Application_(Breast_cancer).ipynb

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    "colab": {
      "name": "Application (Breast-cancer).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/arghac14/UndErNsembled/blob/master/Application_(Breast_cancer).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t15oqb9wsGW"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxdVHpWJwx5P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "335a79bb-4bb4-48ee-c2c3-38e89ba91029"
      },
      "source": [
        "import pandas as pd \n",
        "import io \n",
        "#colon_df = pd.read_csv(io.BytesIO(uploaded['colon.csv']),index_col=0)\n",
        "#colon_df = pd.read_csv(\"https://github.com/raw/disha2sinha/Reducing-Imbalanced-dataset-by-Under-sampling-approach-Consensus-Clustering/main/Datasets/colon.csv\",index_col=0)\n",
        "colon_df = pd.read_csv(\"https://github.com/raw/arghac14/UndErNsembled/master/Datasets/breast-cancer.csv\", index_col=0)\n",
        "colon_df.head()"
      ],
      "execution_count": 1127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>842302</th>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842517</th>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>84300903</th>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84348301</th>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84358402</th>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "id                                ...                                         \n",
              "842302            1        17.99  ...          0.4601                  0.11890\n",
              "842517            1        20.57  ...          0.2750                  0.08902\n",
              "84300903          1        19.69  ...          0.3613                  0.08758\n",
              "84348301          1        11.42  ...          0.6638                  0.17300\n",
              "84358402          1        20.29  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nRGkBidAfOU"
      },
      "source": [
        "target = 'diagnosis'\n",
        "maj_val = 0\n",
        "min_val = 1"
      ],
      "execution_count": 1128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdyFo6S41GYq",
        "outputId": "d0d64919-b97e-4f77-8aed-55715f3f9446"
      },
      "source": [
        "colon_majority_df=colon_df[colon_df[target]==maj_val]\n",
        "colon_majority_df.shape"
      ],
      "execution_count": 1129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(357, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAWaJl6a1htd",
        "outputId": "884391aa-f8eb-4d9d-f72b-d8009d308874"
      },
      "source": [
        "colon_minority_df=colon_df[colon_df[target]==min_val]\n",
        "colon_minority_df.shape"
      ],
      "execution_count": 1130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(212, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RnsGY5Si70j"
      },
      "source": [
        "# colon_df['Class'].replace(to_replace=[['Abnormal'], ['Normal']],value=[0,1],inplace=True)\n",
        "# colon_df"
      ],
      "execution_count": 1131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "6WiGc2xOqpWl",
        "outputId": "c4b4c693-fff7-40b5-cf1e-ab01f174891e"
      },
      "source": [
        "colon_majority_df=colon_df[colon_df[target]==maj_val]\n",
        "colon_majority_df.head()"
      ],
      "execution_count": 1132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8510426</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8510653</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8510824</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>854941</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85713702</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "id                                ...                                         \n",
              "8510426           0       13.540  ...          0.2977                  0.07259\n",
              "8510653           0       13.080  ...          0.3184                  0.08183\n",
              "8510824           0        9.504  ...          0.2450                  0.07773\n",
              "854941            0       13.030  ...          0.1987                  0.06169\n",
              "85713702          0        8.196  ...          0.3105                  0.07409\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "LWzywXyZrEY5",
        "outputId": "00c3647e-4930-48bf-ec9c-0a22a0b0635f"
      },
      "source": [
        "colon_minority_df=colon_df[colon_df[target]==min_val]\n",
        "colon_minority_df.head()"
      ],
      "execution_count": 1133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>842302</th>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842517</th>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84300903</th>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84348301</th>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84358402</th>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "id                                ...                                         \n",
              "842302            1        17.99  ...          0.4601                  0.11890\n",
              "842517            1        20.57  ...          0.2750                  0.08902\n",
              "84300903          1        19.69  ...          0.3613                  0.08758\n",
              "84348301          1        11.42  ...          0.6638                  0.17300\n",
              "84358402          1        20.29  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NZfct52rmss",
        "outputId": "5590e0b6-489d-45b0-8475-419e4aa47152"
      },
      "source": [
        "colon_df.isnull().values.any()"
      ],
      "execution_count": 1134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jdy08_6tbir"
      },
      "source": [
        "![WhatsApp Image 2021-01-03 at 1.38.50 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "-vrFYiCSti73",
        "outputId": "911d40ac-5add-4858-c2ef-b0012adb27bb"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "\n",
        "count_classes = pd.value_counts(colon_df[target], sort = True)\n",
        "\n",
        "count_classes.plot(kind = 'bar', rot=0)\n",
        "\n",
        "plt.title(\"Transaction Class Distribution\")\n",
        "LABELS = [\"0\", \"1\"]\n",
        "plt.xticks(range(2), LABELS)\n",
        "\n",
        "plt.xlabel(\"Class\")\n",
        "\n",
        "plt.ylabel(\"Frequency\")"
      ],
      "execution_count": 1135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1135
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ibv0BJ33uxbM",
        "outputId": "dd5be00e-531b-455e-baae-605e2e274ec3"
      },
      "source": [
        "print(colon_minority_df.shape,colon_majority_df.shape)"
      ],
      "execution_count": 1136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(212, 31) (357, 31)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14MpFzho2T7e"
      },
      "source": [
        "# majority=colon_majority_df['Class']\n",
        "# colon_majority_df=colon_majority_df.drop('Class',axis=1)\n",
        "# colon_majority_df.head()"
      ],
      "execution_count": 1137,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkHzN_850l2R"
      },
      "source": [
        "# Different Clustering :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiAi-63-5Dp3"
      },
      "source": [
        "import pandas as pd\n",
        "columns=['Kmeans','Kmeans++','Minibatch']\n",
        "clustering_result=pd.DataFrame(columns=columns)"
      ],
      "execution_count": 1138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NVKVBWeyIpy"
      },
      "source": [
        "num_of_clusters=7"
      ],
      "execution_count": 1139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsHbq5AoE4xp"
      },
      "source": [
        "##**KMEANS CLUSTERING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "kS52Leu52tRW",
        "outputId": "bde05777-fae4-4bf1-bfc1-bca875f9627a"
      },
      "source": [
        "# Elbow method to find optimal K\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "sse={}\n",
        "for k in range(2,10):\n",
        "  kmeans=KMeans(n_clusters=k,init='random',max_iter=500).fit(colon_majority_df)\n",
        "  sse[k]=kmeans.inertia_\n",
        "plt.plot(list(sse.keys()),list(sse.values()),'bx-')\n",
        "plt.xlabel(\"Number of Cluster\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.savefig(\"elbow_method.png\")"
      ],
      "execution_count": 1140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PaQfXiy00IC"
      },
      "source": [
        "#Kmeans:\n",
        "kmeans=KMeans(n_clusters=num_of_clusters,init='random',max_iter=500).fit(colon_majority_df)\n",
        "clustering_result['Kmeans']=kmeans.labels_"
      ],
      "execution_count": 1141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVhKXIDJKZAA"
      },
      "source": [
        "#import seaborn as sns\n",
        "#sns.lmplot(x='Top10perc',y='S.F.Ratio',data = colon_majority_df,hue='labels',fit_reg=False)\n",
        "#sns.pairplot(colon_majority_df[:0],hue='labels')"
      ],
      "execution_count": 1142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "NgNNkrxMLn8Q",
        "outputId": "47a11fcc-ec9f-4b0d-e0c3-2146fd3d4642"
      },
      "source": [
        "partition1 = colon_majority_df\n",
        "partition1=partition1.reset_index(drop=True)\n",
        "partition1['cluster_label'] = clustering_result['Kmeans']\n",
        "partition1 #with cluster labels "
      ],
      "execution_count": 1143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              "0            0       13.540  ...                  0.07259              0\n",
              "1            0       13.080  ...                  0.08183              0\n",
              "2            0        9.504  ...                  0.07773              6\n",
              "3            0       13.030  ...                  0.06169              1\n",
              "4            0        8.196  ...                  0.07409              6\n",
              "..         ...          ...  ...                      ...            ...\n",
              "352          0       14.590  ...                  0.08004              3\n",
              "353          0       11.510  ...                  0.08732              2\n",
              "354          0       14.050  ...                  0.08321              0\n",
              "355          0       11.200  ...                  0.05905              2\n",
              "356          0        7.760  ...                  0.07039              6\n",
              "\n",
              "[357 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOpcquEM8hJ8"
      },
      "source": [
        "# **CLUSTER VISUALIZATION USING PCA FOR REDUCING DIMENSIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xz9DBR5xmzQ"
      },
      "source": [
        "import plotly as py \n",
        "import plotly.graph_objs as go \n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot"
      ],
      "execution_count": 1144,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BoJarSW2Frx"
      },
      "source": [
        "PCA is an algorithm that is used for dimensionality reduction - meaning, informally, that it can take in a DataFrame with many columns and return a DataFrame with a reduced number of columns that still retains much of the information from the columns of the original DataFrame. The columns of the DataFrame produced from the PCA procedure are called Principal Components. We will use these principal components to help us visualize our clusters in 2-D, and 3-D space, since we cannot easily visualize the data we have in higher dimensions. For example, we can use two principal components to visualize the clusters in 2-D space, or three principal components to visualize the clusters in 3-D space.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GE8D46Ne1Bjj"
      },
      "source": [
        "from sklearn.decomposition import PCA #Principal Component Analysis\n",
        "\n",
        "#PCA with two principal components\n",
        "pca_2d = PCA(n_components=2)\n",
        "\n",
        "#PCA with three principal components\n",
        "pca_3d = PCA(n_components=3)\n",
        "\n",
        "#This DataFrame contains the two principal components that will be used\n",
        "#for the 2-D visualization mentioned above\n",
        "PCs_2d = pd.DataFrame(pca_2d.fit_transform(partition1.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#And this DataFrame contains three principal components that will aid us\n",
        "#in visualizing our clusters in 3-D\n",
        "PCs_3d = pd.DataFrame(pca_3d.fit_transform(partition1.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#\"PC1_2d\" means: 'The first principal component of the components created for 2-D visualization, by PCA.'\n",
        "#And \"PC2_2d\" means: 'The second principal component of the components created for 2-D visualization, by PCA.'\n",
        "PCs_2d.columns = [\"PC1_2d\", \"PC2_2d\"]\n",
        "\n",
        "PCs_3d.columns = [\"PC1_3d\", \"PC2_3d\", \"PC3_3d\"]\n"
      ],
      "execution_count": 1145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IerigAV2g93"
      },
      "source": [
        "partition1_plot= pd.concat([partition1,PCs_2d,PCs_3d], axis=1, join='inner')"
      ],
      "execution_count": 1146,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O733h3D59BRl"
      },
      "source": [
        "### **CLUSTER VISUALIZATION IN TWO DIMENSIONAL SPACE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "HGE88cYEyxlK",
        "outputId": "60978c7e-eeb1-4e85-e19a-c787df5a247e"
      },
      "source": [
        "data=[]\n",
        "for i in partition1_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition1_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter(x = cluster_i[\"PC1_2d\"],y = cluster_i[\"PC2_2d\"],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Two Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)\n"
      ],
      "execution_count": 1147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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              "                        [{\"mode\": \"markers\", \"name\": \"Cluster 0\", \"type\": \"scatter\", \"x\": [184.1364071100868, 92.20924939579554, 171.09069596959264, 145.16806280735736, 91.56153892875162, 97.75321885353095, 167.81218513457986, 223.6516834428136, 114.33105853760733, 209.60399089384651, 97.54546496250836, 207.11343157689427, 140.4437008652505, 104.7496184570675, 105.21209217793667, 181.84984111506336, 162.11503520283964, 169.5378383873246, 210.85450285911088, 168.06354072297728, 195.26859403376352, 94.47286448372937, 142.34477602840724, 205.03966326111185, 209.30541457953157, 165.58728403763536, 155.4178030547121, 107.72554111519112, 174.01902592621323, 131.21825498918727, 91.58257739690659, 188.27594770918833, 101.36075870455687, 137.92215859052095, 160.7164224655774, 157.47558449144202, 151.0740653983118, 100.75772957346356, 229.59287507806425, 190.26598472026018, 151.00228852637773, 88.86581702690097, 100.51139696431427, 188.1040819440304, 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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Visualizing Clusters in Two Dimensions Using PCA\"}, \"xaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"PC1\"}, \"zeroline\": false}, \"yaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"PC2\"}, \"zeroline\": false}},\n",
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              "\n",
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFM1VD3r9Njy"
      },
      "source": [
        "### **CLUSTER VISUALIZATION IN THREE DIMENSIONAL SPACE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "qTljPyTg7SpV",
        "outputId": "b00a6176-12bf-4c3c-eba7-503acca347cc"
      },
      "source": [
        "data=[]\n",
        "for i in partition1_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition1_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter3d(x = cluster_i[\"PC1_3d\"],y = cluster_i[\"PC2_3d\"],z=cluster_i['PC3_3d'],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Three Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)\n"
      ],
      "execution_count": 1148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LzE-ccvYvP0"
      },
      "source": [
        "## **KMEANS++ CLUSTERING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEjsUVMeYuku"
      },
      "source": [
        "#Kmeans++:\n",
        "kmeans=KMeans(n_clusters=num_of_clusters,init='k-means++',max_iter=500).fit(colon_majority_df)\n",
        "clustering_result['Kmeans++']=kmeans.labels_"
      ],
      "execution_count": 1149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "cdq5gnTkZUSA",
        "outputId": "c69d2697-aa35-4de7-af93-c41cbd6999f4"
      },
      "source": [
        "partition2 = colon_majority_df\n",
        "partition2=partition2.reset_index(drop=True)\n",
        "partition2['cluster_label'] = clustering_result['Kmeans++']\n",
        "partition2 #with cluster labels "
      ],
      "execution_count": 1150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>diagnosis</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
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              "      <td>0.06664</td>\n",
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              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
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              "      <td>0.02387</td>\n",
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              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
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              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>6</td>\n",
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              "      <td>0.1967</td>\n",
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              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
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              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
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              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              "0            0       13.540  ...                  0.07259              6\n",
              "1            0       13.080  ...                  0.08183              1\n",
              "2            0        9.504  ...                  0.07773              0\n",
              "3            0       13.030  ...                  0.06169              1\n",
              "4            0        8.196  ...                  0.07409              0\n",
              "..         ...          ...  ...                      ...            ...\n",
              "352          0       14.590  ...                  0.08004              2\n",
              "353          0       11.510  ...                  0.08732              3\n",
              "354          0       14.050  ...                  0.08321              6\n",
              "355          0       11.200  ...                  0.05905              3\n",
              "356          0        7.760  ...                  0.07039              0\n",
              "\n",
              "[357 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNJVgTDfZ4c0"
      },
      "source": [
