Brest_Cancer_Detection_using_NN.ipynb

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  "metadata": {
    "colab": {
      "name": "Brest Cancer Detection using NN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BMQx4I3ggoU",
        "colab_type": "text"
      },
      "source": [
        "**Installing TensorFlow 2.0**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlRoB1kKY5DB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "9b7eab6b-f57e-45d0-af3e-0c7454a5eb49"
      },
      "source": [
        "!pip install tensorflow==2.0.0-rc0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==2.0.0-rc0 in /usr/local/lib/python3.6/dist-packages (2.0.0rc0)\n",
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            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.11.2)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==2.0.0-rc0) (42.0.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0-rc0) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0-rc0) (3.1.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0-rc0) (2.8.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bp0q1T-Badmo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzm3ZfvLbL2V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jn5GnnFrbt5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuV-WYL1gqAJ",
        "colab_type": "text"
      },
      "source": [
        "**Loading Cancer Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "168Yx_wbb-kF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer = datasets.load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBAVGryBcDdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0679f2af-b7a5-4a89-84fa-b1c95933f158"
      },
      "source": [
        "print(cancer.DESCR)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _breast_cancer_dataset:\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.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pk_09R4BcGv_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqf7u7e6dGXf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "2da87ffd-25be-4d72-c209-731dc7c5edf4"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0        17.99         10.38  ...          0.4601                  0.11890\n",
              "1        20.57         17.77  ...          0.2750                  0.08902\n",
              "2        19.69         21.25  ...          0.3613                  0.08758\n",
              "3        11.42         20.38  ...          0.6638                  0.17300\n",
              "4        20.29         14.34  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxds9osWdUE_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VciH6nQdbsX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2fc3bee1-f1e4-4b02-e1ad-cac73e09b7fc"
      },
      "source": [
        "y[:5]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzrX78h0dnRQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5a559dde-d0f2-4ebc-94d2-0b330e692b01"
      },
      "source": [
        "print(X.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(569, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iugKNFuGdne9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6b13f08d-3afd-4fb4-b0b2-d9d5bd838adc"
      },
      "source": [
        "print(y.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(569,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yegzbvfVde_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV6C9Ycddxy_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "46fdd1eb-933b-443d-cff4-cea79ee26073"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(455, 30)\n",
            "(114, 30)\n",
            "(455,)\n",
            "(114,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtTJ-cf8d64s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X06UVOMBfCLv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "f818bfd1-1eae-46cb-ddbe-82853ff90093"
      },
      "source": [
        "X_train[0]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.32315784,  2.57618342,  0.4857203 ,  0.19194201,  0.66421616,\n",
              "        2.00494008,  2.17536136,  1.21130731,  1.20632928,  1.30184276,\n",
              "       -0.53029677, -0.00644456, -0.24670324, -0.40036138, -0.79006026,\n",
              "        1.28234155,  1.54143653,  0.71301503,  0.11989108,  0.92537936,\n",
              "        0.27442622,  2.74323225,  0.65437528,  0.07429717,  0.44573443,\n",
              "        3.44722691,  4.34493009,  1.87472301,  2.06952401,  3.251885  ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qo5B071Tgu0q",
        "colab_type": "text"
      },
      "source": [
        "**Deep Learning Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkud7C-OfGGo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(64, activation='relu', input_shape=(X_train[0].shape)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(34, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gmHN_B0hlm_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "7c4427ac-27e2-4336-d78d-2f0189ea44c0"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 64)                1984      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 34)                2210      \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 34)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 34)                136       \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 35        \n",
