Breast_cancer_CODEX_MAIT.ipynb

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  "metadata": {
    "colab": {
      "name": "Breast_cancer_CODEX_MAIT.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqII670rA5Or",
        "colab_type": "text"
      },
      "source": [
        "#Description : This program detects breast cancer, based off data.\n",
        "##import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NSmmzODuEmS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ABUy2OzBPgl",
        "colab_type": "text"
      },
      "source": [
        "#Load the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkgRQGX9uXyX",
        "colab_type": "code",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "946dfea7-001e-47ac-f594-59717ec4cc6e"
      },
      "source": [
        "#Load the Data\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "df = pd.read_csv('data.csv')\n",
        "df.head(7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "     <output id=\"result-92ff627e-a448-458c-8555-cfdca3b8cc7a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving data.csv to data (1).csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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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              "  </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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>843786</td>\n",
              "      <td>M</td>\n",
              "      <td>12.45</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0.17000</td>\n",
              "      <td>0.1578</td>\n",
              "      <td>0.08089</td>\n",
              "      <td>0.2087</td>\n",
              "      <td>0.07613</td>\n",
              "      <td>0.3345</td>\n",
              "      <td>0.8902</td>\n",
              "      <td>2.217</td>\n",
              "      <td>27.19</td>\n",
              "      <td>0.007510</td>\n",
              "      <td>0.03345</td>\n",
              "      <td>0.03672</td>\n",
              "      <td>0.01137</td>\n",
              "      <td>0.02165</td>\n",
              "      <td>0.005082</td>\n",
              "      <td>15.47</td>\n",
              "      <td>23.75</td>\n",
              "      <td>103.40</td>\n",
              "      <td>741.6</td>\n",
              "      <td>0.1791</td>\n",
              "      <td>0.5249</td>\n",
              "      <td>0.5355</td>\n",
              "      <td>0.1741</td>\n",
              "      <td>0.3985</td>\n",
              "      <td>0.12440</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>844359</td>\n",
              "      <td>M</td>\n",
              "      <td>18.25</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0.10900</td>\n",
              "      <td>0.1127</td>\n",
              "      <td>0.07400</td>\n",
              "      <td>0.1794</td>\n",
              "      <td>0.05742</td>\n",
              "      <td>0.4467</td>\n",
              "      <td>0.7732</td>\n",
              "      <td>3.180</td>\n",
              "      <td>53.91</td>\n",
              "      <td>0.004314</td>\n",
              "      <td>0.01382</td>\n",
              "      <td>0.02254</td>\n",
              "      <td>0.01039</td>\n",
              "      <td>0.01369</td>\n",
              "      <td>0.002179</td>\n",
              "      <td>22.88</td>\n",
              "      <td>27.66</td>\n",
              "      <td>153.20</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>0.1442</td>\n",
              "      <td>0.2576</td>\n",
              "      <td>0.3784</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</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",
              "5    843786         M  ...                  0.12440          NaN\n",
              "6    844359         M  ...                  0.08368          NaN\n",
              "\n",
              "[7 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLXrIzZMBTU4",
        "colab_type": "text"
      },
      "source": [
        "#Number of rows and columns in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_jd6PdzwnDZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cb56422e-47c7-4499-a2fb-810732b62a0e"
      },
      "source": [
        "#Count the number of rows and columns\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBsUcXrEBeVR",
        "colab_type": "text"
      },
      "source": [
        "#Number of empty values in each column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmBk3ZBIw_fx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "outputId": "85970936-c513-442f-b6a6-d7c996b00f73"
      },
      "source": [
        "#Count the number of empty values(NaN ,NAN , na) in each column\n",
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           0\n",
              "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",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwjyNNubBuGo",
        "colab_type": "text"
      },
      "source": [
        "##Drop columns with missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXgAdqC90pAo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Drop the column with all the missing values\n",
        "df = df.dropna(axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAshxMza1imw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "940c0fae-d634-4913-c005-921cce08d85a"
      },
      "source": [
        "#Get the new count of the number\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZS-tNUuB5EV",
        "colab_type": "text"
      },
      "source": [
        "##Count number of Malignant(M) and Benign(B) cells"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLby4fZN10E2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "8b2b9378-feb6-4316-b2c0-c39c1f292b78"
      },
      "source": [
        "#Count the number of Malignant(M) and Benign cells(B)\n",
        "df['diagnosis'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdP_79zNCCl5",
        "colab_type": "text"
      },
      "source": [
        "##Visualize the count"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSXzVADY31Ng",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0a5360c4-968c-4328-cc4a-22a4396ea9d2"
      },
      "source": [
