breastCancer_ClassificationModel(KerasSequential).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "breastCancer_ClassificationModel(KerasSequential).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMlexVyikMM6Xc0FTA0Q6Gq",
      "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/breastCancer_ClassificationModel(KerasSequential).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nccqt_oZMci7",
        "colab_type": "text"
      },
      "source": [
        "### Loading Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxmNjRn9Lum8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "da92075b-6f82-4cd2-c036-721c07693c65"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pandas.util.testing as tm\n",
        "# for data splitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "# keras api\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils.vis_utils import plot_model"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBIHcRB_IhMr",
        "colab_type": "text"
      },
      "source": [
        "Import the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNR5qDeefDbk",
        "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": "b3305e47-0fe2-4a1c-e38e-7754ed94eb70"
      },
      "source": [
        "from google.colab import files\n",
        "file = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c5d61566-b3c6-494a-88c8-71409c201921\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c5d61566-b3c6-494a-88c8-71409c201921\">\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": "markdown",
      "metadata": {
        "id": "DjJ_kXUYIlgj",
        "colab_type": "text"
      },
      "source": [
        "Load the data into pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezOWpFJMIFSV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('BreastCancer_data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6M6_ehgSIPV7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "b6b9c879-a36b-449c-8ac2-36ca26c1b2ce"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
              "1    842517         M  ...                  0.08902          NaN\n",
              "2  84300903         M  ...                  0.08758          NaN\n",
              "3  84348301         M  ...                  0.17300          NaN\n",
              "4  84358402         M  ...                  0.07678          NaN\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Es3_5Hq1IdHG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a979aad2-c1af-4db8-d519-bd3c4ec029d7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GANsS0KitxiJ",
        "colab_type": "text"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cq2cvXZ0I1iM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "4ac0f872-9c5d-43d1-9a61-b25fc4e61b55"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qhqK78yJLgs",
        "colab_type": "text"
      },
      "source": [
        "Check the null column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9XkCPUOI6PE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "48add465-1d48-457a-fa00-9ea59d80ddf8"
      },
      "source": [
        "df[df['Unnamed: 32'].isnull()]"
      ],
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiHkQydfJhEB",
        "colab_type": "text"
      },
      "source": [
        "Drop the null column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhZwvLOeJHAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.dropna(axis=1, inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nQE4llIJpfF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "c2ce839a-d02f-4712-8b9a-b5ed48b3a421"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 9,
      "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>122.80</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>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>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",
              "    </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",
              "    </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",
              "    </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",
              "2  84300903         M  ...          0.3613                  0.08758\n",
              "3  84348301         M  ...          0.6638                  0.17300\n",
              "4  84358402         M  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EKUshFKKX6d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7fd216b3-37cd-4013-faf1-71c3b26e025a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxUAnIsEJzTC",
        "colab_type": "text"
      },
      "source": [
        "Converting the categorical diagnosis column to numerical values\n",