        "#PCA with two principal components\n",
        "pca_2d = PCA(n_components=2)\n",
        "\n",
        "#PCA with three principal components\n",
        "pca_3d = PCA(n_components=3)\n",
        "\n",
        "#This DataFrame contains the two principal components that will be used\n",
        "#for the 2-D visualization mentioned above\n",
        "PCs_2d = pd.DataFrame(pca_2d.fit_transform(partition2.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#And this DataFrame contains three principal components that will aid us\n",
        "#in visualizing our clusters in 3-D\n",
        "PCs_3d = pd.DataFrame(pca_3d.fit_transform(partition2.drop([\"cluster_label\"], axis=1)))\n",
        "\n",
        "#\"PC1_2d\" means: 'The first principal component of the components created for 2-D visualization, by PCA.'\n",
        "#And \"PC2_2d\" means: 'The second principal component of the components created for 2-D visualization, by PCA.'\n",
        "PCs_2d.columns = [\"PC1_2d\", \"PC2_2d\"]\n",
        "\n",
        "PCs_3d.columns = [\"PC1_3d\", \"PC2_3d\", \"PC3_3d\"]"
      ],
      "execution_count": 1151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVVy7u8DaEXh"
      },
      "source": [
        "partition2_plot= pd.concat([partition2,PCs_2d,PCs_3d], axis=1, join='inner')"
      ],
      "execution_count": 1152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "GiLgYRMHaTc3",
        "outputId": "a8668662-f12b-45f0-e49b-faad58be531e"
      },
      "source": [
        "data=[]\n",
        "for i in partition2_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition1_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter(x = cluster_i[\"PC1_2d\"],y = cluster_i[\"PC2_2d\"],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Two Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)\n"
      ],
      "execution_count": 1153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9ffu4DLjap03",
        "outputId": "94a11510-0763-482b-dbb6-1dd1df263fed"
      },
      "source": [
        "data=[]\n",
        "for i in partition2_plot.cluster_label.unique():\n",
        "  cluster_i=partition1_plot[partition2_plot['cluster_label']==i]\n",
        "  trace_i = go.Scatter3d(x = cluster_i[\"PC1_3d\"],y = cluster_i[\"PC2_3d\"],z=cluster_i['PC3_3d'],mode = \"markers\",name = \"Cluster \"+str(i),text = None)\n",
        "  data.append(trace_i)\n",
        "\n",
        "title = \"Visualizing Clusters in Three Dimensions Using PCA\"\n",
        "\n",
        "layout = dict(title = title,xaxis= dict(title= 'PC1',ticklen= 5,zeroline= False),yaxis= dict(title= 'PC2',ticklen= 5,zeroline= False))\n",
        "\n",
        "fig = dict(data = data, layout = layout)\n",
        "\n",
        "iplot(fig)"
      ],
      "execution_count": 1154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "metadata": {
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        "outputId": "7e711704-2973-41f7-c0bc-4381a5c0bc9d"
      },
      "source": [
        "!pip install kmodes"
      ],
      "execution_count": 1155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kmodes in /usr/local/lib/python3.7/dist-packages (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.19.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.0.1)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.7/dist-packages (from kmodes) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Etg8swfMDU3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fef8c1ae-2591-4df8-f25b-884f8ee0b619"
      },
      "source": [
        "from kmodes.kmodes import KModes\n",
        "\n",
        "\n",
        "km = KModes(n_clusters=num_of_clusters, init='Huang', n_init=5, verbose=1)\n",
        "\n",
        "clusters = km.fit_predict(colon_majority_df)\n",
        "\n",
        "df_0_centroids=km.cluster_centroids_\n",
        "clusters"
      ],
      "execution_count": 1156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 1, iteration: 1/100, moves: 11, cost: 10340.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 2, iteration: 1/100, moves: 10, cost: 10335.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 3, iteration: 1/100, moves: 13, cost: 10343.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 4, iteration: 1/100, moves: 13, cost: 10342.0\n",
            "Run 4, iteration: 2/100, moves: 0, cost: 10342.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 5, iteration: 1/100, moves: 9, cost: 10341.0\n",
            "Best run was number 2\n"
          ],
          "name": "stdout"
        },
        {
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    {
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      "metadata": {
        "colab": {
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          "height": 445
        },
        "id": "OwR3vI9HxqbV",
        "outputId": "5de6ab0f-8684-4874-eb96-0e37983e3de4"
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      "source": [
        "partition3 = colon_majority_df\n",
        "partition3=partition3.reset_index(drop=True)\n",
        "partition3['cluster_label'] = clusters\n",
        "partition3 #with cluster labels "
      ],
      "execution_count": 1157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
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              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              "0            0       13.540  ...                  0.07259              5\n",
              "1            0       13.080  ...                  0.08183              0\n",
              "2            0        9.504  ...                  0.07773              0\n",
              "3            0       13.030  ...                  0.06169              0\n",
              "4            0        8.196  ...                  0.07409              0\n",
              "..         ...          ...  ...                      ...            ...\n",
              "352          0       14.590  ...                  0.08004              0\n",
              "353          0       11.510  ...                  0.08732              0\n",
              "354          0       14.050  ...                  0.08321              0\n",
              "355          0       11.200  ...                  0.05905              1\n",
              "356          0        7.760  ...                  0.07039              1\n",
              "\n",
              "[357 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvIFwTLn-c7I"
      },
      "source": [
        "## **K-prototypes Clustering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ-AfOlZ-kq8",
        "outputId": "509f6a61-c5c8-4e74-ed35-c2cc023a5ac1"
      },
      "source": [
        "from kmodes.kprototypes import KPrototypes\n",
        "\n",
        "\n",
        "kproto = KPrototypes(n_clusters=num_of_clusters, init='Cao', verbose=2)\n",
        "clusters = kproto.fit_predict(colon_majority_df, categorical=[1])\n",
        "\n",
        "\n",
        "# Print cluster centroids of the trained model.\n",
        "clusters\n"
      ],
      "execution_count": 1158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 1, iteration: 1/100, moves: 84, ncost: 1101044.9827976972\n",
            "Run: 1, iteration: 2/100, moves: 33, ncost: 1027665.9487187313\n",
            "Run: 1, iteration: 3/100, moves: 9, ncost: 1021496.047633978\n",
            "Run: 1, iteration: 4/100, moves: 4, ncost: 1020674.0386431118\n",
            "Run: 1, iteration: 5/100, moves: 1, ncost: 1020595.24506117\n",
            "Run: 1, iteration: 6/100, moves: 0, ncost: 1020595.24506117\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 2, iteration: 1/100, moves: 106, ncost: 996251.1506643584\n",
            "Run: 2, iteration: 2/100, moves: 31, ncost: 939263.6763858505\n",
            "Run: 2, iteration: 3/100, moves: 19, ncost: 920296.3053737899\n",
            "Run: 2, iteration: 4/100, moves: 14, ncost: 908410.8598860356\n",
            "Run: 2, iteration: 5/100, moves: 5, ncost: 906384.217860423\n",
            "Run: 2, iteration: 6/100, moves: 4, ncost: 905291.40008131\n",
            "Run: 2, iteration: 7/100, moves: 1, ncost: 905148.8043325787\n",
            "Run: 2, iteration: 8/100, moves: 0, ncost: 905148.8043325787\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 3, iteration: 1/100, moves: 106, ncost: 1244962.1984686179\n",
            "Run: 3, iteration: 2/100, moves: 48, ncost: 1168270.6733599524\n",
            "Run: 3, iteration: 3/100, moves: 32, ncost: 1126376.1888910502\n",
            "Run: 3, iteration: 4/100, moves: 31, ncost: 1082934.6511119138\n",
            "Run: 3, iteration: 5/100, moves: 24, ncost: 1050813.9410096586\n",
            "Run: 3, iteration: 6/100, moves: 24, ncost: 1028318.3631612174\n",
            "Run: 3, iteration: 7/100, moves: 13, ncost: 1022301.8125069451\n",
            "Run: 3, iteration: 8/100, moves: 4, ncost: 1021151.0240971134\n",
            "Run: 3, iteration: 9/100, moves: 4, ncost: 1020239.2033803735\n",
            "Run: 3, iteration: 10/100, moves: 0, ncost: 1020239.2033803735\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
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            "Init: initializing centroids\n",
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            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 4, iteration: 1/100, moves: 182, ncost: 1237063.3656312001\n",
            "Run: 4, iteration: 2/100, moves: 41, ncost: 1096202.07310573\n",
            "Run: 4, iteration: 3/100, moves: 34, ncost: 1028163.6818419278\n",
            "Run: 4, iteration: 4/100, moves: 9, ncost: 1021909.0923962658\n",
            "Run: 4, iteration: 5/100, moves: 0, ncost: 1021909.0923962658\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
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            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
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            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
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            "Init: initializing centroids\n",
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            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
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            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 5, iteration: 1/100, moves: 103, ncost: 1047035.6146015441\n",
            "Run: 5, iteration: 2/100, moves: 25, ncost: 1024583.6073076685\n",
            "Run: 5, iteration: 3/100, moves: 8, ncost: 1021341.9676612421\n",
            "Run: 5, iteration: 4/100, moves: 5, ncost: 1020239.2033803727\n",
            "Run: 5, iteration: 5/100, moves: 0, ncost: 1020239.2033803727\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
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            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 6, iteration: 1/100, moves: 120, ncost: 1000544.4211953795\n",
            "Run: 6, iteration: 2/100, moves: 39, ncost: 902038.4798415056\n",
            "Run: 6, iteration: 3/100, moves: 14, ncost: 886191.663186145\n",
            "Run: 6, iteration: 4/100, moves: 4, ncost: 883833.9323782808\n",
            "Run: 6, iteration: 5/100, moves: 3, ncost: 881767.5817916545\n",
            "Run: 6, iteration: 6/100, moves: 4, ncost: 880121.774336327\n",
            "Run: 6, iteration: 7/100, moves: 0, ncost: 880121.774336327\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 7, iteration: 1/100, moves: 140, ncost: 1231485.4821537621\n",
            "Run: 7, iteration: 2/100, moves: 63, ncost: 960993.1141386685\n",
            "Run: 7, iteration: 3/100, moves: 25, ncost: 898943.4465244425\n",
            "Run: 7, iteration: 4/100, moves: 16, ncost: 875806.209795286\n",
            "Run: 7, iteration: 5/100, moves: 4, ncost: 874572.7939170683\n",
            "Run: 7, iteration: 6/100, moves: 3, ncost: 873679.6902361406\n",
            "Run: 7, iteration: 7/100, moves: 1, ncost: 873545.780108382\n",
            "Run: 7, iteration: 8/100, moves: 0, ncost: 873545.780108382\n",
            "Init: initializing centroids\n",
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            "Starting iterations...\n",
            "Run: 8, iteration: 1/100, moves: 105, ncost: 1045076.3581627031\n",
            "Run: 8, iteration: 2/100, moves: 22, ncost: 1023737.9260695725\n",
            "Run: 8, iteration: 3/100, moves: 6, ncost: 1021728.0668084304\n",
            "Run: 8, iteration: 4/100, moves: 4, ncost: 1020992.1186604922\n",
            "Run: 8, iteration: 5/100, moves: 0, ncost: 1020992.1186604922\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
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            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 9, iteration: 1/100, moves: 125, ncost: 1078318.0094205865\n",
            "Run: 9, iteration: 2/100, moves: 33, ncost: 1037908.6833816383\n",
            "Run: 9, iteration: 3/100, moves: 14, ncost: 1026857.5555446617\n",
            "Run: 9, iteration: 4/100, moves: 9, ncost: 1022594.1846832653\n",
            "Run: 9, iteration: 5/100, moves: 4, ncost: 1021009.2375806572\n",
            "Run: 9, iteration: 6/100, moves: 0, ncost: 1021009.2375806572\n",
            "Init: initializing centroids\n",
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            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run: 10, iteration: 1/100, moves: 167, ncost: 1092767.6704634365\n",
            "Run: 10, iteration: 2/100, moves: 36, ncost: 1001208.754330726\n",
            "Run: 10, iteration: 3/100, moves: 30, ncost: 958223.6997435951\n",
            "Run: 10, iteration: 4/100, moves: 27, ncost: 925417.8674242349\n",
            "Run: 10, iteration: 5/100, moves: 16, ncost: 911863.6965563897\n",
            "Run: 10, iteration: 6/100, moves: 9, ncost: 908259.4266957394\n",
            "Run: 10, iteration: 7/100, moves: 3, ncost: 907509.8784349845\n",
            "Run: 10, iteration: 8/100, moves: 0, ncost: 907509.8784349845\n",
            "Best run was number 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3, 5, 3, 5, 3, 2, 6, 2, 6, 6, 3, 5, 1, 5, 5, 5, 6, 5, 3, 5, 3,\n",
              "       3, 3, 6, 3, 3, 3, 0, 0, 0, 2, 6, 1, 6, 5, 3, 1, 1, 6, 3, 6, 1, 3,\n",
              "       2, 1, 5, 3, 5, 6, 0, 2, 2, 0, 3, 4, 6, 6, 6, 1, 6, 3, 1, 6, 0, 0,\n",
              "       2, 3, 5, 1, 6, 2, 3, 4, 6, 6, 6, 3, 0, 1, 0, 3, 1, 1, 5, 1, 3, 3,\n",
              "       6, 1, 6, 6, 3, 3, 5, 3, 3, 2, 1, 2, 4, 6, 6, 1, 2, 2, 1, 2, 0, 1,\n",
              "       0, 3, 6, 6, 1, 2, 0, 2, 3, 6, 2, 1, 3, 3, 6, 6, 6, 1, 2, 3, 1, 2,\n",
              "       6, 1, 6, 6, 2, 2, 6, 3, 3, 3, 3, 6, 6, 0, 0, 3, 6, 3, 2, 1, 0, 1,\n",
              "       3, 1, 6, 6, 2, 5, 2, 2, 6, 0, 3, 6, 5, 3, 6, 5, 3, 1, 3, 3, 3, 0,\n",
              "       6, 3, 6, 6, 3, 3, 1, 0, 1, 6, 6, 1, 3, 0, 6, 6, 6, 6, 3, 3, 2, 5,\n",
              "       1, 3, 2, 3, 4, 2, 3, 0, 2, 4, 1, 2, 2, 6, 6, 6, 3, 2, 3, 2, 6, 1,\n",
              "       5, 3, 2, 2, 3, 6, 3, 3, 3, 3, 3, 6, 4, 2, 3, 6, 6, 5, 0, 6, 1, 3,\n",
              "       6, 6, 0, 6, 2, 1, 1, 1, 6, 1, 3, 6, 0, 3, 0, 2, 2, 6, 2, 1, 6, 0,\n",
              "       0, 6, 6, 0, 3, 2, 6, 2, 3, 1, 0, 6, 2, 2, 2, 1, 6, 1, 3, 0, 3, 1,\n",
              "       3, 0, 2, 6, 3, 0, 2, 2, 0, 3, 0, 6, 3, 4, 3, 2, 0, 3, 3, 0, 3, 5,\n",
              "       5, 6, 1, 4, 6, 0, 0, 6, 2, 3, 5, 6, 2, 1, 5, 2, 3, 2, 3, 6, 6, 2,\n",
              "       1, 6, 5, 5, 6, 0, 0, 2, 2, 2, 1, 1, 1, 6, 1, 6, 3, 5, 3, 1, 1, 5,\n",
              "       0, 6, 2, 6, 5], dtype=uint16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "3lJMacKE--xg",
        "outputId": "aa953cf1-4bfe-431f-ad7e-65b6810d5882"
      },
      "source": [
        "partition4 = colon_majority_df\n",
        "partition4=partition4.reset_index(drop=True)\n",
        "partition4['cluster_label'] = clusters\n",
        "partition4"
      ],
      "execution_count": 1159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              "0            0       13.540  ...                  0.07259              2\n",
              "1            0       13.080  ...                  0.08183              3\n",
              "2            0        9.504  ...                  0.07773              5\n",
              "3            0       13.030  ...                  0.06169              3\n",
              "4            0        8.196  ...                  0.07409              5\n",
              "..         ...          ...  ...                      ...            ...\n",
              "352          0       14.590  ...                  0.08004              0\n",
              "353          0       11.510  ...                  0.08732              6\n",
              "354          0       14.050  ...                  0.08321              2\n",
              "355          0       11.200  ...                  0.05905              6\n",
              "356          0        7.760  ...                  0.07039              5\n",
              "\n",
              "[357 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLyTvUUr6-kB"
      },
      "source": [
        "## **MINIBATCH KMEANS CLUSTERING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_q3dmFr6qZJ"
      },
      "source": [
        "from sklearn.cluster import MiniBatchKMeans\n",
        "mbk = MiniBatchKMeans(init='k-means++', n_clusters=num_of_clusters, batch_size=40, n_init=10, max_no_improvement=10, verbose=0)\n",
        "mbk.fit(colon_majority_df)\n",
        "clustering_result['Minibatch']=mbk.labels_"
      ],
      "execution_count": 1160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLcRAduR88mx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9aaadc4-6fb0-4714-fdb4-b3170017247f"
      },
      "source": [
        "clustering_result['Minibatch'].values"
      ],
      "execution_count": 1161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 5, 3, 5, 3, 1, 3, 1, 3, 6, 1, 5, 0, 5, 5, 0, 6, 5, 3, 5, 3,\n",
              "       1, 1, 6, 1, 3, 3, 4, 4, 4, 1, 3, 0, 6, 5, 3, 0, 0, 3, 3, 6, 0, 3,\n",
              "       1, 0, 5, 3, 5, 6, 4, 1, 1, 4, 3, 2, 3, 6, 6, 0, 6, 1, 6, 3, 4, 4,\n",
              "       1, 1, 5, 0, 6, 1, 3, 2, 3, 6, 3, 3, 4, 6, 4, 3, 0, 0, 5, 0, 1, 3,\n",
              "       6, 0, 3, 6, 3, 3, 5, 3, 3, 1, 0, 1, 2, 3, 3, 0, 1, 1, 0, 1, 4, 0,\n",
              "       4, 1, 6, 6, 0, 1, 4, 1, 3, 6, 1, 0, 1, 1, 6, 6, 6, 6, 1, 3, 0, 1,\n",
              "       6, 0, 6, 6, 1, 1, 3, 3, 3, 3, 3, 6, 6, 4, 4, 3, 3, 3, 1, 0, 4, 0,\n",
              "       3, 0, 6, 6, 1, 5, 1, 1, 6, 4, 1, 6, 5, 3, 3, 5, 3, 0, 3, 3, 3, 4,\n",
              "       3, 1, 6, 6, 3, 3, 0, 4, 0, 6, 3, 0, 3, 4, 6, 6, 3, 6, 3, 3, 1, 5,\n",
              "       0, 3, 1, 3, 2, 1, 3, 4, 1, 2, 0, 1, 1, 6, 6, 6, 3, 1, 3, 4, 6, 0,\n",
              "       5, 3, 1, 1, 3, 6, 3, 3, 1, 3, 3, 6, 2, 1, 3, 6, 6, 5, 4, 3, 0, 3,\n",
              "       6, 6, 4, 6, 1, 0, 0, 6, 6, 6, 3, 3, 4, 1, 4, 4, 1, 6, 1, 0, 3, 4,\n",
              "       4, 3, 3, 4, 1, 1, 3, 1, 1, 0, 4, 6, 1, 1, 1, 0, 3, 0, 3, 4, 3, 6,\n",
              "       3, 4, 1, 6, 3, 4, 1, 1, 4, 3, 4, 3, 3, 2, 3, 1, 4, 1, 3, 4, 3, 5,\n",
              "       0, 3, 6, 2, 6, 4, 4, 6, 1, 1, 5, 6, 1, 0, 5, 1, 3, 1, 3, 3, 3, 4,\n",
              "       6, 6, 5, 5, 6, 4, 4, 1, 1, 1, 0, 0, 0, 6, 6, 6, 3, 5, 3, 0, 0, 0,\n",
              "       4, 6, 1, 6, 5], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "HddUke1y_66Y",
