Breast_Cancer_XGB.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Breast Cancer XGB.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw_iwhqg-6uB",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "bbe579a8-ba09-4dc2-c9ac-3c9af9feeda9"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fe066cc2-896c-493c-9c45-8a7531e6eca8\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-fe066cc2-896c-493c-9c45-8a7531e6eca8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving breast-cancer-wisconsin-data.zip to breast-cancer-wisconsin-data.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfIrtG8U_GWq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "e2f952a7-e6d6-4985-bedd-540c1687a06d"
      },
      "source": [
        "# importing required modules \n",
        "from zipfile import ZipFile \n",
        "  \n",
        "# specifying the zip file name \n",
        "file_name = \"breast-cancer-wisconsin-data.zip\"\n",
        "  \n",
        "# opening the zip file in READ mode \n",
        "with ZipFile(file_name, 'r') as zip: \n",
        "    # printing all the contents of the zip file \n",
        "    zip.printdir() \n",
        "  \n",
        "    # extracting all the files \n",
        "    print('Extracting all the files now...') \n",
        "    zip.extractall() \n",
        "    print('Done!') \n",
        "    zip.close()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File Name                                             Modified             Size\n",
            "data.csv                                       2016-09-25 10:49:04       125204\n",
            "Extracting all the files now...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhRkef29_Qya",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.model_selection import train_test_split\n",
        "from datetime import datetime as dt\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEx7-kbh_hSD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "493ecbf3-566c-42ab-acbd-05df13a65d6d"
      },
      "source": [
        "df = pd.read_csv('data.csv')\n",
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oE7ndsdw_nqF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "7689b551-454d-4f39-85df-fbe5e5fb7880"
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": 5,
      "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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgNraoTQ_v-0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "50f82acc-9db4-478a-e784-c8f912e58b9b"
      },
      "source": [
        "df.drop(['id'], axis = 1, inplace = True)\n",
        "df.drop(['Unnamed: 32'], axis = 1, inplace = True)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "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>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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "0         M        17.99  ...          0.4601                  0.11890\n",
              "1         M        20.57  ...          0.2750                  0.08902\n",
              "2         M        19.69  ...          0.3613                  0.08758\n",
