ConvertingCategoricalvariables.ipynb

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  "metadata": {
    "colab": {
      "name": "ConvertingCategoricalvariables.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMVqdCYT1CwK6Y71ZTZzv0c",
      "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/abidshafee/DataScienceYouTubeTutorials/blob/master/ConvertingCategoricalvariables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RQ1Dp3Sv2XZ",
        "colab_type": "text"
      },
      "source": [
        "### Loading All Required Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2zu9RmTHItB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "938eefe0-b74a-41db-d40c-279ff4253001"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import seaborn as sns\n",
        "import pandas.util.testing as tm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xB_vKSXmLKOC",
        "colab_type": "text"
      },
      "source": [
        "### Importing Data file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRJcI6QaIf58",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "b22da8d6-836a-427a-9d66-2ac02c7baff2"
      },
      "source": [
        "from google.colab import files\n",
        "file = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5c700af8-819f-45d8-aa97-add8305e8005\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5c700af8-819f-45d8-aa97-add8305e8005\">\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 BreastCancer_data.csv to BreastCancer_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZ8jh-0kKlCw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('BreastCancer_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVoSaJmfLDjQ",
        "colab_type": "text"
      },
      "source": [
        "### Quickly Checking the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uxf1FOSyK7-v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "5f4e483f-25c5-42f4-f494-7b85204b1968"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe thead th {\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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
              "1    842517         M  ...                  0.08902          NaN\n",
              "2  84300903         M  ...                  0.08758          NaN\n",
              "3  84348301         M  ...                  0.17300          NaN\n",
              "4  84358402         M  ...                  0.07678          NaN\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2lLp1vmXdO3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79a211fe-6707-4bdb-8772-dbc18b83af2e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTfb6DRztxhK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "a2416205-d9db-4abb-9e29-b7d470c06ed5"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           int64\n",
              "diagnosis                   object\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "Unnamed: 32                float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLxJQMNGXLet",
        "colab_type": "text"
      },
      "source": [
        "### Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_aP6PEBLeub",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "b3bc41c3-33cd-43da-9f84-0f6d81759c87"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           0\n",
              "diagnosis                    0\n",
              "radius_mean                  0\n",
              "texture_mean                 0\n",
              "perimeter_mean               0\n",
              "area_mean                    0\n",
              "smoothness_mean              0\n",
              "compactness_mean             0\n",
              "concavity_mean               0\n",
              "concave points_mean          0\n",
              "symmetry_mean                0\n",
              "fractal_dimension_mean       0\n",
              "radius_se                    0\n",
              "texture_se                   0\n",
              "perimeter_se                 0\n",
              "area_se                      0\n",
              "smoothness_se                0\n",
              "compactness_se               0\n",
              "concavity_se                 0\n",
              "concave points_se            0\n",
              "symmetry_se                  0\n",
              "fractal_dimension_se         0\n",
              "radius_worst                 0\n",
              "texture_worst                0\n",
              "perimeter_worst              0\n",
              "area_worst                   0\n",
              "smoothness_worst             0\n",
              "compactness_worst            0\n",
              "concavity_worst              0\n",
              "concave points_worst         0\n",
              "symmetry_worst               0\n",
              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_e28rA8OKur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec018f3f-34e6-4d55-f3ca-420fc5f873f1"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPMtC6C6Ul40",
        "colab_type": "text"
      },
      "source": [
        "let see the **dataframe** where Unnamed: 32 column is empty."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfsX5VHTVNp-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "6acba4dd-dcee-4400-c176-1f6885434dc3"
      },
      "source": [
        "df[df['Unnamed: 32'].isnull()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <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>0.30010</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>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</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>0.08690</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>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</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>0.19740</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>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</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>0.24140</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>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</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>0.19800</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>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0      842302         M  ...                  0.11890          NaN\n",
              "1      842517         M  ...                  0.08902          NaN\n",
              "2    84300903         M  ...                  0.08758          NaN\n",
              "3    84348301         M  ...                  0.17300          NaN\n",
              "4    84358402         M  ...                  0.07678          NaN\n",
              "..        ...       ...  ...                      ...          ...\n",
              "564    926424         M  ...                  0.07115          NaN\n",
              "565    926682         M  ...                  0.06637          NaN\n",
              "566    926954         M  ...                  0.07820          NaN\n",
              "567    927241         M  ...                  0.12400          NaN\n",
              "568     92751         B  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cISd_PNlTa4j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "e30e4431-f18a-44ba-b664-54e6e840921e"
      },
      "source": [
        "df['Unnamed: 32'].isnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      True\n",
              "1      True\n",
              "2      True\n",
              "3      True\n",
              "4      True\n",
              "       ... \n",
              "564    True\n",
              "565    True\n",
              "566    True\n",
              "567    True\n",
              "568    True\n",
              "Name: Unnamed: 32, Length: 569, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYfM_abxWwyo",
        "colab_type": "text"
      },
      "source": [
        "### Drop the empty Unnamed: 32 Column,\n",
        "axis = 1 indicates empty column should be deleted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKRgdXsKUZEG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.dropna(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-3jxMIpwcDJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "outputId": "2b9563fb-055b-4ab6-91a7-61af49d46569"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.8</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>184.6</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>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.9</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>158.8</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       id diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "0  842302         M  ...          0.4601                  0.11890\n",
              "1  842517         M  ...          0.2750                  0.08902\n",
              "\n",
              "[2 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDz8pbAHwjOc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "081b7fe7-cafc-456f-fe84-bf54d4ab3f1e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dp3ER95GXZm9",
        "colab_type": "text"
      },
      "source": [
        "### Check the Unique Values in categorical diagnosis column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wf3qT_U5K-3y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e6843b8-db87-43ac-bd1e-63cb0cf42604"
      },
      "source": [
        "df['diagnosis'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'B'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUBSJaRAX1iu",
        "colab_type": "text"
      },
      "source": [
        "### Count the number of values for each unique category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79bd080xX0R5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "3627fa50-1c88-4eb6-c08b-8a7d5da043c8"
      },
      "source": [
        "df['diagnosis'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hw3r5qpTYUH_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "6b133bff-dac7-4e34-dbf9-b27b872c60bd"
      },
      "source": [
        "sns.countplot(df['diagnosis'])\n",
        "sns.set(style=\"darkgrid\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "YjdxW811Y1Wz",
        "colab_type": "text"
      },
      "source": [
        "### Now converting categorical Variables in diagonosis column to numerical values using LabelEncoder utility class of scikit learn library. This will replace the original column in the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVjCA43YYwah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encodedOutput_Y = LabelEncoder()\n",
        "df['diagnosis'] = encodedOutput_Y.fit_transform(df.iloc[:,1].values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXmxYC3Tktm8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "41b1b040-e475-4977-cdbe-8c557cde5cea"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "0    842302          1  ...          0.4601                  0.11890\n",
              "1    842517          1  ...          0.2750                  0.08902\n",
              "2  84300903          1  ...          0.3613                  0.08758\n",
              "3  84348301          1  ...          0.6638                  0.17300\n",
              "4  84358402          1  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Yx4kGJBsrCR",
        "colab_type": "text"
      },
      "source": [
        "## Loading the dataframe again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMm7SZPotD5U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('BreastCancer_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3Wef-1z1v2J",
        "colab_type": "text"
      },
      "source": [
        "### Checking the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBHyZAVi12Sl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "761989e5-b80c-447c-bbae-3f9e6545a1f2"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <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>0.30010</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>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</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>0.08690</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>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</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>0.19740</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>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</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>0.24140</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>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</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>0.19800</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>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0      842302         M  ...                  0.11890          NaN\n",
