cancer_classificaiton_ann.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cancer_classificaiton_ann.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN539CKpa6JKoL5AbprTyfC",
      "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/Pravinkumar2629/dl_repo/blob/master/cancer_classificaiton_ann.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3N6cVsSM5ia",
        "colab_type": "text"
      },
      "source": [
        "# Keras TF 2.0 - Code Along Classification Project\n",
        "\n",
        "Let's explore a classification task with Keras API for TF 2.0\n",
        "\n",
        "## The Data\n",
        "\n",
        "### Breast cancer wisconsin (diagnostic) dataset\n",
        "--------------------------------------------\n",
        "\n",
        "**Data Set Characteristics:**\n",
        "\n",
        "    :Number of Instances: 569\n",
        "\n",
        "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
        "\n",
        "    :Attribute Information:\n",
        "        - radius (mean of distances from center to points on the perimeter)\n",
        "        - texture (standard deviation of gray-scale values)\n",
        "        - perimeter\n",
        "        - area\n",
        "        - smoothness (local variation in radius lengths)\n",
        "        - compactness (perimeter^2 / area - 1.0)\n",
        "        - concavity (severity of concave portions of the contour)\n",
        "        - concave points (number of concave portions of the contour)\n",
        "        - symmetry \n",
        "        - fractal dimension (\"coastline approximation\" - 1)\n",
        "\n",
        "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
        "        largest values) of these features were computed for each image,\n",
        "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
        "        13 is Radius SE, field 23 is Worst Radius.\n",
        "\n",
        "        - class:\n",
        "                - WDBC-Malignant\n",
        "                - WDBC-Benign\n",
        "\n",
        "    :Summary Statistics:\n",
        "\n",
        "    ===================================== ====== ======\n",
        "                                           Min    Max\n",
        "    ===================================== ====== ======\n",
        "    radius (mean):                        6.981  28.11\n",
        "    texture (mean):                       9.71   39.28\n",
        "    perimeter (mean):                     43.79  188.5\n",
        "    area (mean):                          143.5  2501.0\n",
        "    smoothness (mean):                    0.053  0.163\n",
        "    compactness (mean):                   0.019  0.345\n",
        "    concavity (mean):                     0.0    0.427\n",
        "    concave points (mean):                0.0    0.201\n",
        "    symmetry (mean):                      0.106  0.304\n",
        "    fractal dimension (mean):             0.05   0.097\n",
        "    radius (standard error):              0.112  2.873\n",
        "    texture (standard error):             0.36   4.885\n",
        "    perimeter (standard error):           0.757  21.98\n",
        "    area (standard error):                6.802  542.2\n",
        "    smoothness (standard error):          0.002  0.031\n",
        "    compactness (standard error):         0.002  0.135\n",
        "    concavity (standard error):           0.0    0.396\n",
        "    concave points (standard error):      0.0    0.053\n",
        "    symmetry (standard error):            0.008  0.079\n",
        "    fractal dimension (standard error):   0.001  0.03\n",
        "    radius (worst):                       7.93   36.04\n",
        "    texture (worst):                      12.02  49.54\n",
        "    perimeter (worst):                    50.41  251.2\n",
        "    area (worst):                         185.2  4254.0\n",
        "    smoothness (worst):                   0.071  0.223\n",
        "    compactness (worst):                  0.027  1.058\n",
        "    concavity (worst):                    0.0    1.252\n",
        "    concave points (worst):               0.0    0.291\n",
        "    symmetry (worst):                     0.156  0.664\n",
        "    fractal dimension (worst):            0.055  0.208\n",
        "    ===================================== ====== ======\n",
        "\n",
        "    :Missing Attribute Values: None\n",
        "\n",
        "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
        "\n",
        "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
        "\n",
        "    :Donor: Nick Street\n",
        "\n",
        "    :Date: November, 1995\n",
        "\n",
        "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
        "https://goo.gl/U2Uwz2\n",
        "\n",
        "Features are computed from a digitized image of a fine needle\n",
        "aspirate (FNA) of a breast mass.  They describe\n",
        "characteristics of the cell nuclei present in the image.\n",
        "\n",
        "Separating plane described above was obtained using\n",
        "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
        "Construction Via Linear Programming.\" Proceedings of the 4th\n",
        "Midwest Artificial Intelligence and Cognitive Science Society,\n",
        "pp. 97-101, 1992], a classification method which uses linear\n",
        "programming to construct a decision tree.  Relevant features\n",
        "were selected using an exhaustive search in the space of 1-4\n",
        "features and 1-3 separating planes.\n",
        "\n",
        "The actual linear program used to obtain the separating plane\n",
        "in the 3-dimensional space is that described in:\n",
        "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
        "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
        "Optimization Methods and Software 1, 1992, 23-34].\n",
        "\n",
        "This database is also available through the UW CS ftp server:\n",
        "\n",
        "ftp ftp.cs.wisc.edu\n",
        "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
        "\n",
        ".. topic:: References\n",
        "\n",
        "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
        "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
        "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
        "     San Jose, CA, 1993.\n",
        "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
        "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
        "     July-August 1995.\n",
        "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
        "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
        "     163-171."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtZC0AkYE8Gu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13HeBrFQGRs7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TJM4C0fGR01",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4dcLsFPGR7O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b22neYpJGSCP",
        "colab_type": "code",
        "outputId": "d9036620-47da-4129-a938-1df547c6df95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--J-guEuF0UZ",
        "colab_type": "code",