        "outputId": "63cb8c78-2efe-47d8-8e53-dcb432c5d3d3"
      },
      "source": [
        "partition5 = colon_majority_df\n",
        "partition5=partition5.reset_index(drop=True)\n",
        "partition5['cluster_label'] = clustering_result['Minibatch']\n",
        "partition5"
      ],
      "execution_count": 1162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              "0            0       13.540  ...                  0.07259              1\n",
              "1            0       13.080  ...                  0.08183              1\n",
              "2            0        9.504  ...                  0.07773              5\n",
              "3            0       13.030  ...                  0.06169              3\n",
              "4            0        8.196  ...                  0.07409              5\n",
              "..         ...          ...  ...                      ...            ...\n",
              "352          0       14.590  ...                  0.08004              4\n",
              "353          0       11.510  ...                  0.08732              6\n",
              "354          0       14.050  ...                  0.08321              1\n",
              "355          0       11.200  ...                  0.05905              6\n",
              "356          0        7.760  ...                  0.07039              5\n",
              "\n",
              "[357 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPk4WYTGsW1N"
      },
      "source": [
        "## **Fuzzy K-Means Clustering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQTbGraNsf6E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3c15fdd-9509-41fb-93b2-f5e8dc8c3c21"
      },
      "source": [
        "!pip install fuzzy-c-means\n",
        "import numpy as np\n",
        "from fcmeans import FCM\n"
      ],
      "execution_count": 1163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fuzzy-c-means in /usr/local/lib/python3.7/dist-packages (1.2.4)\n",
            "Requirement already satisfied: jax<0.3.0,>=0.2.7 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (0.2.13)\n",
            "Requirement already satisfied: jaxlib<0.2.0,>=0.1.57 in /usr/local/lib/python3.7/dist-packages (from fuzzy-c-means) (0.1.66+cuda110)\n",
            "Requirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from jax<0.3.0,>=0.2.7->fuzzy-c-means) (1.19.5)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax<0.3.0,>=0.2.7->fuzzy-c-means) (0.12.0)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax<0.3.0,>=0.2.7->fuzzy-c-means) (3.3.0)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from jaxlib<0.2.0,>=0.1.57->fuzzy-c-means) (1.12)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from jaxlib<0.2.0,>=0.1.57->fuzzy-c-means) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax<0.3.0,>=0.2.7->fuzzy-c-means) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ym4EWq-a1FjT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1d274d0-27c2-4d00-e9eb-32eac6eca0a2"
      },
      "source": [
        "fcm = FCM(n_clusters=num_of_clusters)\n",
        "fcm.fit(colon_majority_df.values)\n",
        "# outputs\n",
        "fcm_centers = fcm.centers\n",
        "fcm_labels = fcm.predict(colon_majority_df.values)\n",
        "fcm_labels"
      ],
      "execution_count": 1164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeviceArray([5, 3, 6, 1, 6, 1, 5, 1, 5, 1, 2, 3, 6, 4, 6, 6, 4, 2, 6, 1,\n",
              "             6, 3, 3, 3, 1, 3, 1, 1, 0, 0, 5, 5, 1, 4, 2, 6, 1, 4, 4, 1,\n",
              "             1, 2, 4, 1, 5, 4, 6, 1, 6, 2, 5, 3, 5, 0, 1, 0, 1, 2, 2, 4,\n",
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              "             1, 1, 3, 2, 2, 0, 0, 3, 1, 1, 5, 4, 0, 4, 1, 4, 2, 2, 3, 6,\n",
              "             5, 3, 2, 0, 3, 2, 6, 1, 1, 6, 1, 4, 3, 1, 1, 5, 1, 3, 2, 2,\n",
              "             1, 3, 4, 0, 4, 2, 1, 4, 1, 0, 2, 2, 1, 2, 1, 3, 5, 6, 4, 3,\n",
              "             3, 3, 0, 3, 3, 0, 5, 0, 4, 5, 5, 2, 2, 1, 3, 3, 1, 5, 2, 4,\n",
              "             6, 1, 5, 5, 3, 2, 1, 1, 3, 3, 1, 2, 0, 3, 3, 2, 2, 6, 0, 1,\n",
              "             4, 1, 2, 1, 0, 2, 5, 4, 4, 2, 2, 2, 3, 1, 0, 3, 5, 5, 5, 2,\n",
              "             5, 4, 1, 0, 0, 1, 1, 5, 3, 5, 1, 3, 3, 4, 5, 2, 5, 5, 5, 4,\n",
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              "             1, 3, 0, 3, 3, 0, 1, 6, 4, 1, 2, 0, 2, 0, 0, 2, 3, 3, 6, 2,\n",
              "             5, 4, 6, 5, 1, 5, 1, 1, 1, 5, 4, 2, 6, 6, 2, 0, 0, 3, 5, 5,\n",
              "             4, 4, 4, 2, 4, 2, 3, 6, 3, 4, 4, 4, 5, 2, 5, 2, 6],            dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUVGk8T-2jSC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "d7196c5f-3872-4061-8217-39d404890277"
      },
      "source": [
        "partition6 = colon_majority_df\n",
        "partition6=partition6.reset_index(drop=True)\n",
        "partition6['cluster_label'] = fcm_labels\n",
        "partition6"
      ],
      "execution_count": 1165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
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              "      <th>concavity_mean</th>\n",
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              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
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              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>cluster_label</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              "0            0       13.540  ...                  0.07259              5\n",
              "1            0       13.080  ...                  0.08183              3\n",
              "2            0        9.504  ...                  0.07773              6\n",
              "3            0       13.030  ...                  0.06169              1\n",
              "4            0        8.196  ...                  0.07409              6\n",
              "..         ...          ...  ...                      ...            ...\n",
              "352          0       14.590  ...                  0.08004              5\n",
              "353          0       11.510  ...                  0.08732              2\n",
              "354          0       14.050  ...                  0.08321              5\n",
              "355          0       11.200  ...                  0.05905              2\n",
              "356          0        7.760  ...                  0.07039              6\n",
              "\n",
              "[357 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1eKJ4_2x0jW",
        "outputId": "5fddfce1-3d17-44b8-a7c5-62ed1db419ac"
      },
      "source": [
        "partition1, partition2, partition3, partition4,partition5,partition6"
      ],
      "execution_count": 1166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              " 0            0       13.540  ...                  0.07259              0\n",
              " 1            0       13.080  ...                  0.08183              0\n",
              " 2            0        9.504  ...                  0.07773              6\n",
              " 3            0       13.030  ...                  0.06169              1\n",
              " 4            0        8.196  ...                  0.07409              6\n",
              " ..         ...          ...  ...                      ...            ...\n",
              " 352          0       14.590  ...                  0.08004              3\n",
              " 353          0       11.510  ...                  0.08732              2\n",
              " 354          0       14.050  ...                  0.08321              0\n",
              " 355          0       11.200  ...                  0.05905              2\n",
              " 356          0        7.760  ...                  0.07039              6\n",
              " \n",
              " [357 rows x 32 columns],\n",
              "      diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              " 0            0       13.540  ...                  0.07259              6\n",
              " 1            0       13.080  ...                  0.08183              1\n",
              " 2            0        9.504  ...                  0.07773              0\n",
              " 3            0       13.030  ...                  0.06169              1\n",
              " 4            0        8.196  ...                  0.07409              0\n",
              " ..         ...          ...  ...                      ...            ...\n",
              " 352          0       14.590  ...                  0.08004              2\n",
              " 353          0       11.510  ...                  0.08732              3\n",
              " 354          0       14.050  ...                  0.08321              6\n",
              " 355          0       11.200  ...                  0.05905              3\n",
              " 356          0        7.760  ...                  0.07039              0\n",
              " \n",
              " [357 rows x 32 columns],\n",
              "      diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              " 0            0       13.540  ...                  0.07259              5\n",
              " 1            0       13.080  ...                  0.08183              0\n",
              " 2            0        9.504  ...                  0.07773              0\n",
              " 3            0       13.030  ...                  0.06169              0\n",
              " 4            0        8.196  ...                  0.07409              0\n",
              " ..         ...          ...  ...                      ...            ...\n",
              " 352          0       14.590  ...                  0.08004              0\n",
              " 353          0       11.510  ...                  0.08732              0\n",
              " 354          0       14.050  ...                  0.08321              0\n",
              " 355          0       11.200  ...                  0.05905              1\n",
              " 356          0        7.760  ...                  0.07039              1\n",
              " \n",
              " [357 rows x 32 columns],\n",
              "      diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              " 0            0       13.540  ...                  0.07259              2\n",
              " 1            0       13.080  ...                  0.08183              3\n",
              " 2            0        9.504  ...                  0.07773              5\n",
              " 3            0       13.030  ...                  0.06169              3\n",
              " 4            0        8.196  ...                  0.07409              5\n",
              " ..         ...          ...  ...                      ...            ...\n",
              " 352          0       14.590  ...                  0.08004              0\n",
              " 353          0       11.510  ...                  0.08732              6\n",
              " 354          0       14.050  ...                  0.08321              2\n",
              " 355          0       11.200  ...                  0.05905              6\n",
              " 356          0        7.760  ...                  0.07039              5\n",
              " \n",
              " [357 rows x 32 columns],\n",
              "      diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              " 0            0       13.540  ...                  0.07259              1\n",
              " 1            0       13.080  ...                  0.08183              1\n",
              " 2            0        9.504  ...                  0.07773              5\n",
              " 3            0       13.030  ...                  0.06169              3\n",
              " 4            0        8.196  ...                  0.07409              5\n",
              " ..         ...          ...  ...                      ...            ...\n",
              " 352          0       14.590  ...                  0.08004              4\n",
              " 353          0       11.510  ...                  0.08732              6\n",
              " 354          0       14.050  ...                  0.08321              1\n",
              " 355          0       11.200  ...                  0.05905              6\n",
              " 356          0        7.760  ...                  0.07039              5\n",
              " \n",
              " [357 rows x 32 columns],\n",
              "      diagnosis  radius_mean  ...  fractal_dimension_worst  cluster_label\n",
              " 0            0       13.540  ...                  0.07259              5\n",
              " 1            0       13.080  ...                  0.08183              3\n",
              " 2            0        9.504  ...                  0.07773              6\n",
              " 3            0       13.030  ...                  0.06169              1\n",
              " 4            0        8.196  ...                  0.07409              6\n",
              " ..         ...          ...  ...                      ...            ...\n",
              " 352          0       14.590  ...                  0.08004              5\n",
              " 353          0       11.510  ...                  0.08732              2\n",
              " 354          0       14.050  ...                  0.08321              5\n",
              " 355          0       11.200  ...                  0.05905              2\n",
              " 356          0        7.760  ...                  0.07039              6\n",
              " \n",
              " [357 rows x 32 columns])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uywOF-D49Sd",
        "outputId": "e14325b0-0a93-4e87-fd89-2973420085c1"
      },
      "source": [
        "c = 0\n",
        "for i in range(len(partition1.cluster_label)):\n",
        "  print(partition1.cluster_label[i], partition2.cluster_label[i], partition3.cluster_label[i],partition4.cluster_label[i],partition5.cluster_label[i],partition6.cluster_label[i])\n",
        "  #if partition1.cluster_label[i]==partition2.cluster_label[i]:\n",
        "    #c+=1"
      ],
      "execution_count": 1167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 6 5 2 1 5\n",
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            "0 6 4 2 1 5\n",
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            "1 3 0 6 3 1\n",
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            "0 1 0 2 1 3\n",
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            "0 6 0 2 1 5\n",
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            "2 3 1 6 6 2\n",
            "3 2 0 0 4 0\n",
            "1 1 0 3 1 3\n",
            "2 3 2 6 6 2\n",
            "6 0 1 5 5 6\n",
            "1 1 0 3 3 1\n",
            "1 3 0 6 3 1\n",
            "6 0 0 5 5 6\n",
            "1 3 0 3 3 1\n",
            "4 4 0 1 0 4\n",
            "1 1 0 3 3 3\n",
            "1 1 2 3 3 1\n",
            "1 1 0 3 3 1\n",
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            "0 1 0 3 1 3\n",
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            "4 4 0 1 0 4\n",
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            "4 4 0 1 0 4\n",
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            "2 3 0 6 6 2\n",
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            "3 2 0 0 4 0\n",
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            "5 5 0 4 2 0\n",
            "4 4 0 1 0 4\n",
            "0 6 0 2 1 5\n",
            "0 6 0 2 1 5\n",
            "2 3 0 6 6 2\n",
            "2 3 1 6 6 2\n",
            "2 3 0 6 6 1\n",
            "1 1 0 3 3 3\n",
            "0 1 0 2 1 3\n",
            "1 3 0 3 3 1\n",
            "0 6 0 2 4 5\n",
            "2 3 0 6 6 2\n",
            "4 4 0 1 0 4\n",
            "6 0 1 5 5 6\n",
            "1 1 0 3 3 1\n",
            "0 6 0 2 1 5\n",
            "0 6 0 2 1 5\n",
            "1 1 0 3 3 3\n",
            "2 3 0 6 6 2\n",
            "1 1 0 3 3 1\n",
            "1 1 0 3 3 1\n",
            "0 1 0 3 1 3\n",
            "1 1 5 3 3 3\n",
            "1 1 3 3 3 1\n",
            "2 3 0 6 6 2\n",
            "5 5 0 4 2 0\n",
            "0 1 0 2 1 3\n",
            "1 1 0 3 3 3\n",
            "2 3 0 6 6 2\n",
            "2 3 5 6 6 2\n",
            "6 0 0 5 5 6\n",
            "3 2 0 0 4 0\n",
            "2 3 0 6 3 1\n",
            "4 4 0 1 0 4\n",
            "1 1 0 3 3 1\n",
            "2 3 0 6 6 2\n",
            "2 3 0 6 6 1\n",
            "3 2 0 0 4 0\n",
            "2 3 0 6 6 2\n",
            "0 6 0 2 1 5\n",
            "4 4 0 1 0 4\n",
            "4 4 0 1 0 4\n",
            "4 4 0 1 6 2\n",
            "2 3 0 6 6 2\n",
            "2 4 0 1 6 2\n",
            "1 1 0 3 3 3\n",
            "1 3 0 6 3 1\n",
            "3 2 0 0 4 0\n",
            "1 1 0 3 1 3\n",
            "3 6 5 0 4 5\n",
            "0 6 0 2 4 5\n",
            "0 6 0 2 1 5\n",
            "2 3 0 6 6 2\n",
            "0 6 0 2 1 5\n",
            "4 4 0 1 0 4\n",
            "2 3 0 6 3 1\n",
            "3 2 0 0 4 0\n",
            "3 2 0 0 4 0\n",
            "2 3 0 6 3 1\n",
            "1 3 0 6 3 1\n",
            "3 2 0 0 4 5\n",
            "1 1 0 3 1 3\n",
            "0 6 0 2 1 5\n",
            "2 3 0 6 3 1\n",
            "0 1 0 2 1 3\n",
            "0 1 0 3 1 3\n",
            "4 4 0 1 0 4\n",
            "3 2 0 0 4 5\n",
            "2 3 0 6 6 2\n",
            "0 6 0 2 1 5\n",
            "0 6 0 2 1 5\n",
            "0 6 0 2 1 5\n",
            "4 4 0 1 0 4\n",
            "2 3 0 6 3 1\n",
            "4 4 0 1 0 4\n",
            "1 1 4 3 3 1\n",
            "3 2 0 0 4 0\n",
            "1 1 1 3 3 1\n",
            "2 4 0 1 6 2\n",
            "1 1 0 3 3 3\n",
            "3 2 0 0 4 0\n",
            "0 6 0 2 1 5\n",
            "2 3 0 6 6 2\n",
            "1 1 3 3 3 1\n",
            "3 2 5 0 4 0\n",
            "0 6 0 2 1 3\n",
            "0 6 0 2 1 5\n",
            "3 2 0 0 4 0\n",
            "1 1 0 3 3 1\n",
            "3 2 0 0 4 0\n",
            "1 3 0 6 3 1\n",
            "1 1 0 3 3 3\n",
            "5 5 0 4 2 0\n",
            "1 1 0 3 3 1\n",
            "0 6 0 2 1 3\n",
            "3 2 4 0 4 0\n",
            "1 1 0 3 1 3\n",
            "1 1 0 3 3 3\n",
            "3 2 0 0 4 0\n",
            "1 1 0 3 3 1\n",
            "6 0 0 5 5 6\n",
            "6 0 0 5 0 4\n",
            "1 3 0 6 3 1\n",
            "4 4 0 1 6 2\n",
            "5 5 0 4 2 0\n",
            "2 3 0 6 6 2\n",
            "3 2 6 0 4 0\n",
            "3 2 0 0 4 0\n",
            "2 3 0 6 6 2\n",
            "0 6 0 2 1 3\n",
            "1 1 0 3 1 3\n",
            "6 0 0 5 5 6\n",
            "2 3 0 6 6 2\n",
            "0 6 5 2 1 5\n",
            "4 4 0 1 0 4\n",
            "6 0 0 5 5 6\n",
            "0 6 0 2 1 5\n",
            "1 1 3 3 3 1\n",
            "0 6 0 2 1 5\n",
            "1 1 0 3 3 1\n",
            "1 3 0 6 3 1\n",
            "1 3 0 6 3 1\n",
            "3 6 4 2 4 5\n",
            "4 4 0 1 6 4\n",
            "2 3 0 6 6 2\n",
            "6 0 1 5 5 6\n",
            "6 0 0 5 5 6\n",
            "2 3 0 6 6 2\n",
            "3 2 0 0 4 0\n",
            "3 2 5 0 4 0\n",
            "0 1 0 2 1 3\n",
