Final_Report(On_balanced_dataset_accuracy).ipynb

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  "metadata": {
    "colab": {
      "name": "Final_Report(On balanced dataset accuracy).ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "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/Final_Report(On_balanced_dataset_accuracy).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "67MMoyVDRAHX",
        "outputId": "3f60771d-89d1-4e7b-d6f4-fe9a564ac19e"
      },
      "source": [
        "import pandas as pd\n",
        "report = {\n",
        "    \"Dataset\":[\"Colon-Cancer\",\"Breast-Cancer\",\"Pima-Diabetes\"],\n",
        "    \"Acc%(KNN)\":[92.2,98.245614,81.23],\n",
        "    \"Acc%(KNN_bagging)\":[71.4,98.421053,76.25],\n",
        "    \"Acc%(KNN_boosting)\":[72.2,98.421053,78.5],\n",
        "    \"Acc%(LR)\":[87.1,97.368421, 73.125],\n",
        "    \"Acc%(LR_bagging)\":[85.7,98.245614,75.87],\n",
        "    \"Acc%(LR_boosting)\":[85.7,97.368421, 75.21],\n",
        "    \"Acc%(DT)\":[78.5,98.245614, 83.64],\n",
        "    \"Acc%(DT_bagging)\":[78.5,98.245614, 81.2],\n",
        "    \"Acc%(DT_boosting)\":[71.4,99.122807, 81.2],\n",
        "    \"Acc%(SVM)\":[71.4,96.491228, 74.79],\n",
        "    \"Acc%(SVM_bagging)\":[92.8,98.245614, 73.12],\n",
        "    \"Acc%(SVM_boosting)\":[91.4,98.24, 73.19],\n",
        "    \"Acc%(RM)\":[92.8,99.120000, 75.62],\n",
        "    \"Acc%(RM_bagging)\":[92.8,99.120000, 75.62],\n",
        "    \"Acc%(RM_boosting)\":[92.8,99.120000, 75.62]\n",
        "}\n",
        "report = pd.DataFrame(report)\n",
        "report"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\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>Dataset</th>\n",
              "      <th>Acc%(KNN)</th>\n",
              "      <th>Acc%(KNN_bagging)</th>\n",
              "      <th>Acc%(KNN_boosting)</th>\n",
              "      <th>Acc%(LR)</th>\n",
              "      <th>Acc%(LR_bagging)</th>\n",
              "      <th>Acc%(LR_boosting)</th>\n",
              "      <th>Acc%(DT)</th>\n",
              "      <th>Acc%(DT_bagging)</th>\n",
              "      <th>Acc%(DT_boosting)</th>\n",
              "      <th>Acc%(SVM)</th>\n",
              "      <th>Acc%(SVM_bagging)</th>\n",
              "      <th>Acc%(SVM_boosting)</th>\n",
              "      <th>Acc%(RM)</th>\n",
              "      <th>Acc%(RM_bagging)</th>\n",
              "      <th>Acc%(RM_boosting)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Colon-Cancer</td>\n",
              "      <td>92.200000</td>\n",
              "      <td>71.400000</td>\n",
              "      <td>72.200000</td>\n",
              "      <td>87.100000</td>\n",
              "      <td>85.700000</td>\n",
              "      <td>85.700000</td>\n",
              "      <td>78.500000</td>\n",
              "      <td>78.500000</td>\n",
              "      <td>71.400000</td>\n",
              "      <td>71.400000</td>\n",
              "      <td>92.800000</td>\n",
              "      <td>91.40</td>\n",
              "      <td>92.80</td>\n",
              "      <td>92.80</td>\n",
              "      <td>92.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Breast-Cancer</td>\n",
              "      <td>98.245614</td>\n",
              "      <td>98.421053</td>\n",
              "      <td>98.421053</td>\n",
              "      <td>97.368421</td>\n",
              "      <td>98.245614</td>\n",
              "      <td>97.368421</td>\n",
              "      <td>98.245614</td>\n",
              "      <td>98.245614</td>\n",
              "      <td>99.122807</td>\n",
              "      <td>96.491228</td>\n",
              "      <td>98.245614</td>\n",
              "      <td>98.24</td>\n",
              "      <td>99.12</td>\n",
              "      <td>99.12</td>\n",
              "      <td>99.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pima-Diabetes</td>\n",
              "      <td>81.230000</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>78.500000</td>\n",
              "      <td>73.125000</td>\n",
              "      <td>75.870000</td>\n",
              "      <td>75.210000</td>\n",
              "      <td>83.640000</td>\n",
              "      <td>81.200000</td>\n",
              "      <td>81.200000</td>\n",
              "      <td>74.790000</td>\n",
              "      <td>73.120000</td>\n",
              "      <td>73.19</td>\n",
              "      <td>75.62</td>\n",
              "      <td>75.62</td>\n",
              "      <td>75.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Dataset  Acc%(KNN)  ...  Acc%(RM_bagging)  Acc%(RM_boosting)\n",
              "0   Colon-Cancer  92.200000  ...             92.80              92.80\n",
              "1  Breast-Cancer  98.245614  ...             99.12              99.12\n",
              "2  Pima-Diabetes  81.230000  ...             75.62              75.62\n",
              "\n",
              "[3 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-DAPedENMdM",
        "outputId": "c93dd8ce-9052-4af0-952d-7496d9d0559a"
      },
      "source": [
        "\n",
        "list(report.mean())"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[90.558538,\n",
              " 82.02368433333334,\n",
              " 83.040351,\n",
              " 85.86447366666665,\n",
              " 86.60520466666667,\n",
              " 86.092807,\n",
              " 86.79520466666666,\n",
              " 85.98187133333333,\n",
              " 83.90760233333333,\n",
              " 80.89374266666668,\n",
              " 88.05520466666667,\n",
              " 87.61,\n",
              " 89.18,\n",
              " 89.18,\n",
              " 89.18]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "QOjhE3Qsq3cz",
        "outputId": "a2ef8b24-ee64-47f5-afae-f899b0da997f"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(report)\n",
        "df.plot.box(grid='True')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1ec2f8590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
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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/",
          "height": 17
        },
        "id": "ZVYCf0j8VQED",
        "outputId": "3301f4b0-16ee-4be1-8b8e-1cae86d48f2d"
      },
      "source": [
        "report.to_csv('Final_report.csv', index = False)\n",
        "from google.colab import files\n",
        "files.download(\"Final_report.csv\")"
      ],
      "execution_count": 32,
      "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_7f5a27f4-18d8-437f-bc3f-3786c6e88776\", \"Final_report.csv\", 572)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}