        "outputId": "c5e98acf-14f8-4130-a594-566cf7ffdde8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmLWe70NF-ss",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_ = pd.read_csv('/content/drive/My Drive/Colab Notebooks/datasets/cancer_classification.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JcVGcR4GPH5",
        "colab_type": "text"
      },
      "source": [
        "Let's copy the data to another sub df, (this is my habit)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Za5QXXlXGzFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df_.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g30m10S-G057",
        "colab_type": "code",
        "outputId": "3be91fbc-7f4b-4b6f-e6f9-4edfbf54dea7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>benign_0__mal_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst fractal dimension  benign_0__mal_1\n",
              "0        17.99         10.38  ...                  0.11890                0\n",
              "1        20.57         17.77  ...                  0.08902                0\n",
              "2        19.69         21.25  ...                  0.08758                0\n",
              "3        11.42         20.38  ...                  0.17300                0\n",
              "4        20.29         14.34  ...                  0.07678                0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymPmTMXCMu-B",
        "colab_type": "code",
        "outputId": "c2f8217c-8682-4088-a9b3-3294ab3b8000",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 31 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   mean radius              569 non-null    float64\n",
            " 1   mean texture             569 non-null    float64\n",
            " 2   mean perimeter           569 non-null    float64\n",
            " 3   mean area                569 non-null    float64\n",
            " 4   mean smoothness          569 non-null    float64\n",
            " 5   mean compactness         569 non-null    float64\n",
            " 6   mean concavity           569 non-null    float64\n",
            " 7   mean concave points      569 non-null    float64\n",
            " 8   mean symmetry            569 non-null    float64\n",
            " 9   mean fractal dimension   569 non-null    float64\n",
            " 10  radius error             569 non-null    float64\n",
            " 11  texture error            569 non-null    float64\n",
            " 12  perimeter error          569 non-null    float64\n",
            " 13  area error               569 non-null    float64\n",
            " 14  smoothness error         569 non-null    float64\n",
            " 15  compactness error        569 non-null    float64\n",
            " 16  concavity error          569 non-null    float64\n",
            " 17  concave points error     569 non-null    float64\n",
            " 18  symmetry error           569 non-null    float64\n",
            " 19  fractal dimension error  569 non-null    float64\n",
            " 20  worst radius             569 non-null    float64\n",
            " 21  worst texture            569 non-null    float64\n",
            " 22  worst perimeter          569 non-null    float64\n",
            " 23  worst area               569 non-null    float64\n",
            " 24  worst smoothness         569 non-null    float64\n",
            " 25  worst compactness        569 non-null    float64\n",
            " 26  worst concavity          569 non-null    float64\n",
            " 27  worst concave points     569 non-null    float64\n",
            " 28  worst symmetry           569 non-null    float64\n",
            " 29  worst fractal dimension  569 non-null    float64\n",
            " 30  benign_0__mal_1          569 non-null    int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 137.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofWVg2VhNjuT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.set_option('display.float_format', lambda x: format(x,'0.3f'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5KL4DnzNVRl",
        "colab_type": "code",
        "outputId": "ab3a6737-e47a-4340-e9e5-dcbbb2999372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        }
      },
      "source": [
        "df.describe().transpose()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean radius</th>\n",
              "      <td>569.000</td>\n",
              "      <td>14.127</td>\n",
              "      <td>3.524</td>\n",
              "      <td>6.981</td>\n",
              "      <td>11.700</td>\n",
              "      <td>13.370</td>\n",
              "      <td>15.780</td>\n",
              "      <td>28.110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean texture</th>\n",
              "      <td>569.000</td>\n",
              "      <td>19.290</td>\n",
              "      <td>4.301</td>\n",
              "      <td>9.710</td>\n",
              "      <td>16.170</td>\n",
              "      <td>18.840</td>\n",
              "      <td>21.800</td>\n",
              "      <td>39.280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean perimeter</th>\n",
              "      <td>569.000</td>\n",
              "      <td>91.969</td>\n",
              "      <td>24.299</td>\n",
              "      <td>43.790</td>\n",
              "      <td>75.170</td>\n",
              "      <td>86.240</td>\n",
              "      <td>104.100</td>\n",
              "      <td>188.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean area</th>\n",
              "      <td>569.000</td>\n",
              "      <td>654.889</td>\n",
              "      <td>351.914</td>\n",
              "      <td>143.500</td>\n",
              "      <td>420.300</td>\n",
              "      <td>551.100</td>\n",
              "      <td>782.700</td>\n",
              "      <td>2501.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean smoothness</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.086</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0.105</td>\n",
              "      <td>0.163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean compactness</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.104</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.093</td>\n",
              "      <td>0.130</td>\n",
              "      <td>0.345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean concavity</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.030</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.131</td>\n",
              "      <td>0.427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean concave points</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.049</td>\n",
              "      <td>0.039</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.034</td>\n",
              "      <td>0.074</td>\n",
              "      <td>0.201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean symmetry</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.181</td>\n",
              "      <td>0.027</td>\n",
              "      <td>0.106</td>\n",
              "      <td>0.162</td>\n",
              "      <td>0.179</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.063</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0.058</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.405</td>\n",
              "      <td>0.277</td>\n",
              "      <td>0.112</td>\n",
              "      <td>0.232</td>\n",
              "      <td>0.324</td>\n",