            "0 6 0 2 1 5\n",
            "0 6 4 2 1 5\n",
            "4 4 0 1 0 4\n",
            "4 4 0 1 0 4\n",
            "4 4 0 1 0 4\n",
            "2 3 0 6 6 2\n",
            "4 4 1 1 6 4\n",
            "2 3 0 6 6 2\n",
            "1 1 0 3 3 3\n",
            "6 0 0 5 5 6\n",
            "1 1 3 3 3 3\n",
            "4 4 0 1 0 4\n",
            "4 4 0 1 0 4\n",
            "6 0 1 5 0 4\n",
            "3 2 0 0 4 5\n",
            "2 3 0 6 6 2\n",
            "0 6 0 2 1 5\n",
            "2 3 1 6 6 2\n",
            "6 0 1 5 5 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wLZB1OwYA1m"
      },
      "source": [
        "## **Finding the 3 Best algorithms for our model:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6h5hXJ-lYAMN",
        "outputId": "cdcb06d8-2c13-4902-841c-98757c9104a2"
      },
      "source": [
        "from sklearn.metrics import davies_bouldin_score\n",
        "print(\"Kmeans (partition 1): \",davies_bouldin_score(colon_majority_df, partition1.cluster_label))\n",
        "print(\"Kmeans++ (partition 2): \",davies_bouldin_score(colon_majority_df, partition2.cluster_label))\n",
        "print(\"KModes (partition 3): \",davies_bouldin_score(colon_majority_df, partition3.cluster_label))\n",
        "print(\"K-prototypes(partition 4): \",davies_bouldin_score(colon_majority_df, partition4.cluster_label))\n",
        "print(\"Minibatch Kmeans (partition 5): \",davies_bouldin_score(colon_majority_df, partition5.cluster_label))\n",
        "print(\"Fuzzy K-means(partition 6): \",davies_bouldin_score(colon_majority_df, partition6.cluster_label))"
      ],
      "execution_count": 1168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Kmeans (partition 1):  0.6338605682534579\n",
            "Kmeans++ (partition 2):  0.6278900539066578\n",
            "KModes (partition 3):  8.531065414855941\n",
            "K-prototypes(partition 4):  0.6343400340514228\n",
            "Minibatch Kmeans (partition 5):  0.6294152226423481\n",
            "Fuzzy K-means(partition 6):  0.6584050294983442\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dm4k1nylAqjz"
      },
      "source": [
        "partitionA, partitionB, partitionC = partition1, partition2, partition5"
      ],
      "execution_count": 1169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "hpmQ6K2eBRBG",
        "outputId": "c40c4860-ef8a-4317-a7d3-6842c1acb6ed"
      },
      "source": [
        "partitionA, partitionB, partitionC = partition1, partition2, partition5\n",
        "colon_majority = colon_majority_df\n",
        "colon_majority['C1'] = partitionA.cluster_label.values\n",
        "colon_majority['C2'] = partitionB.cluster_label.values\n",
        "colon_majority['C3'] = partitionC.cluster_label.values\n",
        "colon_majority"
      ],
      "execution_count": 1170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
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              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
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              "      <th>8510653</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
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              "      <td>14.500</td>\n",
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              "      <td>13.030</td>\n",
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              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
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              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
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              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
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              "      <td>201.9</td>\n",
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              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>6</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925277</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>3</td>\n",
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              "      <th>925291</th>\n",
              "      <td>0</td>\n",
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              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
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              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
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              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>2</td>\n",
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              "      <td>6</td>\n",
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              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
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              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>925311</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92751</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 34 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          diagnosis  radius_mean  texture_mean  ...  C1  C2  C3\n",
              "id                                              ...            \n",
              "8510426           0       13.540         14.36  ...   0   6   1\n",
              "8510653           0       13.080         15.71  ...   0   1   1\n",
              "8510824           0        9.504         12.44  ...   6   0   5\n",
              "854941            0       13.030         18.42  ...   1   1   3\n",
              "85713702          0        8.196         16.84  ...   6   0   5\n",
              "...             ...          ...           ...  ...  ..  ..  ..\n",
              "925277            0       14.590         22.68  ...   3   2   4\n",
              "925291            0       11.510         23.93  ...   2   3   6\n",
              "925292            0       14.050         27.15  ...   0   6   1\n",
              "925311            0       11.200         29.37  ...   2   3   6\n",
              "92751             0        7.760         24.54  ...   6   0   5\n",
              "\n",
              "[357 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zZ-R8RNB8e_"
      },
      "source": [
        "#colon_majority.to_csv('colon_majority.csv', index = False)\n",
        "#from google.colab import files\n",
        "#files.download(\"colon_majority.csv\")"
      ],
      "execution_count": 1171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "AY5Nl2BWDWyh",
        "outputId": "6dd7b72d-60b8-49cb-be0d-3655ad7c9dc9"
      },
      "source": [
        "colon_majority['index'] = range(0, len(colon_majority))\n",
        "\n",
        "colon_majority=colon_majority.reset_index(drop=False)\n",
        "colon_majority.head()"
      ],
      "execution_count": 1172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8510426</td>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8510653</td>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8510824</td>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>854941</td>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85713702</td>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  diagnosis  radius_mean  texture_mean  ...  C1  C2  C3  index\n",
              "0   8510426          0       13.540         14.36  ...   0   6   1      0\n",
              "1   8510653          0       13.080         15.71  ...   0   1   1      1\n",
              "2   8510824          0        9.504         12.44  ...   6   0   5      2\n",
              "3    854941          0       13.030         18.42  ...   1   1   3      3\n",
              "4  85713702          0        8.196         16.84  ...   6   0   5      4\n",
              "\n",
              "[5 rows x 36 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "dR4qn2OxJ3xJ",
        "outputId": "3f40b9d6-7048-4543-87ba-1878bc492d75"
      },
      "source": [
        "colon_majority=colon_majority.drop(['id'], axis=1)\n",
        "colon_majority.head()"
      ],
      "execution_count": 1173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>index</th>\n",
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              "  <tbody>\n",
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              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
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              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
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              "      <td>0.04568</td>\n",
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              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  ...  C1  C2  C3  index\n",
              "0          0       13.540         14.36           87.46  ...   0   6   1      0\n",
              "1          0       13.080         15.71           85.63  ...   0   1   1      1\n",
              "2          0        9.504         12.44           60.34  ...   6   0   5      2\n",
              "3          0       13.030         18.42           82.61  ...   1   1   3      3\n",
              "4          0        8.196         16.84           51.71  ...   6   0   5      4\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "nndsadktKCrx",
        "outputId": "3c1b398d-eb6f-43d3-c037-5b14e9bbe3d1"
      },
      "source": [
        "clustering_result=colon_majority[['index','C1','C2','C3']]\n",
        "clustering_result.rename(columns={'index': 'Datapoint(x)'}, inplace=True)\n",
        "clustering_result"
      ],
      "execution_count": 1174,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4308: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Datapoint(x)</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>352</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>353</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>355</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>356</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C1  C2  C3\n",
              "0               0   0   6   1\n",
              "1               1   0   1   1\n",
              "2               2   6   0   5\n",
              "3               3   1   1   3\n",
              "4               4   6   0   5\n",
              "..            ...  ..  ..  ..\n",
              "352           352   3   2   4\n",
              "353           353   2   3   6\n",
              "354           354   0   6   1\n",
              "355           355   2   3   6\n",
              "356           356   6   0   5\n",
              "\n",
              "[357 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "NQ2doQN2KGUC",
        "outputId": "a5725def-be0c-44c4-9359-c300fa5f79ff"
      },
      "source": [
        "\n",
        "num_of_clusters=7\n",
        "C1_datapoints={}\n",
        "C2_datapoints={}\n",
        "C3_datapoints={}\n",
        "def clusters(dictionary,clustername):\n",
        "  for i in clustering_result.index:\n",
        "    if clustering_result.loc[i,clustername] not in dictionary.keys():\n",
        "      dictionary[clustering_result.loc[i,clustername]]=[clustering_result.loc[i,'Datapoint(x)']]\n",
        "    else:\n",
        "      dictionary[clustering_result.loc[i,clustername]].append(clustering_result.loc[i,'Datapoint(x)'])\n",
        "  return dictionary\n",
        "\n",
        "C1_datapoints=clusters(C1_datapoints,'C1')\n",
        "C2_datapoints=clusters(C2_datapoints,'C2')\n",
        "C3_datapoints=clusters(C3_datapoints,'C3')\n",
        "print(\"C1 clusters :\",C1_datapoints)\n",
        "print(\"C3 clusters :\",C3_datapoints)\n",
        "\n",
        "\n",
        "def common_member(list1,list2):\n",
        "  set1=set(list1)\n",
        "  set2=set(list2)\n",
        "  return len(set1.intersection(set2))\n",
        "\n",
        "# def union(list1, list2):\n",
        "#   l = list1 + list2\n",
        "#   return len(set(l))\n",
        "\n",
        "\n",
        "contingency_matrix1=[]\n",
        "for i in range(7):\n",
        "  common=[]\n",
        "  for j in range(7):\n",
        "    common_element=common_member(C3_datapoints[i],C1_datapoints[j])\n",
        "    common.append(common_element)\n",
        "  contingency_matrix1.append(common)\n",
        "contingency_df1=pd.DataFrame(contingency_matrix1,index=['C30','C31','C32','C33','C34','C35','C36'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "contingency_df1"
      ],
      "execution_count": 1175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C1 clusters : {0: [0, 1, 6, 8, 11, 22, 31, 44, 51, 52, 61, 66, 71, 97, 99, 104, 105, 107, 115, 117, 120, 123, 128, 131, 136, 137, 150, 158, 160, 161, 177, 196, 200, 203, 206, 209, 210, 215, 217, 222, 223, 228, 233, 246, 257, 258, 260, 269, 271, 272, 276, 277, 278, 288, 292, 293, 301, 316, 320, 323, 325, 337, 338, 339, 354], 6: [2, 4, 12, 14, 15, 16, 18, 20, 35, 46, 48, 68, 84, 94, 159, 166, 169, 197, 220, 237, 307, 308, 318, 322, 332, 333, 347, 351, 356], 1: [3, 5, 9, 19, 21, 23, 25, 26, 27, 36, 40, 43, 47, 54, 56, 67, 72, 74, 76, 77, 81, 86, 87, 92, 93, 95, 96, 101, 102, 111, 118, 122, 129, 138, 139, 140, 141, 142, 147, 149, 154, 164, 167, 168, 170, 172, 173, 174, 176, 180, 181, 188, 192, 194, 195, 199, 201, 204, 214, 216, 221, 224, 226, 227, 229, 230, 234, 241, 252, 253, 255, 266, 268, 282, 284, 286, 290, 295, 297, 298, 300, 303, 304, 306, 309, 317, 324, 326, 327, 328, 346, 348], 2: [7, 10, 17, 24, 32, 34, 39, 41, 49, 57, 58, 60, 62, 63, 70, 75, 88, 90, 91, 112, 113, 119, 124, 125, 126, 132, 134, 135, 143, 144, 148, 156, 157, 162, 165, 178, 179, 185, 186, 190, 191, 193, 211, 212, 213, 218, 225, 231, 235, 236, 239, 242, 243, 245, 250, 251, 259, 262, 265, 270, 275, 280, 285, 289, 312, 315, 319, 331, 334, 343, 345, 353, 355], 4: [13, 33, 37, 38, 42, 45, 59, 69, 79, 82, 83, 85, 89, 98, 103, 106, 109, 114, 121, 127, 130, 133, 151, 153, 155, 171, 182, 184, 187, 198, 208, 219, 240, 247, 248, 249, 261, 273, 279, 281, 310, 321, 330, 340, 341, 342, 344, 349, 350], 3: [28, 29, 30, 50, 53, 64, 65, 78, 80, 108, 110, 116, 145, 146, 152, 163, 175, 183, 189, 205, 238, 244, 254, 256, 263, 264, 267, 274, 283, 287, 291, 294, 296, 302, 305, 313, 314, 329, 335, 336, 352], 5: [55, 73, 100, 202, 207, 232, 299, 311]}\n",
            "C3 clusters : {1: [0, 1, 6, 8, 11, 22, 23, 25, 31, 44, 51, 52, 61, 66, 67, 71, 86, 97, 99, 104, 105, 107, 111, 115, 117, 120, 122, 123, 128, 131, 136, 137, 150, 158, 160, 161, 164, 177, 196, 200, 203, 206, 209, 210, 215, 222, 223, 228, 233, 246, 255, 258, 260, 268, 269, 271, 272, 276, 277, 278, 288, 292, 293, 301, 303, 316, 317, 320, 323, 325, 337, 338, 339, 354], 5: [2, 4, 12, 14, 15, 18, 20, 35, 46, 48, 68, 84, 94, 159, 166, 169, 197, 220, 237, 307, 318, 322, 332, 333, 347, 356], 3: [3, 5, 7, 9, 19, 21, 26, 27, 32, 36, 39, 40, 43, 47, 54, 56, 63, 72, 74, 76, 77, 81, 87, 90, 92, 93, 95, 96, 101, 102, 118, 129, 138, 139, 140, 141, 142, 147, 148, 149, 154, 167, 168, 170, 172, 173, 174, 176, 180, 181, 186, 188, 192, 194, 195, 199, 201, 204, 214, 216, 221, 224, 226, 227, 229, 230, 234, 239, 241, 252, 253, 262, 265, 266, 270, 280, 282, 284, 286, 290, 295, 297, 298, 300, 304, 306, 309, 324, 326, 327, 328, 346, 348], 6: [10, 17, 24, 34, 41, 49, 57, 58, 60, 62, 70, 75, 79, 88, 91, 112, 113, 119, 124, 125, 126, 127, 132, 134, 135, 143, 144, 156, 157, 162, 165, 178, 179, 185, 190, 191, 193, 211, 212, 213, 218, 225, 231, 235, 236, 242, 243, 245, 249, 250, 251, 259, 275, 285, 289, 310, 312, 315, 319, 330, 331, 334, 343, 344, 345, 353, 355], 0: [13, 16, 33, 37, 38, 42, 45, 59, 69, 82, 83, 85, 89, 98, 103, 106, 109, 114, 121, 130, 133, 151, 153, 155, 171, 182, 184, 187, 198, 208, 219, 240, 247, 248, 261, 273, 279, 281, 308, 321, 340, 341, 342, 349, 350, 351], 4: [28, 29, 30, 50, 53, 64, 65, 78, 80, 108, 110, 116, 145, 146, 152, 163, 175, 183, 189, 205, 217, 238, 244, 254, 256, 257, 263, 264, 267, 274, 283, 287, 291, 294, 296, 302, 305, 313, 314, 329, 335, 336, 352], 2: [55, 73, 100, 202, 207, 232, 299, 311]}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>63</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0</td>\n",
              "      <td>81</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C10  C11  C12  C13  C14  C15  C16\n",
              "C30    0    0    0    0   43    0    3\n",
              "C31   63   11    0    0    0    0    0\n",
              "C32    0    0    0    0    0    8    0\n",
              "C33    0   81   12    0    0    0    0\n",
              "C34    2    0    0   41    0    0    0\n",
              "C35    0    0    0    0    0    0   26\n",
              "C36    0    0   61    0    6    0    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "qRP1T6MoKI7s",
        "outputId": "3bdfb105-f303-4889-c802-516f0efb0ffe"
      },
      "source": [
        "print(\"C1 clusters :\",C1_datapoints)\n",
        "print(\"C2 clusters :\",C2_datapoints)\n",
        "\n",
        "contingency_matrix2=[]\n",
        "for i in range(7):\n",
        "  common=[]\n",
        "  for j in range(7):\n",
        "    common_element=common_member(C2_datapoints[i],C1_datapoints[j])\n",
        "    common.append(common_element)\n",
        "  contingency_matrix2.append(common)\n",
        "contingency_df2=pd.DataFrame(contingency_matrix2,index=['C20','C21','C22','C23','C24','C25','C26'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "contingency_df2"
      ],
      "execution_count": 1176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C1 clusters : {0: [0, 1, 6, 8, 11, 22, 31, 44, 51, 52, 61, 66, 71, 97, 99, 104, 105, 107, 115, 117, 120, 123, 128, 131, 136, 137, 150, 158, 160, 161, 177, 196, 200, 203, 206, 209, 210, 215, 217, 222, 223, 228, 233, 246, 257, 258, 260, 269, 271, 272, 276, 277, 278, 288, 292, 293, 301, 316, 320, 323, 325, 337, 338, 339, 354], 6: [2, 4, 12, 14, 15, 16, 18, 20, 35, 46, 48, 68, 84, 94, 159, 166, 169, 197, 220, 237, 307, 308, 318, 322, 332, 333, 347, 351, 356], 1: [3, 5, 9, 19, 21, 23, 25, 26, 27, 36, 40, 43, 47, 54, 56, 67, 72, 74, 76, 77, 81, 86, 87, 92, 93, 95, 96, 101, 102, 111, 118, 122, 129, 138, 139, 140, 141, 142, 147, 149, 154, 164, 167, 168, 170, 172, 173, 174, 176, 180, 181, 188, 192, 194, 195, 199, 201, 204, 214, 216, 221, 224, 226, 227, 229, 230, 234, 241, 252, 253, 255, 266, 268, 282, 284, 286, 290, 295, 297, 298, 300, 303, 304, 306, 309, 317, 324, 326, 327, 328, 346, 348], 2: [7, 10, 17, 24, 32, 34, 39, 41, 49, 57, 58, 60, 62, 63, 70, 75, 88, 90, 91, 112, 113, 119, 124, 125, 126, 132, 134, 135, 143, 144, 148, 156, 157, 162, 165, 178, 179, 185, 186, 190, 191, 193, 211, 212, 213, 218, 225, 231, 235, 236, 239, 242, 243, 245, 250, 251, 259, 262, 265, 270, 275, 280, 285, 289, 312, 315, 319, 331, 334, 343, 345, 353, 355], 4: [13, 33, 37, 38, 42, 45, 59, 69, 79, 82, 83, 85, 89, 98, 103, 106, 109, 114, 121, 127, 130, 133, 151, 153, 155, 171, 182, 184, 187, 198, 208, 219, 240, 247, 248, 249, 261, 273, 279, 281, 310, 321, 330, 340, 341, 342, 344, 349, 350], 3: [28, 29, 30, 50, 53, 64, 65, 78, 80, 108, 110, 116, 145, 146, 152, 163, 175, 183, 189, 205, 238, 244, 254, 256, 263, 264, 267, 274, 283, 287, 291, 294, 296, 302, 305, 313, 314, 329, 335, 336, 352], 5: [55, 73, 100, 202, 207, 232, 299, 311]}\n",
