cancer_predict.ipynb

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  "metadata": {
    "colab": {
      "name": "cancer-predict.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMK9tsCUCmj3r6pFj98Fa7L",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akshatagg06/cancer-predict/blob/main/cancer_predict.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cKaTJ-_GhbY"
      },
      "source": [
        "#Description: This program detects breast cancer, based off of data."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBi0GfKN-T87"
      },
      "source": [
        "# import warnings filter\n",
        "from warnings import simplefilter\n",
        "# ignore all future warnings\n",
        "simplefilter(action='ignore', category=FutureWarning)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58Y6su-bGmRj"
      },
      "source": [
        "# import the packages/libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CwIc4cnGnfJ"
      },
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFBO7_VJGpcY"
      },
      "source": [
        "# Import the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "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": 72
        },
        "id": "QHN79gdLGvgx",
        "outputId": "ae3c41a8-9ae8-46e2-fbf4-5daf1a4df1a5"
      },
      "source": [
        "#Load the data \n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a161d91e-f315-48ad-b97a-041c24cb4831\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a161d91e-f315-48ad-b97a-041c24cb4831\">\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 data.csv to data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "WlwRl05yQF1a",
        "outputId": "837cce26-0851-4d3b-f04e-a69f8de8a850"
      },
      "source": [
        "# Use to load data on Google Colab \n",
        "df = pd.read_csv('data.csv') \n",
        "df.head(7)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </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",
              "    <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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgCQ45l3G8f_",
        "outputId": "212fe0c6-c0ac-40a0-ac34-5f2e3d9addbd"
      },
      "source": [
        "#Count the number of rows and columns in the data set\n",
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b28xzuzbHAED"
      },
      "source": [
        "get a count of all of the columns that contain empty (NaN, NAN, na) values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YZXwLOHHETl",
        "outputId": "a78be03d-d0a3-40f0-b32f-d64ac8b8b3dd"
      },
      "source": [
        "#Count the empty (NaN, NAN, na) values in each column\n",
        "df.isna().sum()"
      ],
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zdaW77f_HIyc"
      },
      "source": [
        "#Drop the column with all missing values (na, NAN, NaN)\n",
        "#NOTE: This drops the column Unnamed\n",
        "df = df.dropna(axis=1)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brjlTz7HHLXj",
        "outputId": "3836950c-8ed3-413f-86f6-38681a40a2a0"
      },
      "source": [
        "#Get the new count of the number of rows and cols\n",
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfc5v_elHO0k"
      },
      "source": [