        "because this is our target variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmaR0NywJsfz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['diagnosis'] = np.where(df['diagnosis']=='M', 1, 0)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPOlNFtuKoc-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "1fff4c00-4247-49cc-9f58-bac1f4fe744e"
      },
      "source": [
        "df"
      ],
      "execution_count": 12,
      "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>0.30010</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",
              "    </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>0.08690</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",
              "    </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>0.19740</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",
              "    </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>0.24140</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",
              "    </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>0.19800</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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 32 columns</p>\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",
              "564    926424          1  ...          0.2060                  0.07115\n",
              "565    926682          1  ...          0.2572                  0.06637\n",
              "566    926954          1  ...          0.2218                  0.07820\n",
              "567    927241          1  ...          0.4087                  0.12400\n",
              "568     92751          0  ...          0.2871                  0.07039\n",
              "\n",
              "[569 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3X4KaeYK_Tq",
        "colab_type": "text"
      },
      "source": [
        "Defining input variables X"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhc_FZKuKrsY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.iloc[:,2:]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkTqRIi4LKsk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c88c7600-875e-4101-d65b-bd76d60cb100"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZ8n87LxLQaK",
        "colab_type": "text"
      },
      "source": [
        "Defining the target variable Y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdC8W8ZmLNsx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y = df.iloc[:, 1:2]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6ajXeYaLhQI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d7f00e18-e4a6-4577-c9ba-afd8861e60a9"
      },
      "source": [
        "Y.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZu5KAFGPe0C",
        "colab_type": "text"
      },
      "source": [
        "Spliting data into train and test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYgGdlZXLkay",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=44)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBGLSQ9wQLsb",
        "colab_type": "text"
      },
      "source": [
        "Difining ann Sequencial model for tumar classification "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raVnfo8YP23L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Classification_Model = Sequential()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vKqcNOcLgNh",
        "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": "IvuiDZd_RmlZ",
        "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": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AD0yh-afRzNn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Classification_Model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l56LVHNcR6bn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "cd158666-9c27-44df-8729-a62d477dce16"
      },
      "source": [
        "print(Classification_Model.summary())"
      ],
      "execution_count": 21,
      "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": "ThNQtJ6mR_NL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "e8566599-2c6d-4036-d4cc-cddd25dfa74e"
      },
      "source": [
        "plot_model(Classification_Model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBc1_U6_TIwO",
        "colab_type": "text"
      },
      "source": [
        "### Train the Model with X_train, y_train dataset\n",
        "\n",
        "**Batch:** a set of N samples. The samples in a batch are processed independently, in parallel. If training, a batch results in only one update to the model. A batch generally approximates the distribution of the input data better than a single input. The larger the batch, the better the approximation; however, it is also true that the batch will take longer to process and will still result in only one update. For inference (evaluate/predict), it is recommended to pick a batch size that is as large as you can afford without going out of memory (since larger batches will usually result in faster evaluation/prediction).\n",
        "\n",