        "#Visualize the count\n",
        "sns.countplot(df['diagnosis'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86822f8518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00XXXg5V4y8a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "308eef1a-72a0-4890-f6c0-e3b700b1a91c"
      },
      "source": [
        "#Look at the data types to see which columns need to be encoded\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           int64\n",
              "diagnosis                   object\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4gTjvCcCSIT",
        "colab_type": "text"
      },
      "source": [
        "#Encode the categorical data values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEHzwfBS5zgc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Encode the categoraical data values\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_Y = LabelEncoder()\n",
        "df.iloc[:,1] = labelencoder_Y.fit_transform(df.iloc[:,1].values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0g55uc9CfVN",
        "colab_type": "text"
      },
      "source": [
        "#Creating a pairplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oaSXco0-cvZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "b7bbdc3c-42ee-4727-bd24-bf55f5b98d24"
      },
      "source": [
        "#Create a pair plot\n",
        "sns.pairplot(df.iloc[:,1:5] , hue = 'diagnosis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f86822c9e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 595.5x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1sBuraB-wqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "e04324eb-a8c4-4715-a71a-242b720bc9f2"
      },
      "source": [
        "#Print first five rows of the dataset\n",
        "df.head()"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "0    842302          1  ...          0.4601                  0.11890\n",
              "1    842517          1  ...          0.2750                  0.08902\n",
              "2  84300903          1  ...          0.3613                  0.08758\n",
              "3  84348301          1  ...          0.6638                  0.17300\n",
              "4  84358402          1  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beW7J0FUZvMB",
        "colab_type": "text"
      },
      "source": [
        "#Get the correlation of the columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1l9G1oZUZ77F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "a1238639-37b2-420a-884d-fd93daddc451"
      },
      "source": [
        "df.iloc[:,1:12].corr()"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730029</td>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.330499</td>\n",
              "      <td>-0.012838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>0.730029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>-0.311631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>-0.076437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>-0.261477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>-0.283110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.584792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.565369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.336783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>0.166917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>0.330499</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.479921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>-0.012838</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        diagnosis  ...  fractal_dimension_mean\n",
              "diagnosis                1.000000  ...               -0.012838\n",
              "radius_mean              0.730029  ...               -0.311631\n",
              "texture_mean             0.415185  ...               -0.076437\n",
              "perimeter_mean           0.742636  ...               -0.261477\n",
              "area_mean                0.708984  ...               -0.283110\n",
              "smoothness_mean          0.358560  ...                0.584792\n",
              "compactness_mean         0.596534  ...                0.565369\n",
              "concavity_mean           0.696360  ...                0.336783\n",
              "concave points_mean      0.776614  ...                0.166917\n",
              "symmetry_mean            0.330499  ...                0.479921\n",
              "fractal_dimension_mean  -0.012838  ...                1.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bc4neeLcauAm",
        "colab_type": "text"
      },
      "source": [
        "#Visualize the correlation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TdGEkxkbCDB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "2e4e57d9-02e1-449b-e241-5e56c4d40657"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.iloc[:,1:12].corr() , annot = True , fmt='.0%')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8681f27470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-3ghYPVClPr",
        "colab_type": "text"
      },
      "source": [
        "#Split the data into dependent(X) and independent(Y)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzaoTe8n_k7D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Split the data into dependent(X) and independent(Y) data sets\n",
        "X = df.iloc[: , 2:31].values\n",
        "Y = df.iloc[: , 1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjS3sM2kCsww",
        "colab_type": "text"
      },
      "source": [
        "#Split the data into 75% training data and 25% testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-2Q9VEuCHTa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Split the data into 75% training data and 25% testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train , X_test , Y_train , Y_test = train_test_split(X,Y, test_size = 0.25 , random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pn0kgnQAC26G",
        "colab_type": "text"
      },
      "source": [
        "#Scale the data(Feature Scaling)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ql-tR7YcC8--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Scale the data(Feature scaling)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCIQHmx2C8RT",