        "Get a count of the number of patients with Malignant (M) cancerous and Benign (B) non-cancerous cells."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psj16IuHHPXI",
        "outputId": "48ff11ca-dc66-497d-c42a-412e8b8e4ca4"
      },
      "source": [
        "#Get a count of the number of 'M' & 'B' cells\n",
        "df['diagnosis'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFCvl7D2HTUG"
      },
      "source": [
        "Visualize the counts, by creating a count plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3sjT658-HTxt",
        "outputId": "4b14e517-6362-4cbe-d2d9-07ae3fbff604"
      },
      "source": [
        "#Visualize this count \n",
        "sns.countplot(df['diagnosis'],label=\"Count\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f924a43d860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "wFk4b12FHea7"
      },
      "source": [
        "Look at the data types to see which columns need to be transformed / encoded. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qw94EzEFHiIu",
        "outputId": "ddb11ec2-fa0f-423e-8a49-7542e356cae3"
      },
      "source": [
        "#Look at the data types \n",
        "df.dtypes"
      ],
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9SCN40RHmbj"
      },
      "source": [
        "Encode the categorical data. Change the values in the column ‘diagnosis’ from M and B to 1 and 0 respectively, then print the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdReStEEHnCn",
        "outputId": "1c418f20-f3dd-4f93-c29c-029c13e1c71f"
      },
      "source": [
        "#Encoding categorical data values\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_Y = LabelEncoder()\n",
        "df.iloc[:,1]= labelencoder_Y.fit_transform(df.iloc[:,1].values)\n",
        "print(labelencoder_Y.fit_transform(df.iloc[:,1].values))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1\n",
            " 0 1 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0\n",
            " 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0\n",
            " 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1\n",
            " 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0\n",
            " 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0\n",
            " 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1\n",
            " 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
            " 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0\n",
            " 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0\n",
            " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1\n",
            " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKNcYmVyHrKm"
      },
      "source": [
        "Print the new data set which now has only 32 columns. Print only the first 5 rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "D_dAkitlHwR3",
        "outputId": "f8fe866e-a8b3-4520-c3b2-55900ead78f8"
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": 13,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FboJjv43H3Ec"
      },
      "source": [
        "X = df.iloc[:, 2:31].values \n",
        "Y = df.iloc[:, 1].values "
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jcc8kzTgH6Xi"
      },
      "source": [
        "Split the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dESYi2uH68g"
      },
      "source": [
