1_Breast_cancer_prediction.ipynb

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    "colab": {
      "name": "1-Breast_cancer_prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNR5I0sfvVbR8KZ8mBjdOys",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/novoforce/Exploring-Tensorflow/blob/main/1_Breast_cancer_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4NjCYrFalpi",
        "outputId": "79477570-7780-44ec-85b5-aff0a84a34ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten,Dense,Dropout,BatchNormalization,Conv1D,MaxPool1D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "print('The version of the tensorflow is: ',tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The version of the tensorflow is:  2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q75H8C-DbnLw"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpA-UKqad_e1"
      },
      "source": [
        "from sklearn import datasets,metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSubanDfeNQu"
      },
      "source": [
        "cancer= datasets.load_breast_cancer() #loading the cancer dataset from the sklearn library"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njGOZAN5ea4U",
        "outputId": "f15a898a-5003-4242-971c-e308a0c2db06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(cancer.DESCR)"
      ],
      "execution_count": 5,
      "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": "cjYenK_refRe",
        "outputId": "0dd58756-0a33-4d24-a65e-9081bd3569b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "x= pd.DataFrame(data=cancer.data,columns=cancer.feature_names)\n",
        "x.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
              "    </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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5A3oUSpHfnGm",
        "outputId": "11ece6e6-b5ee-47d0-83b6-cbc43578ebab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "y= cancer.target\n",
        "print(\"The length of y is %d and the y is: \\n %s\"%(len(y),y))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The length of y is 569 and the y is: \n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
            " 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
            " 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
            " 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
            " 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
            " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
            " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
            " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SntMlGSNf11U",
        "outputId": "9ed12a66-94eb-4d90-b309-5180645b5729",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"the target names are: \",cancer.target_names)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the target names are:  ['malignant' 'benign']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WD76vONXg2iU",
        "outputId": "7f920dde-ac5e-468d-83c2-c83b45ddae50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_Gua9zqwb2Q"
      },
      "source": [
        "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2,random_state=0,stratify=y)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwvMXIQJw4_8",
        "outputId": "9c960ff8-8654-4d33-c842-8be677390e25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.shape , y_train.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((455, 30), (455,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Gd40IRFw9eI",
        "outputId": "4ff6b62b-244d-4ce7-f67f-6e2179f6b317",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_test.shape,y_test.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((114, 30), (114,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XrWgeEgyxfY"
      },
      "source": [
        "scaler= StandardScaler()\n",
        "x_train= scaler.fit_transform(x_train)\n",
        "x_test= scaler.transform(x_test)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJMZOBVoxFQ0"
      },
      "source": [
        "#since our data is in 2d format but the neural network accepts 3d data so we will reshape the data for inputting to the neural network\n",
        "x_train= x_train.reshape(x_train.shape[0],x_train.shape[1],1)\n",
        "x_test= x_test.reshape(x_test.shape[0],x_test.shape[1],1)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDu0x84rzeFN"
      },
      "source": [
        "model= Sequential()\n",
        "model.add(Conv1D(filters=32,kernel_size=2,activation='relu',input_shape=(x_train.shape[1],1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(filters=64,kernel_size=2,activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HR1yA0rn1IoK",