            "=================================================================\n",
            "Total params: 30,797\n",
            "Trainable params: 29,961\n",
            "Non-trainable params: 836\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhYeM586hqBW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PfQBeIGiGhI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "48c25c1e-4383-45f7-a47e-bd6d134d2450"
      },
      "source": [
        "history = model.fit(X_train, y_train, batch_size=100, epochs=100)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 455 samples\n",
            "Epoch 1/100\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "455/455 [==============================] - 2s 5ms/sample - loss: 0.8297 - accuracy: 0.5341\n",
            "Epoch 2/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.8024 - accuracy: 0.5385\n",
            "Epoch 3/100\n",
            "455/455 [==============================] - 0s 75us/sample - loss: 0.8124 - accuracy: 0.5473\n",
            "Epoch 4/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.7794 - accuracy: 0.5912\n",
            "Epoch 5/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.7993 - accuracy: 0.5714\n",
            "Epoch 6/100\n",
            "455/455 [==============================] - 0s 85us/sample - loss: 0.7439 - accuracy: 0.5846\n",
            "Epoch 7/100\n",
            "455/455 [==============================] - 0s 82us/sample - loss: 0.7372 - accuracy: 0.6066\n",
            "Epoch 8/100\n",
            "455/455 [==============================] - 0s 73us/sample - loss: 0.7629 - accuracy: 0.5780\n",
            "Epoch 9/100\n",
            "455/455 [==============================] - 0s 74us/sample - loss: 0.6980 - accuracy: 0.5978\n",
            "Epoch 10/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.6942 - accuracy: 0.6264\n",
            "Epoch 11/100\n",
            "455/455 [==============================] - 0s 78us/sample - loss: 0.6139 - accuracy: 0.6571\n",
            "Epoch 12/100\n",
            "455/455 [==============================] - 0s 87us/sample - loss: 0.6345 - accuracy: 0.6901\n",
            "Epoch 13/100\n",
            "455/455 [==============================] - 0s 83us/sample - loss: 0.6609 - accuracy: 0.6813\n",
            "Epoch 14/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.6445 - accuracy: 0.6879\n",
            "Epoch 15/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.6161 - accuracy: 0.7011\n",
            "Epoch 16/100\n",
            "455/455 [==============================] - 0s 73us/sample - loss: 0.6074 - accuracy: 0.6813\n",
            "Epoch 17/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.6533 - accuracy: 0.6791\n",
            "Epoch 18/100\n",
            "455/455 [==============================] - 0s 74us/sample - loss: 0.6054 - accuracy: 0.7187\n",
            "Epoch 19/100\n",
            "455/455 [==============================] - 0s 78us/sample - loss: 0.5495 - accuracy: 0.7319\n",
            "Epoch 20/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.5597 - accuracy: 0.7341\n",
            "Epoch 21/100\n",
            "455/455 [==============================] - 0s 74us/sample - loss: 0.5524 - accuracy: 0.7385\n",
            "Epoch 22/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.5501 - accuracy: 0.7516\n",
            "Epoch 23/100\n",
            "455/455 [==============================] - 0s 74us/sample - loss: 0.5019 - accuracy: 0.7626\n",
            "Epoch 24/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.5107 - accuracy: 0.7758\n",
            "Epoch 25/100\n",
            "455/455 [==============================] - 0s 73us/sample - loss: 0.4838 - accuracy: 0.7824\n",
            "Epoch 26/100\n",
            "455/455 [==============================] - 0s 83us/sample - loss: 0.4918 - accuracy: 0.7824\n",
            "Epoch 27/100\n",
            "455/455 [==============================] - 0s 79us/sample - loss: 0.4688 - accuracy: 0.7978\n",
            "Epoch 28/100\n",
            "455/455 [==============================] - 0s 78us/sample - loss: 0.4714 - accuracy: 0.7890\n",
            "Epoch 29/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.3819 - accuracy: 0.8352\n",
            "Epoch 30/100\n",
            "455/455 [==============================] - 0s 78us/sample - loss: 0.4602 - accuracy: 0.7956\n",
            "Epoch 31/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.4173 - accuracy: 0.8110\n",
            "Epoch 32/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.4294 - accuracy: 0.8176\n",
            "Epoch 33/100\n",
            "455/455 [==============================] - 0s 78us/sample - loss: 0.3622 - accuracy: 0.8484\n",
            "Epoch 34/100\n",
            "455/455 [==============================] - 0s 72us/sample - loss: 0.3740 - accuracy: 0.8330\n",