              "      <td>0.479</td>\n",
              "      <td>2.873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>1.217</td>\n",
              "      <td>0.552</td>\n",
              "      <td>0.360</td>\n",
              "      <td>0.834</td>\n",
              "      <td>1.108</td>\n",
              "      <td>1.474</td>\n",
              "      <td>4.885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>2.866</td>\n",
              "      <td>2.022</td>\n",
              "      <td>0.757</td>\n",
              "      <td>1.606</td>\n",
              "      <td>2.287</td>\n",
              "      <td>3.357</td>\n",
              "      <td>21.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>40.337</td>\n",
              "      <td>45.491</td>\n",
              "      <td>6.802</td>\n",
              "      <td>17.850</td>\n",
              "      <td>24.530</td>\n",
              "      <td>45.190</td>\n",
              "      <td>542.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.030</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal dimension error</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst radius</th>\n",
              "      <td>569.000</td>\n",
              "      <td>16.269</td>\n",
              "      <td>4.833</td>\n",
              "      <td>7.930</td>\n",
              "      <td>13.010</td>\n",
              "      <td>14.970</td>\n",
              "      <td>18.790</td>\n",
              "      <td>36.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst texture</th>\n",
              "      <td>569.000</td>\n",
              "      <td>25.677</td>\n",
              "      <td>6.146</td>\n",
              "      <td>12.020</td>\n",
              "      <td>21.080</td>\n",
              "      <td>25.410</td>\n",
              "      <td>29.720</td>\n",
              "      <td>49.540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst perimeter</th>\n",
              "      <td>569.000</td>\n",
              "      <td>107.261</td>\n",
              "      <td>33.603</td>\n",
              "      <td>50.410</td>\n",
              "      <td>84.110</td>\n",
              "      <td>97.660</td>\n",
              "      <td>125.400</td>\n",
              "      <td>251.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst area</th>\n",
              "      <td>569.000</td>\n",
              "      <td>880.583</td>\n",
              "      <td>569.357</td>\n",
              "      <td>185.200</td>\n",
              "      <td>515.300</td>\n",
              "      <td>686.500</td>\n",
              "      <td>1084.000</td>\n",
              "      <td>4254.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst smoothness</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.132</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.071</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.131</td>\n",
              "      <td>0.146</td>\n",
              "      <td>0.223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst compactness</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.254</td>\n",
              "      <td>0.157</td>\n",
              "      <td>0.027</td>\n",
              "      <td>0.147</td>\n",
              "      <td>0.212</td>\n",
              "      <td>0.339</td>\n",
              "      <td>1.058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst concavity</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.209</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.115</td>\n",
              "      <td>0.227</td>\n",
              "      <td>0.383</td>\n",
              "      <td>1.252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst concave points</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.115</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.161</td>\n",
              "      <td>0.291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst symmetry</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.290</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.157</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.282</td>\n",
              "      <td>0.318</td>\n",
              "      <td>0.664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.071</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>benign_0__mal_1</th>\n",
              "      <td>569.000</td>\n",
              "      <td>0.627</td>\n",
              "      <td>0.484</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          count    mean     std  ...     50%      75%      max\n",
              "mean radius             569.000  14.127   3.524  ...  13.370   15.780   28.110\n",
              "mean texture            569.000  19.290   4.301  ...  18.840   21.800   39.280\n",
              "mean perimeter          569.000  91.969  24.299  ...  86.240  104.100  188.500\n",
              "mean area               569.000 654.889 351.914  ... 551.100  782.700 2501.000\n",
              "mean smoothness         569.000   0.096   0.014  ...   0.096    0.105    0.163\n",
              "mean compactness        569.000   0.104   0.053  ...   0.093    0.130    0.345\n",
              "mean concavity          569.000   0.089   0.080  ...   0.062    0.131    0.427\n",
              "mean concave points     569.000   0.049   0.039  ...   0.034    0.074    0.201\n",
              "mean symmetry           569.000   0.181   0.027  ...   0.179    0.196    0.304\n",
              "mean fractal dimension  569.000   0.063   0.007  ...   0.062    0.066    0.097\n",
              "radius error            569.000   0.405   0.277  ...   0.324    0.479    2.873\n",
              "texture error           569.000   1.217   0.552  ...   1.108    1.474    4.885\n",
              "perimeter error         569.000   2.866   2.022  ...   2.287    3.357   21.980\n",
              "area error              569.000  40.337  45.491  ...  24.530   45.190  542.200\n",
              "smoothness error        569.000   0.007   0.003  ...   0.006    0.008    0.031\n",
              "compactness error       569.000   0.025   0.018  ...   0.020    0.032    0.135\n",
              "concavity error         569.000   0.032   0.030  ...   0.026    0.042    0.396\n",
              "concave points error    569.000   0.012   0.006  ...   0.011    0.015    0.053\n",
              "symmetry error          569.000   0.021   0.008  ...   0.019    0.023    0.079\n",
              "fractal dimension error 569.000   0.004   0.003  ...   0.003    0.005    0.030\n",
              "worst radius            569.000  16.269   4.833  ...  14.970   18.790   36.040\n",
              "worst texture           569.000  25.677   6.146  ...  25.410   29.720   49.540\n",
              "worst perimeter         569.000 107.261  33.603  ...  97.660  125.400  251.200\n",
              "worst area              569.000 880.583 569.357  ... 686.500 1084.000 4254.000\n",
              "worst smoothness        569.000   0.132   0.023  ...   0.131    0.146    0.223\n",
              "worst compactness       569.000   0.254   0.157  ...   0.212    0.339    1.058\n",
              "worst concavity         569.000   0.272   0.209  ...   0.227    0.383    1.252\n",
              "worst concave points    569.000   0.115   0.066  ...   0.100    0.161    0.291\n",
              "worst symmetry          569.000   0.290   0.062  ...   0.282    0.318    0.664\n",