              "3         M        11.42  ...          0.6638                  0.17300\n",
              "4         M        20.29  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQYWGy2g_9yM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "c7579c3a-690c-4005-cef5-da447be54eec"
      },
      "source": [
        "#Encoding Male and Female to 1 and 0\n",
        "df['diagnosis'] = df['diagnosis'].map({'M': 0, 'B': 1})\n",
        "df['diagnosis'].head(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqmexdzwAMjs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4f5470e2-b672-4b36-e8aa-6b9f8da52df0"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhfyN_U-AFgT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "4166b41c-fc05-434c-e13b-73debe5c9b2c"
      },
      "source": [
        "X = df.iloc[:, 1:].values\n",
        "Y = df.iloc[:, 0].values\n",
        "\n",
        "print(\"X: {}\".format(X.shape))\n",
        "print(\"Y: {}\".format(Y.shape))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: (569, 30)\n",
            "Y: (569,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLJe15VoAKmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "a5e465c9-0154-460e-a128-bc290cef7916"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, \n",
        "                                                    Y, \n",
        "                                                    test_size = 0.175,\n",
        "                                                    random_state = 0)\n",
        "\n",
        "print(\"X_train: {}\".format(X_train.shape))\n",
        "print(\"X_test: {}\".format(X_test.shape))\n",
        "print(\"Y_train: {}\".format(Y_train.shape))\n",
        "print(\"Y_test: {}\".format(Y_test.shape))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train: (469, 30)\n",
            "X_test: (100, 30)\n",
            "Y_train: (469,)\n",
            "Y_test: (100,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_wfAtboAT-b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "faeb7083-2945-4c86-b7f8-c0e72869a952"
      },
      "source": [
        "#Building our baseline dummy classifier\n",
        "from sklearn.dummy import DummyClassifier\n",
        "clf = DummyClassifier()\n",
        "clf.fit(X_train, Y_train)\n",
        "\n",
        "#Predicting Results\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "#Calculating Resulta\n",
        "print(\"CM: \\n\",confusion_matrix(Y_test, y_pred))\n",
        "print(\"acc: {0}%\".format(accuracy_score(Y_test, y_pred) * 100))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CM: \n",
            " [[11 30]\n",
            " [23 36]]\n",
            "acc: 47.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dH2-ss5BsDE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "2ccf2380-7645-44a7-c647-968dcc1182ae"
      },
      "source": [
        ""
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ARSjgTPCNws",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e0fe06f2-2e97-4a36-b3eb-033ea89a1ea7"