        "**Epoch:** an arbitrary cutoff, generally defined as \"one pass over the entire dataset\", used to separate training into distinct phases, which is useful for logging and periodic evaluation. When using validation_data or validation_split with the fit method of Keras models, evaluation will be run at the end of every epoch. Within Keras, there is the ability to add callbacks specifically designed to be run at the end of an epoch. Examples of these are learning rate changes and model checkpointing (saving).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zy9W9AUSLxP2",
        "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": "HSgEC5MiSS2J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b8ffeda-4a78-4d50-cea8-5f4a3b674c06"
      },
      "source": [
        "Classification_Model.fit(X_train, y_train, batch_size=1, epochs=50)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "398/398 [==============================] - 0s 918us/step - loss: 4.5350 - accuracy: 0.6759\n",
            "Epoch 2/50\n",
            "398/398 [==============================] - 0s 910us/step - loss: 1.8372 - accuracy: 0.8417\n",
            "Epoch 3/50\n",
            "398/398 [==============================] - 0s 947us/step - loss: 2.1418 - accuracy: 0.8241\n",
            "Epoch 4/50\n",
            "398/398 [==============================] - 0s 902us/step - loss: 1.7037 - accuracy: 0.8342\n",
            "Epoch 5/50\n",
            "398/398 [==============================] - 0s 926us/step - loss: 1.8629 - accuracy: 0.8317\n",
            "Epoch 6/50\n",
            "398/398 [==============================] - 0s 914us/step - loss: 1.8316 - accuracy: 0.8367\n",
            "Epoch 7/50\n",
            "398/398 [==============================] - 0s 906us/step - loss: 1.6659 - accuracy: 0.8492\n",
            "Epoch 8/50\n",
            "398/398 [==============================] - 0s 873us/step - loss: 1.7655 - accuracy: 0.8593\n",
            "Epoch 9/50\n",
            "398/398 [==============================] - 0s 894us/step - loss: 1.7857 - accuracy: 0.8543\n",
            "Epoch 10/50\n",
            "398/398 [==============================] - 0s 932us/step - loss: 1.4508 - accuracy: 0.8065\n",
            "Epoch 11/50\n",
            "398/398 [==============================] - 0s 864us/step - loss: 1.4500 - accuracy: 0.8769\n",
            "Epoch 12/50\n",
            "398/398 [==============================] - 0s 877us/step - loss: 1.2695 - accuracy: 0.8693\n",
            "Epoch 13/50\n",
            "398/398 [==============================] - 0s 891us/step - loss: 1.4774 - accuracy: 0.8518\n",
            "Epoch 14/50\n",
            "398/398 [==============================] - 0s 930us/step - loss: 1.0850 - accuracy: 0.8618\n",
            "Epoch 15/50\n",
            "398/398 [==============================] - 0s 908us/step - loss: 1.2265 - accuracy: 0.8668\n",
            "Epoch 16/50\n",
            "398/398 [==============================] - 0s 880us/step - loss: 1.2980 - accuracy: 0.8693\n",
            "Epoch 17/50\n",
            "398/398 [==============================] - 0s 938us/step - loss: 1.1810 - accuracy: 0.8593\n",
            "Epoch 18/50\n",
            "398/398 [==============================] - 0s 945us/step - loss: 0.9549 - accuracy: 0.8593\n",
            "Epoch 19/50\n",
            "398/398 [==============================] - 0s 924us/step - loss: 1.0197 - accuracy: 0.8719\n",
            "Epoch 20/50\n",
            "398/398 [==============================] - 0s 931us/step - loss: 0.9942 - accuracy: 0.8894\n",
            "Epoch 21/50\n",
            "398/398 [==============================] - 0s 917us/step - loss: 0.9491 - accuracy: 0.8744\n",
            "Epoch 22/50\n",
            "398/398 [==============================] - 0s 903us/step - loss: 1.0074 - accuracy: 0.8568\n",
            "Epoch 23/50\n",
            "398/398 [==============================] - 0s 912us/step - loss: 0.7792 - accuracy: 0.8719\n",
            "Epoch 24/50\n",
            "398/398 [==============================] - 0s 945us/step - loss: 0.8969 - accuracy: 0.8769\n",
            "Epoch 25/50\n",
            "398/398 [==============================] - 0s 902us/step - loss: 0.7344 - accuracy: 0.8794\n",
            "Epoch 26/50\n",
            "398/398 [==============================] - 0s 964us/step - loss: 0.8238 - accuracy: 0.8719\n",
            "Epoch 27/50\n",
            "398/398 [==============================] - 0s 959us/step - loss: 0.5936 - accuracy: 0.9045\n",
            "Epoch 28/50\n",
            "398/398 [==============================] - 0s 934us/step - loss: 0.7698 - accuracy: 0.8744\n",
            "Epoch 29/50\n",
            "398/398 [==============================] - 0s 933us/step - loss: 0.6919 - accuracy: 0.8668\n",
            "Epoch 30/50\n",