              "worst fractal dimension 569.000   0.084   0.018  ...   0.080    0.092    0.207\n",
              "benign_0__mal_1         569.000   0.627   0.484  ...   1.000    1.000    1.000\n",
              "\n",
              "[31 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s2emgMFNZm7",
        "colab_type": "code",
        "outputId": "58289ed0-f206-4dd7-823a-d1ab7de23cee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sns.pairplot(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7febc5b865c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBGlE9KmOcHO",
        "colab_type": "code",
        "outputId": "54f3b1b8-1855-4a3e-d7b8-9ed2f2565ba5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "source": [
        "df.corr()['benign_0__mal_1'].sort_values()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "worst concave points      -0.794\n",
              "worst perimeter           -0.783\n",
              "mean concave points       -0.777\n",
              "worst radius              -0.776\n",
              "mean perimeter            -0.743\n",
              "worst area                -0.734\n",
              "mean radius               -0.730\n",
              "mean area                 -0.709\n",
              "mean concavity            -0.696\n",
              "worst concavity           -0.660\n",
              "mean compactness          -0.597\n",
              "worst compactness         -0.591\n",
              "radius error              -0.567\n",
              "perimeter error           -0.556\n",
              "area error                -0.548\n",
              "worst texture             -0.457\n",
              "worst smoothness          -0.421\n",
              "worst symmetry            -0.416\n",
              "mean texture              -0.415\n",
              "concave points error      -0.408\n",
              "mean smoothness           -0.359\n",
              "mean symmetry             -0.330\n",
              "worst fractal dimension   -0.324\n",
              "compactness error         -0.293\n",
              "concavity error           -0.254\n",
              "fractal dimension error   -0.078\n",
              "symmetry error             0.007\n",
              "texture error              0.008\n",
              "mean fractal dimension     0.013\n",
              "smoothness error           0.067\n",
              "benign_0__mal_1            1.000\n",
              "Name: benign_0__mal_1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP6zo3YLOk3q",
        "colab_type": "code",
        "outputId": "18a14ced-4409-423a-f1be-048b57c3c554",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(df.corr(), cmap='coolwarm', annot=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fec0f4365c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uv3QkoT_PGfk",
        "colab_type": "text"
      },
      "source": [
        "### Ha!.. Nothing seems to correlate more to the target variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cx2O3F2QUdlx",
        "colab_type": "text"
      },
      "source": [
        "## Let's standardize the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5JuNjLPUhMj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeBJvKXyUpj7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62Z2irOiVgz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_features = df.drop('benign_0__mal_1', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXcSPj2PVnbe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_features_scaled = scaler.fit_transform(df_features)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exndhA8xcgEM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_features_scaled = pd.DataFrame(df_features_scaled, columns=df_features.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFxwWcGAc3KU",
        "colab_type": "text"
      },
      "source": [
        "## Let's split the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmj53EtJdHi_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfS1ilHbdSD_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_features_scaled, df['benign_0__mal_1'], random_state=29, test_size=0.3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TP5Q3xJsdimN",
        "colab_type": "text"
      },
      "source": [
        "## Let's import our model and optimizers,"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uzstfr33doZd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6opnyPJdxoZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Dense, Dropout, Flatten"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx78Sy-8d42_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JF_CwbtzeHON",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCaxkbfrfONm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss', mode='min', patience=40,verbose=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wyq5Af03fu2y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_checkpoint = ModelCheckpoint('cancer_classifier.h5', monitor='val_loss',verbose=1, save_best_only=True, mode='min')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnnVURY-gUnA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepare_model():\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Dense(50, activation='relu'))\n",
        "  model.add(Dropout(0.4))\n",
        "\n",
        "  model.add(Dense(20, activation='relu'))\n",
        "  model.add(Dropout(0.4))\n",
        "\n",
        "  model.add(Dense(5, activation='relu'))\n",
        "\n",
        "  model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "  model.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sjewJp6lpui",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = prepare_model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGje6cLElvk9",
        "colab_type": "code",
        "outputId": "16200c0a-5158-48cf-cd5c-2a421717dfe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(x=X_train,y=y_train, epochs=200,verbose=1, callbacks=[early_stop, model_checkpoint], validation_data=(X_test, y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "WARNING:tensorflow:Layer dense_5 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.7180\n",
            "Epoch 00001: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 12ms/step - loss: 0.7063 - val_loss: 0.6712\n",
            "Epoch 2/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.6976\n",
            "Epoch 00002: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.6753 - val_loss: 0.6476\n",
            "Epoch 3/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.6479\n",
            "Epoch 00003: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.6468 - val_loss: 0.6141\n",
            "Epoch 4/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.6437\n",
            "Epoch 00004: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.6244 - val_loss: 0.5684\n",
            "Epoch 5/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.6176\n",
            "Epoch 00005: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.5899 - val_loss: 0.5222\n",
            "Epoch 6/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.5551\n",
            "Epoch 00006: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.5462 - val_loss: 0.4681\n",
            "Epoch 7/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.5193\n",
            "Epoch 00007: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.5232 - val_loss: 0.4083\n",