              "1      842517         M  ...                  0.08902          NaN\n",
              "2    84300903         M  ...                  0.08758          NaN\n",
              "3    84348301         M  ...                  0.17300          NaN\n",
              "4    84358402         M  ...                  0.07678          NaN\n",
              "..        ...       ...  ...                      ...          ...\n",
              "564    926424         M  ...                  0.07115          NaN\n",
              "565    926682         M  ...                  0.06637          NaN\n",
              "566    926954         M  ...                  0.07820          NaN\n",
              "567    927241         M  ...                  0.12400          NaN\n",
              "568     92751         B  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKfdt2ZGtmek",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e10af5d9-5c66-4fe0-9354-e2fcb38b8a1a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYgjt1K7l2qa",
        "colab_type": "text"
      },
      "source": [
        "### Converting Categorical data to numerical values using numpy where() method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2oVCMvymDWb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%timeit\n",
        "df['diagnosis'] = np.where(df['diagnosis']=='M', 0, 1)\n",
        "\n",
        "# df['dignosis'] = np.where(df['diagnosis'].str.contains('M'),1,0)\n",
        "%timeit"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3x-keYHsgPh",
        "colab_type": "text"
      },
      "source": [
        "Droping Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fMz5bzomUeU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "40514d9d-91e2-42fc-a5e4-1c18fb99e787"
      },
      "source": [
        "df.head(30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>0</td>\n",
              "      <td>17.990</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>0.30010</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>0.049040</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.015870</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>0.71190</td>\n",
              "      <td>0.26540</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>0</td>\n",
              "      <td>20.570</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>0.08690</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>0.013080</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.013400</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>0.24160</td>\n",
              "      <td>0.18600</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>0</td>\n",
              "      <td>19.690</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>0.19740</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>0.040060</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.020580</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>0.45040</td>\n",
              "      <td>0.24300</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>0</td>\n",
              "      <td>11.420</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>0.24140</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>0.074580</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.018670</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>0.68690</td>\n",
              "      <td>0.25750</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>0</td>\n",
              "      <td>20.290</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>0.19800</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>0.024610</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.018850</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>0.40000</td>\n",
              "      <td>0.16250</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>0</td>\n",
              "      <td>12.450</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>0.15780</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>0.033450</td>\n",
              "      <td>0.03672</td>\n",
              "      <td>0.011370</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>0.53550</td>\n",
              "      <td>0.17410</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>0</td>\n",
              "      <td>18.250</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>0.11270</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>0.013820</td>\n",
              "      <td>0.02254</td>\n",
              "      <td>0.010390</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>0.37840</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>84458202</td>\n",
              "      <td>0</td>\n",
              "      <td>13.710</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>0.05985</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>0.07451</td>\n",
              "      <td>0.5835</td>\n",
              "      <td>1.3770</td>\n",
              "      <td>3.856</td>\n",
              "      <td>50.96</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>0.02488</td>\n",
              "      <td>0.014480</td>\n",
              "      <td>0.01486</td>\n",
              "      <td>0.005412</td>\n",
              "      <td>17.06</td>\n",
              "      <td>28.14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.1654</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.26780</td>\n",
              "      <td>0.15560</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>844981</td>\n",
              "      <td>0</td>\n",
              "      <td>13.000</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.18590</td>\n",
              "      <td>0.09353</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>0.07389</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>1.0020</td>\n",
              "      <td>2.406</td>\n",
              "      <td>24.32</td>\n",
              "      <td>0.005731</td>\n",
              "      <td>0.035020</td>\n",
              "      <td>0.03553</td>\n",
              "      <td>0.012260</td>\n",
              "      <td>0.02143</td>\n",
              "      <td>0.003749</td>\n",
              "      <td>15.49</td>\n",
              "      <td>30.73</td>\n",
              "      <td>106.20</td>\n",
              "      <td>739.3</td>\n",
              "      <td>0.1703</td>\n",
              "      <td>0.5401</td>\n",
              "      <td>0.53900</td>\n",
              "      <td>0.20600</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>0.10720</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>84501001</td>\n",
              "      <td>0</td>\n",
              "      <td>12.460</td>\n",
              "      <td>24.04</td>\n",
              "      <td>83.97</td>\n",
              "      <td>475.9</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0.23960</td>\n",
              "      <td>0.22730</td>\n",
              "      <td>0.08543</td>\n",
              "      <td>0.2030</td>\n",
              "      <td>0.08243</td>\n",
              "      <td>0.2976</td>\n",
              "      <td>1.5990</td>\n",
              "      <td>2.039</td>\n",
              "      <td>23.94</td>\n",
              "      <td>0.007149</td>\n",
              "      <td>0.072170</td>\n",
              "      <td>0.07743</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01789</td>\n",
              "      <td>0.010080</td>\n",
              "      <td>15.09</td>\n",
              "      <td>40.68</td>\n",
              "      <td>97.65</td>\n",
              "      <td>711.4</td>\n",
              "      <td>0.1853</td>\n",
              "      <td>1.0580</td>\n",
              "      <td>1.10500</td>\n",
              "      <td>0.22100</td>\n",
              "      <td>0.4366</td>\n",
              "      <td>0.20750</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>845636</td>\n",
              "      <td>0</td>\n",
              "      <td>16.020</td>\n",
              "      <td>23.24</td>\n",
              "      <td>102.70</td>\n",
              "      <td>797.8</td>\n",
              "      <td>0.08206</td>\n",
              "      <td>0.06669</td>\n",
              "      <td>0.03299</td>\n",
              "      <td>0.03323</td>\n",
              "      <td>0.1528</td>\n",
              "      <td>0.05697</td>\n",
              "      <td>0.3795</td>\n",
              "      <td>1.1870</td>\n",
              "      <td>2.466</td>\n",
              "      <td>40.51</td>\n",
              "      <td>0.004029</td>\n",
              "      <td>0.009269</td>\n",
              "      <td>0.01101</td>\n",
              "      <td>0.007591</td>\n",
              "      <td>0.01460</td>\n",
              "      <td>0.003042</td>\n",
              "      <td>19.19</td>\n",
              "      <td>33.88</td>\n",
              "      <td>123.80</td>\n",
              "      <td>1150.0</td>\n",
              "      <td>0.1181</td>\n",
              "      <td>0.1551</td>\n",
              "      <td>0.14590</td>\n",
              "      <td>0.09975</td>\n",
              "      <td>0.2948</td>\n",
              "      <td>0.08452</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>84610002</td>\n",
              "      <td>0</td>\n",
              "      <td>15.780</td>\n",
              "      <td>17.89</td>\n",
              "      <td>103.60</td>\n",
              "      <td>781.0</td>\n",
              "      <td>0.09710</td>\n",
              "      <td>0.12920</td>\n",
              "      <td>0.09954</td>\n",
              "      <td>0.06606</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.06082</td>\n",
              "      <td>0.5058</td>\n",
              "      <td>0.9849</td>\n",
              "      <td>3.564</td>\n",
              "      <td>54.16</td>\n",
              "      <td>0.005771</td>\n",
              "      <td>0.040610</td>\n",
              "      <td>0.02791</td>\n",
              "      <td>0.012820</td>\n",
              "      <td>0.02008</td>\n",
              "      <td>0.004144</td>\n",
              "      <td>20.42</td>\n",
              "      <td>27.28</td>\n",
              "      <td>136.50</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>0.1396</td>\n",
              "      <td>0.5609</td>\n",
              "      <td>0.39650</td>\n",
              "      <td>0.18100</td>\n",
              "      <td>0.3792</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>846226</td>\n",
              "      <td>0</td>\n",
              "      <td>19.170</td>\n",
              "      <td>24.80</td>\n",
              "      <td>132.40</td>\n",
              "      <td>1123.0</td>\n",
              "      <td>0.09740</td>\n",
              "      <td>0.24580</td>\n",
              "      <td>0.20650</td>\n",
              "      <td>0.11180</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07800</td>\n",
              "      <td>0.9555</td>\n",
              "      <td>3.5680</td>\n",
              "      <td>11.070</td>\n",
              "      <td>116.20</td>\n",
              "      <td>0.003139</td>\n",
              "      <td>0.082970</td>\n",
              "      <td>0.08890</td>\n",
              "      <td>0.040900</td>\n",
              "      <td>0.04484</td>\n",
              "      <td>0.012840</td>\n",
              "      <td>20.96</td>\n",
              "      <td>29.94</td>\n",
              "      <td>151.70</td>\n",
              "      <td>1332.0</td>\n",
              "      <td>0.1037</td>\n",
              "      <td>0.3903</td>\n",
              "      <td>0.36390</td>\n",
              "      <td>0.17670</td>\n",
              "      <td>0.3176</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>846381</td>\n",
              "      <td>0</td>\n",
              "      <td>15.850</td>\n",
              "      <td>23.95</td>\n",
              "      <td>103.70</td>\n",
              "      <td>782.7</td>\n",
              "      <td>0.08401</td>\n",
              "      <td>0.10020</td>\n",
              "      <td>0.09938</td>\n",
              "      <td>0.05364</td>\n",
              "      <td>0.1847</td>\n",
              "      <td>0.05338</td>\n",