            "C2 clusters : {6: [0, 6, 8, 31, 44, 51, 52, 66, 71, 104, 105, 107, 115, 117, 120, 128, 131, 136, 137, 150, 160, 161, 175, 196, 203, 206, 209, 210, 217, 222, 223, 246, 256, 257, 258, 260, 269, 276, 277, 278, 288, 292, 293, 301, 316, 320, 323, 325, 329, 338, 339, 354], 1: [1, 3, 5, 11, 19, 21, 22, 23, 25, 26, 27, 36, 40, 43, 47, 54, 61, 67, 72, 77, 81, 86, 87, 92, 93, 95, 96, 97, 99, 111, 118, 122, 123, 129, 139, 140, 142, 147, 149, 154, 158, 164, 167, 172, 173, 174, 177, 180, 181, 188, 194, 195, 199, 200, 201, 204, 214, 215, 221, 224, 226, 227, 228, 229, 230, 233, 234, 241, 252, 255, 268, 271, 272, 282, 284, 286, 290, 295, 298, 300, 303, 304, 306, 317, 324, 326, 337, 346, 348], 0: [2, 4, 12, 14, 15, 16, 18, 20, 35, 46, 48, 68, 84, 94, 159, 166, 169, 197, 220, 237, 307, 308, 318, 322, 332, 333, 347, 351, 356], 3: [7, 9, 10, 17, 24, 32, 34, 39, 41, 49, 56, 57, 58, 60, 63, 70, 74, 75, 76, 88, 90, 91, 101, 102, 112, 113, 119, 124, 125, 126, 132, 134, 135, 138, 141, 143, 144, 148, 156, 157, 162, 165, 168, 170, 176, 178, 179, 185, 186, 190, 191, 192, 193, 211, 212, 213, 216, 218, 225, 231, 235, 236, 239, 242, 243, 245, 250, 253, 259, 262, 265, 266, 270, 275, 280, 289, 297, 309, 312, 315, 319, 327, 328, 331, 334, 343, 345, 353, 355], 4: [13, 33, 37, 38, 42, 45, 59, 62, 69, 79, 82, 83, 85, 89, 98, 103, 106, 109, 114, 121, 127, 130, 133, 151, 153, 155, 171, 182, 184, 187, 198, 208, 219, 240, 247, 248, 249, 251, 261, 273, 279, 281, 285, 310, 321, 330, 340, 341, 342, 344, 349, 350], 2: [28, 29, 30, 50, 53, 64, 65, 78, 80, 108, 110, 116, 145, 146, 152, 163, 183, 189, 205, 238, 244, 254, 263, 264, 267, 274, 283, 287, 291, 294, 296, 302, 305, 313, 314, 335, 336, 352], 5: [55, 73, 100, 202, 207, 232, 299, 311]}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>16</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C10  C11  C12  C13  C14  C15  C16\n",
              "C20    0    0    0    0    0    0   29\n",
              "C21   16   73    0    0    0    0    0\n",
              "C22    0    0    0   38    0    0    0\n",
              "C23    0   19   70    0    0    0    0\n",
              "C24    0    0    3    0   49    0    0\n",
              "C25    0    0    0    0    0    8    0\n",
              "C26   49    0    0    3    0    0    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "Wsoo6oreKLDk",
        "outputId": "804a15d3-b197-41a8-de53-5a36394d7a61"
      },
      "source": [
        "contigency_mat=pd.concat([contingency_df2, contingency_df1])\n",
        "print(\"-----------Contrigency matrix----------\")\n",
        "contigency_mat"
      ],
      "execution_count": 1177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-----------Contrigency matrix----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>16</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>63</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0</td>\n",
              "      <td>81</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C10  C11  C12  C13  C14  C15  C16\n",
              "C20    0    0    0    0    0    0   29\n",
              "C21   16   73    0    0    0    0    0\n",
              "C22    0    0    0   38    0    0    0\n",
              "C23    0   19   70    0    0    0    0\n",
              "C24    0    0    3    0   49    0    0\n",
              "C25    0    0    0    0    0    8    0\n",
              "C26   49    0    0    3    0    0    0\n",
              "C30    0    0    0    0   43    0    3\n",
              "C31   63   11    0    0    0    0    0\n",
              "C32    0    0    0    0    0    8    0\n",
              "C33    0   81   12    0    0    0    0\n",
              "C34    2    0    0   41    0    0    0\n",
              "C35    0    0    0    0    0    0   26\n",
              "C36    0    0   61    0    6    0    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "uf5157BcKNah",
        "outputId": "ab1c5f81-42ee-4e69-d14a-e770d770d0c8"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "C1_datapoints={}\n",
        "C2_datapoints={}\n",
        "C3_datapoints={}\n",
        "\n",
        "def clusters(dictionary,clustername):\n",
        "  for i in clustering_result.index:\n",
        "    if clustering_result.loc[i,clustername] not in dictionary.keys():\n",
        "      dictionary[clustering_result.loc[i,clustername]]=[clustering_result.loc[i,'Datapoint(x)']]\n",
        "    else:\n",
        "      dictionary[clustering_result.loc[i,clustername]].append(clustering_result.loc[i,'Datapoint(x)'])\n",
        "  return dictionary\n",
        "\n",
        "C1_datapoints=clusters(C1_datapoints,'C1')\n",
        "C2_datapoints=clusters(C2_datapoints,'C2')\n",
        "C3_datapoints=clusters(C3_datapoints,'C3')\n",
        "\n",
        "\n",
        "\n",
        "def union(list1, list2):\n",
        "  l = list1 + list2\n",
        "  return len(set(l))\n",
        "\n",
        "def common_member(list1,list2):\n",
        "  set1=set(list1)\n",
        "  set2=set(list2)\n",
        "  return len(set1.intersection(set2))\n",
        "\n",
        "\n",
        "union_df1 = []\n",
        "common_df1 = []\n",
        "\n",
        "for i in range(7):\n",
        "  uni = []\n",
        "  common = []\n",
        "  for j in range(7):\n",
        "    union_element = union(C3_datapoints[i],C1_datapoints[j])\n",
        "    common_element = common_member(C3_datapoints[i],C1_datapoints[j])\n",
        "    uni.append(union_element)\n",
        "    common.append(common_element)\n",
        "   \n",
        "  union_df1.append(uni)\n",
        "  common_df1.append(common)\n",
        "\n",
        "confusion_mat1 = np.divide(common_df1, union_df1)\n",
        "confusion_mat1=pd.DataFrame(confusion_mat1,index=['C30','C31','C32','C33','C34','C35','C36'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "common_df1, union_df1\n",
        "confusion_mat1"
      ],
      "execution_count": 1178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.826923</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0.828947</td>\n",
              "      <td>0.070968</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.778846</td>\n",
              "      <td>0.077922</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.953488</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.896552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.772152</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.054545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12       C13       C14  C15       C16\n",
              "C30  0.000000  0.000000  0.000000  0.000000  0.826923  0.0  0.041667\n",
              "C31  0.828947  0.070968  0.000000  0.000000  0.000000  0.0  0.000000\n",
              "C32  0.000000  0.000000  0.000000  0.000000  0.000000  1.0  0.000000\n",
              "C33  0.000000  0.778846  0.077922  0.000000  0.000000  0.0  0.000000\n",
              "C34  0.018868  0.000000  0.000000  0.953488  0.000000  0.0  0.000000\n",
              "C35  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.896552\n",
              "C36  0.000000  0.000000  0.772152  0.000000  0.054545  0.0  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "oIDbQ2vdKPyi",
        "outputId": "bf50cb17-e299-4956-b8c3-c817dfa19d60"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "C1_datapoints={}\n",
        "C2_datapoints={}\n",
        "C3_datapoints={}\n",
        "\n",
        "def clusters(dictionary,clustername):\n",
        "  for i in clustering_result.index:\n",
        "    if clustering_result.loc[i,clustername] not in dictionary.keys():\n",
        "      dictionary[clustering_result.loc[i,clustername]]=[clustering_result.loc[i,'Datapoint(x)']]\n",
        "    else:\n",
        "      dictionary[clustering_result.loc[i,clustername]].append(clustering_result.loc[i,'Datapoint(x)'])\n",
        "  return dictionary\n",
        "\n",
        "C1_datapoints=clusters(C1_datapoints,'C1')\n",
        "C2_datapoints=clusters(C2_datapoints,'C2')\n",
        "C3_datapoints=clusters(C3_datapoints,'C3')\n",
        "\n",
        "\n",
        "union_df2 = []\n",
        "common_df2 = []\n",
        "\n",
        "for i in range(7):\n",
        "  uni = []\n",
        "  common = []\n",
        "  for j in range(7):\n",
        "    union_element = union(C2_datapoints[i],C1_datapoints[j])\n",
        "    common_element = common_member(C2_datapoints[i],C1_datapoints[j])\n",
        "    uni.append(union_element)\n",
        "    common.append(common_element)\n",
        "   \n",
        "  union_df2.append(uni)\n",
        "  common_df2.append(common)\n",
        "\n",
        "confusion_mat2 = np.divide(common_df2, union_df2)\n",
        "\n",
        "confusion_mat2=pd.DataFrame(confusion_mat2,index=['C20','C21','C22','C23','C24','C25','C26'],columns=['C10','C11','C12','C13','C14','C15','C16'])\n",
        "common_df2, union_df2\n",
        "confusion_mat2"
      ],
      "execution_count": 1179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>0.115942</td>\n",
              "      <td>0.675926</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.926829</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.117284</td>\n",
              "      <td>0.76087</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02459</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.942308</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>0.720588</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11      C12       C13       C14  C15  C16\n",
              "C20  0.000000  0.000000  0.00000  0.000000  0.000000  0.0  1.0\n",
              "C21  0.115942  0.675926  0.00000  0.000000  0.000000  0.0  0.0\n",
              "C22  0.000000  0.000000  0.00000  0.926829  0.000000  0.0  0.0\n",
              "C23  0.000000  0.117284  0.76087  0.000000  0.000000  0.0  0.0\n",
              "C24  0.000000  0.000000  0.02459  0.000000  0.942308  0.0  0.0\n",
              "C25  0.000000  0.000000  0.00000  0.000000  0.000000  1.0  0.0\n",
              "C26  0.720588  0.000000  0.00000  0.033333  0.000000  0.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "gY0bKAGuKSE0",
        "outputId": "eb24bb27-10b0-43c9-86d8-e54f8ad78660"
      },
      "source": [
        "confusion_mat=pd.concat([confusion_mat2, confusion_mat1])\n",
        "print(\"-----------Confusion matrix----------\")\n",
        "confusion_mat"
      ],
      "execution_count": 1180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-----------Confusion matrix----------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C21</th>\n",
              "      <td>0.115942</td>\n",
              "      <td>0.675926</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.926829</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.117284</td>\n",
              "      <td>0.760870</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.024590</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.942308</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>0.720588</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.826923</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0.828947</td>\n",
              "      <td>0.070968</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.778846</td>\n",
              "      <td>0.077922</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.953488</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.896552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.772152</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.054545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12       C13       C14  C15       C16\n",
              "C20  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  1.000000\n",
              "C21  0.115942  0.675926  0.000000  0.000000  0.000000  0.0  0.000000\n",
              "C22  0.000000  0.000000  0.000000  0.926829  0.000000  0.0  0.000000\n",
              "C23  0.000000  0.117284  0.760870  0.000000  0.000000  0.0  0.000000\n",
              "C24  0.000000  0.000000  0.024590  0.000000  0.942308  0.0  0.000000\n",
              "C25  0.000000  0.000000  0.000000  0.000000  0.000000  1.0  0.000000\n",
              "C26  0.720588  0.000000  0.000000  0.033333  0.000000  0.0  0.000000\n",
              "C30  0.000000  0.000000  0.000000  0.000000  0.826923  0.0  0.041667\n",
              "C31  0.828947  0.070968  0.000000  0.000000  0.000000  0.0  0.000000\n",
              "C32  0.000000  0.000000  0.000000  0.000000  0.000000  1.0  0.000000\n",
              "C33  0.000000  0.778846  0.077922  0.000000  0.000000  0.0  0.000000\n",
              "C34  0.018868  0.000000  0.000000  0.953488  0.000000  0.0  0.000000\n",
              "C35  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.896552\n",
              "C36  0.000000  0.000000  0.772152  0.000000  0.054545  0.0  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gm_E9FMeKUR5",
        "outputId": "e97e9d3f-a1ba-4475-f01c-ca7a9e273b9d"
      },
      "source": [
        "CCluster = []\n",
        "CLabel = ['C10', 'C11',\t'C12', 'C13',\t'C14', 'C15',\t'C16']\n",
        "\n",
        "for i in range(len(confusion_mat)):\n",
        "  l =confusion_mat.iloc[i]\n",
        "  l = list(l)\n",
        "  ind = l.index(max(l))\n",
        "  CCluster.append(CLabel[ind])\n",
        "CCluster"
      ],
      "execution_count": 1181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['C16',\n",
              " 'C11',\n",
              " 'C13',\n",
              " 'C12',\n",
              " 'C14',\n",
              " 'C15',\n",
              " 'C10',\n",
              " 'C14',\n",
              " 'C10',\n",
              " 'C15',\n",
              " 'C11',\n",
              " 'C13',\n",
              " 'C16',\n",
              " 'C12']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "fr35OlX0KWSD",
        "outputId": "6e925457-ca18-4d3e-f48b-ed09dc2528d7"
      },
      "source": [
        "confusion_matrix = confusion_mat\n",
        "confusion_matrix['CCluster'] = CCluster\n",
        "confusion_matrix.tail(8)"
      ],
      "execution_count": 1182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C10</th>\n",
              "      <th>C11</th>\n",
              "      <th>C12</th>\n",
              "      <th>C13</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "      <th>CCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C26</th>\n",
              "      <td>0.720588</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.826923</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>C14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C31</th>\n",
              "      <td>0.828947</td>\n",
              "      <td>0.070968</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C32</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C33</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.778846</td>\n",
              "      <td>0.077922</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C34</th>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.953488</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.896552</td>\n",
              "      <td>C16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C36</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.772152</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.054545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>C12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C10       C11       C12       C13       C14  C15       C16 CCluster\n",
              "C26  0.720588  0.000000  0.000000  0.033333  0.000000  0.0  0.000000      C10\n",
              "C30  0.000000  0.000000  0.000000  0.000000  0.826923  0.0  0.041667      C14\n",
              "C31  0.828947  0.070968  0.000000  0.000000  0.000000  0.0  0.000000      C10\n",
              "C32  0.000000  0.000000  0.000000  0.000000  0.000000  1.0  0.000000      C15\n",
              "C33  0.000000  0.778846  0.077922  0.000000  0.000000  0.0  0.000000      C11\n",
              "C34  0.018868  0.000000  0.000000  0.953488  0.000000  0.0  0.000000      C13\n",
              "C35  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.896552      C16\n",
              "C36  0.000000  0.000000  0.772152  0.000000  0.054545  0.0  0.000000      C12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BPBNTagKZV6",
        "outputId": "799ac848-09cc-4978-a8f4-fa30fa4592a2"
      },
      "source": [
        "clustering_result['C1'].values, clustering_result['C2'].values, clustering_result['C3'].values"
      ],
      "execution_count": 1183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 6, 1, 6, 1, 0, 2, 0, 1, 2, 0, 6, 4, 6, 6, 6, 2, 6, 1, 6, 1,\n",
              "        0, 1, 2, 1, 1, 1, 3, 3, 3, 0, 2, 4, 2, 6, 1, 4, 4, 2, 1, 2, 4, 1,\n",
              "        0, 4, 6, 1, 6, 2, 3, 0, 0, 3, 1, 5, 1, 2, 2, 4, 2, 0, 2, 2, 3, 3,\n",
              "        0, 1, 6, 4, 2, 0, 1, 5, 1, 2, 1, 1, 3, 4, 3, 1, 4, 4, 6, 4, 1, 1,\n",
              "        2, 4, 2, 2, 1, 1, 6, 1, 1, 0, 4, 0, 5, 1, 1, 4, 0, 0, 4, 0, 3, 4,\n",
              "        3, 1, 2, 2, 4, 0, 3, 0, 1, 2, 0, 4, 1, 0, 2, 2, 2, 4, 0, 1, 4, 0,\n",
              "        2, 4, 2, 2, 0, 0, 1, 1, 1, 1, 1, 2, 2, 3, 3, 1, 2, 1, 0, 4, 3, 4,\n",
              "        1, 4, 2, 2, 0, 6, 0, 0, 2, 3, 1, 2, 6, 1, 1, 6, 1, 4, 1, 1, 1, 3,\n",
              "        1, 0, 2, 2, 1, 1, 4, 3, 4, 2, 2, 4, 1, 3, 2, 2, 1, 2, 1, 1, 0, 6,\n",
              "        4, 1, 0, 1, 5, 0, 1, 3, 0, 5, 4, 0, 0, 2, 2, 2, 1, 0, 1, 0, 2, 4,\n",
              "        6, 1, 0, 0, 1, 2, 1, 1, 0, 1, 1, 2, 5, 0, 1, 2, 2, 6, 3, 2, 4, 1,\n",
              "        2, 2, 3, 2, 0, 4, 4, 4, 2, 2, 1, 1, 3, 1, 3, 0, 0, 2, 0, 4, 2, 3,\n",
              "        3, 2, 1, 3, 1, 0, 2, 0, 0, 4, 3, 2, 0, 0, 0, 4, 2, 4, 1, 3, 1, 2,\n",
              "        1, 3, 0, 2, 1, 3, 0, 0, 3, 1, 3, 1, 1, 5, 1, 0, 3, 1, 1, 3, 1, 6,\n",
              "        6, 1, 4, 5, 2, 3, 3, 2, 0, 1, 6, 2, 0, 4, 6, 0, 1, 0, 1, 1, 1, 3,\n",
              "        4, 2, 6, 6, 2, 3, 3, 0, 0, 0, 4, 4, 4, 2, 4, 2, 1, 6, 1, 4, 4, 6,\n",
              "        3, 2, 0, 2, 6], dtype=int32),\n",
              " array([6, 1, 0, 1, 0, 1, 6, 3, 6, 3, 3, 1, 0, 4, 0, 0, 0, 3, 0, 1, 0, 1,\n",
              "        1, 1, 3, 1, 1, 1, 2, 2, 2, 6, 3, 4, 3, 0, 1, 4, 4, 3, 1, 3, 4, 1,\n",
              "        6, 4, 0, 1, 0, 3, 2, 6, 6, 2, 1, 5, 3, 3, 3, 4, 3, 1, 4, 3, 2, 2,\n",
              "        6, 1, 0, 4, 3, 6, 1, 5, 3, 3, 3, 1, 2, 4, 2, 1, 4, 4, 0, 4, 1, 1,\n",
              "        3, 4, 3, 3, 1, 1, 0, 1, 1, 1, 4, 1, 5, 3, 3, 4, 6, 6, 4, 6, 2, 4,\n",
              "        2, 1, 3, 3, 4, 6, 2, 6, 1, 3, 6, 4, 1, 1, 3, 3, 3, 4, 6, 1, 4, 6,\n",
              "        3, 4, 3, 3, 6, 6, 3, 1, 1, 3, 1, 3, 3, 2, 2, 1, 3, 1, 6, 4, 2, 4,\n",
              "        1, 4, 3, 3, 1, 0, 6, 6, 3, 2, 1, 3, 0, 1, 3, 0, 3, 4, 1, 1, 1, 6,\n",
              "        3, 1, 3, 3, 1, 1, 4, 2, 4, 3, 3, 4, 1, 2, 3, 3, 3, 3, 1, 1, 6, 0,\n",
              "        4, 1, 1, 1, 5, 6, 1, 2, 6, 5, 4, 6, 6, 3, 3, 3, 1, 1, 3, 6, 3, 4,\n",
              "        0, 1, 6, 6, 1, 3, 1, 1, 1, 1, 1, 3, 5, 1, 1, 3, 3, 0, 2, 3, 4, 1,\n",
              "        3, 3, 2, 3, 6, 4, 4, 4, 3, 4, 1, 3, 2, 1, 6, 6, 6, 3, 6, 4, 3, 2,\n",
              "        2, 3, 3, 2, 1, 6, 3, 1, 1, 4, 2, 3, 6, 6, 6, 4, 3, 4, 1, 2, 1, 4,\n",
              "        1, 2, 6, 3, 1, 2, 6, 6, 2, 1, 2, 3, 1, 5, 1, 6, 2, 1, 1, 2, 1, 0,\n",
              "        0, 3, 4, 5, 3, 2, 2, 3, 6, 1, 0, 3, 6, 4, 0, 6, 1, 6, 1, 3, 3, 6,\n",
              "        4, 3, 0, 0, 3, 2, 2, 1, 6, 6, 4, 4, 4, 3, 4, 3, 1, 0, 1, 4, 4, 0,\n",
              "        2, 3, 6, 3, 0], dtype=int32),\n",