        "colab_type": "text"
      },
      "source": [
        "##Creating (3)Models in a function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auCB1fqkEC3o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create a function for the models\n",
        "def models(X_train,Y_train):\n",
        "\n",
        "  #Logistic Regression\n",
        "  from sklearn.linear_model import LogisticRegression\n",
        "  log = LogisticRegression(random_state = 0)\n",
        "  log.fit(X_train,Y_train)\n",
        "\n",
        "  #Decision Tree\n",
        "  from sklearn.tree import DecisionTreeClassifier\n",
        "  tree = DecisionTreeClassifier(criterion = 'entropy' , random_state = 0)\n",
        "  tree.fit(X_train, Y_train)\n",
        "\n",
        "  #Random Forest Classifier\n",
        "  from sklearn.ensemble import RandomForestClassifier\n",
        "  forest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy' , random_state = 0)\n",
        "  forest.fit(X_train,Y_train)\n",
        "\n",
        "  #Print the models accuracy on the training data \n",
        "  print('[0] Logistic Regression Training Accuracy:', log.score(X_train,Y_train))\n",
        "  print('[1] Decision Tree Classifier Training Accuracy:', tree.score(X_train,Y_train))\n",
        "  print('[2] Random Forest Classifier Training Accuracy:', forest.score(X_train,Y_train))\n",
        "\n",
        "  return log,tree,forest\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElrHi7ryDHPC",
        "colab_type": "text"
      },
      "source": [
        "#Getting all of the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IueeEn2QIfSf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "fbc3ef04-0355-44e9-bf7f-5a23c19122d0"
      },
      "source": [
        "#Getting all of the models\n",
        "model = models(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0] Logistic Regression Training Accuracy: 0.9906103286384976\n",
            "[1] Decision Tree Classifier Training Accuracy: 1.0\n",
            "[2] Random Forest Classifier Training Accuracy: 0.9953051643192489\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GK_cgEeODrBr",
        "colab_type": "text"
      },
      "source": [
        "#Testing model accuracy on test data on confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOBKqzzuJa6_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "c6d2b082-9f14-494a-c696-5a9495d24956"
      },
      "source": [
        "#Test model accuracy on test data on confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "for i in range(len(model)):\n",
        "  print('Model' , i)\n",
        "  cm = confusion_matrix(Y_test, model[i].predict(X_test))\n",
        "  TP = cm[0][0]\n",
        "  FP = cm[0][1]\n",
        "  TN = cm[1][1]\n",
        "  FN = cm[1][0]\n",
        "  print(cm)\n",
        "  print('Testing Accuracy =',( TP + TN )/(TP + TN + FP + FN))\n",
        "  print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model 0\n",
            "[[86  4]\n",
            " [ 3 50]]\n",
            "Testing Accuracy = 0.951048951048951\n",
            "\n",
            "Model 1\n",
            "[[83  7]\n",
            " [ 2 51]]\n",
            "Testing Accuracy = 0.9370629370629371\n",
            "\n",
            "Model 2\n",
            "[[87  3]\n",
            " [ 2 51]]\n",
            "Testing Accuracy = 0.965034965034965\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8arAZY5pD5Mg",
        "colab_type": "text"
      },
      "source": [
        "#Metrics of the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMUbwGxkNHSb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "c95d1e4d-0904-49d6-e6f6-759eb4a82980"
      },
      "source": [
        "#Another way to get metrics of the models\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "for i in range(len(model)):\n",
        "  print(classification_report(Y_test,model[i].predict(X_test)))\n",
        "  print(accuracy_score(Y_test , model[i].predict(X_test)))\n",
        "  print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.96        90\n",
            "           1       0.93      0.94      0.93        53\n",
            "\n",
            "    accuracy                           0.95       143\n",
            "   macro avg       0.95      0.95      0.95       143\n",
            "weighted avg       0.95      0.95      0.95       143\n",
            "\n",
            "0.951048951048951\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.92      0.95        90\n",
            "           1       0.88      0.96      0.92        53\n",
            "\n",
            "    accuracy                           0.94       143\n",
            "   macro avg       0.93      0.94      0.93       143\n",
            "weighted avg       0.94      0.94      0.94       143\n",
            "\n",
            "0.9370629370629371\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.97        90\n",
            "           1       0.94      0.96      0.95        53\n",
            "\n",
            "    accuracy                           0.97       143\n",
            "   macro avg       0.96      0.96      0.96       143\n",
            "weighted avg       0.97      0.97      0.97       143\n",
            "\n",
            "0.965034965034965\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bS9Pl9F0ENW9",
        "colab_type": "text"
      },
      "source": [
        "#Prediction of the best model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O6iIjebO6rD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "d9db343c-43a2-4f8d-a57f-c080f5fdd1c6"
      },
      "source": [
        "#Print the prediction of Random Forest Classifier model\n",
        "pred = model[2].predict(X_test)\n",
        "print(pred)\n",
        "print()\n",
        "print(Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0\n",
            " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
            " 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]\n",
            "\n",
            "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1\n",
            " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
            " 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}