Breast Cancer Model_1.ipynb

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    "colab": {
      "name": "Breast Cancer Model_1.ipynb",
      "provenance": [],
      "mount_file_id": "1y9laTWanKC04hxeWGNTBzu1K1E6RkV8B",
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/EOHFA-GOAT/machine-learning-projects/blob/master/Breast%20Cancer%20Model_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LT7xoIUpqt42"
      },
      "source": [
        "##Introduction\n",
        "In this project, I will build and test several machine learning classification models on breast cancer data. The dataset used comes from the [UCI Machine Learning Repository for breast cancer](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)). The goal is of the project is to help detect early diagnosis of breat cancer using ML to drastically improve the prognosis and chance of survival. \n",
        "\n",
        "Please note that this project is in its early stages of development."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzRoFrbJt5iF"
      },
      "source": [
        "##Information on the Dataset\n",
        "This dataset is publicly available online, and was created by Dr. William H. Wolberg, a physician at the University Of Wisconsin Hospital.\n",
        "\n",
        "Attribute Information:\n",
        "1. ID number 2) Diagnosis (M = malignant, B = benign) 3–32)\n",
        "\n",
        "Ten real-valued features are computed for each cell nucleus:\n",
        "1. radius (mean of distances from center to points on the perimeter)\n",
        "2. texture (standard deviation of gray-scale values)\n",
        "3. perimeter\n",
        "4. area\n",
        "5. smoothness (local variation in radius lengths)\n",
        "6. compactness (perimeter² / area — 1.0)\n",
        "7. concavity (severity of concave portions of the contour)\n",
        "8. concave points (number of concave portions of the contour)\n",
        "9. symmetry\n",
        "10. fractal dimension (“coastline approximation” — 1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0jloTJSvIZM"
      },
      "source": [
        "##Importing Necessary Libraries & Files\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuuPLMbymc28"
      },
      "source": [
        "#importing EDA libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set() # Making sns as default for plots \n",
        "#importing scikit-learn libraries\n",
        "from sklearn.linear_model import LogisticRegression # to apply the Logistic regression\n",
        "from sklearn.model_selection import train_test_split # to split the data into two parts\n",
        "from sklearn.model_selection import KFold # use for cross validation\n",
        "from sklearn.ensemble import RandomForestClassifier # for random forest classifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import svm # for Support Vector Machine\n",
        "from sklearn import metrics\n",
        "\n",
        "#importing breast cancer dataset\n",
        "data = pd.read_csv('./drive/My Drive/breastcancerdata.csv')\n",
        "X = data.iloc[:, 1:31].values\n",
        "Y = data.iloc[:, 31].values"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "height": 241
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        "id": "lSyUH9Ic0Xuc",
        "outputId": "afb62c5f-6e46-4ac5-9c0d-0b487b061009"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>Unnamed: 32</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</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",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</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",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
              "1    842517         M  ...                  0.08902          NaN\n",
              "2  84300903         M  ...                  0.08758          NaN\n",
              "3  84348301         M  ...                  0.17300          NaN\n",
              "4  84358402         M  ...                  0.07678          NaN\n",
              "\n",
              "[5 rows x 33 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yL5vbjwM3dS-",
        "outputId": "27f764d3-854b-418c-8e36-42c9727c6874"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTdanLsZxuSK",
        "outputId": "c55b290a-0f48-44f0-c210-705513e1bf1f"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zz5-if2z7eJ"
      },
      "source": [
        "Checking for NaN or Null Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJARojBa9hCo"
      },
      "source": [
        "data = data.drop(labels=\"Unnamed: 32\", axis=1)\n",
        "data = data.drop(labels=\"id\", axis=1)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hh_TJcFA9xcT",
        "outputId": "bb1cf25f-85ed-4f0d-f16c-cd8b1dca1863"
      },
      "source": [
        "data.isnull().sum()\n",
        "data.isna().sum()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBNccxMi4hyb"
      },
      "source": [
        "##EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "cQ9iIFLi4jfZ",
        "outputId": "d423c67c-b554-4e62-b41c-6ad273e76ab5"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "count   569.000000    569.000000  ...      569.000000               569.000000\n",
              "mean     14.127292     19.289649  ...        0.290076                 0.083946\n",
              "std       3.524049      4.301036  ...        0.061867                 0.018061\n",
              "min       6.981000      9.710000  ...        0.156500                 0.055040\n",
              "25%      11.700000     16.170000  ...        0.250400                 0.071460\n",