            "398/398 [==============================] - 0s 926us/step - loss: 0.5812 - accuracy: 0.8744\n",
            "Epoch 31/50\n",
            "398/398 [==============================] - 0s 961us/step - loss: 0.6329 - accuracy: 0.8920\n",
            "Epoch 32/50\n",
            "398/398 [==============================] - 0s 927us/step - loss: 0.5834 - accuracy: 0.8819\n",
            "Epoch 33/50\n",
            "398/398 [==============================] - 0s 933us/step - loss: 0.6606 - accuracy: 0.8744\n",
            "Epoch 34/50\n",
            "398/398 [==============================] - 0s 943us/step - loss: 0.5206 - accuracy: 0.8894\n",
            "Epoch 35/50\n",
            "398/398 [==============================] - 0s 901us/step - loss: 0.6295 - accuracy: 0.8920\n",
            "Epoch 36/50\n",
            "398/398 [==============================] - 0s 942us/step - loss: 0.5574 - accuracy: 0.8920\n",
            "Epoch 37/50\n",
            "398/398 [==============================] - 0s 936us/step - loss: 0.6172 - accuracy: 0.8643\n",
            "Epoch 38/50\n",
            "398/398 [==============================] - 0s 938us/step - loss: 0.4973 - accuracy: 0.8869\n",
            "Epoch 39/50\n",
            "398/398 [==============================] - 0s 929us/step - loss: 0.5413 - accuracy: 0.8618\n",
            "Epoch 40/50\n",
            "398/398 [==============================] - 0s 875us/step - loss: 0.4874 - accuracy: 0.8995\n",
            "Epoch 41/50\n",
            "398/398 [==============================] - 0s 909us/step - loss: 0.5138 - accuracy: 0.8945\n",
            "Epoch 42/50\n",
            "398/398 [==============================] - 0s 906us/step - loss: 0.4371 - accuracy: 0.8869\n",
            "Epoch 43/50\n",
            "398/398 [==============================] - 0s 923us/step - loss: 0.4977 - accuracy: 0.8844\n",
            "Epoch 44/50\n",
            "398/398 [==============================] - 0s 923us/step - loss: 0.4578 - accuracy: 0.8945\n",
            "Epoch 45/50\n",
            "398/398 [==============================] - 0s 949us/step - loss: 0.4114 - accuracy: 0.9095\n",
            "Epoch 46/50\n",
            "398/398 [==============================] - 0s 917us/step - loss: 0.4067 - accuracy: 0.8945\n",
            "Epoch 47/50\n",
            "398/398 [==============================] - 0s 911us/step - loss: 0.4302 - accuracy: 0.9045\n",
            "Epoch 48/50\n",
            "398/398 [==============================] - 0s 949us/step - loss: 0.4472 - accuracy: 0.8920\n",
            "Epoch 49/50\n",
            "398/398 [==============================] - 0s 904us/step - loss: 0.3882 - accuracy: 0.8819\n",
            "Epoch 50/50\n",
            "398/398 [==============================] - 0s 943us/step - loss: 0.4546 - accuracy: 0.9045\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f6010c5eda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehsvVsLBm2Sn",
        "colab_type": "text"
      },
      "source": [
        "Lets quickly check our testing dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whPkPwSVSz3w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "28aeab7f-6c90-497e-bf83-bdc3b5a7af62"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 24,
      "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>43</th>\n",
              "      <td>13.280</td>\n",
              "      <td>20.28</td>\n",
              "      <td>87.32</td>\n",
              "      <td>545.2</td>\n",
              "      <td>0.10410</td>\n",
              "      <td>0.14360</td>\n",
              "      <td>0.09847</td>\n",
              "      <td>0.061580</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.06782</td>\n",
              "      <td>0.3704</td>\n",
              "      <td>0.8249</td>\n",
              "      <td>2.427</td>\n",
              "      <td>31.330</td>\n",
              "      <td>0.005072</td>\n",
              "      <td>0.021470</td>\n",
              "      <td>0.021850</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.01719</td>\n",
              "      <td>0.003317</td>\n",
              "      <td>17.380</td>\n",
              "      <td>28.00</td>\n",
              "      <td>113.10</td>\n",
              "      <td>907.2</td>\n",
              "      <td>0.15300</td>\n",
              "      <td>0.37240</td>\n",
              "      <td>0.36640</td>\n",
              "      <td>0.14920</td>\n",
              "      <td>0.3739</td>\n",
              "      <td>0.10270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>20.590</td>\n",
              "      <td>21.24</td>\n",
              "      <td>137.80</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>0.10850</td>\n",
              "      <td>0.16440</td>\n",
              "      <td>0.21880</td>\n",