            "Epoch 35/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.3660 - accuracy: 0.8505\n",
            "Epoch 36/100\n",
            "455/455 [==============================] - 0s 70us/sample - loss: 0.3575 - accuracy: 0.8484\n",
            "Epoch 37/100\n",
            "455/455 [==============================] - 0s 85us/sample - loss: 0.3347 - accuracy: 0.8703\n",
            "Epoch 38/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.3169 - accuracy: 0.8725\n",
            "Epoch 39/100\n",
            "455/455 [==============================] - 0s 76us/sample - loss: 0.3617 - accuracy: 0.8527\n",
            "Epoch 40/100\n",
            "455/455 [==============================] - 0s 74us/sample - loss: 0.3380 - accuracy: 0.8571\n",
            "Epoch 41/100\n",
            "455/455 [==============================] - 0s 75us/sample - loss: 0.2916 - accuracy: 0.8813\n",
            "Epoch 42/100\n",
            "455/455 [==============================] - 0s 81us/sample - loss: 0.3242 - accuracy: 0.8725\n",
            "Epoch 43/100\n",
            "455/455 [==============================] - 0s 88us/sample - loss: 0.3223 - accuracy: 0.8835\n",
            "Epoch 44/100\n",
            "455/455 [==============================] - 0s 81us/sample - loss: 0.2788 - accuracy: 0.8923\n",
            "Epoch 45/100\n",
            "455/455 [==============================] - 0s 75us/sample - loss: 0.2801 - accuracy: 0.8989\n",
            "Epoch 46/100\n",
            "455/455 [==============================] - 0s 74us/sample - loss: 0.3073 - accuracy: 0.8747\n",
            "Epoch 47/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.2770 - accuracy: 0.8945\n",
            "Epoch 48/100\n",
            "455/455 [==============================] - 0s 89us/sample - loss: 0.3073 - accuracy: 0.8769\n",
            "Epoch 49/100\n",
            "455/455 [==============================] - 0s 94us/sample - loss: 0.2704 - accuracy: 0.9011\n",
            "Epoch 50/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.2661 - accuracy: 0.8989\n",
            "Epoch 51/100\n",
            "455/455 [==============================] - 0s 83us/sample - loss: 0.2391 - accuracy: 0.9011\n",
            "Epoch 52/100\n",
            "455/455 [==============================] - 0s 85us/sample - loss: 0.2595 - accuracy: 0.9187\n",
            "Epoch 53/100\n",
            "455/455 [==============================] - 0s 86us/sample - loss: 0.2608 - accuracy: 0.9077\n",
            "Epoch 54/100\n",
            "455/455 [==============================] - 0s 83us/sample - loss: 0.2135 - accuracy: 0.9341\n",
            "Epoch 55/100\n",
            "455/455 [==============================] - 0s 88us/sample - loss: 0.2588 - accuracy: 0.9121\n",
            "Epoch 56/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.2354 - accuracy: 0.9165\n",
            "Epoch 57/100\n",
            "455/455 [==============================] - 0s 88us/sample - loss: 0.2367 - accuracy: 0.9099\n",
            "Epoch 58/100\n",
            "455/455 [==============================] - 0s 92us/sample - loss: 0.2295 - accuracy: 0.9121\n",
            "Epoch 59/100\n",
            "455/455 [==============================] - 0s 91us/sample - loss: 0.2226 - accuracy: 0.9297\n",
            "Epoch 60/100\n",
            "455/455 [==============================] - 0s 87us/sample - loss: 0.2357 - accuracy: 0.9143\n",
            "Epoch 61/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1913 - accuracy: 0.9319\n",
            "Epoch 62/100\n",
            "455/455 [==============================] - 0s 86us/sample - loss: 0.2273 - accuracy: 0.9121\n",
            "Epoch 63/100\n",
            "455/455 [==============================] - 0s 90us/sample - loss: 0.2178 - accuracy: 0.9165\n",
            "Epoch 64/100\n",
            "455/455 [==============================] - 0s 79us/sample - loss: 0.2013 - accuracy: 0.9275\n",
            "Epoch 65/100\n",
            "455/455 [==============================] - 0s 87us/sample - loss: 0.2192 - accuracy: 0.9055\n",
            "Epoch 66/100\n",
            "455/455 [==============================] - 0s 87us/sample - loss: 0.1701 - accuracy: 0.9538\n",
            "Epoch 67/100\n",
            "455/455 [==============================] - 0s 93us/sample - loss: 0.1650 - accuracy: 0.9495\n",
            "Epoch 68/100\n",
            "455/455 [==============================] - 0s 82us/sample - loss: 0.1956 - accuracy: 0.9297\n",
            "Epoch 69/100\n",
            "455/455 [==============================] - 0s 77us/sample - loss: 0.1679 - accuracy: 0.9538\n",
            "Epoch 70/100\n",
            "455/455 [==============================] - 0s 90us/sample - loss: 0.1966 - accuracy: 0.9297\n",
            "Epoch 71/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1443 - accuracy: 0.9516\n",
            "Epoch 72/100\n",
            "455/455 [==============================] - 0s 79us/sample - loss: 0.1962 - accuracy: 0.9495\n",
            "Epoch 73/100\n",
            "455/455 [==============================] - 0s 86us/sample - loss: 0.1890 - accuracy: 0.9341\n",
