breast_cancer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "breast_cancer.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CODvwEo1x-1P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Description: This program detects breast cancer, based off of data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsNAOrZZy8RJ",
        "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\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9NnQuJjzMqV",
        "colab_type": "code",
        "outputId": "3f28e05c-f902-497f-c3da-52dcbc371419",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "#load data\n",
        "from google.colab import files\n",
        "uploaded=files.upload()\n",
        "df = pd.read_csv('data.csv')\n",
        "df.head(7)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "     <input type=\"file\" id=\"files-fbc46f33-6cfa-4fef-b216-e68e800d84fa\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-fbc46f33-6cfa-4fef-b216-e68e800d84fa\">\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 (2).csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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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",
              "    </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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1uelWer1Iev",
        "colab_type": "code",
        "outputId": "660dd603-9621-48ed-9ffc-845c47eb6b67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Count the number of rows and columns in the daha set\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XgMHKF81Z0y",
        "colab_type": "code",
        "outputId": "a7201e19-e5da-4da4-ae50-1fd3060b21e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "#count the number of empty values(NaN, NAN, na) in each column\n",
        "df.isna().sum()"
      ],
      "execution_count": 0,
      "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": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVjBpfdo1w-Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#drop the column with all missing values\n",
        "df = df.dropna(axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNfieo042IUS",
        "colab_type": "code",
        "outputId": "d879e904-98f5-4268-cc91-70b1878485fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#get the new count of the number of rows and columns\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oI5kf9ZS2P9U",
        "colab_type": "code",
        "outputId": "0ac53c27-4b34-46a5-d42b-acc5e26587ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#get a count ofthe number of Malignant (M) or Benign (B) cells\n",
        "df.diagnosis.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdetLgAd25hL",
        "colab_type": "code",
        "outputId": "ea436ab7-8a4f-43de-a108-d50e5b04a63a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "#visualize the count\n",
        "sns.countplot(df.diagnosis,label=\"count\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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py4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1LbmASHJOkvuT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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EG5YEb-m3CjA",
        "colab_type": "code",
        "outputId": "4715477e-e3d5-490f-f4ff-0da6a7ccb8b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "source": [
        "#look at the data types to see which columns need to be encoded\n",
        "df.dtypes"
      ],
      "execution_count": 0,
      "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": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3v8gLIr30j7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#encode the 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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnCxokBr40vt",
        "colab_type": "code",
        "outputId": "3d14bf5d-0fa4-420e-b1d3-4fe2d09e5122",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        }
      },
      "source": [
        "#create a pair plot\n",
        "sns.pairplot(df.iloc[:,1:6],hue=\"diagnosis\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3zx9WF86tT+i0G57Quw6cq7Pe+Xqdv+bp0Pr2g4fM04XHHqQLAq/nxMjKIzS7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yuiQfp8+mf83kH484amv3OAwwLn94a1X4U54O8EufYFg+DCu6D8C3hsDBz8sqN7pDkCUolK3bBs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\nRNbTpg4/9a1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hcp4Ev0IIIYQQQgghcp4Ev0IIIYQQQgghcp4Ev0IIIYQQQgghcp4Ev0II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g8LMQRJ8CuEEEII0eaZ1dtYtameltYoO5paWV3dSGMwwgFFHn504kQOHN6+rrRowz/Jq30P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\nd64dkdSMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 955.5x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "263LuV-P6Roe",
        "colab_type": "code",
        "outputId": "203e3a31-9d6b-4ed2-f5ac-59c8c97e1412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "#print the first 5 rows of the new data\n",
        "df.head()"
      ],
      "execution_count": 0,
      "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": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csDy8qeY6fBP",
        "colab_type": "code",
        "outputId": "e2c912a7-5fc1-490f-85e7-cd35136c376c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "#get the correlation of the columns\n",
        "df.iloc[:,1:12].corr()"
      ],
      "execution_count": 0,
      "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": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "es1V56fh691v",
        "colab_type": "code",
        "outputId": "3acf4612-c862-42a9-f530-cb38b7116356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "source": [
        "#visualize the correlation\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.iloc[:,1:12].corr(), annot=True,fmt=\".0%\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j1wsW88PSn9Fa0+7JprzwbGuOnjzNyHGfk5JiJX++CEYPjSY4KJDdBw4zctxk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NapWuuTgEcpFQ/UBKZrrQNa6+PATxgNuOymX8pirXWy1toNbAdKhKZ7gTmhxxswug2A0fB9L1SV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\nl3as9wQvjZtJUIAfk159zqv54NHYzyo4G4FvjTRumMyk7dyI8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GQ8cBPwBvCoqu5zC65L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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAPLPFGg76zY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Split the data set into independent(x) and dependent (y) data sets\n",
        "x=df.iloc[:,2:31].values\n",
        "y=df.iloc[:,1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FF5L4GA28kMf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#split the data set into 75% training 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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0emuHWE9dQm",
        "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "winzlxAA_Ci9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#create a function for the models\n",
        "def models(x_train,y_train):\n",
        "  #Logistic Regression Model\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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkwtX6mjAuoh",
        "colab_type": "code",
        "outputId": "c1bc3b6b-529e-4d4f-d180-d65a431b552c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#Getting all of the models\n",
        "model = models(x_train,y_train)"
      ],
      "execution_count": 0,
      "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": "code",
      "metadata": {
        "id": "4Y7lvt1NBCs_",
        "colab_type": "code",
        "outputId": "88ce2b56-49e7-462a-c8c6-c906f2d50fad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "#test model accuracy on confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "for i in range(len(model)):\n",
        "  print(\"Model \", i)\n",
        "  cm =confusion_matrix(y_test,model[i].predict(x_test))\n",
        "\n",
        "  TP=cm[0][0]\n",
        "  TN=cm[1][1]\n",
        "  FN=cm[1][0]\n",
        "  FP=cm[0][1]\n",
        "\n",
        "  print(cm)\n",
        "  print(\"Testing Accuracy = \", (TP+TN) / (TP+TN+FN+FP))\n",
        "  print()"
      ],
      "execution_count": 0,
      "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": "code",
      "metadata": {
        "id": "KhTpKjOHE2XB",
        "colab_type": "code",
        "outputId": "174f26aa-bc7b-4189-98a3-5e7f86ddc3ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        }
      },
      "source": [
        "#show another way to get metrics of the models\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "for i in range(len(model) ):\n",
        "  print(\"Model \",i)\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": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model  0\n",
            "              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",
            "Model  1\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",
            "Model  2\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": "code",
      "metadata": {
        "id": "gn4Q1DfpHOVz",
        "colab_type": "code",
        "outputId": "ee4ce7b7-b900-4f19-87c4-1240b72ed70e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "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": 0,
      "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"
        }
      ]
    }
  ]
}