              "      <td>0.112100</td>\n",
              "      <td>0.1848</td>\n",
              "      <td>0.06222</td>\n",
              "      <td>0.5904</td>\n",
              "      <td>1.2160</td>\n",
              "      <td>4.206</td>\n",
              "      <td>75.090</td>\n",
              "      <td>0.006666</td>\n",
              "      <td>0.027910</td>\n",
              "      <td>0.040620</td>\n",
              "      <td>0.014790</td>\n",
              "      <td>0.01117</td>\n",
              "      <td>0.003727</td>\n",
              "      <td>23.860</td>\n",
              "      <td>30.76</td>\n",
              "      <td>163.20</td>\n",
              "      <td>1760.0</td>\n",
              "      <td>0.14640</td>\n",
              "      <td>0.35970</td>\n",
              "      <td>0.51790</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.2480</td>\n",
              "      <td>0.08999</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>0.017700</td>\n",
              "      <td>0.1739</td>\n",
              "      <td>0.05677</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>1.5710</td>\n",
              "      <td>1.183</td>\n",
              "      <td>14.680</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",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>19.730</td>\n",
              "      <td>19.82</td>\n",
              "      <td>130.70</td>\n",
              "      <td>1206.0</td>\n",
              "      <td>0.10620</td>\n",
              "      <td>0.18490</td>\n",
              "      <td>0.24170</td>\n",
              "      <td>0.097400</td>\n",
              "      <td>0.1733</td>\n",
              "      <td>0.06697</td>\n",
              "      <td>0.7661</td>\n",
              "      <td>0.7800</td>\n",
              "      <td>4.115</td>\n",
              "      <td>92.810</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.050570</td>\n",
              "      <td>0.068000</td>\n",
              "      <td>0.019710</td>\n",
              "      <td>0.01467</td>\n",
              "      <td>0.007259</td>\n",
              "      <td>25.280</td>\n",
              "      <td>25.59</td>\n",
              "      <td>159.80</td>\n",
              "      <td>1933.0</td>\n",
              "      <td>0.17100</td>\n",
              "      <td>0.59550</td>\n",
              "      <td>0.84890</td>\n",
              "      <td>0.25070</td>\n",
              "      <td>0.2749</td>\n",
              "      <td>0.12970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542</th>\n",
              "      <td>14.740</td>\n",
              "      <td>25.42</td>\n",
              "      <td>94.70</td>\n",
              "      <td>668.6</td>\n",
              "      <td>0.08275</td>\n",
              "      <td>0.07214</td>\n",
              "      <td>0.04105</td>\n",
              "      <td>0.030270</td>\n",
              "      <td>0.1840</td>\n",
              "      <td>0.05680</td>\n",
              "      <td>0.3031</td>\n",
              "      <td>1.3850</td>\n",
              "      <td>2.177</td>\n",
              "      <td>27.410</td>\n",
              "      <td>0.004775</td>\n",
              "      <td>0.011720</td>\n",
              "      <td>0.019470</td>\n",
              "      <td>0.012690</td>\n",
              "      <td>0.01870</td>\n",
              "      <td>0.002626</td>\n",
              "      <td>16.510</td>\n",
              "      <td>32.29</td>\n",
              "      <td>107.40</td>\n",
              "      <td>826.4</td>\n",
              "      <td>0.10600</td>\n",
              "      <td>0.13760</td>\n",
              "      <td>0.16110</td>\n",
              "      <td>0.10950</td>\n",
              "      <td>0.2722</td>\n",
              "      <td>0.06956</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>65</th>\n",
              "      <td>14.780</td>\n",
              "      <td>23.94</td>\n",
              "      <td>97.40</td>\n",
              "      <td>668.3</td>\n",
              "      <td>0.11720</td>\n",
              "      <td>0.14790</td>\n",
              "      <td>0.12670</td>\n",
              "      <td>0.090290</td>\n",
              "      <td>0.1953</td>\n",
              "      <td>0.06654</td>\n",
              "      <td>0.3577</td>\n",
              "      <td>1.2810</td>\n",
              "      <td>2.450</td>\n",
              "      <td>35.240</td>\n",
              "      <td>0.006703</td>\n",
              "      <td>0.023100</td>\n",
              "      <td>0.023150</td>\n",
              "      <td>0.011840</td>\n",
              "      <td>0.01900</td>\n",
              "      <td>0.003224</td>\n",
              "      <td>17.310</td>\n",
              "      <td>33.39</td>\n",
              "      <td>114.60</td>\n",