            "Epoch 8/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.5521\n",
            "Epoch 00008: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.4883 - val_loss: 0.3616\n",
            "Epoch 9/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.4880\n",
            "Epoch 00009: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.4406 - val_loss: 0.3199\n",
            "Epoch 10/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.4317\n",
            "Epoch 00010: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.4382 - val_loss: 0.2908\n",
            "Epoch 11/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.4144\n",
            "Epoch 00011: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3890 - val_loss: 0.2592\n",
            "Epoch 12/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2898\n",
            "Epoch 00012: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3553 - val_loss: 0.2306\n",
            "Epoch 13/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.3030\n",
            "Epoch 00013: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3495 - val_loss: 0.2106\n",
            "Epoch 14/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2967\n",
            "Epoch 00014: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3081 - val_loss: 0.1903\n",
            "Epoch 15/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.3374\n",
            "Epoch 00015: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.3080 - val_loss: 0.1735\n",
            "Epoch 16/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2815\n",
            "Epoch 00016: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2707 - val_loss: 0.1599\n",
            "Epoch 17/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1629\n",
            "Epoch 00017: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2651 - val_loss: 0.1497\n",
            "Epoch 18/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1597\n",
            "Epoch 00018: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2541 - val_loss: 0.1408\n",
            "Epoch 19/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1203\n",
            "Epoch 00019: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2297 - val_loss: 0.1326\n",
            "Epoch 20/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2448\n",
            "Epoch 00020: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2262 - val_loss: 0.1195\n",
            "Epoch 21/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1956\n",
            "Epoch 00021: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2420 - val_loss: 0.1132\n",
            "Epoch 22/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2453\n",
            "Epoch 00022: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2124 - val_loss: 0.1113\n",
            "Epoch 23/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1518\n",
            "Epoch 00023: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1881 - val_loss: 0.1051\n",
            "Epoch 24/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2666\n",
            "Epoch 00024: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.2089 - val_loss: 0.0970\n",
            "Epoch 25/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2324\n",
            "Epoch 00025: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1855 - val_loss: 0.0937\n",
            "Epoch 26/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2924\n",
            "Epoch 00026: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1958 - val_loss: 0.0897\n",
            "Epoch 27/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1461\n",
            "Epoch 00027: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1657 - val_loss: 0.0849\n",
            "Epoch 28/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2452\n",
            "Epoch 00028: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1895 - val_loss: 0.0853\n",
            "Epoch 29/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1497\n",
            "Epoch 00029: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1802 - val_loss: 0.0823\n",
            "Epoch 30/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1359\n",
            "Epoch 00030: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1791 - val_loss: 0.0814\n",
            "Epoch 31/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1821\n",
            "Epoch 00031: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1558 - val_loss: 0.0777\n",
            "Epoch 32/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1848\n",
            "Epoch 00032: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1700 - val_loss: 0.0730\n",
            "Epoch 33/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1010\n",
            "Epoch 00033: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1360 - val_loss: 0.0706\n",
            "Epoch 34/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1680\n",
            "Epoch 00034: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1790 - val_loss: 0.0706\n",
            "Epoch 35/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2422\n",
            "Epoch 00035: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1374 - val_loss: 0.0664\n",
            "Epoch 36/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0969\n",
            "Epoch 00036: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1456 - val_loss: 0.0693\n",
            "Epoch 37/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0957\n",
            "Epoch 00037: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1587 - val_loss: 0.0618\n",
            "Epoch 38/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1129\n",
            "Epoch 00038: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1456 - val_loss: 0.0656\n",
            "Epoch 39/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2091\n",
            "Epoch 00039: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1130 - val_loss: 0.0641\n",
            "Epoch 40/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1290\n",
            "Epoch 00040: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1259 - val_loss: 0.0591\n",
            "Epoch 41/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0608\n",
            "Epoch 00041: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1277 - val_loss: 0.0566\n",
            "Epoch 42/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1338\n",
            "Epoch 00042: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1180 - val_loss: 0.0561\n",
            "Epoch 43/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1071\n",
            "Epoch 00043: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1297 - val_loss: 0.0543\n",
            "Epoch 44/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1543\n",
            "Epoch 00044: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1173 - val_loss: 0.0510\n",
            "Epoch 45/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1768\n",
            "Epoch 00045: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1113 - val_loss: 0.0471\n",
            "Epoch 46/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0466\n",
            "Epoch 00046: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1156 - val_loss: 0.0487\n",