              "50%      13.370000     18.840000  ...        0.282200                 0.080040\n",
              "75%      15.780000     21.800000  ...        0.317900                 0.092080\n",
              "max      28.110000     39.280000  ...        0.663800                 0.207500\n",
              "\n",
              "[8 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssBh7wel5kKe",
        "outputId": "0aa0c367-0602-4975-d49c-400c7ccab6e7"
      },
      "source": [
        "features_mean= list(data.columns[1:11])\n",
        "features_se= list(data.columns[11:20])\n",
        "features_worst=list(data.columns[21:31])\n",
        "print(features_mean)\n",
        "print(\"-----------------------------------\")\n",
        "print(features_se)\n",
        "print(\"------------------------------------\")\n",
        "print(features_worst)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n",
            "-----------------------------------\n",
            "['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se']\n",
            "------------------------------------\n",
            "['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YklOChnz5u9P"
      },
      "source": [
        "data['diagnosis']=data['diagnosis'].map({'M':1,'B':0})"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "fykULMEI4qqK",
        "outputId": "e046562d-0616-44e1-fe64-db78e125038a"
      },
      "source": [
        "sns.countplot(data['diagnosis'],label=\"Count\")"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68792f4128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_j6xVENG4uL4"
      },
      "source": [
        "From this plot, I can see that there is a more number of bengin stages of cancer, which can be cured."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "id": "SYKPEkyz4_KI",
        "outputId": "c3df4910-efc3-41e1-bca8-729fc8306813"
      },
      "source": [
        "corr = data[features_mean].corr() # .corr is used for find corelation\n",
        "plt.figure(figsize=(14,14))\n",
        "sns.heatmap(corr, cbar = True,  square = True, annot=True, fmt= '.2f',annot_kws={'size': 15},\n",
        "           xticklabels= features_mean, yticklabels= features_mean,\n",
        "           cmap= 'coolwarm')"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f687955a908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36vNC_KT6CHj"
      },
      "source": [
        "##Splitting Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XLnBBHO599K"
      },
      "source": [
        "prediction_var = ['texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean'] #variables which I will use for prediction"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1DeuSolX6SMp",
        "outputId": "46cfddcb-8b6c-4412-f4b7-72d4a28dc30e"
      },
      "source": [
        "train, test = train_test_split(data, test_size = 0.3)\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(398, 31)\n",
            "(171, 31)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEJpJcbL6aXV"
      },
      "source": [
        "train_X = train[prediction_var] #taking the training data input \n",
        "train_y=train.diagnosis #output of our training data\n",
        "test_X= test[prediction_var] #taking test data inputs\n",
        "test_y =test.diagnosis #output value of test data\n"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6WM2Pfg6no_"
      },
      "source": [
        "##Building and Testing Appropriate Classifer Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUuNQpl_AXF0"
      },
      "source": [
        "def classification_model(model,data,prediction_input,output):\n",
        "    # here the model means the model \n",
        "    # data is used for the data \n",
        "    #prediction_input means the inputs used for prediction\n",
        "    # output mean the value which are to be predicted\n",
        "    # here we will try to find out the Accuarcy of model by using same data for fiiting and \n",
        "    #comparison for same data\n",
        "    #Fit the model:\n",
        "    model.fit(data[prediction_input],data[output]) #Here we fit the model using training set\n",
        "  \n",
        "    #Make predictions on training set:\n",
        "    predictions = model.predict(data[prediction_input])\n",
        "  \n",
        "    #Print accuracy\n",
        "    # now checkin accuracy for same data\n",
        "    accuracy = metrics.accuracy_score(predictions,data[output])\n",
        "    print(\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))\n",
        " \n",
        "    \n",
        "    kf = KFold(data.shape[0], n_splits=5)\n",
        "    #I am going to use cross validation\n",
        "    #data.shape[0] means number of rows in data\n",
        "    #n_splits is for number of folds (updated library, those who code with this are probably used to n_folds)\n",
        "    error = []\n",
        "    for train, test in kf:\n",
        "        # The data will be divided into train and test using KFold\n",
        "        # I am going to fit model\n",
        "        #in the cross validation, the data in train and test will change for every iteration\n",
        "        train_X = (data[prediction_input].iloc[train,:])\n",
        "        train_y = data[output].iloc[train]\n",
        "        # Training the algorithm using the predictors and target.\n",
        "        model.fit(train_X, train_y)\n",
        "    \n",
        "        #for test data\n",
        "        test_X=data[prediction_input].iloc[test,:]\n",