              "      <td>0.4033</td>\n",
              "      <td>1.0780</td>\n",
              "      <td>2.903</td>\n",
              "      <td>36.58</td>\n",
              "      <td>0.009769</td>\n",
              "      <td>0.031260</td>\n",
              "      <td>0.05051</td>\n",
              "      <td>0.019920</td>\n",
              "      <td>0.02981</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>16.84</td>\n",
              "      <td>27.66</td>\n",
              "      <td>112.00</td>\n",
              "      <td>876.5</td>\n",
              "      <td>0.1131</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.23220</td>\n",
              "      <td>0.11190</td>\n",
              "      <td>0.2809</td>\n",
              "      <td>0.06287</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>84667401</td>\n",
              "      <td>0</td>\n",
              "      <td>13.730</td>\n",
              "      <td>22.61</td>\n",
              "      <td>93.60</td>\n",
              "      <td>578.3</td>\n",
              "      <td>0.11310</td>\n",
              "      <td>0.22930</td>\n",
              "      <td>0.21280</td>\n",
              "      <td>0.08025</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.07682</td>\n",
              "      <td>0.2121</td>\n",
              "      <td>1.1690</td>\n",
              "      <td>2.061</td>\n",
              "      <td>19.21</td>\n",
              "      <td>0.006429</td>\n",
              "      <td>0.059360</td>\n",
              "      <td>0.05501</td>\n",
              "      <td>0.016280</td>\n",
              "      <td>0.01961</td>\n",
              "      <td>0.008093</td>\n",
              "      <td>15.03</td>\n",
              "      <td>32.01</td>\n",
              "      <td>108.80</td>\n",
              "      <td>697.7</td>\n",
              "      <td>0.1651</td>\n",
              "      <td>0.7725</td>\n",
              "      <td>0.69430</td>\n",
              "      <td>0.22080</td>\n",
              "      <td>0.3596</td>\n",
              "      <td>0.14310</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>84799002</td>\n",
              "      <td>0</td>\n",
              "      <td>14.540</td>\n",
              "      <td>27.54</td>\n",
              "      <td>96.73</td>\n",
              "      <td>658.8</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.15950</td>\n",
              "      <td>0.16390</td>\n",
              "      <td>0.07364</td>\n",
              "      <td>0.2303</td>\n",
              "      <td>0.07077</td>\n",
              "      <td>0.3700</td>\n",
              "      <td>1.0330</td>\n",
              "      <td>2.879</td>\n",
              "      <td>32.55</td>\n",
              "      <td>0.005607</td>\n",
              "      <td>0.042400</td>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.010900</td>\n",
              "      <td>0.01857</td>\n",
              "      <td>0.005466</td>\n",
              "      <td>17.46</td>\n",
              "      <td>37.13</td>\n",
              "      <td>124.10</td>\n",
              "      <td>943.2</td>\n",
              "      <td>0.1678</td>\n",
              "      <td>0.6577</td>\n",
              "      <td>0.70260</td>\n",
              "      <td>0.17120</td>\n",
              "      <td>0.4218</td>\n",
              "      <td>0.13410</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>848406</td>\n",
              "      <td>0</td>\n",
              "      <td>14.680</td>\n",
              "      <td>20.13</td>\n",
              "      <td>94.74</td>\n",
              "      <td>684.5</td>\n",
              "      <td>0.09867</td>\n",
              "      <td>0.07200</td>\n",
              "      <td>0.07395</td>\n",
              "      <td>0.05259</td>\n",
              "      <td>0.1586</td>\n",
              "      <td>0.05922</td>\n",
              "      <td>0.4727</td>\n",
              "      <td>1.2400</td>\n",
              "      <td>3.195</td>\n",
              "      <td>45.40</td>\n",
              "      <td>0.005718</td>\n",
              "      <td>0.011620</td>\n",
              "      <td>0.01998</td>\n",
              "      <td>0.011090</td>\n",
              "      <td>0.01410</td>\n",
              "      <td>0.002085</td>\n",
              "      <td>19.07</td>\n",
              "      <td>30.88</td>\n",
              "      <td>123.40</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>0.1464</td>\n",
              "      <td>0.1871</td>\n",
              "      <td>0.29140</td>\n",
              "      <td>0.16090</td>\n",
              "      <td>0.3029</td>\n",
              "      <td>0.08216</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>84862001</td>\n",
              "      <td>0</td>\n",
              "      <td>16.130</td>\n",
              "      <td>20.68</td>\n",
              "      <td>108.10</td>\n",
              "      <td>798.8</td>\n",
              "      <td>0.11700</td>\n",
              "      <td>0.20220</td>\n",
              "      <td>0.17220</td>\n",
              "      <td>0.10280</td>\n",
              "      <td>0.2164</td>\n",
              "      <td>0.07356</td>\n",
              "      <td>0.5692</td>\n",
              "      <td>1.0730</td>\n",
              "      <td>3.854</td>\n",
              "      <td>54.18</td>\n",
              "      <td>0.007026</td>\n",
              "      <td>0.025010</td>\n",
              "      <td>0.03188</td>\n",
              "      <td>0.012970</td>\n",
              "      <td>0.01689</td>\n",
              "      <td>0.004142</td>\n",
              "      <td>20.96</td>\n",
              "      <td>31.48</td>\n",
              "      <td>136.80</td>\n",
              "      <td>1315.0</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>0.4233</td>\n",
              "      <td>0.47840</td>\n",
              "      <td>0.20730</td>\n",
              "      <td>0.3706</td>\n",
              "      <td>0.11420</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>849014</td>\n",
              "      <td>0</td>\n",
              "      <td>19.810</td>\n",
              "      <td>22.15</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.09831</td>\n",
              "      <td>0.10270</td>\n",
              "      <td>0.14790</td>\n",
              "      <td>0.09498</td>\n",
              "      <td>0.1582</td>\n",
              "      <td>0.05395</td>\n",
              "      <td>0.7582</td>\n",
              "      <td>1.0170</td>\n",
              "      <td>5.865</td>\n",
              "      <td>112.40</td>\n",
              "      <td>0.006494</td>\n",
              "      <td>0.018930</td>\n",
              "      <td>0.03391</td>\n",
              "      <td>0.015210</td>\n",
              "      <td>0.01356</td>\n",
              "      <td>0.001997</td>\n",
              "      <td>27.32</td>\n",
              "      <td>30.88</td>\n",
              "      <td>186.80</td>\n",
              "      <td>2398.0</td>\n",
              "      <td>0.1512</td>\n",
              "      <td>0.3150</td>\n",
              "      <td>0.53720</td>\n",
              "      <td>0.23880</td>\n",
              "      <td>0.2768</td>\n",
              "      <td>0.07615</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8510426</td>\n",
              "      <td>1</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.04781</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.56</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.11</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>0.1773</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8510653</td>\n",
              "      <td>1</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.03110</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.67</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.50</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.1312</td>\n",
              "      <td>0.2776</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8510824</td>\n",
              "      <td>1</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.02076</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.70</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.02027</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.23</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.1324</td>\n",
              "      <td>0.1148</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8511133</td>\n",
              "      <td>0</td>\n",
              "      <td>15.340</td>\n",
              "      <td>14.26</td>\n",
              "      <td>102.50</td>\n",
              "      <td>704.4</td>\n",
              "      <td>0.10730</td>\n",
              "      <td>0.21350</td>\n",
              "      <td>0.20770</td>\n",
              "      <td>0.09756</td>\n",
              "      <td>0.2521</td>\n",
              "      <td>0.07032</td>\n",
              "      <td>0.4388</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>3.384</td>\n",
              "      <td>44.91</td>\n",
              "      <td>0.006789</td>\n",
              "      <td>0.053280</td>\n",
              "      <td>0.06446</td>\n",
              "      <td>0.022520</td>\n",
              "      <td>0.03672</td>\n",
              "      <td>0.004394</td>\n",
              "      <td>18.07</td>\n",
              "      <td>19.08</td>\n",
              "      <td>125.10</td>\n",
              "      <td>980.9</td>\n",
              "      <td>0.1390</td>\n",
              "      <td>0.5954</td>\n",
              "      <td>0.63050</td>\n",
              "      <td>0.23930</td>\n",
              "      <td>0.4667</td>\n",
              "      <td>0.09946</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>851509</td>\n",
              "      <td>0</td>\n",
              "      <td>21.160</td>\n",
              "      <td>23.04</td>\n",
              "      <td>137.20</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>0.09428</td>\n",
              "      <td>0.10220</td>\n",
              "      <td>0.10970</td>\n",
              "      <td>0.08632</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.05278</td>\n",
              "      <td>0.6917</td>\n",
              "      <td>1.1270</td>\n",
              "      <td>4.303</td>\n",
              "      <td>93.99</td>\n",
              "      <td>0.004728</td>\n",
              "      <td>0.012590</td>\n",
              "      <td>0.01715</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.01083</td>\n",
              "      <td>0.001987</td>\n",
              "      <td>29.17</td>\n",
              "      <td>35.59</td>\n",
              "      <td>188.00</td>\n",
              "      <td>2615.0</td>\n",
              "      <td>0.1401</td>\n",
              "      <td>0.2600</td>\n",
              "      <td>0.31550</td>\n",
              "      <td>0.20090</td>\n",
              "      <td>0.2822</td>\n",
              "      <td>0.07526</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>852552</td>\n",
              "      <td>0</td>\n",
              "      <td>16.650</td>\n",
              "      <td>21.38</td>\n",
              "      <td>110.00</td>\n",
              "      <td>904.6</td>\n",
              "      <td>0.11210</td>\n",
              "      <td>0.14570</td>\n",
              "      <td>0.15250</td>\n",
              "      <td>0.09170</td>\n",
              "      <td>0.1995</td>\n",
              "      <td>0.06330</td>\n",
              "      <td>0.8068</td>\n",
              "      <td>0.9017</td>\n",
              "      <td>5.455</td>\n",
              "      <td>102.60</td>\n",
              "      <td>0.006048</td>\n",
              "      <td>0.018820</td>\n",
              "      <td>0.02741</td>\n",
              "      <td>0.011300</td>\n",
              "      <td>0.01468</td>\n",
              "      <td>0.002801</td>\n",
              "      <td>26.46</td>\n",
              "      <td>31.56</td>\n",
              "      <td>177.00</td>\n",
              "      <td>2215.0</td>\n",
              "      <td>0.1805</td>\n",
              "      <td>0.3578</td>\n",
              "      <td>0.46950</td>\n",
              "      <td>0.20950</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.09564</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>852631</td>\n",
              "      <td>0</td>\n",
              "      <td>17.140</td>\n",
              "      <td>16.40</td>\n",