        "outputId": "1b5c1dee-313e-47d6-affb-cc5e4ae9cfb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_1 (Conv1D)            (None, 29, 32)            96        \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 29, 32)            128       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 29, 32)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 28, 64)            4160      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 28, 64)            256       \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 28, 64)            0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1792)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                114752    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 119,457\n",
            "Trainable params: 119,265\n",
            "Non-trainable params: 192\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fTamWy11jTT"
      },
      "source": [
        "model.compile(optimizer=Adam(lr=0.00005),loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVGTvYis14h5",
        "outputId": "773314ee-58e2-4cb0-9fe1-438fff75b8c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs=50\n",
        "history= model.fit(x_train,y_train,epochs=epochs,validation_data=(x_test,y_test),verbose=1)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 1.0489 - accuracy: 0.5714 - val_loss: 0.6728 - val_accuracy: 0.7018\n",
            "Epoch 2/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.7774 - accuracy: 0.6527 - val_loss: 0.6326 - val_accuracy: 0.7368\n",
            "Epoch 3/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.5817 - accuracy: 0.7253 - val_loss: 0.5949 - val_accuracy: 0.8070\n",
            "Epoch 4/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.4376 - accuracy: 0.8022 - val_loss: 0.5604 - val_accuracy: 0.8158\n",
            "Epoch 5/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.3777 - accuracy: 0.8264 - val_loss: 0.5305 - val_accuracy: 0.8421\n",
            "Epoch 6/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.3853 - accuracy: 0.8330 - val_loss: 0.5020 - val_accuracy: 0.8509\n",
            "Epoch 7/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.3194 - accuracy: 0.8703 - val_loss: 0.4739 - val_accuracy: 0.8596\n",
            "Epoch 8/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.3244 - accuracy: 0.8703 - val_loss: 0.4473 - val_accuracy: 0.8684\n",
            "Epoch 9/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.2426 - accuracy: 0.9077 - val_loss: 0.4222 - val_accuracy: 0.8684\n",
            "Epoch 10/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.2841 - accuracy: 0.8703 - val_loss: 0.3971 - val_accuracy: 0.8684\n",
            "Epoch 11/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.2576 - accuracy: 0.8967 - val_loss: 0.3752 - val_accuracy: 0.8684\n",
            "Epoch 12/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.2323 - accuracy: 0.9077 - val_loss: 0.3532 - val_accuracy: 0.8684\n",
            "Epoch 13/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.2386 - accuracy: 0.9055 - val_loss: 0.3311 - val_accuracy: 0.8860\n",
            "Epoch 14/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.2157 - accuracy: 0.9165 - val_loss: 0.3103 - val_accuracy: 0.8947\n",
            "Epoch 15/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.2219 - accuracy: 0.9341 - val_loss: 0.2919 - val_accuracy: 0.8947\n",
            "Epoch 16/50\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.2214 - accuracy: 0.9121 - val_loss: 0.2711 - val_accuracy: 0.9123\n",
            "Epoch 17/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1796 - accuracy: 0.9319 - val_loss: 0.2536 - val_accuracy: 0.9211\n",
            "Epoch 18/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1780 - accuracy: 0.9341 - val_loss: 0.2383 - val_accuracy: 0.9386\n",
            "Epoch 19/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1665 - accuracy: 0.9165 - val_loss: 0.2216 - val_accuracy: 0.9474\n",
            "Epoch 20/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.2271 - accuracy: 0.9121 - val_loss: 0.2097 - val_accuracy: 0.9474\n",
            "Epoch 21/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1797 - accuracy: 0.9253 - val_loss: 0.1983 - val_accuracy: 0.9474\n",
            "Epoch 22/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1654 - accuracy: 0.9297 - val_loss: 0.1850 - val_accuracy: 0.9474\n",
            "Epoch 23/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1691 - accuracy: 0.9297 - val_loss: 0.1752 - val_accuracy: 0.9561\n",
            "Epoch 24/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1679 - accuracy: 0.9319 - val_loss: 0.1667 - val_accuracy: 0.9561\n",
            "Epoch 25/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1736 - accuracy: 0.9319 - val_loss: 0.1578 - val_accuracy: 0.9561\n",