              " array([1, 1, 5, 3, 5, 3, 1, 3, 1, 3, 6, 1, 5, 0, 5, 5, 0, 6, 5, 3, 5, 3,\n",
              "        1, 1, 6, 1, 3, 3, 4, 4, 4, 1, 3, 0, 6, 5, 3, 0, 0, 3, 3, 6, 0, 3,\n",
              "        1, 0, 5, 3, 5, 6, 4, 1, 1, 4, 3, 2, 3, 6, 6, 0, 6, 1, 6, 3, 4, 4,\n",
              "        1, 1, 5, 0, 6, 1, 3, 2, 3, 6, 3, 3, 4, 6, 4, 3, 0, 0, 5, 0, 1, 3,\n",
              "        6, 0, 3, 6, 3, 3, 5, 3, 3, 1, 0, 1, 2, 3, 3, 0, 1, 1, 0, 1, 4, 0,\n",
              "        4, 1, 6, 6, 0, 1, 4, 1, 3, 6, 1, 0, 1, 1, 6, 6, 6, 6, 1, 3, 0, 1,\n",
              "        6, 0, 6, 6, 1, 1, 3, 3, 3, 3, 3, 6, 6, 4, 4, 3, 3, 3, 1, 0, 4, 0,\n",
              "        3, 0, 6, 6, 1, 5, 1, 1, 6, 4, 1, 6, 5, 3, 3, 5, 3, 0, 3, 3, 3, 4,\n",
              "        3, 1, 6, 6, 3, 3, 0, 4, 0, 6, 3, 0, 3, 4, 6, 6, 3, 6, 3, 3, 1, 5,\n",
              "        0, 3, 1, 3, 2, 1, 3, 4, 1, 2, 0, 1, 1, 6, 6, 6, 3, 1, 3, 4, 6, 0,\n",
              "        5, 3, 1, 1, 3, 6, 3, 3, 1, 3, 3, 6, 2, 1, 3, 6, 6, 5, 4, 3, 0, 3,\n",
              "        6, 6, 4, 6, 1, 0, 0, 6, 6, 6, 3, 3, 4, 1, 4, 4, 1, 6, 1, 0, 3, 4,\n",
              "        4, 3, 3, 4, 1, 1, 3, 1, 1, 0, 4, 6, 1, 1, 1, 0, 3, 0, 3, 4, 3, 6,\n",
              "        3, 4, 1, 6, 3, 4, 1, 1, 4, 3, 4, 3, 3, 2, 3, 1, 4, 1, 3, 4, 3, 5,\n",
              "        0, 3, 6, 2, 6, 4, 4, 6, 1, 1, 5, 6, 1, 0, 5, 1, 3, 1, 3, 3, 3, 4,\n",
              "        6, 6, 5, 5, 6, 4, 4, 1, 1, 1, 0, 0, 0, 6, 6, 6, 3, 5, 3, 0, 0, 0,\n",
              "        4, 6, 1, 6, 5], dtype=int32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1183
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTI7d914KbJv"
      },
      "source": [
        "# C2 = [5, 1, 5, 5, 4, 6, 5, 4, 5, 5, 4, 5, 4, 5, 5, 6, 6, 6, 6, 5, 5, 4, 5, 5, 4, 5, 4, 5, 2, 6, 2, 2, 5, 2, 4, 5, 1, 5, 4, 5]\n",
        "# C3 = [5, 1, 5, 5, 4, 6, 5, 4, 5, 5, 4, 5, 4, 5, 5, 6, 6, 6, 6, 5, 5, 4, 5, 5, 4, 5, 4, 5, 2, 6, 2, 2, 5, 2, 4, 5, 1, 5, 4, 5]"
      ],
      "execution_count": 1184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        },
        "id": "z2Hc2fVAKfIR",
        "outputId": "fc6ea70b-e82e-4ecf-c116-e71b1b1861ba"
      },
      "source": [
        "#Relabeling\n",
        "new_clustering_result = clustering_result\n",
        "new_clustering_result['C2'] = clustering_result['C2'].values\n",
        "new_clustering_result['C3'] = clustering_result['C3'].values\n",
        "new_clustering_result"
      ],
      "execution_count": 1185,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Datapoint(x)</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>352</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>353</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>355</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>356</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C1  C2  C3\n",
              "0               0   0   6   1\n",
              "1               1   0   1   1\n",
              "2               2   6   0   5\n",
              "3               3   1   1   3\n",
              "4               4   6   0   5\n",
              "..            ...  ..  ..  ..\n",
              "352           352   3   2   4\n",
              "353           353   2   3   6\n",
              "354           354   0   6   1\n",
              "355           355   2   3   6\n",
              "356           356   6   0   5\n",
              "\n",
              "[357 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "dnw7lIRpMU92",
        "outputId": "ecfd1f30-4969-4571-9f18-1e31cc35fb53"
      },
      "source": [
        "#Majority Voting\n",
        "\n",
        "C = []\n",
        "for i in range(len(new_clustering_result)):\n",
        "  l = new_clustering_result.iloc[i]\n",
        "  l = list(l[1:])\n",
        "  majority_label = max(set(l), key = l.count)\n",
        "  C.append(majority_label)\n",
        "\n",
        "new_clustering_result['C'] = C\n",
        "new_clustering_result"
      ],
      "execution_count": 1186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Datapoint(x)</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>352</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>353</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>355</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>356</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C1  C2  C3  C\n",
              "0               0   0   6   1  0\n",
              "1               1   0   1   1  1\n",
              "2               2   6   0   5  0\n",
              "3               3   1   1   3  1\n",
              "4               4   6   0   5  0\n",
              "..            ...  ..  ..  .. ..\n",
              "352           352   3   2   4  2\n",
              "353           353   2   3   6  2\n",
              "354           354   0   6   1  0\n",
              "355           355   2   3   6  2\n",
              "356           356   6   0   5  0\n",
              "\n",
              "[357 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "Qz4N10kPMdhy",
        "outputId": "7cfce712-a66d-4ee1-d33c-aa05eb89c8e9"
      },
      "source": [
        "consensus_clustering_result=new_clustering_result.drop(['C1', 'C2', 'C3'], axis = 1)\n",
        "consensus_clustering_result"
      ],
      "execution_count": 1187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Datapoint(x)</th>\n",
              "      <th>C</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>352</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>353</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>355</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>356</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Datapoint(x)  C\n",
              "0               0  0\n",
              "1               1  1\n",
              "2               2  0\n",
              "3               3  1\n",
              "4               4  0\n",
              "..            ... ..\n",
              "352           352  2\n",
              "353           353  2\n",
              "354           354  0\n",
              "355           355  2\n",
              "356           356  0\n",
              "\n",
              "[357 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1187
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "S_hT9r3NMozr",
        "outputId": "55356d7d-8625-4a9b-c406-35523dbf9fe4"
      },
      "source": [
        "colon_majority.rename(columns={'index': 'Datapoint(x)'}, inplace=True)\n",
        "colon_majority=colon_majority.merge(consensus_clustering_result,on='Datapoint(x)')\n",
        "colon_majority=colon_majority.drop(columns=['C1','C2','C3'])\n",
        "colon_majority"
      ],
      "execution_count": 1188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
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              "      <th>concave points_mean</th>\n",
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              "      <th>texture_se</th>\n",
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              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Datapoint(x)</th>\n",
              "      <th>C</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.02671</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.02574</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>0</td>\n",
              "      <td>14.590</td>\n",
              "      <td>22.68</td>\n",
              "      <td>96.39</td>\n",
              "      <td>657.1</td>\n",
              "      <td>0.08473</td>\n",
              "      <td>0.13300</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.037360</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>0.06147</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>2.224</td>\n",
              "      <td>19.540</td>\n",
              "      <td>0.004242</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.06578</td>\n",
              "      <td>0.016060</td>\n",
              "      <td>0.01638</td>\n",
              "      <td>0.004406</td>\n",
              "      <td>15.480</td>\n",
              "      <td>27.27</td>\n",
              "      <td>105.90</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.10260</td>\n",
              "      <td>0.31710</td>\n",
              "      <td>0.36620</td>\n",
              "      <td>0.11050</td>\n",
              "      <td>0.2258</td>\n",
              "      <td>0.08004</td>\n",
              "      <td>352</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0</td>\n",
              "      <td>11.510</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.041050</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>0.06570</td>\n",
              "      <td>0.2388</td>\n",
              "      <td>2.9040</td>\n",
              "      <td>1.936</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008200</td>\n",
              "      <td>0.029820</td>\n",
              "      <td>0.05738</td>\n",
              "      <td>0.012670</td>\n",
              "      <td>0.01488</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>12.480</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.12980</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "      <td>353</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0</td>\n",
              "      <td>14.050</td>\n",
              "      <td>27.15</td>\n",
              "      <td>91.38</td>\n",
              "      <td>600.4</td>\n",
              "      <td>0.09929</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.043040</td>\n",
              "      <td>0.1537</td>\n",
              "      <td>0.06171</td>\n",
              "      <td>0.3645</td>\n",
              "      <td>1.4920</td>\n",
              "      <td>2.888</td>\n",
              "      <td>29.840</td>\n",
              "      <td>0.007256</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.02071</td>\n",
              "      <td>0.016260</td>\n",
              "      <td>0.02080</td>\n",
              "      <td>0.005304</td>\n",
              "      <td>15.300</td>\n",
              "      <td>33.17</td>\n",
              "      <td>100.20</td>\n",
              "      <td>706.7</td>\n",
              "      <td>0.12410</td>\n",
              "      <td>0.22640</td>\n",
              "      <td>0.13260</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>0</td>\n",
              "      <td>11.200</td>\n",
              "      <td>29.37</td>\n",
              "      <td>70.67</td>\n",
              "      <td>386.0</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>0.03558</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.05502</td>\n",
              "      <td>0.3141</td>\n",
              "      <td>3.8960</td>\n",
              "      <td>2.041</td>\n",
              "      <td>22.810</td>\n",
              "      <td>0.007594</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.01989</td>\n",
              "      <td>0.001773</td>\n",
              "      <td>11.920</td>\n",
              "      <td>38.30</td>\n",
              "      <td>75.19</td>\n",
              "      <td>439.6</td>\n",
              "      <td>0.09267</td>\n",
              "      <td>0.05494</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1566</td>\n",
              "      <td>0.05905</td>\n",
              "      <td>355</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.150</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>356</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>357 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  Datapoint(x)  C\n",
              "0            0       13.540  ...             0  0\n",
              "1            0       13.080  ...             1  1\n",
              "2            0        9.504  ...             2  0\n",
              "3            0       13.030  ...             3  1\n",
              "4            0        8.196  ...             4  0\n",
              "..         ...          ...  ...           ... ..\n",
              "352          0       14.590  ...           352  2\n",
              "353          0       11.510  ...           353  2\n",
              "354          0       14.050  ...           354  0\n",
              "355          0       11.200  ...           355  2\n",
              "356          0        7.760  ...           356  0\n",
              "\n",
              "[357 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJVCphT0M0bk",
        "outputId": "62b41122-4aa4-4fed-f17e-d4b3bc74268d"
      },
      "source": [
        "cluster_0 = colon_majority[colon_majority['C']==0]\n",
        "cluster_0.shape"
      ],
      "execution_count": 1189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(78, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVXsopE1M23Q",
        "outputId": "66db2aa4-aa05-4ea1-83c8-d9177e9b8896"
      },
      "source": [
        "cluster_1 = colon_majority[colon_majority['C']==1]\n",
        "cluster_1.shape"
      ],
      "execution_count": 1190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(89, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cau-797TM5wY",
        "outputId": "90ec0eb7-f401-4cc9-82f5-74e6e6bed876"
      },
      "source": [
        "cluster_2 = colon_majority[colon_majority['C']==2]\n",
        "cluster_2.shape"
      ],
      "execution_count": 1191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6o5u2K8M-CS",
        "outputId": "c48c01e2-8218-42f1-9002-bf90237041cd"
      },
      "source": [
        "cluster_3 = colon_majority[colon_majority['C']==3]\n",
        "cluster_3.shape"
      ],
      "execution_count": 1192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKw98d1oNAac",
        "outputId": "abb7178e-7615-47d2-db1e-001253b16519"
      },
      "source": [
        "cluster_4 = colon_majority[colon_majority['C']==4]\n",
        "cluster_4.shape"
      ],
      "execution_count": 1193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(49, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVIo0Dg-NC0h",
        "outputId": "04de2f69-05c4-42ef-c249-0206a6ce3695"
      },
      "source": [
        "cluster_5 = colon_majority[colon_majority['C']==5]\n",
        "cluster_5.shape"
      ],
      "execution_count": 1194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7UlgdSnNFgI",
        "outputId": "263a04e0-0498-47e5-e770-69b5770b45de"
      },
      "source": [
        "cluster_6 = colon_majority[colon_majority['C']==6]\n",
        "cluster_6.shape"
      ],
      "execution_count": 1195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WT5MO3Q0Us4g",
        "outputId": "dcfe958c-6da9-478a-d136-f2374a3e72c4"
      },
      "source": [
        "cluster_0.shape, cluster_1.shape, cluster_2.shape, cluster_3.shape, cluster_4.shape, cluster_5.shape, cluster_6.shape"
      ],
      "execution_count": 1196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((78, 33), (89, 33), (99, 33), (34, 33), (49, 33), (8, 33), (0, 33))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1196
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "vi9lE9RdNHmI",
        "outputId": "0b370466-318a-4c7d-e8f4-f0bc5657ab85"
      },
      "source": [
        "#centroid of different clusters\n",
        "\n",
        "from sklearn.neighbors import NearestCentroid\n",
        "colon_majority\n",
        "X=colon_majority.drop(columns=['Datapoint(x)','diagnosis','C'])\n",
        "y=colon_majority.C\n",
        "clf=NearestCentroid()\n",
        "clf.fit(X,y)\n",
        "data_centroids=clf.centroids_\n",
        "pd.DataFrame(data_centroids)"
      ],
      "execution_count": 1197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11.829385</td>\n",
              "      <td>17.936667</td>\n",
              "      <td>76.008333</td>\n",
              "      <td>446.480769</td>\n",
              "      <td>0.091979</td>\n",
              "      <td>0.083887</td>\n",
              "      <td>0.048670</td>\n",
              "      <td>0.024002</td>\n",
              "      <td>0.172715</td>\n",
              "      <td>0.064791</td>\n",
              "      <td>0.282755</td>\n",
              "      <td>1.228435</td>\n",
              "      <td>2.015088</td>\n",
              "      <td>20.322962</td>\n",
              "      <td>0.007590</td>\n",
              "      <td>0.022956</td>\n",
              "      <td>0.028394</td>\n",
              "      <td>0.009620</td>\n",
              "      <td>0.021046</td>\n",
              "      <td>0.004010</td>\n",
              "      <td>13.041782</td>\n",
              "      <td>23.308462</td>\n",
              "      <td>84.939359</td>\n",
              "      <td>541.117949</td>\n",
              "      <td>0.125248</td>\n",
              "      <td>0.188048</td>\n",
              "      <td>0.171438</td>\n",
              "      <td>0.070963</td>\n",
              "      <td>0.268918</td>\n",
              "      <td>0.082417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.634157</td>\n",
              "      <td>18.004157</td>\n",
              "      <td>81.187753</td>\n",
              "      <td>491.315730</td>\n",
              "      <td>0.090948</td>\n",
              "      <td>0.076161</td>\n",
              "      <td>0.041619</td>\n",
              "      <td>0.025184</td>\n",
              "      <td>0.171769</td>\n",
              "      <td>0.060841</td>\n",
              "      <td>0.267796</td>\n",
              "      <td>1.116879</td>\n",
              "      <td>1.888733</td>\n",
              "      <td>20.555371</td>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.018937</td>\n",
              "      <td>0.022126</td>\n",
              "      <td>0.008832</td>\n",
              "      <td>0.019578</td>\n",
              "      <td>0.003055</td>\n",
              "      <td>13.886067</td>\n",
              "      <td>23.694719</td>\n",
              "      <td>90.155169</td>\n",
              "      <td>589.776404</td>\n",
              "      <td>0.123041</td>\n",
              "      <td>0.186077</td>\n",
              "      <td>0.166997</td>\n",
              "      <td>0.074262</td>\n",
              "      <td>0.275006</td>\n",
              "      <td>0.077269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.608283</td>\n",
              "      <td>17.941919</td>\n",
              "      <td>81.243232</td>\n",
              "      <td>496.953535</td>\n",
              "      <td>0.091761</td>\n",
              "      <td>0.082609</td>\n",
              "      <td>0.049855</td>\n",
              "      <td>0.029022</td>\n",
              "      <td>0.174285</td>\n",
              "      <td>0.061546</td>\n",
              "      <td>0.283872</td>\n",
              "      <td>1.182861</td>\n",
              "      <td>2.008719</td>\n",
              "      <td>22.278636</td>\n",
              "      <td>0.006523</td>\n",
              "      <td>0.022163</td>\n",
              "      <td>0.025256</td>\n",
              "      <td>0.010226</td>\n",
              "      <td>0.019800</td>\n",
              "      <td>0.003362</td>\n",
              "      <td>13.920505</td>\n",
              "      <td>23.824646</td>\n",
              "      <td>90.930505</td>\n",
              "      <td>602.726263</td>\n",
              "      <td>0.123094</td>\n",
              "      <td>0.192865</td>\n",
              "      <td>0.180102</td>\n",
              "      <td>0.082269</td>\n",