              "      <td>116.00</td>\n",
              "      <td>912.7</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0.22760</td>\n",
              "      <td>0.22290</td>\n",
              "      <td>0.14010</td>\n",
              "      <td>0.3040</td>\n",
              "      <td>0.07413</td>\n",
              "      <td>1.0460</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>7.276</td>\n",
              "      <td>111.40</td>\n",
              "      <td>0.008029</td>\n",
              "      <td>0.037990</td>\n",
              "      <td>0.03732</td>\n",
              "      <td>0.023970</td>\n",
              "      <td>0.02308</td>\n",
              "      <td>0.007444</td>\n",
              "      <td>22.25</td>\n",
              "      <td>21.40</td>\n",
              "      <td>152.40</td>\n",
              "      <td>1461.0</td>\n",
              "      <td>0.1545</td>\n",
              "      <td>0.3949</td>\n",
              "      <td>0.38530</td>\n",
              "      <td>0.25500</td>\n",
              "      <td>0.4066</td>\n",
              "      <td>0.10590</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>852763</td>\n",
              "      <td>0</td>\n",
              "      <td>14.580</td>\n",
              "      <td>21.53</td>\n",
              "      <td>97.41</td>\n",
              "      <td>644.8</td>\n",
              "      <td>0.10540</td>\n",
              "      <td>0.18680</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.08783</td>\n",
              "      <td>0.2252</td>\n",
              "      <td>0.06924</td>\n",
              "      <td>0.2545</td>\n",
              "      <td>0.9832</td>\n",
              "      <td>2.110</td>\n",
              "      <td>21.05</td>\n",
              "      <td>0.004452</td>\n",
              "      <td>0.030550</td>\n",
              "      <td>0.02681</td>\n",
              "      <td>0.013520</td>\n",
              "      <td>0.01454</td>\n",
              "      <td>0.003711</td>\n",
              "      <td>17.62</td>\n",
              "      <td>33.21</td>\n",
              "      <td>122.40</td>\n",
              "      <td>896.9</td>\n",
              "      <td>0.1525</td>\n",
              "      <td>0.6643</td>\n",
              "      <td>0.55390</td>\n",
              "      <td>0.27010</td>\n",
              "      <td>0.4264</td>\n",
              "      <td>0.12750</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>852781</td>\n",
              "      <td>0</td>\n",
              "      <td>18.610</td>\n",
              "      <td>20.25</td>\n",
              "      <td>122.10</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>0.09440</td>\n",
              "      <td>0.10660</td>\n",
              "      <td>0.14900</td>\n",
              "      <td>0.07731</td>\n",
              "      <td>0.1697</td>\n",
              "      <td>0.05699</td>\n",
              "      <td>0.8529</td>\n",
              "      <td>1.8490</td>\n",
              "      <td>5.632</td>\n",
              "      <td>93.54</td>\n",
              "      <td>0.010750</td>\n",
              "      <td>0.027220</td>\n",
              "      <td>0.05081</td>\n",
              "      <td>0.019110</td>\n",
              "      <td>0.02293</td>\n",
              "      <td>0.004217</td>\n",
              "      <td>21.31</td>\n",
              "      <td>27.26</td>\n",
              "      <td>139.90</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>0.1338</td>\n",
              "      <td>0.2117</td>\n",
              "      <td>0.34460</td>\n",
              "      <td>0.14900</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.07421</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>852973</td>\n",
              "      <td>0</td>\n",
              "      <td>15.300</td>\n",
              "      <td>25.27</td>\n",
              "      <td>102.40</td>\n",
              "      <td>732.4</td>\n",
              "      <td>0.10820</td>\n",
              "      <td>0.16970</td>\n",
              "      <td>0.16830</td>\n",
              "      <td>0.08751</td>\n",
              "      <td>0.1926</td>\n",
              "      <td>0.06540</td>\n",
              "      <td>0.4390</td>\n",
              "      <td>1.0120</td>\n",
              "      <td>3.498</td>\n",
              "      <td>43.50</td>\n",
              "      <td>0.005233</td>\n",
              "      <td>0.030570</td>\n",
              "      <td>0.03576</td>\n",
              "      <td>0.010830</td>\n",
              "      <td>0.01768</td>\n",
              "      <td>0.002967</td>\n",
              "      <td>20.27</td>\n",
              "      <td>36.71</td>\n",
              "      <td>149.30</td>\n",
              "      <td>1269.0</td>\n",
              "      <td>0.1641</td>\n",
              "      <td>0.6110</td>\n",
              "      <td>0.63350</td>\n",
              "      <td>0.20240</td>\n",
              "      <td>0.4027</td>\n",
              "      <td>0.09876</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>853201</td>\n",
              "      <td>0</td>\n",
              "      <td>17.570</td>\n",
              "      <td>15.05</td>\n",
              "      <td>115.00</td>\n",
              "      <td>955.1</td>\n",
              "      <td>0.09847</td>\n",
              "      <td>0.11570</td>\n",
              "      <td>0.09875</td>\n",
              "      <td>0.07953</td>\n",
              "      <td>0.1739</td>\n",
              "      <td>0.06149</td>\n",
              "      <td>0.6003</td>\n",
              "      <td>0.8225</td>\n",
              "      <td>4.655</td>\n",
              "      <td>61.10</td>\n",
              "      <td>0.005627</td>\n",
              "      <td>0.030330</td>\n",
              "      <td>0.03407</td>\n",
              "      <td>0.013540</td>\n",
              "      <td>0.01925</td>\n",
              "      <td>0.003742</td>\n",
              "      <td>20.01</td>\n",
              "      <td>19.52</td>\n",
              "      <td>134.90</td>\n",
              "      <td>1227.0</td>\n",
              "      <td>0.1255</td>\n",
              "      <td>0.2812</td>\n",
              "      <td>0.24890</td>\n",
              "      <td>0.14560</td>\n",
              "      <td>0.2756</td>\n",
              "      <td>0.07919</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          0  ...                  0.11890          NaN\n",
              "1     842517          0  ...                  0.08902          NaN\n",
              "2   84300903          0  ...                  0.08758          NaN\n",
              "3   84348301          0  ...                  0.17300          NaN\n",
              "4   84358402          0  ...                  0.07678          NaN\n",
              "5     843786          0  ...                  0.12440          NaN\n",
              "6     844359          0  ...                  0.08368          NaN\n",
              "7   84458202          0  ...                  0.11510          NaN\n",
              "8     844981          0  ...                  0.10720          NaN\n",
              "9   84501001          0  ...                  0.20750          NaN\n",
              "10    845636          0  ...                  0.08452          NaN\n",
              "11  84610002          0  ...                  0.10480          NaN\n",
              "12    846226          0  ...                  0.10230          NaN\n",
              "13    846381          0  ...                  0.06287          NaN\n",
              "14  84667401          0  ...                  0.14310          NaN\n",
              "15  84799002          0  ...                  0.13410          NaN\n",
              "16    848406          0  ...                  0.08216          NaN\n",
              "17  84862001          0  ...                  0.11420          NaN\n",
              "18    849014          0  ...                  0.07615          NaN\n",
              "19   8510426          1  ...                  0.07259          NaN\n",
              "20   8510653          1  ...                  0.08183          NaN\n",
              "21   8510824          1  ...                  0.07773          NaN\n",
              "22   8511133          0  ...                  0.09946          NaN\n",
              "23    851509          0  ...                  0.07526          NaN\n",
              "24    852552          0  ...                  0.09564          NaN\n",
              "25    852631          0  ...                  0.10590          NaN\n",
              "26    852763          0  ...                  0.12750          NaN\n",
              "27    852781          0  ...                  0.07421          NaN\n",
              "28    852973          0  ...                  0.09876          NaN\n",
              "29    853201          0  ...                  0.07919          NaN\n",
              "\n",
              "[30 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ulqft0ug4o3d",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdnEM6Q54t3Z",
        "colab_type": "text"
      },
      "source": [
        "### Drop a column in dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zG8ZG2h9rI4v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "de8d01ca-f676-4cb8-9ee9-0f69a1009a1f"
      },
      "source": [
        "'''\n",
        "df.drop('diagnosis',\n",
        "  axis='columns', inplace=True)\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\ndf.drop('diagnosis',\\n  axis='columns', inplace=True)\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBeiDQcsp7A0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.dropna(axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntTDtL8q7W1h",
        "colab_type": "text"
      },
      "source": [
        "# Tensorflow classification Model\n",
        "### Spliting Data into test set, and train set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6OJKPHX5fgl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lC8GCH1v599y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "2fe46ba4-986f-4f5a-c364-728c38453ede"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 32 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    int64  \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            "dtypes: float64(30), int64(2)\n",
            "memory usage: 142.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdUJpKm06GPp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.iloc[:,2:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwgAFRBv62VK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9fef0524-a991-47fd-9c57-4adf2bf0f000"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldvZZMFh63Ty",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y = df.iloc[:, 1:2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpcGQukA8Fcc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnV-tmNX9ZJH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "1536b366-867f-45d6-d9b1-63042c3acbc6"
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>172</th>\n",
              "      <td>15.460</td>\n",
              "      <td>11.89</td>\n",
              "      <td>102.50</td>\n",
              "      <td>736.9</td>\n",
              "      <td>0.12570</td>\n",
              "      <td>0.15550</td>\n",
              "      <td>0.20320</td>\n",
              "      <td>0.10970</td>\n",
              "      <td>0.1966</td>\n",
              "      <td>0.07069</td>\n",
              "      <td>0.4209</td>\n",
              "      <td>0.6583</td>\n",
              "      <td>2.8050</td>\n",
              "      <td>44.64</td>\n",
              "      <td>0.005393</td>\n",
              "      <td>0.023210</td>\n",
              "      <td>0.043030</td>\n",
              "      <td>0.013200</td>\n",
              "      <td>0.01792</td>\n",
              "      <td>0.004168</td>\n",
              "      <td>18.790</td>\n",
              "      <td>17.04</td>\n",
              "      <td>125.00</td>\n",
              "      <td>1102.0</td>\n",
              "      <td>0.15310</td>\n",
              "      <td>0.35830</td>\n",
              "      <td>0.58300</td>\n",
              "      <td>0.18270</td>\n",
              "      <td>0.3216</td>\n",
              "      <td>0.10100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>407</th>\n",
              "      <td>12.850</td>\n",
              "      <td>21.37</td>\n",
