Breast_Cancer_Classification.ipynb

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  "metadata": {
    "colab": {
      "name": "Breast Cancer Classification.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kingketan9/Breast-Cancer-Classificaton/blob/main/Breast_Cancer_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BREAST CANCER CLASSIFICATION WITH NEURAL NETWORK"
      ],
      "metadata": {
        "id": "oyEZp8nBTpP1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the libraries**"
      ],
      "metadata": {
        "id": "gg5N0iOIT7pm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "78u-PGpGGOM_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading the dataset**"
      ],
      "metadata": {
        "id": "4fIP1d-DT_dw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_dataset = sklearn.datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "_DX9xrU9T69S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY_77_AcUM6x",
        "outputId": "3be82bbf-afb1-4e46-8234-640c25e1796d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
            "        1.189e-01],\n",
            "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
            "        8.902e-02],\n",
            "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
            "        8.758e-02],\n",
            "       ...,\n",
            "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
            "        7.820e-02],\n",
            "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
            "        1.240e-01],\n",
            "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
            "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
            "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
            "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
            "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
            "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
            "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast 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        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 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\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd 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.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
            "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
            "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
            "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
            "       'smoothness error', 'compactness error', 'concavity error',\n",
            "       'concave points error', 'symmetry error',\n",
            "       'fractal dimension error', 'worst radius', 'worst texture',\n",
            "       'worst perimeter', 'worst area', 'worst smoothness',\n",
            "       'worst compactness', 'worst concavity', 'worst concave points',\n",
            "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Collection & PreProcessing**"
      ],
      "metadata": {
        "id": "H4_dBFjHUpjz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#loading the data inside a dataframe\n",
        "data_frame = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)"
      ],
      "metadata": {
        "id": "3Xe78069URkT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "bSB1T71xVsc7",
        "outputId": "8fa6a6f3-0f56-48b8-85a2-3328f0260cd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
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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",
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              "      <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",
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              "      </button>\n",
              "      \n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-6a9bef26-a18e-4137-90e2-d4b2489c32fa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#adding the 'target' column to the dataframe\n",
        "data_frame['label'] = breast_cancer_dataset.target"
      ],
      "metadata": {
        "id": "p1vzXx5dVz_a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "gYErylD8WHg5",
        "outputId": "5ced3ad6-afda-42f0-b660-95975a848bc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  ...  worst fractal dimension  label\n",
              "564        21.56         22.39  ...                  0.07115      0\n",
              "565        20.13         28.25  ...                  0.06637      0\n",
              "566        16.60         28.08  ...                  0.07820      0\n",
              "567        20.60         29.33  ...                  0.12400      0\n",
              "568         7.76         24.54  ...                  0.07039      1\n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <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",
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              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
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              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
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              "      <th>565</th>\n",
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              "      <td>131.20</td>\n",