              "      <td>925.1</td>\n",
              "      <td>0.16480</td>\n",
              "      <td>0.34160</td>\n",
              "      <td>0.30240</td>\n",
              "      <td>0.16140</td>\n",
              "      <td>0.3321</td>\n",
              "      <td>0.08911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387</th>\n",
              "      <td>13.880</td>\n",
              "      <td>16.16</td>\n",
              "      <td>88.37</td>\n",
              "      <td>596.6</td>\n",
              "      <td>0.07026</td>\n",
              "      <td>0.04831</td>\n",
              "      <td>0.02045</td>\n",
              "      <td>0.008507</td>\n",
              "      <td>0.1607</td>\n",
              "      <td>0.05474</td>\n",
              "      <td>0.2541</td>\n",
              "      <td>0.6218</td>\n",
              "      <td>1.709</td>\n",
              "      <td>23.120</td>\n",
              "      <td>0.003728</td>\n",
              "      <td>0.014150</td>\n",
              "      <td>0.019880</td>\n",
              "      <td>0.007016</td>\n",
              "      <td>0.01647</td>\n",
              "      <td>0.001970</td>\n",
              "      <td>15.510</td>\n",
              "      <td>19.97</td>\n",
              "      <td>99.66</td>\n",
              "      <td>745.3</td>\n",
              "      <td>0.08484</td>\n",
              "      <td>0.12330</td>\n",
              "      <td>0.10910</td>\n",
              "      <td>0.04537</td>\n",
              "      <td>0.2542</td>\n",
              "      <td>0.06623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>12.420</td>\n",
              "      <td>15.04</td>\n",
              "      <td>78.61</td>\n",
              "      <td>476.5</td>\n",
              "      <td>0.07926</td>\n",
              "      <td>0.03393</td>\n",
              "      <td>0.01053</td>\n",
              "      <td>0.011080</td>\n",
              "      <td>0.1546</td>\n",
              "      <td>0.05754</td>\n",
              "      <td>0.1153</td>\n",
              "      <td>0.6745</td>\n",
              "      <td>0.757</td>\n",
              "      <td>9.006</td>\n",
              "      <td>0.003265</td>\n",
              "      <td>0.004930</td>\n",
              "      <td>0.006493</td>\n",
              "      <td>0.003762</td>\n",
              "      <td>0.01720</td>\n",
              "      <td>0.001360</td>\n",
              "      <td>13.200</td>\n",
              "      <td>20.37</td>\n",
              "      <td>83.85</td>\n",
              "      <td>543.4</td>\n",
              "      <td>0.10370</td>\n",
              "      <td>0.07776</td>\n",
              "      <td>0.06243</td>\n",
              "      <td>0.04052</td>\n",
              "      <td>0.2901</td>\n",
              "      <td>0.06783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>8.671</td>\n",
              "      <td>14.45</td>\n",
              "      <td>54.42</td>\n",
              "      <td>227.2</td>\n",
              "      <td>0.09138</td>\n",
              "      <td>0.04276</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1722</td>\n",
              "      <td>0.06724</td>\n",
              "      <td>0.2204</td>\n",
              "      <td>0.7873</td>\n",
              "      <td>1.435</td>\n",
              "      <td>11.360</td>\n",
              "      <td>0.009172</td>\n",
              "      <td>0.008007</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02711</td>\n",
              "      <td>0.003399</td>\n",
              "      <td>9.262</td>\n",
              "      <td>17.04</td>\n",
              "      <td>58.36</td>\n",
              "      <td>259.2</td>\n",
              "      <td>0.11620</td>\n",
              "      <td>0.07057</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2592</td>\n",
              "      <td>0.07848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>508</th>\n",
              "      <td>16.300</td>\n",
              "      <td>15.70</td>\n",
              "      <td>104.70</td>\n",
              "      <td>819.8</td>\n",
              "      <td>0.09427</td>\n",
              "      <td>0.06712</td>\n",
              "      <td>0.05526</td>\n",
              "      <td>0.045630</td>\n",
              "      <td>0.1711</td>\n",
              "      <td>0.05657</td>\n",
              "      <td>0.2067</td>\n",
              "      <td>0.4706</td>\n",
              "      <td>1.146</td>\n",
              "      <td>20.670</td>\n",
              "      <td>0.007394</td>\n",
              "      <td>0.012030</td>\n",
              "      <td>0.024700</td>\n",
              "      <td>0.014310</td>\n",
              "      <td>0.01344</td>\n",
              "      <td>0.002569</td>\n",
              "      <td>17.320</td>\n",
              "      <td>17.76</td>\n",