              "      <td>82.63</td>\n",
              "      <td>514.5</td>\n",
              "      <td>0.07551</td>\n",
              "      <td>0.08316</td>\n",
              "      <td>0.06126</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.1580</td>\n",
              "      <td>0.06114</td>\n",
              "      <td>0.4993</td>\n",
              "      <td>1.7980</td>\n",
              "      <td>2.5520</td>\n",
              "      <td>41.24</td>\n",
              "      <td>0.006011</td>\n",
              "      <td>0.044800</td>\n",
              "      <td>0.051750</td>\n",
              "      <td>0.013410</td>\n",
              "      <td>0.02669</td>\n",
              "      <td>0.007731</td>\n",
              "      <td>14.400</td>\n",
              "      <td>27.01</td>\n",
              "      <td>91.63</td>\n",
              "      <td>645.8</td>\n",
              "      <td>0.09402</td>\n",
              "      <td>0.19360</td>\n",
              "      <td>0.18380</td>\n",
              "      <td>0.05601</td>\n",
              "      <td>0.2488</td>\n",
              "      <td>0.08151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>19.210</td>\n",
              "      <td>18.57</td>\n",
              "      <td>125.50</td>\n",
              "      <td>1152.0</td>\n",
              "      <td>0.10530</td>\n",
              "      <td>0.12670</td>\n",
              "      <td>0.13230</td>\n",
              "      <td>0.08994</td>\n",
              "      <td>0.1917</td>\n",
              "      <td>0.05961</td>\n",
              "      <td>0.7275</td>\n",
              "      <td>1.1930</td>\n",
              "      <td>4.8370</td>\n",
              "      <td>102.50</td>\n",
              "      <td>0.006458</td>\n",
              "      <td>0.023060</td>\n",
              "      <td>0.029450</td>\n",
              "      <td>0.015380</td>\n",
              "      <td>0.01852</td>\n",
              "      <td>0.002608</td>\n",
              "      <td>26.140</td>\n",
              "      <td>28.14</td>\n",
              "      <td>170.10</td>\n",
              "      <td>2145.0</td>\n",
              "      <td>0.16240</td>\n",
              "      <td>0.35110</td>\n",
              "      <td>0.38790</td>\n",
              "      <td>0.20910</td>\n",
              "      <td>0.3537</td>\n",
              "      <td>0.08294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>12.470</td>\n",
              "      <td>17.31</td>\n",
              "      <td>80.45</td>\n",
              "      <td>480.1</td>\n",
              "      <td>0.08928</td>\n",
              "      <td>0.07630</td>\n",
              "      <td>0.03609</td>\n",
              "      <td>0.02369</td>\n",
              "      <td>0.1526</td>\n",
              "      <td>0.06046</td>\n",
              "      <td>0.1532</td>\n",
              "      <td>0.7810</td>\n",
              "      <td>1.2530</td>\n",
              "      <td>11.91</td>\n",
              "      <td>0.003796</td>\n",
              "      <td>0.013710</td>\n",
              "      <td>0.013460</td>\n",
              "      <td>0.007096</td>\n",
              "      <td>0.01536</td>\n",
              "      <td>0.001541</td>\n",
              "      <td>14.060</td>\n",
              "      <td>24.34</td>\n",
              "      <td>92.82</td>\n",
              "      <td>607.3</td>\n",
              "      <td>0.12760</td>\n",
              "      <td>0.25060</td>\n",
              "      <td>0.20280</td>\n",
              "      <td>0.10530</td>\n",
              "      <td>0.3035</td>\n",
              "      <td>0.07661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>12.460</td>\n",
              "      <td>19.89</td>\n",
              "      <td>80.43</td>\n",
              "      <td>471.3</td>\n",
              "      <td>0.08451</td>\n",
              "      <td>0.10140</td>\n",
              "      <td>0.06830</td>\n",
              "      <td>0.03099</td>\n",
              "      <td>0.1781</td>\n",
              "      <td>0.06249</td>\n",
              "      <td>0.3642</td>\n",
              "      <td>1.0400</td>\n",
              "      <td>2.5790</td>\n",
              "      <td>28.32</td>\n",
              "      <td>0.006530</td>\n",
              "      <td>0.033690</td>\n",
              "      <td>0.047120</td>\n",
              "      <td>0.014030</td>\n",
              "      <td>0.02740</td>\n",
              "      <td>0.004651</td>\n",
              "      <td>13.460</td>\n",
              "      <td>23.07</td>\n",
              "      <td>88.13</td>\n",
              "      <td>551.3</td>\n",
              "      <td>0.10500</td>\n",
              "      <td>0.21580</td>\n",
              "      <td>0.19040</td>\n",
              "      <td>0.07625</td>\n",
              "      <td>0.2685</td>\n",
              "      <td>0.07764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>8.888</td>\n",
              "      <td>14.64</td>\n",
              "      <td>58.79</td>\n",
              "      <td>244.0</td>\n",
              "      <td>0.09783</td>\n",
              "      <td>0.15310</td>\n",
              "      <td>0.08606</td>\n",
              "      <td>0.02872</td>\n",
              "      <td>0.1902</td>\n",
              "      <td>0.08980</td>\n",
              "      <td>0.5262</td>\n",
              "      <td>0.8522</td>\n",
              "      <td>3.1680</td>\n",
              "      <td>25.44</td>\n",
              "      <td>0.017210</td>\n",
              "      <td>0.093680</td>\n",
              "      <td>0.056710</td>\n",
              "      <td>0.017660</td>\n",
              "      <td>0.02541</td>\n",
              "      <td>0.021930</td>\n",
              "      <td>9.733</td>\n",
              "      <td>15.67</td>\n",
              "      <td>62.56</td>\n",
              "      <td>284.4</td>\n",
              "      <td>0.12070</td>\n",
              "      <td>0.24360</td>\n",
              "      <td>0.14340</td>\n",
              "      <td>0.04786</td>\n",
              "      <td>0.2254</td>\n",
              "      <td>0.10840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>11.640</td>\n",
              "      <td>18.33</td>\n",
              "      <td>75.17</td>\n",
              "      <td>412.5</td>\n",
              "      <td>0.11420</td>\n",
              "      <td>0.10170</td>\n",
              "      <td>0.07070</td>\n",
              "      <td>0.03485</td>\n",
              "      <td>0.1801</td>\n",
              "      <td>0.06520</td>\n",
              "      <td>0.3060</td>\n",
              "      <td>1.6570</td>\n",
              "      <td>2.1550</td>\n",
              "      <td>20.62</td>\n",
              "      <td>0.008540</td>\n",
              "      <td>0.023100</td>\n",
              "      <td>0.029450</td>\n",
              "      <td>0.013980</td>\n",
              "      <td>0.01565</td>\n",
              "      <td>0.003840</td>\n",
              "      <td>13.140</td>\n",
              "      <td>29.26</td>\n",
              "      <td>85.51</td>\n",
              "      <td>521.7</td>\n",
              "      <td>0.16880</td>\n",
              "      <td>0.26600</td>\n",
              "      <td>0.28730</td>\n",
              "      <td>0.12180</td>\n",
              "      <td>0.2806</td>\n",
              "      <td>0.09097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>14.290</td>\n",
              "      <td>16.82</td>\n",
              "      <td>90.30</td>\n",
              "      <td>632.6</td>\n",
              "      <td>0.06429</td>\n",
              "      <td>0.02675</td>\n",
              "      <td>0.00725</td>\n",
              "      <td>0.00625</td>\n",
              "      <td>0.1508</td>\n",
              "      <td>0.05376</td>\n",
              "      <td>0.1302</td>\n",
              "      <td>0.7198</td>\n",
              "      <td>0.8439</td>\n",
              "      <td>10.77</td>\n",
              "      <td>0.003492</td>\n",
              "      <td>0.003710</td>\n",
              "      <td>0.004826</td>\n",
              "      <td>0.003608</td>\n",
              "      <td>0.01536</td>\n",
              "      <td>0.001381</td>\n",
              "      <td>14.910</td>\n",
              "      <td>20.65</td>\n",
              "      <td>94.44</td>\n",
              "      <td>684.6</td>\n",
              "      <td>0.08567</td>\n",
              "      <td>0.05036</td>\n",
              "      <td>0.03866</td>\n",
              "      <td>0.03333</td>\n",
              "      <td>0.2458</td>\n",
              "      <td>0.06120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>13.980</td>\n",
              "      <td>19.62</td>\n",
              "      <td>91.12</td>\n",
              "      <td>599.5</td>\n",
              "      <td>0.10600</td>\n",
              "      <td>0.11330</td>\n",
              "      <td>0.11260</td>\n",
              "      <td>0.06463</td>\n",
              "      <td>0.1669</td>\n",
              "      <td>0.06544</td>\n",
              "      <td>0.2208</td>\n",
              "      <td>0.9533</td>\n",
              "      <td>1.6020</td>\n",
              "      <td>18.85</td>\n",
              "      <td>0.005314</td>\n",
              "      <td>0.017910</td>\n",
              "      <td>0.021850</td>\n",
              "      <td>0.009567</td>\n",
              "      <td>0.01223</td>\n",
              "      <td>0.002846</td>\n",
              "      <td>17.040</td>\n",
              "      <td>30.80</td>\n",
              "      <td>113.90</td>\n",
              "      <td>869.3</td>\n",
              "      <td>0.16130</td>\n",
              "      <td>0.35680</td>\n",
              "      <td>0.40690</td>\n",
              "      <td>0.18270</td>\n",
              "      <td>0.3179</td>\n",
              "      <td>0.10550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>12.180</td>\n",
              "      <td>20.52</td>\n",
              "      <td>77.22</td>\n",
              "      <td>458.7</td>\n",
              "      <td>0.08013</td>\n",
              "      <td>0.04038</td>\n",
              "      <td>0.02383</td>\n",
              "      <td>0.01770</td>\n",
              "      <td>0.1739</td>\n",
              "      <td>0.05677</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>1.5710</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>14.68</td>\n",
              "      <td>0.005080</td>\n",
              "      <td>0.006098</td>\n",
              "      <td>0.010690</td>\n",
              "      <td>0.006797</td>\n",
              "      <td>0.01447</td>\n",
              "      <td>0.001532</td>\n",
              "      <td>13.340</td>\n",
              "      <td>32.84</td>\n",
              "      <td>84.58</td>\n",
              "      <td>547.8</td>\n",
              "      <td>0.11230</td>\n",
              "      <td>0.08862</td>\n",
              "      <td>0.11450</td>\n",
              "      <td>0.07431</td>\n",
              "      <td>0.2694</td>\n",
              "      <td>0.06878</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>381 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "172       15.460         11.89  ...          0.3216                  0.10100\n",
              "407       12.850         21.37  ...          0.2488                  0.08151\n",
              "56        19.210         18.57  ...          0.3537                  0.08294\n",
              "497       12.470         17.31  ...          0.3035                  0.07661\n",
              "301       12.460         19.89  ...          0.2685                  0.07764\n",
              "..           ...           ...  ...             ...                      ...\n",
              "71         8.888         14.64  ...          0.2254                  0.10840\n",
              "106       11.640         18.33  ...          0.2806                  0.09097\n",
              "270       14.290         16.82  ...          0.2458                  0.06120\n",
              "435       13.980         19.62  ...          0.3179                  0.10550\n",
              "102       12.180         20.52  ...          0.2694                  0.06878\n",
              "\n",
              "[381 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ufzxh1hs9cP7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "ae83f1ac-8474-4d8c-92cf-8285fc859a07"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>431</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>188 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis\n",