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              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.463</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
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              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
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              "      <th>566</th>\n",
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              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
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              "      <td>0.05648</td>\n",
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              "      <td>1.075</td>\n",
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              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
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              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.595</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
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              "      <td>0.02324</td>\n",
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              "      <td>0.86810</td>\n",
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              "      <td>0.4087</td>\n",
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              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.428</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0066042b-fb91-41a1-9026-1b041165cb50')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-0066042b-fb91-41a1-9026-1b041165cb50 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0066042b-fb91-41a1-9026-1b041165cb50');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iH6fLGxxWLUv",
        "outputId": "aa687491-0891-40cd-f23d-c638a1010f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6p7grkJWRKB",
        "outputId": "44d100c0-3373-4510-8563-a93caa7d48fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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  label                    569 non-null    int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 137.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking for the null values**"
      ],
      "metadata": {
        "id": "5pTBrbesWWrA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFdo-jLLWUsB",
        "outputId": "9c5df303-08a5-406b-a5fc-e5122dc74a1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean radius                0\n",
              "mean texture               0\n",
              "mean perimeter             0\n",
              "mean area                  0\n",
              "mean smoothness            0\n",
              "mean compactness           0\n",
              "mean concavity             0\n",
              "mean concave points        0\n",
              "mean symmetry              0\n",
              "mean fractal dimension     0\n",
              "radius error               0\n",
              "texture error              0\n",
              "perimeter error            0\n",
              "area error                 0\n",
              "smoothness error           0\n",
              "compactness error          0\n",
              "concavity error            0\n",
              "concave points error       0\n",
              "symmetry error             0\n",
              "fractal dimension error    0\n",
              "worst radius               0\n",
              "worst texture              0\n",
              "worst perimeter            0\n",
              "worst area                 0\n",
              "worst smoothness           0\n",
              "worst compactness          0\n",
              "worst concavity            0\n",
              "worst concave points       0\n",
              "worst symmetry             0\n",
              "worst fractal dimension    0\n",
              "label                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "mQgoz0W6WdfW",
        "outputId": "5c94561c-98c6-4c66-e1df-aab7f131691e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean radius  mean texture  ...  worst fractal dimension       label\n",
              "count   569.000000    569.000000  ...               569.000000  569.000000\n",
              "mean     14.127292     19.289649  ...                 0.083946    0.627417\n",
              "std       3.524049      4.301036  ...                 0.018061    0.483918\n",
              "min       6.981000      9.710000  ...                 0.055040    0.000000\n",
              "25%      11.700000     16.170000  ...                 0.071460    0.000000\n",
              "50%      13.370000     18.840000  ...                 0.080040    1.000000\n",
              "75%      15.780000     21.800000  ...                 0.092080    1.000000\n",
              "max      28.110000     39.280000  ...                 0.207500    1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "    <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>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>0.627417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>0.483918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>1.000000</td>\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0cccf4a4-43ff-44c5-bcd9-c9e15c3be553 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0cccf4a4-43ff-44c5-bcd9-c9e15c3be553');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the distribution of the target variable\n",