        "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": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-5R9H1uIBP1"
      },
      "source": [
        "Scale the data to bring all features to the same level of magnitude"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnxk16AoICKU"
      },
      "source": [
        "#Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQpa1HiOIHz3"
      },
      "source": [
        "Create a function to hold many different models (e.g. Logistic Regression, Decision Tree Classifier, Random Forest Classifier) to make the classification."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZPMmK7xIIYU"
      },
      "source": [
        "def models(X_train,Y_train):\n",
        "  \n",
        "  #Using Logistic Regression \n",
        "  from sklearn.linear_model import LogisticRegression\n",
        "  log = LogisticRegression(random_state = 0)\n",
        "  log.fit(X_train, Y_train)\n",
        "  \n",
        "  #Using KNeighborsClassifier \n",
        "  from sklearn.neighbors import KNeighborsClassifier\n",
        "  knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "  knn.fit(X_train, Y_train)\n",
        "\n",
        "  #Using SVC linear\n",
        "  from sklearn.svm import SVC\n",
        "  svc_lin = SVC(kernel = 'linear', random_state = 0)\n",
        "  svc_lin.fit(X_train, Y_train)\n",
        "\n",
        "  #Using SVC rbf\n",
        "  from sklearn.svm import SVC\n",
        "  svc_rbf = SVC(kernel = 'rbf', random_state = 0)\n",
        "  svc_rbf.fit(X_train, Y_train)\n",
        "\n",
        "  #Using GaussianNB \n",
        "  from sklearn.naive_bayes import GaussianNB\n",
        "  gauss = GaussianNB()\n",
        "  gauss.fit(X_train, Y_train)\n",
        "\n",
        "  #Using DecisionTreeClassifier \n",
        "  from sklearn.tree import DecisionTreeClassifier\n",
        "  tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "  tree.fit(X_train, Y_train)\n",
        "\n",
        "  #Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm\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 model accuracy on the training data.\n",
        "  print('[0]Logistic Regression Training Accuracy:', log.score(X_train, Y_train))\n",
        "  print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, Y_train))\n",
        "  print('[2]Support Vector Machine (Linear Classifier) Training Accuracy:', svc_lin.score(X_train, Y_train))\n",
        "  print('[3]Support Vector Machine (RBF Classifier) Training Accuracy:', svc_rbf.score(X_train, Y_train))\n",
        "  print('[4]Gaussian Naive Bayes Training Accuracy:', gauss.score(X_train, Y_train))\n",
        "  print('[5]Decision Tree Classifier Training Accuracy:', tree.score(X_train, Y_train))\n",
        "  print('[6]Random Forest Classifier Training Accuracy:', forest.score(X_train, Y_train))\n",
        "  \n",
        "  return log, knn, svc_lin, svc_rbf, gauss, tree, forest"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ozf-QksINjk"
      },
      "source": [
        "Create the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdxCm00OIUau",
        "outputId": "0df2d05e-6848-4660-fed1-ce471f5d87c2"
      },
      "source": [
        "model = models(X_train,Y_train)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]Logistic Regression Training Accuracy: 0.9906103286384976\n",