              "204          1\n",
              "70           0\n",
              "131          0\n",
              "431          1\n",
              "540          1\n",
              "..         ...\n",
              "141          0\n",
              "498          0\n",
              "7            0\n",
              "541          1\n",
              "19           1\n",
              "\n",
              "[188 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6zjL43T9khO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Odhk26zSPITt",
        "colab_type": "text"
      },
      "source": [
        "Defining ANN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLxWoHf9-Wsa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Classification_Model = Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQzD6m-S_yuk",
        "colab_type": "text"
      },
      "source": [
        "### Arguments of Dense Layer\n",
        "\n",
        "units -\tPositive integer, dimensionality of the output space.\n",
        "\n",
        "activation -\tActivation function to use. If you don't specify anything, no activation is applied (ie. \"linear\" activation: a(x) = x).\n",
        "\n",
        "use_bias -\tBoolean, whether the layer uses a bias vector.\n",
        "\n",
        "kernel_initializer -\tInitializer for the kernel weights matrix.\n",
        "\n",
        "bias_initializer\t- Initializer for the bias vector.\n",
        "\n",
        "kernel_regularizer - \tRegularizer function applied to the kernel weights matrix.\n",
        "\n",
        "bias_regularizer\t- Regularizer function applied to the bias vector.\n",
        "\n",
        "activity_regularizer\t- Regularizer function applied to the output of the layer (its \"activation\").\n",
        "\n",
        "kernel_constraint\t- Constraint function applied to the kernel weights matrix.\n",
        "\n",
        "bias_constraint\t- Constraint function applied to the bias vector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPuiW6LK_E-i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Classification_Model.add(Dense(units= 16, activation = 'relu', input_dim=30)) \n",
        "# units indicates here the ooutput dimention of Dense layer\n",
        "Classification_Model.add(Dense(units=8, activation='relu'))\n",
        "Classification_Model.add(Dense(units=6, activation='relu'))\n",
        "Classification_Model.add(Dense(units=1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XN3x-9_lhxnw",
        "colab_type": "text"
      },
      "source": [
        "This model we have 4 Layers:\n",
        "- First Layer input dimention is 30, output dimmention 16\n",
        "- 2nd Layer input dim 16, output dim 8,\n",
        "- 3rd Layer input dim is 8, output dim is 6\n",
        "- 4th Layer input dim is 6, output dim is 1, this the ANN's output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0GJvj4xPCx1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Classification_Model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vuXkPiFvA1g",
        "colab_type": "text"
      },
      "source": [
        "## Summerize the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atdPJwVCvEjf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "2ca91adc-9199-477b-c8a7-bd59c82da149"
      },
      "source": [
        "print(Classification_Model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                496       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 6)                 54        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 693\n",
            "Trainable params: 693\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XOOpqwf4v3WN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "7c08fe25-db3c-4484-dcf5-9fb9e5d7eb2b"
      },
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(Classification_Model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9Ukc15KY4Gq",
        "colab_type": "text"
      },
      "source": [
        "### Train the Neural Network using classification_Model.fit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJkKiWACZ_Pm",
        "colab_type": "text"
      },
      "source": [
        "batch_size: Integer or `None`.  \n",
        "        Number of samples per gradient update.  \n",
        "        If unspecified, `batch_size` will default to 32.  \n",
        "        Do not specify the `batch_size` if your data is in the  \n",
        "        form of datasets, generators, or `keras.utils.Sequence` instances  \n",
        "        (since they generate `batches`).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-hXnCLhYvym",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f8a57de-7870-404d-98fc-9313e7430c1b"
      },
      "source": [
        "Classification_Model.fit(X_train, y_train, batch_size=1, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2838 - accuracy: 0.9265\n",
            "Epoch 2/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3113 - accuracy: 0.9160\n",
            "Epoch 3/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3652 - accuracy: 0.9003\n",
            "Epoch 4/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2670 - accuracy: 0.8924\n",
            "Epoch 5/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4185 - accuracy: 0.9055\n",
            "Epoch 6/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4116 - accuracy: 0.9213\n",
            "Epoch 7/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3786 - accuracy: 0.9081\n",
            "Epoch 8/100\n",
            "381/381 [==============================] - 0s 992us/step - loss: 0.2796 - accuracy: 0.9108\n",
            "Epoch 9/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4707 - accuracy: 0.9081\n",
            "Epoch 10/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3902 - accuracy: 0.9160\n",
            "Epoch 11/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3005 - accuracy: 0.9081\n",
            "Epoch 12/100\n",
            "381/381 [==============================] - 0s 990us/step - loss: 0.3346 - accuracy: 0.9134\n",
            "Epoch 13/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5247 - accuracy: 0.9055\n",
            "Epoch 14/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3884 - accuracy: 0.9003\n",
            "Epoch 15/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3227 - accuracy: 0.9134\n",
            "Epoch 16/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2900 - accuracy: 0.8976\n",
            "Epoch 17/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2991 - accuracy: 0.9265\n",
            "Epoch 18/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3784 - accuracy: 0.9081\n",
            "Epoch 19/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2884 - accuracy: 0.8976\n",
            "Epoch 20/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3891 - accuracy: 0.9055\n",
            "Epoch 21/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3954 - accuracy: 0.8950\n",
            "Epoch 22/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.3852 - accuracy: 0.9370\n",
            "Epoch 23/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3916 - accuracy: 0.9003\n",
            "Epoch 24/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.2853 - accuracy: 0.9134\n",
            "Epoch 25/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.3545 - accuracy: 0.9108\n",
            "Epoch 26/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.6635 - accuracy: 0.9003\n",
            "Epoch 27/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.3099 - accuracy: 0.9055\n",
            "Epoch 28/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.3197 - accuracy: 0.8976\n",
            "Epoch 29/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.2472 - accuracy: 0.9160\n",
            "Epoch 30/100\n",
            "381/381 [==============================] - 1s 1ms/step - loss: 0.3224 - accuracy: 0.8976\n",
            "Epoch 31/100\n",
            "381/381 [==============================] - 1s 2ms/step - loss: 0.3028 - accuracy: 0.9081\n",
            "Epoch 32/100\n",
            "381/381 [==============================] - 1s 2ms/step - loss: 0.4129 - accuracy: 0.9160\n",
            "Epoch 33/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4834 - accuracy: 0.9108\n",
            "Epoch 34/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3166 - accuracy: 0.9081\n",
            "Epoch 35/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3358 - accuracy: 0.9003\n",
            "Epoch 36/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3939 - accuracy: 0.9055\n",
            "Epoch 37/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5603 - accuracy: 0.9003\n",
            "Epoch 38/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3101 - accuracy: 0.9160\n",
            "Epoch 39/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3810 - accuracy: 0.9160\n",
            "Epoch 40/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3640 - accuracy: 0.9081\n",
            "Epoch 41/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3298 - accuracy: 0.8898\n",
            "Epoch 42/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3804 - accuracy: 0.9081\n",
            "Epoch 43/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3056 - accuracy: 0.9081\n",
            "Epoch 44/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3697 - accuracy: 0.9003\n",
            "Epoch 45/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3138 - accuracy: 0.9108\n",
            "Epoch 46/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3460 - accuracy: 0.9055\n",
            "Epoch 47/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.9029\n",
            "Epoch 48/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3301 - accuracy: 0.9029\n",
            "Epoch 49/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2753 - accuracy: 0.9029\n",
            "Epoch 50/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3248 - accuracy: 0.9108\n",
            "Epoch 51/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4105 - accuracy: 0.8950\n",
            "Epoch 52/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3623 - accuracy: 0.8950\n",
            "Epoch 53/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3111 - accuracy: 0.8871\n",
            "Epoch 54/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4724 - accuracy: 0.9003\n",
            "Epoch 55/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3947 - accuracy: 0.9081\n",
            "Epoch 56/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3643 - accuracy: 0.9029\n",
            "Epoch 57/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5694 - accuracy: 0.8898\n",
            "Epoch 58/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3224 - accuracy: 0.8845\n",
            "Epoch 59/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.6469 - accuracy: 0.8740\n",
            "Epoch 60/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4518 - accuracy: 0.9003\n",
            "Epoch 61/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4433 - accuracy: 0.8766\n",
            "Epoch 62/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4665 - accuracy: 0.8898\n",
            "Epoch 63/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3904 - accuracy: 0.8976\n",
            "Epoch 64/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4119 - accuracy: 0.8793\n",
            "Epoch 65/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4333 - accuracy: 0.9108\n",
            "Epoch 66/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3571 - accuracy: 0.9029\n",
            "Epoch 67/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3430 - accuracy: 0.8845\n",
            "Epoch 68/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4195 - accuracy: 0.8819\n",
            "Epoch 69/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.2921 - accuracy: 0.9055\n",
            "Epoch 70/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4754 - accuracy: 0.8740\n",
            "Epoch 71/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4374 - accuracy: 0.8688\n",
            "Epoch 72/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3294 - accuracy: 0.9055\n",
            "Epoch 73/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5124 - accuracy: 0.9029\n",