        "data_frame['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHajRqI4WhLY",
        "outputId": "44de75ad-c713-4306-930c-40d1ff77bd32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    357\n",
              "0    212\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1 --> Benign**\n",
        "\n",
        "**0 --> Malignant**"
      ],
      "metadata": {
        "id": "575tqfspWuen"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.groupby('label').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "ssHEJfXkWs1S",
        "outputId": "e4dee872-0b5c-4952-f761-44f71f81098a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "label                             ...                                         \n",
              "0        17.462830     21.604906  ...        0.323468                 0.091530\n",
              "1        12.146524     17.914762  ...        0.270246                 0.079442\n",
              "\n",
              "[2 rows x 30 columns]"
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              "      <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",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
              "      <td>0.609083</td>\n",
              "      <td>1.210915</td>\n",
              "      <td>4.323929</td>\n",
              "      <td>72.672406</td>\n",
              "      <td>0.006780</td>\n",
              "      <td>0.032281</td>\n",
              "      <td>0.041824</td>\n",
              "      <td>0.015060</td>\n",
              "      <td>0.020472</td>\n",
              "      <td>0.004062</td>\n",
              "      <td>21.134811</td>\n",
              "      <td>29.318208</td>\n",
              "      <td>141.370330</td>\n",
              "      <td>1422.286321</td>\n",
              "      <td>0.144845</td>\n",
              "      <td>0.374824</td>\n",
              "      <td>0.450606</td>\n",
              "      <td>0.182237</td>\n",
              "      <td>0.323468</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.146524</td>\n",
              "      <td>17.914762</td>\n",
              "      <td>78.075406</td>\n",
              "      <td>462.790196</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.080085</td>\n",
              "      <td>0.046058</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.174186</td>\n",
              "      <td>0.062867</td>\n",
              "      <td>0.284082</td>\n",
              "      <td>1.220380</td>\n",
              "      <td>2.000321</td>\n",
              "      <td>21.135148</td>\n",
              "      <td>0.007196</td>\n",
              "      <td>0.021438</td>\n",
              "      <td>0.025997</td>\n",
              "      <td>0.009858</td>\n",
              "      <td>0.020584</td>\n",
              "      <td>0.003636</td>\n",
              "      <td>13.379801</td>\n",
              "      <td>23.515070</td>\n",
              "      <td>87.005938</td>\n",
              "      <td>558.899440</td>\n",
              "      <td>0.124959</td>\n",
              "      <td>0.182673</td>\n",
              "      <td>0.166238</td>\n",
              "      <td>0.074444</td>\n",
              "      <td>0.270246</td>\n",
              "      <td>0.079442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6cf282a-3079-44b8-85a7-e4cce912a4e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e6cf282a-3079-44b8-85a7-e4cce912a4e9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e6cf282a-3079-44b8-85a7-e4cce912a4e9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Separating the features and Target**"
      ],
      "metadata": {
        "id": "XZsgD4eCXLOz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_frame.drop(columns = 'label', axis = 1)\n",
        "Y = data_frame['label']"
      ],
      "metadata": {
        "id": "kvSfi6DRXJlA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6xthDAbXbOO",
        "outputId": "59532f11-72b6-48e5-9d6a-5b90ee9ae973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     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",
            "564        21.56         22.39  ...          0.2060                  0.07115\n",
            "565        20.13         28.25  ...          0.2572                  0.06637\n",
            "566        16.60         28.08  ...          0.2218                  0.07820\n",
            "567        20.60         29.33  ...          0.4087                  0.12400\n",
            "568         7.76         24.54  ...          0.2871                  0.07039\n",
            "\n",
            "[569 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTkBvzuIXfxW",
        "outputId": "8312b8bd-83dc-41ee-a472-482151956eaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      0\n",
            "1      0\n",
            "2      0\n",
            "3      0\n",
            "4      0\n",
            "      ..\n",
            "564    0\n",
            "565    0\n",
            "566    0\n",
            "567    0\n",
            "568    1\n",
            "Name: label, Length: 569, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the data into Training & Testing data**"
      ],
      "metadata": {
        "id": "TP4DzOIwXlq5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
      ],
      "metadata": {
        "id": "uaGgUKGMXhrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6D9rSbtX3uN",