              "      <td>109.80</td>\n",
              "      <td>928.2</td>\n",
              "      <td>0.13540</td>\n",
              "      <td>0.13610</td>\n",
              "      <td>0.19470</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.2300</td>\n",
              "      <td>0.07230</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>171 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "43        13.280         20.28  ...          0.3739                  0.10270\n",
              "499       20.590         21.24  ...          0.2480                  0.08999\n",
              "102       12.180         20.52  ...          0.2694                  0.06878\n",
              "252       19.730         19.82  ...          0.2749                  0.12970\n",
              "542       14.740         25.42  ...          0.2722                  0.06956\n",
              "..           ...           ...  ...             ...                      ...\n",
              "65        14.780         23.94  ...          0.3321                  0.08911\n",
              "387       13.880         16.16  ...          0.2542                  0.06623\n",
              "241       12.420         15.04  ...          0.2901                  0.06783\n",
              "175        8.671         14.45  ...          0.2592                  0.07848\n",
              "508       16.300         15.70  ...          0.2300                  0.07230\n",
              "\n",
              "[171 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7D4BTagmnG-7",
        "colab_type": "text"
      },
      "source": [
        "### Now let's predict the target variable on test data Using our Sequential Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJ2llnOem9qv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = Classification_Model.predict(X_test)\n",
        "prediction = [1 if y>0.5 else 0 for y in prediction] #list\n",
        "prediction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oerzR4S9oUPg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "030465d2-27ea-4064-8e4e-f6b056cf7723"
      },
      "source": [
        "len(prediction)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRZtW5Ucnwxa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "09f7467c-b185-43d9-8ad2-6e447936354c"
      },
      "source": [
        "type(prediction)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IcEthVdn4NH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ae4c50f0-6438-4988-e2bd-4e64bd010b15"
      },
      "source": [
        "type(y_test)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jx9UGU0spl4y",
        "colab_type": "text"
      },
      "source": [
        "COnverting Prediction list to pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zMV4VoUoRE2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred = pd.DataFrame(prediction)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVdhcW1qpyyr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7d60a648-d3f7-4a82-ef7c-3ecfb19c437a"
      },
      "source": [
        "type(pred)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f44V_Q9yp5xM",
        "colab_type": "text"
      },
      "source": [
        "### Now converting both pred dataframe and y_test dataframe to numpy ndarray to calculate prediction Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HemRmaW8p1wK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred = pred.values"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhKh0iLIq3ue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_test_ndarray = y_test.values"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fK7jQIeorQw_",
        "colab_type": "text"
      },
      "source": [
        "### Prediction Accurecy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFF2pze9rJrr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "e5ddd69d-fad5-4bb6-a6d7-bd904794642b"
      },
      "source": [
        "total = 0\n",
        "correct = 0\n",
        "wrong =0\n",
        "\n",
        "for i in range(len(pred)):\n",
        "    total = total+1\n",
        "    if(y_test_ndarray[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": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total = 171\n",
            "Correct = 165 - Parcentage correct: 96.49%\n",
            "Wrong = 6 Parcentage wrong: 3.51%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RLqD5vUroC_",
        "colab_type": "text"
      },
      "source": [
        "### Testing the model for individual input data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eb9yVsWr5iK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a900b258-b5f0-4506-e686-66cd8c5f87f1"
      },
      "source": [
        "len(X_test)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uop_z_GfrhWK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "be1c2b72-12c6-4332-c713-b79536e4731d"