            "Epoch 74/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3406 - accuracy: 0.8504\n",
            "Epoch 75/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3593 - accuracy: 0.8976\n",
            "Epoch 76/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5427 - accuracy: 0.8976\n",
            "Epoch 77/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3656 - accuracy: 0.9081\n",
            "Epoch 78/100\n",
            "381/381 [==============================] - 0s 991us/step - loss: 0.6593 - accuracy: 0.9081\n",
            "Epoch 79/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3355 - accuracy: 0.8898\n",
            "Epoch 80/100\n",
            "381/381 [==============================] - 0s 996us/step - loss: 0.4605 - accuracy: 0.8898\n",
            "Epoch 81/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5439 - accuracy: 0.8504\n",
            "Epoch 82/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3528 - accuracy: 0.8924\n",
            "Epoch 83/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3712 - accuracy: 0.8714\n",
            "Epoch 84/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.9052 - accuracy: 0.9055\n",
            "Epoch 85/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3024 - accuracy: 0.8793\n",
            "Epoch 86/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3872 - accuracy: 0.9134\n",
            "Epoch 87/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3515 - accuracy: 0.8924\n",
            "Epoch 88/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4676 - accuracy: 0.9081\n",
            "Epoch 89/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.6808 - accuracy: 0.8793\n",
            "Epoch 90/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4525 - accuracy: 0.9003\n",
            "Epoch 91/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3517 - accuracy: 0.8976\n",
            "Epoch 92/100\n",
            "381/381 [==============================] - 0s 994us/step - loss: 0.3355 - accuracy: 0.8898\n",
            "Epoch 93/100\n",
            "381/381 [==============================] - 0s 994us/step - loss: 0.5324 - accuracy: 0.8871\n",
            "Epoch 94/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3640 - accuracy: 0.8740\n",
            "Epoch 95/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5392 - accuracy: 0.8924\n",
            "Epoch 96/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.3899 - accuracy: 0.8950\n",
            "Epoch 97/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4166 - accuracy: 0.9081\n",
            "Epoch 98/100\n",
            "381/381 [==============================] - 0s 999us/step - loss: 0.3953 - accuracy: 0.9160\n",
            "Epoch 99/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.5255 - accuracy: 0.9003\n",
            "Epoch 100/100\n",
            "381/381 [==============================] - 0s 1ms/step - loss: 0.4622 - accuracy: 0.8898\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f012d9c4ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUxuTqC-ztlO",
        "colab_type": "text"
      },
      "source": [
        "### Checking Input Text Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAYvbUZmzrwq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "6963d9e1-3a5c-46e7-d3c1-6147fe21cb41"
      },
      "source": [
        "X_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>204</th>\n",
              "      <td>12.47</td>\n",
              "      <td>18.60</td>\n",
              "      <td>81.09</td>\n",
              "      <td>481.9</td>\n",
              "      <td>0.09965</td>\n",
              "      <td>0.10580</td>\n",
              "      <td>0.08005</td>\n",
              "      <td>0.03821</td>\n",
              "      <td>0.1925</td>\n",
              "      <td>0.06373</td>\n",
              "      <td>0.3961</td>\n",
              "      <td>1.0440</td>\n",
              "      <td>2.497</td>\n",
              "      <td>30.29</td>\n",
              "      <td>0.006953</td>\n",
              "      <td>0.01911</td>\n",
              "      <td>0.02701</td>\n",
              "      <td>0.01037</td>\n",
              "      <td>0.01782</td>\n",
              "      <td>0.003586</td>\n",
              "      <td>14.97</td>\n",
              "      <td>24.64</td>\n",
              "      <td>96.05</td>\n",
              "      <td>677.9</td>\n",
              "      <td>0.1426</td>\n",
              "      <td>0.2378</td>\n",
              "      <td>0.2671</td>\n",
              "      <td>0.10150</td>\n",
              "      <td>0.3014</td>\n",
              "      <td>0.08750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>18.94</td>\n",
              "      <td>21.31</td>\n",
              "      <td>123.60</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>0.09009</td>\n",
              "      <td>0.10290</td>\n",
              "      <td>0.10800</td>\n",
              "      <td>0.07951</td>\n",
              "      <td>0.1582</td>\n",
              "      <td>0.05461</td>\n",
              "      <td>0.7888</td>\n",
              "      <td>0.7975</td>\n",
              "      <td>5.486</td>\n",
              "      <td>96.05</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.01652</td>\n",
              "      <td>0.02269</td>\n",
              "      <td>0.01370</td>\n",
              "      <td>0.01386</td>\n",
              "      <td>0.001698</td>\n",
              "      <td>24.86</td>\n",
              "      <td>26.58</td>\n",
              "      <td>165.90</td>\n",
              "      <td>1866.0</td>\n",
              "      <td>0.1193</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.2687</td>\n",
              "      <td>0.17890</td>\n",
              "      <td>0.2551</td>\n",
              "      <td>0.06589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>15.46</td>\n",
              "      <td>19.48</td>\n",
              "      <td>101.70</td>\n",
              "      <td>748.9</td>\n",
              "      <td>0.10920</td>\n",
              "      <td>0.12230</td>\n",
              "      <td>0.14660</td>\n",
              "      <td>0.08087</td>\n",
              "      <td>0.1931</td>\n",
              "      <td>0.05796</td>\n",
              "      <td>0.4743</td>\n",
              "      <td>0.7859</td>\n",
              "      <td>3.094</td>\n",
              "      <td>48.31</td>\n",
              "      <td>0.006240</td>\n",
              "      <td>0.01484</td>\n",
              "      <td>0.02813</td>\n",
              "      <td>0.01093</td>\n",
              "      <td>0.01397</td>\n",
              "      <td>0.002461</td>\n",
              "      <td>19.26</td>\n",
              "      <td>26.00</td>\n",
              "      <td>124.90</td>\n",
              "      <td>1156.0</td>\n",
              "      <td>0.1546</td>\n",
              "      <td>0.2394</td>\n",
              "      <td>0.3791</td>\n",
              "      <td>0.15140</td>\n",
              "      <td>0.2837</td>\n",
              "      <td>0.08019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>431</th>\n",
              "      <td>12.40</td>\n",
              "      <td>17.68</td>\n",
              "      <td>81.47</td>\n",
              "      <td>467.8</td>\n",
              "      <td>0.10540</td>\n",
              "      <td>0.13160</td>\n",
              "      <td>0.07741</td>\n",
              "      <td>0.02799</td>\n",
              "      <td>0.1811</td>\n",
              "      <td>0.07102</td>\n",
              "      <td>0.1767</td>\n",
              "      <td>1.4600</td>\n",
              "      <td>2.204</td>\n",
              "      <td>15.43</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.03295</td>\n",
              "      <td>0.04861</td>\n",
              "      <td>0.01167</td>\n",
              "      <td>0.02187</td>\n",
              "      <td>0.006005</td>\n",
              "      <td>12.88</td>\n",
              "      <td>22.91</td>\n",
              "      <td>89.61</td>\n",
              "      <td>515.8</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.2629</td>\n",
              "      <td>0.2403</td>\n",
              "      <td>0.07370</td>\n",
              "      <td>0.2556</td>\n",
              "      <td>0.09359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540</th>\n",
              "      <td>11.54</td>\n",
              "      <td>14.44</td>\n",
              "      <td>74.65</td>\n",
              "      <td>402.9</td>\n",
              "      <td>0.09984</td>\n",
              "      <td>0.11200</td>\n",
              "      <td>0.06737</td>\n",
              "      <td>0.02594</td>\n",
              "      <td>0.1818</td>\n",
              "      <td>0.06782</td>\n",
              "      <td>0.2784</td>\n",
              "      <td>1.7680</td>\n",
              "      <td>1.628</td>\n",
              "      <td>20.86</td>\n",
              "      <td>0.012150</td>\n",
              "      <td>0.04112</td>\n",
              "      <td>0.05553</td>\n",
              "      <td>0.01494</td>\n",
              "      <td>0.01840</td>\n",
              "      <td>0.005512</td>\n",
              "      <td>12.26</td>\n",
              "      <td>19.68</td>\n",
              "      <td>78.78</td>\n",
              "      <td>457.8</td>\n",
              "      <td>0.1345</td>\n",
              "      <td>0.2118</td>\n",
              "      <td>0.1797</td>\n",
              "      <td>0.06918</td>\n",
              "      <td>0.2329</td>\n",
              "      <td>0.08134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>16.11</td>\n",
              "      <td>18.05</td>\n",
              "      <td>105.10</td>\n",
              "      <td>813.0</td>\n",
              "      <td>0.09721</td>\n",
              "      <td>0.11370</td>\n",
              "      <td>0.09447</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.1861</td>\n",
              "      <td>0.06248</td>\n",
              "      <td>0.7049</td>\n",
              "      <td>1.3320</td>\n",
              "      <td>4.533</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.006770</td>\n",
              "      <td>0.01938</td>\n",
              "      <td>0.03067</td>\n",
              "      <td>0.01167</td>\n",
              "      <td>0.01875</td>\n",
              "      <td>0.003434</td>\n",
              "      <td>19.92</td>\n",
              "      <td>25.27</td>\n",
              "      <td>129.00</td>\n",
              "      <td>1233.0</td>\n",
              "      <td>0.1314</td>\n",
              "      <td>0.2236</td>\n",
              "      <td>0.2802</td>\n",
              "      <td>0.12160</td>\n",
              "      <td>0.2792</td>\n",
              "      <td>0.08158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>18.49</td>\n",
              "      <td>17.52</td>\n",
              "      <td>121.30</td>\n",
              "      <td>1068.0</td>\n",
              "      <td>0.10120</td>\n",
              "      <td>0.13170</td>\n",
              "      <td>0.14910</td>\n",
              "      <td>0.09183</td>\n",
              "      <td>0.1832</td>\n",
              "      <td>0.06697</td>\n",
              "      <td>0.7923</td>\n",
              "      <td>1.0450</td>\n",
              "      <td>4.851</td>\n",
              "      <td>95.77</td>\n",
              "      <td>0.007974</td>\n",
              "      <td>0.03214</td>\n",
              "      <td>0.04435</td>\n",
              "      <td>0.01573</td>\n",
              "      <td>0.01617</td>\n",
              "      <td>0.005255</td>\n",
              "      <td>22.75</td>\n",
              "      <td>22.88</td>\n",
              "      <td>146.40</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>0.1412</td>\n",
              "      <td>0.3089</td>\n",
              "      <td>0.3533</td>\n",
              "      <td>0.16630</td>\n",
              "      <td>0.2510</td>\n",
              "      <td>0.09445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>13.71</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>0.05985</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>0.07451</td>\n",
              "      <td>0.5835</td>\n",
              "      <td>1.3770</td>\n",
              "      <td>3.856</td>\n",
              "      <td>50.96</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>0.03029</td>\n",