            "Epoch 47/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1838\n",
            "Epoch 00047: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1290 - val_loss: 0.0468\n",
            "Epoch 48/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0994\n",
            "Epoch 00048: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0992 - val_loss: 0.0528\n",
            "Epoch 49/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1727\n",
            "Epoch 00049: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1384 - val_loss: 0.0526\n",
            "Epoch 50/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0759\n",
            "Epoch 00050: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1127 - val_loss: 0.0505\n",
            "Epoch 51/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0756\n",
            "Epoch 00051: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1054 - val_loss: 0.0561\n",
            "Epoch 52/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0641\n",
            "Epoch 00052: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1151 - val_loss: 0.0497\n",
            "Epoch 53/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0262\n",
            "Epoch 00053: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1026 - val_loss: 0.0466\n",
            "Epoch 54/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1174\n",
            "Epoch 00054: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1126 - val_loss: 0.0492\n",
            "Epoch 55/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0487\n",
            "Epoch 00055: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1128 - val_loss: 0.0498\n",
            "Epoch 56/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2182\n",
            "Epoch 00056: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1308 - val_loss: 0.0465\n",
            "Epoch 57/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0448\n",
            "Epoch 00057: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0998 - val_loss: 0.0440\n",
            "Epoch 58/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0654\n",
            "Epoch 00058: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1072 - val_loss: 0.0454\n",
            "Epoch 59/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0891\n",
            "Epoch 00059: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0951 - val_loss: 0.0442\n",
            "Epoch 60/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1850\n",
            "Epoch 00060: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1003 - val_loss: 0.0455\n",
            "Epoch 61/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0837\n",
            "Epoch 00061: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0853 - val_loss: 0.0434\n",
            "Epoch 62/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0566\n",
            "Epoch 00062: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0874 - val_loss: 0.0423\n",
            "Epoch 63/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1325\n",
            "Epoch 00063: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.1133 - val_loss: 0.0428\n",
            "Epoch 64/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1277\n",
            "Epoch 00064: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0789 - val_loss: 0.0418\n",
            "Epoch 65/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0367\n",
            "Epoch 00065: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0861 - val_loss: 0.0496\n",
            "Epoch 66/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2215\n",
            "Epoch 00066: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0844 - val_loss: 0.0398\n",
            "Epoch 67/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0685\n",
            "Epoch 00067: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1067 - val_loss: 0.0424\n",
            "Epoch 68/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0341\n",
            "Epoch 00068: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0922 - val_loss: 0.0375\n",
            "Epoch 69/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1663\n",
            "Epoch 00069: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0954 - val_loss: 0.0356\n",
            "Epoch 70/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0546\n",
            "Epoch 00070: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0880 - val_loss: 0.0406\n",
            "Epoch 71/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0779\n",
            "Epoch 00071: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0961 - val_loss: 0.0377\n",
            "Epoch 72/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0168\n",
            "Epoch 00072: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0809 - val_loss: 0.0332\n",
            "Epoch 73/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0690\n",
            "Epoch 00073: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0951 - val_loss: 0.0383\n",
            "Epoch 74/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0335\n",
            "Epoch 00074: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0730 - val_loss: 0.0320\n",
            "Epoch 75/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0901\n",
            "Epoch 00075: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0861 - val_loss: 0.0377\n",
            "Epoch 76/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0657\n",
            "Epoch 00076: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0983 - val_loss: 0.0358\n",
            "Epoch 77/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0320\n",
            "Epoch 00077: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0970 - val_loss: 0.0384\n",
            "Epoch 78/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0393\n",
            "Epoch 00078: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0938 - val_loss: 0.0389\n",
            "Epoch 79/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0338\n",
            "Epoch 00079: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0938 - val_loss: 0.0398\n",
            "Epoch 80/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0550\n",
            "Epoch 00080: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0883 - val_loss: 0.0345\n",
            "Epoch 81/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0294\n",
            "Epoch 00081: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0970 - val_loss: 0.0390\n",
            "Epoch 82/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1275\n",
            "Epoch 00082: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0861 - val_loss: 0.0391\n",
            "Epoch 83/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0668\n",
            "Epoch 00083: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0908 - val_loss: 0.0347\n",
            "Epoch 84/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1537\n",
            "Epoch 00084: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0881 - val_loss: 0.0452\n",
            "Epoch 85/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1580\n",
            "Epoch 00085: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0856 - val_loss: 0.0356\n",