        "outputId": "42dd87ed-5a57-4539-901b-f9824014d74a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (455, 30) (114, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Standardise the data**"
      ],
      "metadata": {
        "id": "DMPCDQ7yYECb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "FBA7GIiFYBz0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scalar = StandardScaler()\n",
        "\n",
        "X_train_std = scalar.fit_transform(X_train)\n",
        "\n",
        "X_test_std = scalar.transform(X_test)"
      ],
      "metadata": {
        "id": "iJ3V-rKtYPYO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building a Neural Network "
      ],
      "metadata": {
        "id": "F5ZMXY_KYqCA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "7k84XgjyYuqT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Tensorflow and Keras libraries**"
      ],
      "metadata": {
        "id": "av_GZcpbO-Bn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf.random.set_seed(3)\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "c0od-bIPY5_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Setting up the layers of Neural Networks**"
      ],
      "metadata": {
        "id": "tQozg-0GPa_s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    #down below is the input layer\n",
        "    keras.layers.Flatten(input_shape = (30,)), # 30 --> number of columns in the data ie number of features\n",
        "    # down below is the hidden layer\n",
        "    keras.layers.Dense(20, activation = 'relu'), # 20 -->  number of neurons\n",
        "    #down below is the output layer\n",
        "    keras.layers.Dense(2, activation = 'sigmoid') # 2 --> number of classes(here the number should be specific to the number of classes you have in the model)\n",
        "    # here the number is 2 as we have 2 classes in our model (beign and malignant)\n",
        "])"
      ],
      "metadata": {
        "id": "cuUH8nJgPZAl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Compiling the neural network**"
      ],
      "metadata": {
        "id": "PONYoR60Rl3S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "shT0i80WRkj7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training the Neural Network**"
      ],
      "metadata": {
        "id": "RDy-FJZQSmQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_std, Y_train, validation_split = 0.1, epochs = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s07ZYYPhSV-X",
        "outputId": "a3c86a9e-6124-4bd4-aeac-ca917f20f081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.5798 - accuracy: 0.7237 - val_loss: 0.3884 - val_accuracy: 0.9130\n",
            "Epoch 2/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.4069 - accuracy: 0.8631 - val_loss: 0.2845 - val_accuracy: 0.9783\n",
            "Epoch 3/10\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.3059 - accuracy: 0.8924 - val_loss: 0.2272 - val_accuracy: 0.9783\n",
            "Epoch 4/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.2491 - accuracy: 0.9095 - val_loss: 0.1939 - val_accuracy: 0.9783\n",
            "Epoch 5/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.2119 - accuracy: 0.9218 - val_loss: 0.1719 - val_accuracy: 0.9783\n",
            "Epoch 6/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1870 - accuracy: 0.9364 - val_loss: 0.1557 - val_accuracy: 0.9783\n",
            "Epoch 7/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1682 - accuracy: 0.9389 - val_loss: 0.1433 - val_accuracy: 0.9783\n",
            "Epoch 8/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1537 - accuracy: 0.9487 - val_loss: 0.1328 - val_accuracy: 0.9783\n",
            "Epoch 9/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1419 - accuracy: 0.9535 - val_loss: 0.1236 - val_accuracy: 0.9783\n",
            "Epoch 10/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1314 - accuracy: 0.9633 - val_loss: 0.1169 - val_accuracy: 0.9783\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizing the Accuracy and Loss**"
      ],
      "metadata": {
        "id": "B8ABgOPLTepU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "\n",
        "plt.title('model_accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['training data', 'validation data'], loc = 'lower right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "FrLJMk02TjRa",
        "outputId": "c3b37b17-345c-478e-fd0f-6793398a120e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe148ddefd0>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['training data', 'validation data'], loc = 'upper right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "OeMyUXJ3UufZ",
        "outputId": "c40e7d87-5171-4fd0-9bf3-77ed1f728d1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe148d53e90>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accuracy of the model on test data**"
      ],
      "metadata": {
        "id": "d_4CmRZnU7f3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test_std, Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCm51rEFU7NO",
        "outputId": "f4a19249-3bb7-49db-f60e-9052609330da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1273 - accuracy: 0.9737\n",
            "0.9736841917037964\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std.shape)\n",
        "print(X_test_std[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwh62UblVU_I",
        "outputId": "1321174a-8489-4fc2-9b66-01ca90fab57d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(114, 30)\n",