              "      <td>0.02488</td>\n",
              "      <td>0.01448</td>\n",
              "      <td>0.01486</td>\n",
              "      <td>0.005412</td>\n",
              "      <td>17.06</td>\n",
              "      <td>28.14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.1654</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.2678</td>\n",
              "      <td>0.15560</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>14.47</td>\n",
              "      <td>24.99</td>\n",
              "      <td>95.81</td>\n",
              "      <td>656.4</td>\n",
              "      <td>0.08837</td>\n",
              "      <td>0.12300</td>\n",
              "      <td>0.10090</td>\n",
              "      <td>0.03890</td>\n",
              "      <td>0.1872</td>\n",
              "      <td>0.06341</td>\n",
              "      <td>0.2542</td>\n",
              "      <td>1.0790</td>\n",
              "      <td>2.615</td>\n",
              "      <td>23.11</td>\n",
              "      <td>0.007138</td>\n",
              "      <td>0.04653</td>\n",
              "      <td>0.03829</td>\n",
              "      <td>0.01162</td>\n",
              "      <td>0.02068</td>\n",
              "      <td>0.006111</td>\n",
              "      <td>16.22</td>\n",
              "      <td>31.73</td>\n",
              "      <td>113.50</td>\n",
              "      <td>808.9</td>\n",
              "      <td>0.1340</td>\n",
              "      <td>0.4202</td>\n",
              "      <td>0.4040</td>\n",
              "      <td>0.12050</td>\n",
              "      <td>0.3187</td>\n",
              "      <td>0.10230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>13.54</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.04781</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.56</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.01460</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.01315</td>\n",
              "      <td>0.01980</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.11</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>0.1773</td>\n",
              "      <td>0.2390</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>188 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "204        12.47         18.60  ...          0.3014                  0.08750\n",
              "70         18.94         21.31  ...          0.2551                  0.06589\n",
              "131        15.46         19.48  ...          0.2837                  0.08019\n",
              "431        12.40         17.68  ...          0.2556                  0.09359\n",
              "540        11.54         14.44  ...          0.2329                  0.08134\n",
              "..           ...           ...  ...             ...                      ...\n",
              "141        16.11         18.05  ...          0.2792                  0.08158\n",
              "498        18.49         17.52  ...          0.2510                  0.09445\n",
              "7          13.71         20.83  ...          0.3196                  0.11510\n",
              "541        14.47         24.99  ...          0.3187                  0.10230\n",
              "19         13.54         14.36  ...          0.2977                  0.07259\n",
              "\n",
              "[188 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PrcVIqB90wBe",
        "colab_type": "text"
      },
      "source": [
        "### Predicting breast canceer diagnosis report for all X_test input data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OkW-8Mha-7W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = Classification_Model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cw2uTBr4bjJl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = [1 if y>0.5 else 0 for y in prediction]\n",
        "prediction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2fUAhn_yr5c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "af9671ee-d563-4329-cb15-02528e1e0f60"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YG-hPSgQ1BcF",
        "colab_type": "text"
      },
      "source": [
        "### Taking individual input data from X_test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbu67lkCyACN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "b1b89606-ea60-4eb5-d3ee-e3e2727722dc"
      },
      "source": [
        "input1 = X_test.iloc[[1]]\n",
        "input1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>70</th>\n",
              "      <td>18.94</td>\n",
              "      <td>21.31</td>\n",
              "      <td>123.6</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>0.09009</td>\n",
              "      <td>0.1029</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.07951</td>\n",
              "      <td>0.1582</td>\n",
              "      <td>0.05461</td>\n",
              "      <td>0.7888</td>\n",
              "      <td>0.7975</td>\n",
              "      <td>5.486</td>\n",
              "      <td>96.05</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.01652</td>\n",
              "      <td>0.02269</td>\n",
              "      <td>0.0137</td>\n",
              "      <td>0.01386</td>\n",
              "      <td>0.001698</td>\n",
              "      <td>24.86</td>\n",
              "      <td>26.58</td>\n",
              "      <td>165.9</td>\n",
              "      <td>1866.0</td>\n",
              "      <td>0.1193</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.2687</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>0.2551</td>\n",
              "      <td>0.06589</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "70        18.94         21.31  ...          0.2551                  0.06589\n",
              "\n",
              "[1 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm4frOpEzPk6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9de1c7cd-e802-46c7-b032-ae119ebb820e"
      },
      "source": [
        "input1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwuX2tWm0P87",
        "colab_type": "text"
      },
      "source": [
        "## Predicting for Individual Input:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0R4Nsc2q3NmB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5ce9a282-5913-4091-fd1d-a0c2ef764d65"
      },
      "source": [
        "prediction1 = Classification_Model.predict(input1)\n",
        "prediction1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.9596268e-29]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74f4rKowbhHl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction1 = Classification_Model.predict(input1)\n",
        "\n",
        "# prediction1 = ['1' if y>0.5 else '0' for y in prediction1]\n",
        "\n",
        "\n",
        "prediction1 = ['1' if prediction1 > 0.5 else '0' for y in prediction1]"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_q4FVFvMczHB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad609f6a-53c9-4885-b1c5-2133e6f83e34"
      },
      "source": [
        "prediction1"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4dJveHg3l5t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "63fe793a-fb43-4d82-b368-5383365a6813"
      },
      "source": [
        "df.loc[df['area_mean']==1130.0]"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>859575</td>\n",
              "      <td>M</td>\n",
              "      <td>18.94</td>\n",
              "      <td>21.31</td>\n",
              "      <td>123.6</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>0.09009</td>\n",
              "      <td>0.1029</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.07951</td>\n",
              "      <td>0.1582</td>\n",
              "      <td>0.05461</td>\n",
              "      <td>0.7888</td>\n",
              "      <td>0.7975</td>\n",
              "      <td>5.486</td>\n",
              "      <td>96.05</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.01652</td>\n",
              "      <td>0.02269</td>\n",
              "      <td>0.0137</td>\n",
              "      <td>0.01386</td>\n",
              "      <td>0.001698</td>\n",
              "      <td>24.86</td>\n",
              "      <td>26.58</td>\n",
              "      <td>165.9</td>\n",
              "      <td>1866.0</td>\n",
              "      <td>0.1193</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.2687</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>0.2551</td>\n",
              "      <td>0.06589</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "70  859575         M  ...                  0.06589          NaN\n",
              "\n",
              "[1 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDm72cQqlWkr",
        "colab_type": "text"
      },
      "source": [
        "**So Our Model predict the input variable correctly:**\n",
        "*as we have converted categorical M to 0, and B to 1*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5brc_em5Tnj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "82180cfe-a090-49a1-965b-fdd87c65505d"
      },
      "source": [
        "'''\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "# define 10-fold cross validation test harness\n",
        "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)\n",
        "cvscores = []\n",
        "for train, test in kfold.split(X, Y):\n",
        "\tscores = Classification_Model.evaluate(X_test, y_test, verbose=0)\n",
        "\tprint(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
        "\tcvscores.append(scores[1] * 100)\n",
        "print(\"%.2f%% (+/- %.2f%%)\" % (numpy.mean(cvscores), numpy.std(cvscores)))\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nfrom sklearn.model_selection import StratifiedKFold\\n# define 10-fold cross validation test harness\\nkfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)\\ncvscores = []\\nfor train, test in kfold.split(X, Y):\\n\\tscores = Classification_Model.evaluate(X_test, y_test, verbose=0)\\n\\tprint(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\\n\\tcvscores.append(scores[1] * 100)\\nprint(\"%.2f%% (+/- %.2f%%)\" % (numpy.mean(cvscores), numpy.std(cvscores)))\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYWLEC03WCMh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "95edff08-3c72-4eb9-824f-a2e532b5fd61"
      },
      "source": [
        "type(y_test.values[0])"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmX9_lo5WW6p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d9cdeb16-f7d2-4889-cfb7-cc660ecd51b8"
      },
      "source": [
        "type(prediction)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nY66FI7aXF8P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6b4691aa-0a3c-40c0-981b-af132f98ce41"
      },
      "source": [
        "prediction = pd.DataFrame(prediction)\n",
        "type(prediction.values[0])"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmgWr-Gzizhv",
        "colab_type": "text"
      },
      "source": [
        "### Converting both prediction values and test data to numpy ndArray"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHVpJgeqTbzd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred = prediction.values"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VaElw6mdh5r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yTest = y_test.values"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqU8ou_qiqMG",
        "colab_type": "text"
      },
      "source": [
        "### Prediction Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYa465IqdlnJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4682c5b4-7b18-4012-dca8-a7d4344f4a20"
      },
      "source": [
        "total = 0\n",
        "correct = 0\n",
        "wrong =0\n",
        "\n",
        "for i in range(len(pred)):\n",
        "    total = total+1\n",
        "    if(yTest[i]== pred[i]):\n",
        "      correct = correct+1\n",
        "    else:\n",
        "       wrong = wrong+1\n",
        "print('Total = ' + str(total))\n",
        "print('Correct = ' + str(correct) + ' - Parcentage correct: ' + str(round((correct/total)*100, 2))+'%')\n",
        "print('Wrong = ' + str(wrong) + ' Parcentage wrong: ' + str(round((wrong/total)*100, 2)) + '%')"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total = 188\n",
            "Correct = 180 - Parcentage correct: 95.74%\n",
            "Wrong = 8 Parcentage wrong: 4.26%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOh2kPDPd7F0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}