              "      <td>0.273097</td>\n",
              "      <td>0.077921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.078824</td>\n",
              "      <td>18.310294</td>\n",
              "      <td>77.580000</td>\n",
              "      <td>449.429412</td>\n",
              "      <td>0.094378</td>\n",
              "      <td>0.078239</td>\n",
              "      <td>0.042099</td>\n",
              "      <td>0.025626</td>\n",
              "      <td>0.173341</td>\n",
              "      <td>0.063116</td>\n",
              "      <td>0.304753</td>\n",
              "      <td>1.243132</td>\n",
              "      <td>2.173226</td>\n",
              "      <td>22.529706</td>\n",
              "      <td>0.007435</td>\n",
              "      <td>0.020498</td>\n",
              "      <td>0.024430</td>\n",
              "      <td>0.010755</td>\n",
              "      <td>0.020454</td>\n",
              "      <td>0.003563</td>\n",
              "      <td>13.355588</td>\n",
              "      <td>24.053529</td>\n",
              "      <td>86.725882</td>\n",
              "      <td>547.414706</td>\n",
              "      <td>0.127484</td>\n",
              "      <td>0.172582</td>\n",
              "      <td>0.156767</td>\n",
              "      <td>0.078150</td>\n",
              "      <td>0.263044</td>\n",
              "      <td>0.079085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.193816</td>\n",
              "      <td>17.773265</td>\n",
              "      <td>65.156939</td>\n",
              "      <td>317.930612</td>\n",
              "      <td>0.096704</td>\n",
              "      <td>0.076701</td>\n",
              "      <td>0.043135</td>\n",
              "      <td>0.019785</td>\n",
              "      <td>0.182865</td>\n",
              "      <td>0.066971</td>\n",
              "      <td>0.298318</td>\n",
              "      <td>1.505424</td>\n",
              "      <td>2.028178</td>\n",
              "      <td>18.827429</td>\n",
              "      <td>0.009703</td>\n",
              "      <td>0.023742</td>\n",
              "      <td>0.032813</td>\n",
              "      <td>0.010628</td>\n",
              "      <td>0.024279</td>\n",
              "      <td>0.004953</td>\n",
              "      <td>11.187755</td>\n",
              "      <td>23.019184</td>\n",
              "      <td>72.037551</td>\n",
              "      <td>381.708163</td>\n",
              "      <td>0.131135</td>\n",
              "      <td>0.155278</td>\n",
              "      <td>0.130372</td>\n",
              "      <td>0.056264</td>\n",
              "      <td>0.266302</td>\n",
              "      <td>0.083504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>16.347500</td>\n",
              "      <td>15.556250</td>\n",
              "      <td>105.633750</td>\n",
              "      <td>825.737500</td>\n",
              "      <td>0.089269</td>\n",
              "      <td>0.083991</td>\n",
              "      <td>0.057701</td>\n",
              "      <td>0.044202</td>\n",
              "      <td>0.164625</td>\n",
              "      <td>0.056821</td>\n",
              "      <td>0.305775</td>\n",
              "      <td>0.915013</td>\n",
              "      <td>2.088375</td>\n",
              "      <td>29.561250</td>\n",
              "      <td>0.005055</td>\n",
              "      <td>0.015382</td>\n",
              "      <td>0.019765</td>\n",
              "      <td>0.010492</td>\n",
              "      <td>0.014896</td>\n",
              "      <td>0.002088</td>\n",
              "      <td>17.881250</td>\n",
              "      <td>20.448750</td>\n",
              "      <td>116.425000</td>\n",
              "      <td>980.512500</td>\n",
              "      <td>0.118020</td>\n",
              "      <td>0.176932</td>\n",
              "      <td>0.195438</td>\n",
              "      <td>0.109200</td>\n",
              "      <td>0.249725</td>\n",
              "      <td>0.070074</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0          1           2   ...        27        28        29\n",
              "0  11.829385  17.936667   76.008333  ...  0.070963  0.268918  0.082417\n",
              "1  12.634157  18.004157   81.187753  ...  0.074262  0.275006  0.077269\n",
              "2  12.608283  17.941919   81.243232  ...  0.082269  0.273097  0.077921\n",
              "3  12.078824  18.310294   77.580000  ...  0.078150  0.263044  0.079085\n",
              "4  10.193816  17.773265   65.156939  ...  0.056264  0.266302  0.083504\n",
              "5  16.347500  15.556250  105.633750  ...  0.109200  0.249725  0.070074\n",
              "\n",
              "[6 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIMU4IhQNSwx",
        "outputId": "d71477a8-f05c-4930-936f-54574b6cc7fd"
      },
      "source": [
        "datapoints4 = cluster_3+cluster_5\n",
        "datapoints4.shape\n"
      ],
      "execution_count": 1202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2Zqx9d8NU9T",
        "outputId": "54e6728c-c95b-4674-8ac1-dbb521cd993e"
      },
      "source": [
        "#Finding 42 datapoints in cluster 4\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_4 = list(data_centroids[3])\n",
        "\n",
        "\n",
        "l=list(cluster_4.values[0])\n",
        "l=l[1:-2]\n",
        "print(len(centroid_3),len(l))\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_4)):\n",
        "  l=list(cluster_4.values[i])\n",
        "  l=l[1:-2]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_3[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_4_with_distance = cluster_4\n",
        "cluster_4_with_distance['Distance from centroid'] = distances\n",
        "cluster_4_with_distance = cluster_4_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_4_with_distance\n"
      ],
      "execution_count": 1200,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30 30\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_O5Rf8gYP_N4",
        "outputId": "db5edfb7-a1ac-49ea-ea99-a7229623f5d4"
      },
      "source": [
        "datapoints5 = cluster_4_with_distance[:42]\n",
        "datapoints5.shape"
      ],
      "execution_count": 1209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CozfEEh1QB82",
        "outputId": "a816be5c-a793-45bb-a281-87159e4a406a"
      },
      "source": [
        "#Finding 42 datapoints in cluster 1\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_1 = list(data_centroids[0])\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_1)):\n",
        "  l=list(cluster_1.values[i])\n",
        "  l=l[1:-2]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_1[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_1_with_distance = cluster_1\n",
        "cluster_1_with_distance['Distance from centroid'] = distances\n",
        "cluster_1_with_distance = cluster_1_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_1_with_distance"
      ],
      "execution_count": 1205,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNuzp05vQnyb",
        "outputId": "aeb7c47c-55d8-4124-c674-724042d441a0"
      },
      "source": [
        "datapoints1 = cluster_0_with_distance[:42]\n",
        "datapoints1.shape"
      ],
      "execution_count": 1208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T41-e0mGRsNf",
        "outputId": "21e3a686-927b-47c9-99fa-0efd99f31417"
      },
      "source": [
        "#Finding 42 datapoints in cluster 2\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_2 = list(data_centroids[1])\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_2)):\n",
        "  l=list(cluster_2.values[i])\n",
        "  l=l[1:-2]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_2[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_2_with_distance = cluster_2\n",
        "cluster_2_with_distance['Distance from centroid'] = distances\n",
        "cluster_2_with_distance = cluster_2_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_2_with_distance"
      ],
      "execution_count": 1210,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjiAHvGJR6Zy",
        "outputId": "97977fe0-fdba-4e83-b391-931732264ca0"
      },
      "source": [
        "datapoints2 = cluster_1_with_distance[:42]\n",
        "datapoints2.shape"
      ],
      "execution_count": 1213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIAxPhEER80o",
        "outputId": "0cd8d8ac-c688-4c55-b30b-f8f5a73a8722"
      },
      "source": [
        "#Finding 42 datapoints in cluster 3\n",
        "\n",
        "import math\n",
        "\n",
        "centroid_3 = list(data_centroids[2])\n",
        "\n",
        "distances = []\n",
        "\n",
        "for i in range(len(cluster_3)):\n",
        "  l=list(cluster_3.values[i])\n",
        "  l=l[1:-2]\n",
        "  dist = 0\n",
        "  for i in range(len(l)):\n",
        "    dist += (l[i] - centroid_3[i])**2\n",
        "  #print(dist)\n",
        "  dist = math.sqrt(dist)\n",
        "  distances.append(dist)\n",
        "\n",
        "cluster_3_with_distance = cluster_3\n",
        "cluster_3_with_distance['Distance from centroid'] = distances\n",
        "cluster_3_with_distance = cluster_3_with_distance.sort_values('Distance from centroid')\n",
        "#cluster_4_with_distance"
      ],
      "execution_count": 1214,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfYsbmL8R89O",
        "outputId": "cbdcdb2c-119e-4c03-858a-fa5f0ed5b7c1"
      },
      "source": [
        "datapoints3 = cluster_2_with_distance[:42]\n",
        "datapoints3.shape"
      ],
      "execution_count": 1217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1217
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "YV3a7jqvYSdO",
        "outputId": "56a25e43-b12c-47c4-8795-fcb21c5ed74b"
      },
      "source": [
        "balanced_dataset_with_class0=pd.concat([datapoints1, datapoints2, datapoints3, datapoints4, datapoints5])\n",
        "balanced_dataset_with_class0 = balanced_dataset_with_class0.drop(['C','Distance from centroid', 'Datapoint(x)'], axis=1)\n",
        "balanced_dataset_with_class0=balanced_dataset_with_class0.drop(['diagnosis'], axis=1)\n",
        "balanced_dataset_with_class0['diagnosis'] =  [0]*len(balanced_dataset_with_class0)\n",
        "balanced_dataset_with_class0"
      ],
      "execution_count": 1227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>11.710</td>\n",
              "      <td>15.45</td>\n",
              "      <td>75.03</td>\n",
              "      <td>420.3</td>\n",
              "      <td>0.11500</td>\n",
              "      <td>0.07281</td>\n",
              "      <td>0.040060</td>\n",
              "      <td>0.032500</td>\n",
              "      <td>0.2009</td>\n",
              "      <td>0.06506</td>\n",
              "      <td>0.3446</td>\n",
              "      <td>0.7395</td>\n",
              "      <td>2.355</td>\n",
              "      <td>24.53</td>\n",
              "      <td>0.009536</td>\n",
              "      <td>0.010970</td>\n",
              "      <td>0.016510</td>\n",
              "      <td>0.011210</td>\n",
              "      <td>0.01953</td>\n",
              "      <td>0.003100</td>\n",
              "      <td>13.06</td>\n",
              "      <td>18.16</td>\n",
              "      <td>84.16</td>\n",
              "      <td>516.4</td>\n",
              "      <td>0.14600</td>\n",
              "      <td>0.11150</td>\n",
              "      <td>0.10870</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.2765</td>\n",
              "      <td>0.07806</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>11.870</td>\n",
              "      <td>21.54</td>\n",
              "      <td>76.83</td>\n",
              "      <td>432.0</td>\n",
              "      <td>0.06613</td>\n",
              "      <td>0.10640</td>\n",
              "      <td>0.087770</td>\n",
              "      <td>0.023860</td>\n",
              "      <td>0.1349</td>\n",
              "      <td>0.06612</td>\n",
              "      <td>0.2560</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>1.955</td>\n",
              "      <td>20.24</td>\n",
              "      <td>0.006854</td>\n",
              "      <td>0.060630</td>\n",
              "      <td>0.066630</td>\n",
              "      <td>0.015530</td>\n",
              "      <td>0.02354</td>\n",
              "      <td>0.008925</td>\n",
              "      <td>12.79</td>\n",
              "      <td>28.18</td>\n",
              "      <td>83.51</td>\n",
              "      <td>507.2</td>\n",
              "      <td>0.09457</td>\n",
              "      <td>0.33990</td>\n",
              "      <td>0.32180</td>\n",
              "      <td>0.08750</td>\n",
              "      <td>0.2305</td>\n",
              "      <td>0.09952</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.760</td>\n",
              "      <td>21.60</td>\n",
              "      <td>74.72</td>\n",
              "      <td>427.9</td>\n",
              "      <td>0.08637</td>\n",
              "      <td>0.04966</td>\n",
              "      <td>0.016570</td>\n",
              "      <td>0.011150</td>\n",
              "      <td>0.1495</td>\n",
              "      <td>0.05888</td>\n",
              "      <td>0.4062</td>\n",
              "      <td>1.2100</td>\n",
              "      <td>2.635</td>\n",
              "      <td>28.47</td>\n",
              "      <td>0.005857</td>\n",
              "      <td>0.009758</td>\n",
              "      <td>0.011680</td>\n",
              "      <td>0.007445</td>\n",
              "      <td>0.02406</td>\n",
              "      <td>0.001769</td>\n",
              "      <td>12.98</td>\n",
              "      <td>25.72</td>\n",
              "      <td>82.98</td>\n",
              "      <td>516.5</td>\n",
              "      <td>0.10850</td>\n",
              "      <td>0.08615</td>\n",
              "      <td>0.05523</td>\n",
              "      <td>0.03715</td>\n",
              "      <td>0.2433</td>\n",
              "      <td>0.06563</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>11.710</td>\n",
              "      <td>17.19</td>\n",
              "      <td>74.68</td>\n",
              "      <td>420.3</td>\n",
              "      <td>0.09774</td>\n",
              "      <td>0.06141</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.032390</td>\n",
              "      <td>0.1516</td>\n",
              "      <td>0.06095</td>\n",
              "      <td>0.2451</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>1.742</td>\n",
              "      <td>17.86</td>\n",
              "      <td>0.006905</td>\n",
              "      <td>0.008704</td>\n",
              "      <td>0.019780</td>\n",
              "      <td>0.011850</td>\n",
              "      <td>0.01897</td>\n",
              "      <td>0.001671</td>\n",
              "      <td>13.01</td>\n",
              "      <td>21.39</td>\n",
              "      <td>84.42</td>\n",
              "      <td>521.5</td>\n",
              "      <td>0.13230</td>\n",
              "      <td>0.10400</td>\n",
              "      <td>0.15210</td>\n",
              "      <td>0.10990</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.07097</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>11.900</td>\n",
              "      <td>14.65</td>\n",
              "      <td>78.11</td>\n",
              "      <td>432.8</td>\n",
              "      <td>0.11520</td>\n",
              "      <td>0.12960</td>\n",
              "      <td>0.037100</td>\n",
              "      <td>0.030030</td>\n",
              "      <td>0.1995</td>\n",
              "      <td>0.07839</td>\n",
              "      <td>0.3962</td>\n",
              "      <td>0.6538</td>\n",
              "      <td>3.021</td>\n",
              "      <td>25.03</td>\n",
              "      <td>0.010170</td>\n",
              "      <td>0.047410</td>\n",
              "      <td>0.027890</td>\n",
              "      <td>0.011100</td>\n",
              "      <td>0.03127</td>\n",
              "      <td>0.009423</td>\n",
              "      <td>13.15</td>\n",
              "      <td>16.51</td>\n",
              "      <td>86.26</td>\n",
              "      <td>509.6</td>\n",
              "      <td>0.14240</td>\n",
              "      <td>0.25170</td>\n",
              "      <td>0.09420</td>\n",
              "      <td>0.06042</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.10360</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>9.787</td>\n",
              "      <td>19.94</td>\n",
              "      <td>62.11</td>\n",
              "      <td>294.5</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.05301</td>\n",
              "      <td>0.006829</td>\n",
              "      <td>0.007937</td>\n",
              "      <td>0.1350</td>\n",
              "      <td>0.06890</td>\n",
              "      <td>0.3350</td>\n",
              "      <td>2.0430</td>\n",
              "      <td>2.132</td>\n",
              "      <td>20.05</td>\n",
              "      <td>0.011130</td>\n",
              "      <td>0.014630</td>\n",
              "      <td>0.005308</td>\n",
              "      <td>0.005250</td>\n",
              "      <td>0.01801</td>\n",
              "      <td>0.005667</td>\n",
              "      <td>10.92</td>\n",
              "      <td>26.29</td>\n",
              "      <td>68.81</td>\n",
              "      <td>366.1</td>\n",
              "      <td>0.13160</td>\n",
              "      <td>0.09473</td>\n",
              "      <td>0.02049</td>\n",
              "      <td>0.02381</td>\n",
              "      <td>0.1934</td>\n",
              "      <td>0.08988</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>9.876</td>\n",
              "      <td>19.40</td>\n",
              "      <td>63.95</td>\n",
              "      <td>298.3</td>\n",
              "      <td>0.10050</td>\n",
              "      <td>0.09697</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>0.1945</td>\n",
              "      <td>0.06322</td>\n",
              "      <td>0.1803</td>\n",
              "      <td>1.2220</td>\n",
              "      <td>1.528</td>\n",
              "      <td>11.77</td>\n",
              "      <td>0.009058</td>\n",
              "      <td>0.021960</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>0.011120</td>\n",
              "      <td>0.01609</td>\n",
              "      <td>0.003570</td>\n",
              "      <td>10.76</td>\n",
              "      <td>26.83</td>\n",
              "      <td>72.22</td>\n",
              "      <td>361.2</td>\n",
              "      <td>0.15590</td>\n",
              "      <td>0.23020</td>\n",
              "      <td>0.26440</td>\n",
              "      <td>0.09749</td>\n",
              "      <td>0.2622</td>\n",
              "      <td>0.08490</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>9.777</td>\n",
              "      <td>16.99</td>\n",
              "      <td>62.50</td>\n",
              "      <td>290.2</td>\n",
              "      <td>0.10370</td>\n",
              "      <td>0.08404</td>\n",
              "      <td>0.043340</td>\n",
              "      <td>0.017780</td>\n",
              "      <td>0.1584</td>\n",
              "      <td>0.07065</td>\n",
              "      <td>0.4030</td>\n",
              "      <td>1.4240</td>\n",