            "[1]K Nearest Neighbor Training Accuracy: 0.9765258215962441\n",
            "[2]Support Vector Machine (Linear Classifier) Training Accuracy: 0.9882629107981221\n",
            "[3]Support Vector Machine (RBF Classifier) Training Accuracy: 0.9835680751173709\n",
            "[4]Gaussian Naive Bayes Training Accuracy: 0.9507042253521126\n",
            "[5]Decision Tree Classifier Training Accuracy: 1.0\n",
            "[6]Random Forest Classifier Training Accuracy: 0.9953051643192489\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky2b2yg9IaHs"
      },
      "source": [
        "Get performance metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ba4yK8IsIcDD",
        "outputId": "03307808-2001-4d7e-f208-1ca1b16abd8b"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "for i in range(len(model)):\n",
        "  # confusion matrix\n",
        "  fig, ax = plt.subplots()\n",
        "  sns.heatmap(confusion_matrix(Y_test, model[i].predict(X_test), normalize='true'), annot=True, ax=ax)\n",
        "  ax.set_title(\"Confusion Matrix\")\n",
        "  plt.show()\n",
        "\n",
        "  cm = confusion_matrix(Y_test, model[i].predict(X_test))  \n",
        "  TN = cm[0][0]\n",
        "  TP = cm[1][1]\n",
        "  FN = cm[1][0]\n",
        "  FP = cm[0][1]\n",
        "  print('Model[{}] Testing Accuracy = \"{}\"'.format(i,  (TP + TN) / (TP + TN + FN + FP)))\n",
        "  print()# Print a new line\n",
        "  print()# Print a new line"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[0] Testing Accuracy = \"0.9440559440559441\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[1] Testing Accuracy = \"0.958041958041958\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[2] Testing Accuracy = \"0.965034965034965\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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keNwy3tKYCuxkZtuZWRFwGjC2VpvnSWS5mFk7EuWGuQ11qvKCiERKJkvBMurHvcrMLgZeBgqBh9291MxuAkrcfWzy3JFmNguIAb919xUN9WvuTfvIXOXyufn9TJ7kRMtOfXM9BMlDVWvKMy/I1qP8gJ9lHHOK3319g6/XWMp0RSRSspXpNhUFXRGJlHgjVi/kgoKuiERKBhNkOaWgKyKRoqArIhJQE68N2GAKuiISKcp0RUQCclfQFREJJqbVCyIi4SjTFREJSDVdEZGAtHpBRCQgZboiIgHF4vn9xloFXRGJFJUXREQCimv1gohIOFoyJiIS0EZfXmjX/YimvoQ0Q999MibXQ5CIUnlBRCQgrV4QEQkoz6sLCroiEi0qL4iIBKTVCyIiAeX5LwNW0BWRaHGU6YqIBFOl8oKISDjKdEVEAlJNV0QkIGW6IiIBKdMVEQkopkxXRCScPP9tPQq6IhItcWW6IiLh6IU3IiIBaSJNRCSguKm8ICISTCzXA0hDQVdEIiXfVy/k9++1EBFppDiW8ZaOmfU3s0/NbI6ZXd1AuxPNzM1sn3R9KuiKSKR4I7aGmFkhMAwYAPQETjeznnW0awVcAryfyfgUdEUkUuKW+ZZGH2COu8919zXA08DgOtrdDNwO/JDJ+BR0RSRS4o3YzGyomZWkbENTuioGFqTsL0weq2ZmewFd3P3FTMeniTQRiZRYIybS3H04MHx9rmNmBcCdwNmN+ZyCrohEShYfjigHuqTsd04eW6sV0At4wxJrgzsAY81skLuX1Nepgq6IREoWg+5UYCcz245EsD0NGLL2pLuvBtqt3TezN4ArGgq4oJquiESMW+Zbg/24VwEXAy8Ds4FR7l5qZjeZ2aD1HZ8yXRGJlGy+e8HdxwPjax27vp62h2bSp4KuiESKHgMWEQko3x8DVtAVkUjRqx1FRAJS0BURCUi/OUJEJCDVdEVEAtLqBRGRgOJ5XmBQ0BWRSNFEmohIQPmd5yroikjEKNMVEQmoyvI711XQFZFIye+Qq6ArIhGj8oKISEBaMiYiElB+h1wFXRGJGJUXREQCiuV5rqugKyKRokxXRCQgV6YrIhJOvme6+hXsG+Cww39KyYevMm3G61x62S/XOV9UVMQjj97DtBmvM3HSc3TtWgxA167FLF5WyttTxvH2lHH89e6bAdhyyy2qj709ZRxz503lttuvC3pPkl2TSz7i2PN/y9HnXs5Do8atc75iyXLOv/o2TrjgGs658hYWL/uqxvlvvv2ew878Nbfc92ioITd7cTzjLReU6a6ngoIC7rjzBo4bdBbl5YuZ9NYYxo+fyKefzKlu84uzTmbVqtXs2ftnnHjSQG68+SrOOevXAJSVzafvgcfW6PObb76tcezNt//JuLEvh7khybpYLM4twx5l+K1X0aFdG0675Hr67bcXO3Qrrm7zl4ee5NjDDmbwEX