            "Epoch 86/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0395\n",
            "Epoch 00086: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0740 - val_loss: 0.0300\n",
            "Epoch 87/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0815\n",
            "Epoch 00087: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0999 - val_loss: 0.0390\n",
            "Epoch 88/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0126\n",
            "Epoch 00088: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0867 - val_loss: 0.0290\n",
            "Epoch 89/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0287\n",
            "Epoch 00089: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0690 - val_loss: 0.0373\n",
            "Epoch 90/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0275\n",
            "Epoch 00090: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0780 - val_loss: 0.0334\n",
            "Epoch 91/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2465\n",
            "Epoch 00091: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0851 - val_loss: 0.0332\n",
            "Epoch 92/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.2090\n",
            "Epoch 00092: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0916 - val_loss: 0.0369\n",
            "Epoch 93/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1137\n",
            "Epoch 00093: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0676 - val_loss: 0.0281\n",
            "Epoch 94/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0435\n",
            "Epoch 00094: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0634 - val_loss: 0.0312\n",
            "Epoch 95/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0993\n",
            "Epoch 00095: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0720 - val_loss: 0.0299\n",
            "Epoch 96/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1035\n",
            "Epoch 00096: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0807 - val_loss: 0.0288\n",
            "Epoch 97/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0932\n",
            "Epoch 00097: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0894 - val_loss: 0.0301\n",
            "Epoch 98/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0165\n",
            "Epoch 00098: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0643 - val_loss: 0.0314\n",
            "Epoch 99/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0535\n",
            "Epoch 00099: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0717 - val_loss: 0.0303\n",
            "Epoch 100/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0699\n",
            "Epoch 00100: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0849 - val_loss: 0.0274\n",
            "Epoch 101/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0807\n",
            "Epoch 00101: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0752 - val_loss: 0.0320\n",
            "Epoch 102/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0800\n",
            "Epoch 00102: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0709 - val_loss: 0.0419\n",
            "Epoch 103/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0649\n",
            "Epoch 00103: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0718 - val_loss: 0.0335\n",
            "Epoch 104/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0601\n",
            "Epoch 00104: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0730 - val_loss: 0.0278\n",
            "Epoch 105/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0487\n",
            "Epoch 00105: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0736 - val_loss: 0.0374\n",
            "Epoch 106/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1491\n",
            "Epoch 00106: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0834 - val_loss: 0.0266\n",
            "Epoch 107/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0868\n",
            "Epoch 00107: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0758 - val_loss: 0.0321\n",
            "Epoch 108/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0540\n",
            "Epoch 00108: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0748 - val_loss: 0.0269\n",
            "Epoch 109/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0627\n",
            "Epoch 00109: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0700 - val_loss: 0.0288\n",
            "Epoch 110/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1569\n",
            "Epoch 00110: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0721 - val_loss: 0.0294\n",
            "Epoch 111/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0488\n",
            "Epoch 00111: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0716 - val_loss: 0.0311\n",
            "Epoch 112/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0308\n",
            "Epoch 00112: val_loss did not improve from 0.02386\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0682 - val_loss: 0.0316\n",
            "Epoch 113/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0183\n",
            "Epoch 00113: val_loss improved from 0.02386 to 0.02208, saving model to cancer_classifier.h5\n",
            "13/13 [==============================] - 0s 9ms/step - loss: 0.0653 - val_loss: 0.0221\n",
            "Epoch 114/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1440\n",
            "Epoch 00114: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0733 - val_loss: 0.0264\n",
            "Epoch 115/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0329\n",
            "Epoch 00115: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0793 - val_loss: 0.0245\n",
            "Epoch 116/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0211\n",
            "Epoch 00116: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0917 - val_loss: 0.0303\n",
            "Epoch 117/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0378\n",
            "Epoch 00117: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0758 - val_loss: 0.0279\n",
            "Epoch 118/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0509\n",
            "Epoch 00118: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0627 - val_loss: 0.0286\n",
            "Epoch 119/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0306\n",
            "Epoch 00119: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0598 - val_loss: 0.0292\n",
            "Epoch 120/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1573\n",
            "Epoch 00120: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0598 - val_loss: 0.0274\n",
            "Epoch 121/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1273\n",
            "Epoch 00121: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0672 - val_loss: 0.0270\n",
            "Epoch 122/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0768\n",
            "Epoch 00122: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0640 - val_loss: 0.0291\n",
            "Epoch 123/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0187\n",
            "Epoch 00123: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0592 - val_loss: 0.0278\n",