        "        test_y=data[output].iloc[test]\n",
        "        error.append(model.score(test_X,test_y))\n",
        "        # printing the score \n",
        "        print(\"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error)))\n"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d358YeEX-0O5"
      },
      "source": [
        "***Random Forest Classifier***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqG2sgRf6lqz"
      },
      "source": [
        "model=RandomForestClassifier(n_estimators=100)# a simple random forest model"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csFYiwtA6xP8",
        "outputId": "65fdcf39-0a24-4c56-d086-118cfc61cffd"
      },
      "source": [
        "model.fit(train_X,train_y)# fit model for training data"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0lAe7EG-7ga"
      },
      "source": [
        "prediction=model.predict(test_X)"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elhD7naY-9Gs",
        "outputId": "06f1c0c8-5226-437f-d2da-0de77e453928"
      },
      "source": [
        "metrics.accuracy_score(prediction,test_y) # to check the accuracy"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.935672514619883"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0u1JjOs_B7g"
      },
      "source": [
        "The Random Forest Classifer Model predicts with an accuracy of 93.57%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDzb-FBa_NrN"
      },
      "source": [
        "***Support Vector Machine***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BJYBZeH_S4-",
        "outputId": "dd15424e-afcd-4620-e544-b0363da5d757"
      },
      "source": [
        "model = svm.SVC()\n",
        "model.fit(train_X,train_y)\n",
        "prediction=model.predict(test_X)\n",
        "metrics.accuracy_score(prediction,test_y)"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8830409356725146"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_28x4IG_Wwj"
      },
      "source": [
        "Support Vector Machine Model predicts with an accuracy of 88.3%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgn0PnU9_g31"
      },
      "source": [
        "***K-Nearest Neighbors Algorithm***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "67ZdSSjxAbKb",
        "outputId": "af05dad2-61b3-422d-9763-5e2cba423e29"
      },
      "source": [
        "model = KNeighborsClassifier()\n",
        "classification_model(model,data,prediction_var,outcome_var)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 92.970%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-144-210d7807293a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassification_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction_var\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-133-cdc9280eed2e>\u001b[0m in \u001b[0;36mclassification_model\u001b[0;34m(model, data, prediction_input, output)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mkf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0;31m#I am going to use cross validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;31m#data.shape[0] means number of rows in data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() got multiple values for argument 'n_splits'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Px5e7zIeCQFs"
      },
      "source": [
        "Not sure if this is an error in the updated Library?\n",
        "\n",
        "Anyways, the K-Nearest Neighbors Algorithm predicts with an accuracy of 92.97%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqxxuAsZDHNm"
      },
      "source": [
        "***Logistic Regression***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "6nm1tzqzDGzM",
        "outputId": "0cdda5ee-d4ae-4cde-9f4a-2dbc07e1999c"
      },
      "source": [
        "model=LogisticRegression()\n",
        "classification_model(model,data,prediction_var,outcome_var)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 89.807%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-145-06f3e6071355>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassification_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction_var\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-133-cdc9280eed2e>\u001b[0m in \u001b[0;36mclassification_model\u001b[0;34m(model, data, prediction_input, output)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mkf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0;31m#I am going to use cross validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;31m#data.shape[0] means number of rows in data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() got multiple values for argument 'n_splits'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnlitG0kDPV8"
      },
      "source": [
        "Logistic Regression Predicts with an accuracy of 89.8%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dx9DmFiDD25j"
      },
      "source": [
        "##Conclusion\n",
        "\n",
        "The best model for breast cancer detection for this project is currently Random Forest Algorithm. Going forward in my ML Github Repository, I could tune the parameters (hyper optimization), or try out other ML models that I have not used in this project. I hope to bring this project to university, where I could closely work with individuals who are just as passionate about machine learning and image classification as I am.\n",
        "\n",
        "\n",
        "Emini Offutt, Hume-Fogg Academic High School, C/O 2021\n",
        "\n",
        "28 November 2020\n",
        "\n"
      ]
    }
  ]
}