      },
      "source": [
        "# Fitting XGBoost to the Training set\n",
        "from xgboost import XGBClassifier\n",
        "classifier = XGBClassifier()\n",
        "classifier.fit(X_train, Y_train)\n",
        "\n",
        "pred_xgb = classifier.predict(X_test)\n",
        "\n",
        "#Model Evaluation\n",
        "xgb_acc = accuracy_score(Y_test, pred_xgb)\n",
        "\n",
        "print('Accuracy Score: ' + str(xgb_acc))\n",
        "\n",
        "print('Precision Score: ' + str(precision_score(Y_test, pred_xgb)))\n",
        "\n",
        "print('Recall Score: ' + str(recall_score(Y_test, pred_xgb)))\n",
        "\n",
        "print('F1 Score: ' + str(f1_score(Y_test, pred_xgb)))\n",
        "\n",
        "print('Classification Report: \\n' + str(classification_report(Y_test, pred_xgb)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score: 0.98\n",
            "Precision Score: 0.9830508474576272\n",
            "Recall Score: 0.9830508474576272\n",
            "F1 Score: 0.9830508474576272\n",
            "Classification Report: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98        41\n",
            "           1       0.98      0.98      0.98        59\n",
            "\n",
            "    accuracy                           0.98       100\n",
            "   macro avg       0.98      0.98      0.98       100\n",
            "weighted avg       0.98      0.98      0.98       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xl2bQGIhCUY8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "db641eb0-8600-49e2-9184-19b1048e9586"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "import scikitplot as skplt\n",
        "skplt.metrics.plot_confusion_matrix(Y_test, pred_xgb)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VAtprEGSShgDvpiG2AfARkon+3wFHAD8HxgPXVWvLTxo3s9xqdGX/FsCUdJ6sDbg6Im6Q\n9Ajwc0nnkqy0U/UuIweZmeVWiyCLiIeAXdewfx6wZ562MgeZpD4RsSxP42bWepJLK0p2i5KkPSX9\nGXgi3R4t6T/qXpmZFVbpHtAL/BA4DHgRICIeJHlgr5mto2pxQWwtZRlatkXE/C5dyRV1qsfMCk5A\nR8GGllmC7BlJewKRfrvwBeDx+pZlZkVWsBzLFGSnkAwvhwMvAL9J95nZOkjKdYtSQ2S513IRcFQD\najGzkihYjmVaIfanrOGey4iYWJeKzKzwSrfUNclQslNf4B+AZ+pTjpkVnaDqoomNlmVoudqy1pKu\nAO6sW0VmVmwNvkYsi57corQtMLTWhZhZeaihK/JXl2WO7GX+NkfWRvLA3jPrWZSZFVfpHgen5CrY\n0fxtPaCVEbHWxRbNbN1QtCDr9halNLRuiogV6cshZma1evhIzWS513K2pPcstWFm66bkcXDZXo3S\n3Zr9nUvN7grcJ+kpYCnJEDkiYrcG1WhmBVOmK/v/BOwGfKJBtZhZCZRtsl+QPF28QbWYWUkUrEPW\nbZANkXT62j6MiO/VoR4zKzzRVqLryNqBflCwis2sqUS5emQLI+LfGlaJmZWDoKNgk2RV58jMzCqV\nrUd2UMOqMLNSKc3lFxHxUiMLMbPyKFiO+QG9ZpaPyHZLUCM5yMwsH5VoaGlmtibJlf0OMjMruWLF\nmIPMzHqgYB2yws3ZmVnhZVuLrNp6ZJK2lvQ7SY9ImiPpi+n+gZJul/RE+uem1SpykJlZLp3fWmZ5\nVbEc+FJEjAL2Ak6VNIpkKf0ZETESmEGGpfUdZGaWW1v6tPFqr+5ExMKIuD99/zrwKDAMOByYkh42\nBRhXrR7PkZlZPiLPMtaDJc2s2J4cEZPf06Q0gmQR13uBoRGxMP3oeTI8tc1BZma55LwgdklEjOm2\nPakfcA1wWkS8VhmSERGSqj4rxENLM8utVg8fkbQeSYj9LCJ+le5+QdIW6edbAIuqteMgM7PclPHV\nbRtJ0l0CPNplodbrgfHp+/HAddXq8dDSzHIR0F6bC8n2AY4D/ixpdrrvLOB84GpJE4D5wJHVGnKQ\nmVlutcixiLiTtXfcci0j5iAzs5yECnaTkoPMzHIr2i1KDjIzyyW5/KJYSeYgM7N85B6ZmbUAr0dm\nZqWWLKzY7CpW5yAzs9z8raWZlV7BRpYOsnr63GdP5OabbmDIZpsxa