      },
      "source": [
        "input1 = X_test.iloc[[49]]\n",
        "input1"
      ],
      "execution_count": 56,
      "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>87</th>\n",
              "      <td>19.02</td>\n",
              "      <td>24.59</td>\n",
              "      <td>122.0</td>\n",
              "      <td>1076.0</td>\n",
              "      <td>0.09029</td>\n",
              "      <td>0.1206</td>\n",
              "      <td>0.1468</td>\n",
              "      <td>0.08271</td>\n",
              "      <td>0.1953</td>\n",
              "      <td>0.05629</td>\n",
              "      <td>0.5495</td>\n",
              "      <td>0.6636</td>\n",
              "      <td>3.055</td>\n",
              "      <td>57.65</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>0.01842</td>\n",
              "      <td>0.0371</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.01964</td>\n",
              "      <td>0.003337</td>\n",
              "      <td>24.56</td>\n",
              "      <td>30.41</td>\n",
              "      <td>152.9</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>0.1249</td>\n",
              "      <td>0.3206</td>\n",
              "      <td>0.5755</td>\n",
              "      <td>0.1956</td>\n",
              "      <td>0.3956</td>\n",
              "      <td>0.09288</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "87        19.02         24.59  ...          0.3956                  0.09288\n",
              "\n",
              "[1 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4r9_HF_sN6b",
        "colab_type": "text"
      },
      "source": [
        "This individual iput has 30 attributes and 1 row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GY9p6szrsDNl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "530d01fb-e781-4451-e7c3-b640344d3b54"
      },
      "source": [
        "input1.shape"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PY8yrpisal0",
        "colab_type": "text"
      },
      "source": [
        "Predict individual input data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fMVzM3VsKVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0ff3a711-0072-4368-d6c9-e88ea5549307"
      },
      "source": [
        "prediction1 = Classification_Model.predict(input1)\n",
        "prediction1"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lIKDhJsseoz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6bea23b0-c594-4103-8fb1-50865bbdcb2d"
      },
      "source": [
        "prediction1 = ['1' if prediction1>0.5 else '0' for y in prediction1]\n",
        "prediction1"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66OpPrXAxyrE",
        "colab_type": "text"
      },
      "source": [
        "### Manually checking Prediction Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ngh7gj0dsoLJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "7bd74d3b-e2c5-4251-becf-0c9abf423f76"
      },
      "source": [
        "df.loc[(df['area_mean']==1076.0\t) & (df['concavity_mean']==0.1468)]"
      ],
      "execution_count": 60,
      "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>87</th>\n",
              "      <td>86135502</td>\n",
              "      <td>1</td>\n",
              "      <td>19.02</td>\n",
              "      <td>24.59</td>\n",
              "      <td>122.0</td>\n",
              "      <td>1076.0</td>\n",
              "      <td>0.09029</td>\n",
              "      <td>0.1206</td>\n",
              "      <td>0.1468</td>\n",
              "      <td>0.08271</td>\n",
              "      <td>0.1953</td>\n",
              "      <td>0.05629</td>\n",
              "      <td>0.5495</td>\n",
              "      <td>0.6636</td>\n",
              "      <td>3.055</td>\n",
              "      <td>57.65</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>0.01842</td>\n",
              "      <td>0.0371</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.01964</td>\n",
              "      <td>0.003337</td>\n",
              "      <td>24.56</td>\n",
              "      <td>30.41</td>\n",
              "      <td>152.9</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>0.1249</td>\n",
              "      <td>0.3206</td>\n",
              "      <td>0.5755</td>\n",
              "      <td>0.1956</td>\n",
              "      <td>0.3956</td>\n",
              "      <td>0.09288</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "87  86135502          1  ...          0.3956                  0.09288\n",
              "\n",
              "[1 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUftFeBtyyUJ",
        "colab_type": "text"
      },
      "source": [
        "As We can See that our Model predict the individual input accurately."
      ]
    }
  ]
}