              "      <td>2.747</td>\n",
              "      <td>22.87</td>\n",
              "      <td>0.013850</td>\n",
              "      <td>0.029320</td>\n",
              "      <td>0.027220</td>\n",
              "      <td>0.010230</td>\n",
              "      <td>0.03281</td>\n",
              "      <td>0.004638</td>\n",
              "      <td>11.05</td>\n",
              "      <td>21.47</td>\n",
              "      <td>71.68</td>\n",
              "      <td>367.0</td>\n",
              "      <td>0.14670</td>\n",
              "      <td>0.17650</td>\n",
              "      <td>0.13000</td>\n",
              "      <td>0.05334</td>\n",
              "      <td>0.2533</td>\n",
              "      <td>0.08468</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>10.160</td>\n",
              "      <td>19.59</td>\n",
              "      <td>64.73</td>\n",
              "      <td>311.7</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.07504</td>\n",
              "      <td>0.005025</td>\n",
              "      <td>0.011160</td>\n",
              "      <td>0.1791</td>\n",
              "      <td>0.06331</td>\n",
              "      <td>0.2441</td>\n",
              "      <td>2.0900</td>\n",
              "      <td>1.648</td>\n",
              "      <td>16.80</td>\n",
              "      <td>0.012910</td>\n",
              "      <td>0.022220</td>\n",
              "      <td>0.004174</td>\n",
              "      <td>0.007082</td>\n",
              "      <td>0.02572</td>\n",
              "      <td>0.002278</td>\n",
              "      <td>10.65</td>\n",
              "      <td>22.88</td>\n",
              "      <td>67.88</td>\n",
              "      <td>347.3</td>\n",
              "      <td>0.12650</td>\n",
              "      <td>0.12000</td>\n",
              "      <td>0.01005</td>\n",
              "      <td>0.02232</td>\n",
              "      <td>0.2262</td>\n",
              "      <td>0.06742</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>9.683</td>\n",
              "      <td>19.34</td>\n",
              "      <td>61.05</td>\n",
              "      <td>285.7</td>\n",
              "      <td>0.08491</td>\n",
              "      <td>0.05030</td>\n",
              "      <td>0.023370</td>\n",
              "      <td>0.009615</td>\n",
              "      <td>0.1580</td>\n",
              "      <td>0.06235</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>1.3630</td>\n",
              "      <td>2.054</td>\n",
              "      <td>18.24</td>\n",
              "      <td>0.007440</td>\n",
              "      <td>0.011230</td>\n",
              "      <td>0.023370</td>\n",
              "      <td>0.009615</td>\n",
              "      <td>0.02203</td>\n",
              "      <td>0.004154</td>\n",
              "      <td>10.93</td>\n",
              "      <td>25.59</td>\n",
              "      <td>69.10</td>\n",
              "      <td>364.2</td>\n",
              "      <td>0.11990</td>\n",
              "      <td>0.09546</td>\n",
              "      <td>0.09350</td>\n",
              "      <td>0.03846</td>\n",
              "      <td>0.2552</td>\n",
              "      <td>0.07920</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>210 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  texture_mean  ...  fractal_dimension_worst  diagnosis\n",
              "186       11.710         15.45  ...                  0.07806          0\n",
              "265       11.870         21.54  ...                  0.09952          0\n",
              "7         11.760         21.60  ...                  0.06563          0\n",
              "90        11.710         17.19  ...                  0.07097          0\n",
              "63        11.900         14.65  ...                  0.10360          0\n",
              "..           ...           ...  ...                      ...        ...\n",
              "33         9.787         19.94  ...                  0.08988          0\n",
              "37         9.876         19.40  ...                  0.08490          0\n",
              "42         9.777         16.99  ...                  0.08468          0\n",
              "350       10.160         19.59  ...                  0.06742          0\n",
              "342        9.683         19.34  ...                  0.07920          0\n",
              "\n",
              "[210 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbm6os9aaVB7",
        "outputId": "1e150628-93b9-4dc0-f32f-12ba5e7d3c06"
      },
      "source": [
        "og_dataset = pd.read_csv(\"https://github.com/raw/arghac14/UndErNsembled/master/Datasets/breast-cancer.csv\",index_col=0)\n",
        "minority_dataset = og_dataset[og_dataset['diagnosis']==1]\n",
        "minority_dataset.shape"
      ],
      "execution_count": 1230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(212, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "L-EF76WxandF",
        "outputId": "f5fcb035-bcc3-4365-9c61-3fd72555c7dd"
      },
      "source": [
        "balanced_dataset = pd.concat([balanced_dataset_with_class0, minority_dataset])\n",
        "balanced_dataset = balanced_dataset.sample(frac=1)\n",
        "balanced_dataset = balanced_dataset.reset_index(drop=True)\n",
        "balanced_dataset"
      ],
      "execution_count": 1239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.09</td>\n",
              "      <td>26.57</td>\n",
              "      <td>142.70</td>\n",
              "      <td>1311.0</td>\n",
              "      <td>0.11410</td>\n",
              "      <td>0.28320</td>\n",
              "      <td>0.24870</td>\n",
              "      <td>0.14960</td>\n",
              "      <td>0.2395</td>\n",
              "      <td>0.07398</td>\n",
              "      <td>0.6298</td>\n",
              "      <td>0.7629</td>\n",
              "      <td>4.414</td>\n",
              "      <td>81.46</td>\n",
              "      <td>0.004253</td>\n",
              "      <td>0.04759</td>\n",
              "      <td>0.03872</td>\n",
              "      <td>0.01567</td>\n",
              "      <td>0.01798</td>\n",
              "      <td>0.005295</td>\n",
              "      <td>26.68</td>\n",
              "      <td>33.48</td>\n",
              "      <td>176.50</td>\n",
              "      <td>2089.0</td>\n",
              "      <td>0.14910</td>\n",
              "      <td>0.7584</td>\n",
              "      <td>0.6780</td>\n",
              "      <td>0.29030</td>\n",
              "      <td>0.4098</td>\n",
              "      <td>0.12840</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.94</td>\n",
              "      <td>21.31</td>\n",
              "      <td>123.60</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>0.09009</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.10800</td>\n",
              "      <td>0.07951</td>\n",
              "      <td>0.1582</td>\n",
              "      <td>0.05461</td>\n",
              "      <td>0.7888</td>\n",
              "      <td>0.7975</td>\n",
              "      <td>5.486</td>\n",
              "      <td>96.05</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.01652</td>\n",
              "      <td>0.02269</td>\n",
              "      <td>0.01370</td>\n",
              "      <td>0.01386</td>\n",
              "      <td>0.001698</td>\n",
              "      <td>24.86</td>\n",
              "      <td>26.58</td>\n",
              "      <td>165.90</td>\n",
              "      <td>1866.0</td>\n",
              "      <td>0.11930</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.2687</td>\n",
              "      <td>0.17890</td>\n",
              "      <td>0.2551</td>\n",
              "      <td>0.06589</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.54</td>\n",
              "      <td>19.32</td>\n",
              "      <td>115.10</td>\n",
              "      <td>951.6</td>\n",
              "      <td>0.08968</td>\n",
              "      <td>0.11980</td>\n",
              "      <td>0.10360</td>\n",
              "      <td>0.07488</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>0.05491</td>\n",
              "      <td>0.3971</td>\n",
              "      <td>0.8282</td>\n",
              "      <td>3.088</td>\n",
              "      <td>40.73</td>\n",
              "      <td>0.006090</td>\n",
              "      <td>0.02569</td>\n",
              "      <td>0.02713</td>\n",
              "      <td>0.01345</td>\n",
              "      <td>0.01594</td>\n",
              "      <td>0.002658</td>\n",
              "      <td>20.42</td>\n",
              "      <td>25.84</td>\n",
              "      <td>139.50</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>0.13810</td>\n",
              "      <td>0.3420</td>\n",
              "      <td>0.3508</td>\n",
              "      <td>0.19390</td>\n",
              "      <td>0.2928</td>\n",
              "      <td>0.07867</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21.16</td>\n",
              "      <td>23.04</td>\n",
              "      <td>137.20</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>0.09428</td>\n",
              "      <td>0.10220</td>\n",
              "      <td>0.10970</td>\n",
              "      <td>0.08632</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.05278</td>\n",
              "      <td>0.6917</td>\n",
              "      <td>1.1270</td>\n",
              "      <td>4.303</td>\n",
              "      <td>93.99</td>\n",
              "      <td>0.004728</td>\n",
              "      <td>0.01259</td>\n",
              "      <td>0.01715</td>\n",
              "      <td>0.01038</td>\n",
              "      <td>0.01083</td>\n",
              "      <td>0.001987</td>\n",
              "      <td>29.17</td>\n",
              "      <td>35.59</td>\n",
              "      <td>188.00</td>\n",
              "      <td>2615.0</td>\n",
              "      <td>0.14010</td>\n",
              "      <td>0.2600</td>\n",
              "      <td>0.3155</td>\n",
              "      <td>0.20090</td>\n",
              "      <td>0.2822</td>\n",
              "      <td>0.07526</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.71</td>\n",
              "      <td>16.67</td>\n",
              "      <td>74.72</td>\n",
              "      <td>423.6</td>\n",
              "      <td>0.10510</td>\n",
              "      <td>0.06095</td>\n",
              "      <td>0.03592</td>\n",
              "      <td>0.02600</td>\n",
              "      <td>0.1339</td>\n",
              "      <td>0.05945</td>\n",
              "      <td>0.4489</td>\n",
              "      <td>2.5080</td>\n",
              "      <td>3.258</td>\n",
              "      <td>34.37</td>\n",
              "      <td>0.006578</td>\n",
              "      <td>0.01380</td>\n",
              "      <td>0.02662</td>\n",
              "      <td>0.01307</td>\n",
              "      <td>0.01359</td>\n",
              "      <td>0.003707</td>\n",
              "      <td>13.33</td>\n",
              "      <td>25.48</td>\n",
              "      <td>86.16</td>\n",
              "      <td>546.7</td>\n",
              "      <td>0.12710</td>\n",
              "      <td>0.1028</td>\n",
              "      <td>0.1046</td>\n",
              "      <td>0.06968</td>\n",
              "      <td>0.1712</td>\n",
              "      <td>0.07343</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>19.59</td>\n",
              "      <td>18.15</td>\n",
              "      <td>130.70</td>\n",
              "      <td>1214.0</td>\n",
              "      <td>0.11200</td>\n",
              "      <td>0.16660</td>\n",
              "      <td>0.25080</td>\n",
              "      <td>0.12860</td>\n",
              "      <td>0.2027</td>\n",
              "      <td>0.06082</td>\n",
              "      <td>0.7364</td>\n",
              "      <td>1.0480</td>\n",
              "      <td>4.792</td>\n",
              "      <td>97.07</td>\n",
              "      <td>0.004057</td>\n",
              "      <td>0.02277</td>\n",
              "      <td>0.04029</td>\n",
              "      <td>0.01303</td>\n",
              "      <td>0.01686</td>\n",
              "      <td>0.003318</td>\n",
              "      <td>26.73</td>\n",
              "      <td>26.39</td>\n",
              "      <td>174.90</td>\n",
              "      <td>2232.0</td>\n",
              "      <td>0.14380</td>\n",
              "      <td>0.3846</td>\n",
              "      <td>0.6810</td>\n",
              "      <td>0.22470</td>\n",
              "      <td>0.3643</td>\n",
              "      <td>0.09223</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>15.37</td>\n",
              "      <td>22.76</td>\n",
              "      <td>100.20</td>\n",
              "      <td>728.2</td>\n",
              "      <td>0.09200</td>\n",
              "      <td>0.10360</td>\n",
              "      <td>0.11220</td>\n",
              "      <td>0.07483</td>\n",
              "      <td>0.1717</td>\n",
              "      <td>0.06097</td>\n",
              "      <td>0.3129</td>\n",
              "      <td>0.8413</td>\n",
              "      <td>2.075</td>\n",
              "      <td>29.44</td>\n",
              "      <td>0.009882</td>\n",
              "      <td>0.02444</td>\n",
              "      <td>0.04531</td>\n",
              "      <td>0.01763</td>\n",
              "      <td>0.02471</td>\n",
              "      <td>0.002142</td>\n",
              "      <td>16.43</td>\n",
              "      <td>25.84</td>\n",
              "      <td>107.50</td>\n",
              "      <td>830.9</td>\n",
              "      <td>0.12570</td>\n",
              "      <td>0.1997</td>\n",
              "      <td>0.2846</td>\n",
              "      <td>0.14760</td>\n",
              "      <td>0.2556</td>\n",
              "      <td>0.06828</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>11.27</td>\n",
              "      <td>12.96</td>\n",
              "      <td>73.16</td>\n",
              "      <td>386.3</td>\n",
              "      <td>0.12370</td>\n",
              "      <td>0.11110</td>\n",
              "      <td>0.07900</td>\n",
              "      <td>0.05550</td>\n",
              "      <td>0.2018</td>\n",
              "      <td>0.06914</td>\n",
              "      <td>0.2562</td>\n",
              "      <td>0.9858</td>\n",
              "      <td>1.809</td>\n",
              "      <td>16.04</td>\n",
              "      <td>0.006635</td>\n",
              "      <td>0.01777</td>\n",
              "      <td>0.02101</td>\n",
              "      <td>0.01164</td>\n",
              "      <td>0.02108</td>\n",
              "      <td>0.003721</td>\n",
              "      <td>12.84</td>\n",
              "      <td>20.53</td>\n",
              "      <td>84.93</td>\n",
              "      <td>476.1</td>\n",
              "      <td>0.16100</td>\n",
              "      <td>0.2429</td>\n",
              "      <td>0.2247</td>\n",
              "      <td>0.13180</td>\n",
              "      <td>0.3343</td>\n",
              "      <td>0.09215</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>18.08</td>\n",
              "      <td>21.84</td>\n",
              "      <td>117.40</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>0.07371</td>\n",
              "      <td>0.08642</td>\n",
              "      <td>0.11030</td>\n",
              "      <td>0.05778</td>\n",
              "      <td>0.1770</td>\n",
              "      <td>0.05340</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>1.3050</td>\n",
              "      <td>4.312</td>\n",
              "      <td>76.36</td>\n",
              "      <td>0.005530</td>\n",
              "      <td>0.05296</td>\n",
              "      <td>0.06110</td>\n",
              "      <td>0.01444</td>\n",
              "      <td>0.02140</td>\n",
              "      <td>0.005036</td>\n",
              "      <td>19.76</td>\n",
              "      <td>24.70</td>\n",
              "      <td>129.10</td>\n",
              "      <td>1228.0</td>\n",
              "      <td>0.08822</td>\n",
              "      <td>0.1963</td>\n",
              "      <td>0.2535</td>\n",
              "      <td>0.09181</td>\n",
              "      <td>0.2369</td>\n",
              "      <td>0.06558</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421</th>\n",
              "      <td>19.02</td>\n",
              "      <td>24.59</td>\n",
              "      <td>122.00</td>\n",
              "      <td>1076.0</td>\n",
              "      <td>0.09029</td>\n",
              "      <td>0.12060</td>\n",
              "      <td>0.14680</td>\n",
              "      <td>0.08271</td>\n",
              "      <td>0.1953</td>\n",
              "      <td>0.05629</td>\n",
              "      <td>0.5495</td>\n",
              "      <td>0.6636</td>\n",
              "      <td>3.055</td>\n",
              "      <td>57.65</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>0.01842</td>\n",
              "      <td>0.03710</td>\n",
              "      <td>0.01200</td>\n",
              "      <td>0.01964</td>\n",
              "      <td>0.003337</td>\n",
              "      <td>24.56</td>\n",
              "      <td>30.41</td>\n",
              "      <td>152.90</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>0.12490</td>\n",
              "      <td>0.3206</td>\n",
              "      <td>0.5755</td>\n",
              "      <td>0.19560</td>\n",
              "      <td>0.3956</td>\n",
              "      <td>0.09288</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>422 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  texture_mean  ...  fractal_dimension_worst  diagnosis\n",
              "0          21.09         26.57  ...                  0.12840          1\n",
              "1          18.94         21.31  ...                  0.06589          1\n",
              "2          17.54         19.32  ...                  0.07867          1\n",
              "3          21.16         23.04  ...                  0.07526          1\n",
              "4          11.71         16.67  ...                  0.07343          0\n",
              "..           ...           ...  ...                      ...        ...\n",
              "417        19.59         18.15  ...                  0.09223          1\n",
              "418        15.37         22.76  ...                  0.06828          1\n",
              "419        11.27         12.96  ...                  0.09215          0\n",
              "420        18.08         21.84  ...                  0.06558          1\n",
              "421        19.02         24.59  ...                  0.09288          1\n",
              "\n",
              "[422 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "LNhq5VNqaxaX",
        "outputId": "8eb62a59-0a0f-4ca7-d2f8-449b60f21abc"
      },
      "source": [
        "balanced_dataset.to_csv('balanced_dataset_breast.csv', index = False)\n",
        "from google.colab import files\n",
        "files.download(\"balanced_dataset_breast.csv\")"
      ],
      "execution_count": 1240,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_84ce292a-efc6-4c2d-a39b-f7f09728d294\", \"balanced_dataset_breast.csv\", 99487)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}