/bDzS7H1uKxG8/h9aXLWLFy\nJctXrGTfY77NB3YYxn987Sj69FmP5StWctq/T2PmnPnNLrUw1pkemaRLgcOARRGxc73OU2THjT+B\nkz8/ic+eeHyzS7Eqxk68kBdfWbpq+7zTxnHe5Ju57a5H+Oi+ozjvtHF89KQLm1hhcRRxjqyeN41f\nBoytY/uFt+9+H2bgwIHNLsN6IAI23qgvAJv024CFi19tckUFknFRxUZ+s1m3HllE/D5dLM2s0CKC\nX/9oEhHBJdfcxaW/uosv/79f8uuLT+Vb//IPtLWJA074brPLLJSCdciaP0cmaSIwEWDr4cObXI2t\niw76zPd5bvGrDNm0Hzf8ZBJzn36eTx68K1/57q+4dsZs/vEju/LjbxzDx06+qNmlFkIRn2vZ9PXI\nImJyRIyJiDFDBg9pdjm2DnouHTYufvkNrv/tQ+zx/hEcc9gHuXZGsrLMNbc/wJj3b9PMEgunFuuR\n1VLTg8ysmTbsuz79Nuyz6v3Be+/EnKeeY+HiV9lv95EA7L/nDjz518XNLLN4CpZkTR9atrLjjz2a\nP/z3HSxZsoTtRmzFv379HE44cUKzy7IKmw3qz7TvnQRAR3s7026eye1/fJRT37yK73z5CDo62li2\nbDmTzp3a5EqLpWhDy3pefjEV2J/kKSrPAt+IiEvqdb4iuvxK/8dfdE8veJEPfvr89+z/4+x57HPM\nt5tQUTkUK8bq+63l0fVq28yarGBJ5qGlmeWSTH8VK8kcZGaWj9cjM7NWULAcc5CZWV7ZHr7bSA4y\nM8utYDnmIDOzfBp91X4WDjIzy69gSeYgM7PcfPmFmZWe58jMrNx8HZmZtYKiDS29jI+Z5SKSHlmW\nV9W2pEslLZL0cMW+gZJul/RE+uem1dpxkJlZbjVcjuwy3vtsjzOBGRExEpiRbnfLQWZm+dUoySLi\n98BLXXYfDkxJ308BxlVrx3NkZpZbjoUVB0uaWbE9OSImV/mZoRGxMH3/PDC02kkcZGaWW46p/iUR\nMaan54mIkBTVjvPQ0szyq++a/S9I2gIg/XNRtR9wkJlZLp0LK2b5vx66Hhifvh8PXFftBxxkZpZP\nxksvMl5+MRW4G9hR0rOSJgDnAx+R9ARwcLrdLc+RmVlutboctptnexyUpx0HmZnl5IUVzawFFCzH\nHGRmlo8XVjSz1lCwJHOQmVluRVv9wkFmZrl5jszMyk3Q5iAzs/IrVpI5yMwsl86FFYvEQWZmuRUs\nxxxkZpafe2RmVnq+RcnMSq9YMeYgM7Ocsi7R00gOMjPLzVf2m1n5FSvHHGRmll/BcsxBZmZ5Kc/j\n4BrCQWZmuRTxyn4/fMTMSs89MjPLrWg9MgeZmeXmyy/MrNx8QayZlV0RJ/sdZGaWm4eWZlZ67pGZ\nWekVLMccZGbWAwVLMgeZmeUiKNwtSoqIZtewiqTFwPxm11EHg4ElzS7CcmnVf7NtImJIbxqQdAvJ\n308WSyJibG/Ol0WhgqxVSZoZEWOaXYdl53+zcvG9lmZWeg4yMys9B1ljTG52AZab/81KxHNkZlZ6\n7pGZWek5yMys9BxkdSRprKS5kp6UdGaz67HqJF0qaZGkh5tdi2XnIKsTSe3AxcChwCjgaEmjmluV\nZXAZUPcLOK22HGT1syfwZETMi4h3gJ8Dhze5JqsiIn4PvNTsOiwfB1n9DAOeqdh+Nt1nZjXmIDOz\n0nOQ1c8CYOuK7a3SfWZWYw6y+rkPGClpW0nrA0cB1ze5JrOW5CCrk4hYDkwCbgUeBa6OiDnNrcqq\nkTQVuBvYUdKzkiY0uyarzrcomVnpuUdmZqXnIDOz0nOQmVnpOcjMrPQcZGZWeg6yEpG0QtJsSQ9L\n+oWkDXvR1v6Sbkjff6K71TkkDZD0+R6c45uSzsi6v8sxl0k6Ise5RnjFinWXg6xc3oqIXSJiZ+Ad\n4OTKD5XI/W8aEddHxPndHDIAyB1kZo3iICuvPwDbpz2RuZIuBx4GtpZ0iKS7Jd2f9tz6war10R6T\ndD/wyc6GJJ0g6aL0/VBJ0yU9mL4+BJwPbJf2Br+THvdlSfdJekjSORVtfU3S45LuBHas9ktIOilt\n50FJ13TpZR4saWba3mHp8e2SvlNx7s/19i/Sys9BVkKSOkjWOftzumsk8KOIeD+wFDgbODgidgNm\nAqdL6gv8FPg4sDuw+Vqa/yHw3xExGtgNmAOcCTyV9ga/LOmQ9Jx7ArsAu0v6sKTdSW7F2gX4e2CP\nDL/OryJij/R8jwKVV9KPSM/xMeAn6e8wAXg1IvZI2z9J0rYZzmMtrKPZBVguG0ianb7/A3AJsCUw\nPyLuSffvRbKQ411KHmu/PsktNzsBf4mIJwAkXQlMXMM5DgSOB4iIFcCrkjbtcswh6euBdLsfSbD1\nB6ZHxJvpObLcW7qzpHNJhq/9SG7p6nR1RKwEnpA0L/0dDgE+UDF/tkl67scznMtalIOsXN6KiF0q\nd6RhtbRyF3B7RBzd5bjVfq6XBHwrIv5/l3Oc1oO2LgPGRcSDkk4A9q/4rOv9c5Ge+wsRURl4SBrR\ng3Nbi/DQsvXcA+wjaXsASRtJ2gF4DBghabv0uKPX8vMzgFPSn22XtAnwOklvq9OtwIkVc2/DJG0G\n/B4YJ2kDSf1JhrHV9AcWSloPOKbLZ5+S1JbW/L+Auem5T0mPR9IOkjbKcB5rYe6RtZiIWJz2bKZK\n6pPuPjsiHpc0EbhR0pskQ9P+a2jii8DkdNWHFcApEXG3pLvSyxtuTufJ3gfcnfYI3wCOjYj7JU0D\nHgQWkSxlVM2/AvcCi9M/K2v6K/AnYGPg5Ih4W9J/ksyd3a/k5IuBcdn+dqxVefULMys9Dy3NrPQc\nZGZWeg4yMys9B5mZlZ6DzMxKz0FmZqXnIDOz0vsfE63600HMHoMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyv8iyB_CdJK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}