            "Epoch 124/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0814\n",
            "Epoch 00124: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0578 - val_loss: 0.0265\n",
            "Epoch 125/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0337\n",
            "Epoch 00125: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0788 - val_loss: 0.0285\n",
            "Epoch 126/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0519\n",
            "Epoch 00126: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0545 - val_loss: 0.0252\n",
            "Epoch 127/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1281\n",
            "Epoch 00127: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0597 - val_loss: 0.0300\n",
            "Epoch 128/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0087\n",
            "Epoch 00128: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0532 - val_loss: 0.0239\n",
            "Epoch 129/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0144\n",
            "Epoch 00129: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0647 - val_loss: 0.0331\n",
            "Epoch 130/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0900\n",
            "Epoch 00130: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0812 - val_loss: 0.0253\n",
            "Epoch 131/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0613\n",
            "Epoch 00131: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0537 - val_loss: 0.0270\n",
            "Epoch 132/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0307\n",
            "Epoch 00132: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0649 - val_loss: 0.0348\n",
            "Epoch 133/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0860\n",
            "Epoch 00133: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0676 - val_loss: 0.0295\n",
            "Epoch 134/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0528\n",
            "Epoch 00134: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0618 - val_loss: 0.0254\n",
            "Epoch 135/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0549\n",
            "Epoch 00135: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0627 - val_loss: 0.0276\n",
            "Epoch 136/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0204\n",
            "Epoch 00136: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0690 - val_loss: 0.0311\n",
            "Epoch 137/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0310\n",
            "Epoch 00137: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0749 - val_loss: 0.0288\n",
            "Epoch 138/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0233\n",
            "Epoch 00138: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0612 - val_loss: 0.0322\n",
            "Epoch 139/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0619\n",
            "Epoch 00139: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0638 - val_loss: 0.0263\n",
            "Epoch 140/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0397\n",
            "Epoch 00140: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0546 - val_loss: 0.0245\n",
            "Epoch 141/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0625\n",
            "Epoch 00141: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0640 - val_loss: 0.0359\n",
            "Epoch 142/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0728\n",
            "Epoch 00142: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0547 - val_loss: 0.0274\n",
            "Epoch 143/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0398\n",
            "Epoch 00143: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0553 - val_loss: 0.0247\n",
            "Epoch 144/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1807\n",
            "Epoch 00144: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0719 - val_loss: 0.0258\n",
            "Epoch 145/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0155\n",
            "Epoch 00145: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0506 - val_loss: 0.0249\n",
            "Epoch 146/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0521\n",
            "Epoch 00146: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0684 - val_loss: 0.0266\n",
            "Epoch 147/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0581\n",
            "Epoch 00147: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0556 - val_loss: 0.0237\n",
            "Epoch 148/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.1790\n",
            "Epoch 00148: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0486 - val_loss: 0.0241\n",
            "Epoch 149/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0269\n",
            "Epoch 00149: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0582 - val_loss: 0.0308\n",
            "Epoch 150/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0452\n",
            "Epoch 00150: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0502 - val_loss: 0.0239\n",
            "Epoch 151/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0177\n",
            "Epoch 00151: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0749 - val_loss: 0.0309\n",
            "Epoch 152/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0127\n",
            "Epoch 00152: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.0470 - val_loss: 0.0309\n",
            "Epoch 153/200\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0065\n",
            "Epoch 00153: val_loss did not improve from 0.02208\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.0595 - val_loss: 0.0243\n",
            "Epoch 00153: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0oFdH_PmQoA",
        "colab_type": "code",
        "outputId": "04ed06fe-f3c8-401f-a616-3b4bf268b283",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsfGfHfBpBFf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhC4tVvbmoQS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = (model.predict(X_test) > 0.5).astype('int32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQ0EYaKrnQPx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = y_pred[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdsX_k4_nRlY",
        "colab_type": "text"
      },
      "source": [
        "## Let's import metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmXo87r5nd-C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iu4e6e4Ani_o",
        "colab_type": "code",
        "outputId": "29a850b1-3a95-4776-a995-06d6ec132685",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 66   1]\n",
            " [  0 104]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        67\n",
            "           1       0.99      1.00      1.00       104\n",
            "\n",
            "    accuracy                           0.99       171\n",
            "   macro avg       1.00      0.99      0.99       171\n",
            "weighted avg       0.99      0.99      0.99       171\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlvNEyeinp_z",
        "colab_type": "text"
      },
      "source": [
        "Hie!... Model performed great. Well done : )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZMf8i4xqM0l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}