            "Epoch 26/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1771 - accuracy: 0.9363 - val_loss: 0.1489 - val_accuracy: 0.9561\n",
            "Epoch 27/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1416 - accuracy: 0.9429 - val_loss: 0.1427 - val_accuracy: 0.9561\n",
            "Epoch 28/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1137 - accuracy: 0.9560 - val_loss: 0.1380 - val_accuracy: 0.9561\n",
            "Epoch 29/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1223 - accuracy: 0.9429 - val_loss: 0.1330 - val_accuracy: 0.9561\n",
            "Epoch 30/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1805 - accuracy: 0.9275 - val_loss: 0.1277 - val_accuracy: 0.9561\n",
            "Epoch 31/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1378 - accuracy: 0.9516 - val_loss: 0.1229 - val_accuracy: 0.9649\n",
            "Epoch 32/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1465 - accuracy: 0.9626 - val_loss: 0.1168 - val_accuracy: 0.9649\n",
            "Epoch 33/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1570 - accuracy: 0.9319 - val_loss: 0.1125 - val_accuracy: 0.9737\n",
            "Epoch 34/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1639 - accuracy: 0.9385 - val_loss: 0.1074 - val_accuracy: 0.9737\n",
            "Epoch 35/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1300 - accuracy: 0.9473 - val_loss: 0.1045 - val_accuracy: 0.9737\n",
            "Epoch 36/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1360 - accuracy: 0.9495 - val_loss: 0.1026 - val_accuracy: 0.9737\n",
            "Epoch 37/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1112 - accuracy: 0.9582 - val_loss: 0.1011 - val_accuracy: 0.9737\n",
            "Epoch 38/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1135 - accuracy: 0.9538 - val_loss: 0.0998 - val_accuracy: 0.9737\n",
            "Epoch 39/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1407 - accuracy: 0.9473 - val_loss: 0.0984 - val_accuracy: 0.9737\n",
            "Epoch 40/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1167 - accuracy: 0.9516 - val_loss: 0.0971 - val_accuracy: 0.9737\n",
            "Epoch 41/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1166 - accuracy: 0.9604 - val_loss: 0.0958 - val_accuracy: 0.9737\n",
            "Epoch 42/50\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1194 - accuracy: 0.9516 - val_loss: 0.0953 - val_accuracy: 0.9737\n",
            "Epoch 43/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.0992 - accuracy: 0.9692 - val_loss: 0.0950 - val_accuracy: 0.9737\n",
            "Epoch 44/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1301 - accuracy: 0.9429 - val_loss: 0.0936 - val_accuracy: 0.9649\n",
            "Epoch 45/50\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1294 - accuracy: 0.9429 - val_loss: 0.0924 - val_accuracy: 0.9737\n",
            "Epoch 46/50\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1216 - accuracy: 0.9451 - val_loss: 0.0917 - val_accuracy: 0.9649\n",
            "Epoch 47/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1098 - accuracy: 0.9736 - val_loss: 0.0907 - val_accuracy: 0.9649\n",
            "Epoch 48/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.0722 - accuracy: 0.9736 - val_loss: 0.0907 - val_accuracy: 0.9649\n",
            "Epoch 49/50\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1037 - accuracy: 0.9538 - val_loss: 0.0905 - val_accuracy: 0.9649\n",
            "Epoch 50/50\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.1578 - accuracy: 0.9385 - val_loss: 0.0902 - val_accuracy: 0.9649\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNI8B5H82Nie"
      },
      "source": [
        "def plots(history,epochs):\n",
        "  epoch_range= range(1,epochs+1)\n",
        "  plt.plot(epoch_range,history.history['accuracy'])\n",
        "  plt.plot(epoch_range,history.history['val_accuracy'])\n",
        "  plt.title('Model accuracy')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.legend(['Train','Val'],loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  epoch_range= range(1,epochs+1)\n",
        "  plt.plot(epoch_range,history.history['loss'])\n",
        "  plt.plot(epoch_range,history.history['val_loss'])\n",
        "  plt.title('Model loss')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.legend(['Train','Val'],loc='upper left')\n",
        "  plt.show()\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAxP7l_k3WIk",
        "outputId": "adadda83-cd51-4156-965d-0190ab0fec0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "plots(history,epochs)"
      ],
      "execution_count": 36,
      "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"
          }
        },
        {
          "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": "O72ET50b3ud-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}