15f3opd48YxW2//VX1+XtHjmbv3XfJxfCbrfwuLijTXW9779ObuXPn8eWXC6isrOQfo1/gmGMOr9Hm6GMO58kn/gHA82MmcMihB2Tc/w47dqfdNm2Z8s7UrI5bwpn52Rd07dSeLh23pUWLTRhwyP5Meu+DGm3mzq9gvz16AtCnd08mvfuf86Wfl7Fi5WoO3KtX0HE3d1V4xlsuKOiup06d2lO+cFH1fnn5Yjp2al+jTcdOHarbxGIxvl79b9q03RqAbt068/Y7Y3nxpSc54MB91un/xJMGMua5F5vwDqSpLV2+kg7btKneb9+uDUtWrKzRpsf2XXntnRIAJk4p4dvvf2DV1/8mHo/zlwef5PLzhwQdcxR4I/7LhfUOumZ2TgPnhppZiZmVrKn8en0vEVmLFy9jt1370vegQVx79a089PBdtGq1ZY02J540kNHPrlsDlGi54vzTKZn5CSdfdB0lMz9h27ZbU1BQwNMvTKTvvr1rBG3JTLwRWy5sSE33RuCRuk64+3BgOEDrLXfI9xLLeqmoWEJx547V+8XFHVhUsaRGm0UViynu3JGKisUUFhayVetWfJXMdNZ8tQaA6dM/pqxsHjvuuB3Tps0EoFevXdikcBOmT/840N1IU9i23dY1JsaWLP+K9smfdKrbtN2au35/CQDfff8Dr06eylZbbsGM2Z/zYelnPPPCRL774QcqK6vYfLPNuPTcU4PeQ3PUrJeMmdlH9Z0C2tdzbqPw4QcfscMO3enWrTMVFUs44aSBnH/upTXajB8/kSFnnMDUf03juOMH8Nab7wLQtl0bVn61ing8TvfuXdhhh+58+eX86s+ddPKxjB6tLLe569Vje+ZVLGbh4qW0b9uGCW++x+1XXVijzcrV/6Z1qy0oKCjgoWfGcfyRhwDUaPf8q29R+nmZAm6G8n3JWLpMtz1wFLCy1nEDpjTJiJqJWCzGFZffyD+eH0FhYQGPjxzNJ7M/55rrfsO0D2cyYfxERj46iuEP3cG0Ga+zcuUqzj07kdEcdNC+XHPdb6isrMLjcS695PesXLm6uu/jTziak048L1e3JlmySWEh11zwC3513f8Si8U5/sifsmO3ztz72HPs1mM7+u2/F1M/ms3dI0ZhZuzda2euvfCsXA+72Yt5fme65g0M0Mz+Djzi7pPrOPeku6et8ke1vCAbZtlHT+V6CJKHirbvYxvax5Bux2ccc56cN2aDr9dYDWa67l5vupVJwBURCa1Z13RFRJqb5l7TFRFpVvSbI0REAlJ5QUQkoHxfvaCgKyKRku/lBb17QUQiJZuPAZtZfzP71MzmmNnVdZy/zMxmmdlHZjbRzLql61NBV0QiJVsvvDGzQmAYMADoCZxuZj1rNZsG7OPuPwFGA39ONz4FXRGJlCy+xLwPMMfd57r7GuBpYHBqA3ef5O7fJXffAzqn61RBV0Qixd0z3lLfiJjchqZ0VQwsSNlfmDxWn/OACenGp4k0EYmUxvwK9tQ3Im4IMzsT2Ac4JF1bBV0RiZQsrl4oB7qk7HdOHqvBzA4HrgUOcfcf03WqoCsikdLQS7waaSqwk5ltRyLYngbUeOeMme0J/A3o7+5LM+lUQVdEIiVbma67V5nZxcDLQCHwsLuXmtlNQIm7jwX+F9gSeNbMAOa7+6CG+lXQFZFIyeZjwO4+Hhhf69j1KV8fvs6H0lDQFZFI0WPAIiIB5ftjwAq6IhIpCroiIgFlcfVCk1DQFZFIUaYrIhKQXmIuIhJQzPP7t6Qp6IpIpKimKyISkGq6IiIBqaYrIhJQXOUFEZFwlOmKiASk1QsiIgGpvCAiEpDKCyIiASnTFREJSJmuiEhAMY/leggNUtAVkUjRY8AiIgHpMWARkYCU6YqIBKTVCyIiAWn1gohIQHoMWEQkINV0RUQCUk1XRCQgZboiIgFpna6ISEDKdEVEAtLqBRGRgDSRJiISkMoLIiIB6Yk0EZGAlOmKiASU7zVdy/fvClFiZkPdfXiuxyH5RX8vNi4FuR7ARmZorgcgeUl/LzYiCroiIgEp6IqIBKSgG5bqdlIX/b3YiGgiTUQkIGW6IiIBKeiKiASkoBuImfU3s0/NbI6ZXZ3r8UjumdnDZrbUzD7O9VgkHAXdAMysEBgGDAB6AqebWc/cjkrywAigf64HIWEp6IbRB5jj7nPdfQ3wNDA4x2OSHHP3t4Cvcj0OCUtBN4xiYEHK/sLkMRHZyCjoiogEpKAbRjnQJWW/c/KYiGxkFHTDmArsZGbbmVkRcBowNsdjEpEcUNANwN2rgIuBl4HZwCh3L83tqCTXzOwp4F1gZzNbaGbn5XpM0vT0GLCISEDKdEVEAlLQFREJSEFXRCQgBV0RkYAUdEVEAlLQFREJSEFXRCSg/wfNi3zM8rdt2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[3] Testing Accuracy = \"0.965034965034965\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[4] Testing Accuracy = \"0.9230769230769231\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[5] Testing Accuracy = \"0.951048951048951\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model[6] Testing Accuracy = \"0.965034965034965\"\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJiO2qkNJxwR",
        "outputId": "a169cd82-ab89-4abe-e4b1-289b394a6d98"
      },
      "source": [
        "#Print Prediction of Random Forest Classifier model\n",
        "pred = model[6].predict(X_test)\n",
        "print(pred)\n",
        "\n",
        "#Print a space\n",
        "print()\n",
        "\n",
        "#Print the actual values\n",
        "print(Y_test)"
      ],
      "execution_count": 21,
      "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"
        }
      ]
    }
  ]
}