            "Epoch 74/100\n",
            "455/455 [==============================] - 0s 82us/sample - loss: 0.1379 - accuracy: 0.9582\n",
            "Epoch 75/100\n",
            "455/455 [==============================] - 0s 87us/sample - loss: 0.1496 - accuracy: 0.9582\n",
            "Epoch 76/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.1576 - accuracy: 0.9560\n",
            "Epoch 77/100\n",
            "455/455 [==============================] - 0s 95us/sample - loss: 0.1679 - accuracy: 0.9626\n",
            "Epoch 78/100\n",
            "455/455 [==============================] - 0s 89us/sample - loss: 0.1400 - accuracy: 0.9582\n",
            "Epoch 79/100\n",
            "455/455 [==============================] - 0s 86us/sample - loss: 0.1552 - accuracy: 0.9495\n",
            "Epoch 80/100\n",
            "455/455 [==============================] - 0s 86us/sample - loss: 0.1622 - accuracy: 0.9582\n",
            "Epoch 81/100\n",
            "455/455 [==============================] - 0s 91us/sample - loss: 0.1443 - accuracy: 0.9473\n",
            "Epoch 82/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1691 - accuracy: 0.9429\n",
            "Epoch 83/100\n",
            "455/455 [==============================] - 0s 85us/sample - loss: 0.1431 - accuracy: 0.9516\n",
            "Epoch 84/100\n",
            "455/455 [==============================] - 0s 87us/sample - loss: 0.1352 - accuracy: 0.9626\n",
            "Epoch 85/100\n",
            "455/455 [==============================] - 0s 92us/sample - loss: 0.1661 - accuracy: 0.9560\n",
            "Epoch 86/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1753 - accuracy: 0.9451\n",
            "Epoch 87/100\n",
            "455/455 [==============================] - 0s 85us/sample - loss: 0.1425 - accuracy: 0.9582\n",
            "Epoch 88/100\n",
            "455/455 [==============================] - 0s 82us/sample - loss: 0.1389 - accuracy: 0.9516\n",
            "Epoch 89/100\n",
            "455/455 [==============================] - 0s 80us/sample - loss: 0.1539 - accuracy: 0.9538\n",
            "Epoch 90/100\n",
            "455/455 [==============================] - 0s 85us/sample - loss: 0.1106 - accuracy: 0.9692\n",
            "Epoch 91/100\n",
            "455/455 [==============================] - 0s 94us/sample - loss: 0.1447 - accuracy: 0.9670\n",
            "Epoch 92/100\n",
            "455/455 [==============================] - 0s 94us/sample - loss: 0.1104 - accuracy: 0.9648\n",
            "Epoch 93/100\n",
            "455/455 [==============================] - 0s 92us/sample - loss: 0.1279 - accuracy: 0.9626\n",
            "Epoch 94/100\n",
            "455/455 [==============================] - 0s 90us/sample - loss: 0.1689 - accuracy: 0.9385\n",
            "Epoch 95/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1367 - accuracy: 0.9670\n",
            "Epoch 96/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1437 - accuracy: 0.9516\n",
            "Epoch 97/100\n",
            "455/455 [==============================] - 0s 82us/sample - loss: 0.1921 - accuracy: 0.9297\n",
            "Epoch 98/100\n",
            "455/455 [==============================] - 0s 83us/sample - loss: 0.1353 - accuracy: 0.9516\n",
            "Epoch 99/100\n",
            "455/455 [==============================] - 0s 84us/sample - loss: 0.1139 - accuracy: 0.9626\n",
            "Epoch 100/100\n",
            "455/455 [==============================] - 0s 82us/sample - loss: 0.1180 - accuracy: 0.9692\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hE99ICP6iTbM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Plotting Accuracy with No. of Epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aisZOUudibld",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "ef1477ea-7d75-4d61-f270-447a32dfc665"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Accuracy')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v5YNdR3liZTL7jh4/5b47M0sQgUlRIYgIN58di8PAhKEhJAzR5wCUchdNBOpb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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_qxtQtPin2B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Plotting Loss with No. of Epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzcbYzQPirlf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "f9618855-d536-48f1-dcb4-0060747c90aa"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Loss')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kauq4elIqqbFhXHXmEAZFh7Ii79QL2hnjT6xpyPQrv7hyXKfnUqJDyUgIZ3V+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\nxhg/Z4nAGGP8nCUCY4zxc5YIjDHGz/1/z5nVw5SJPpYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMRr0Zhii4r6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict_classes(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zE7Kljw1i9Pz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "967f691f-a823-4b68-cded-2fc582a45568"
      },
      "source": [
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9385964912280702\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}