            "[-0.04462793 -1.41612656 -0.05903514 -0.16234067  2.0202457  -0.11323672\n",
            "  0.18500609  0.47102419  0.63336386  0.26335737  0.53209124  2.62763999\n",
            "  0.62351167  0.11405261  1.01246781  0.41126289  0.63848593  2.88971815\n",
            " -0.41675911  0.74270853 -0.32983699 -1.67435595 -0.36854552 -0.38767294\n",
            "  0.32655007 -0.74858917 -0.54689089 -0.18278004 -1.23064515 -0.6268286 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test_std)"
      ],
      "metadata": {
        "id": "oxkJFAaBVU5X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred.shape)\n",
        "print(Y_pred[0])\n",
        "#when you use predict function, it will give us two values ie first one what is the probability of number having 0(class - malignant)\n",
        "# and the other what is the probability of number having 1(class - benign)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hm_9-H-uVjCe",
        "outputId": "7cb4502b-88da-4393-d629-10417457a155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(114, 2)\n",
            "[0.35253245 0.5096884 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prBM91x9VtwE",
        "outputId": "e9c2181a-f302-446e-a714-4a357be9bbe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.04462793 -1.41612656 -0.05903514 ... -0.18278004 -1.23064515\n",
            "  -0.6268286 ]\n",
            " [ 0.24583601 -0.06219797  0.21802678 ...  0.54129749  0.11047691\n",
            "   0.0483572 ]\n",
            " [-1.26115925 -0.29051645 -1.26499659 ... -1.35138617  0.269338\n",
            "  -0.28231213]\n",
            " ...\n",
            " [ 0.72709489  0.45836817  0.75277276 ...  1.46701686  1.19909344\n",
            "   0.65319961]\n",
            " [ 0.25437907  1.33054477  0.15659489 ... -1.29043534 -2.22561725\n",
            "  -1.59557344]\n",
            " [ 0.84100232 -0.06676434  0.8929529  ...  2.15137705  0.35629355\n",
            "   0.37459546]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "model.predict() --> gives the prediction of each class for the data point"
      ],
      "metadata": {
        "id": "YISCLPWcXeuA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgneGANGV7Qy",
        "outputId": "2f44da5d-6c04-497a-e814-8d36b0e1ad54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3.52532446e-01 5.09688377e-01]\n",
            " [4.48819518e-01 6.39671981e-01]\n",
            " [7.77235925e-02 9.54797506e-01]\n",
            " [9.87053990e-01 6.04152679e-04]\n",
            " [3.99637222e-01 3.86358976e-01]\n",
            " [8.81420374e-01 2.54445374e-02]\n",
            " [2.31196851e-01 7.56302118e-01]\n",
            " [7.98084438e-02 9.39291596e-01]\n",
            " [1.82437211e-01 9.08723235e-01]\n",
            " [2.17808127e-01 8.38581443e-01]\n",
            " [5.17215490e-01 5.76673090e-01]\n",
            " [2.69769698e-01 8.00358713e-01]\n",
            " [2.57872075e-01 8.07288885e-01]\n",
            " [3.74741703e-01 7.93492496e-01]\n",
            " [1.10800087e-01 9.33896542e-01]\n",
            " [7.91043758e-01 2.42272735e-01]\n",
            " [1.21738434e-01 9.04611230e-01]\n",
            " [1.19124174e-01 8.48879695e-01]\n",
            " [1.99983060e-01 8.58543038e-01]\n",
            " [7.99560428e-01 2.34141350e-02]\n",
            " [2.80697346e-02 3.97514343e-01]\n",
            " [5.96571267e-02 8.42884183e-01]\n",
            " [2.31051236e-01 9.11577702e-01]\n",
            " [6.37716651e-02 9.06915426e-01]\n",
            " [1.26598924e-01 6.47980928e-01]\n",
            " [7.87011266e-01 6.83519840e-02]\n",
            " [2.18806237e-01 7.18833804e-01]\n",
            " [2.77573079e-01 3.83661151e-01]\n",
            " [8.56613696e-01 1.27740741e-01]\n",
            " [8.38154256e-01 1.21800542e-01]\n",
            " [3.02804530e-01 6.56933904e-01]\n",
            " [2.01736271e-01 9.23902273e-01]\n",
            " [7.41788149e-02 7.73730218e-01]\n",
            " [9.45674539e-01 1.98404491e-02]\n",
            " [7.98260808e-01 3.62116098e-02]\n",
            " [1.80579156e-01 6.20296717e-01]\n",
            " [1.37931734e-01 9.64400291e-01]\n",
            " [3.03043097e-01 8.73271823e-01]\n",
            " [5.79419136e-02 9.34768021e-01]\n",
            " [1.85705751e-01 9.22273993e-01]\n",
            " [9.49324846e-01 1.64973736e-03]\n",
            " [6.56987488e-01 3.12119305e-01]\n",
            " [3.20477843e-01 9.80843425e-01]\n",
            " [4.38446403e-02 7.81281471e-01]\n",
            " [4.58011776e-01 8.14981759e-02]\n",
            " [9.80421901e-02 9.09805417e-01]\n",
            " [1.89368129e-02 8.99510205e-01]\n",
            " [1.50504440e-01 9.83127117e-01]\n",
            " [9.55030501e-01 4.59823906e-02]\n",
            " [7.50581264e-01 1.15957439e-01]\n",
            " [1.89819396e-01 8.51266861e-01]\n",
            " [7.00565934e-01 2.26073384e-01]\n",
            " [5.12941599e-01 6.07427776e-01]\n",
            " [1.04046434e-01 8.92997444e-01]\n",
            " [5.16062081e-02 9.43519115e-01]\n",
            " [5.66863894e-01 3.76570165e-01]\n",
            " [1.59559369e-01 7.33134985e-01]\n",
            " [7.21621215e-02 9.53346848e-01]\n",
            " [6.81772232e-01 6.86144829e-03]\n",
            " [9.31151211e-02 7.84259319e-01]\n",
            " [2.75227845e-01 6.74018204e-01]\n",
            " [7.04416752e-01 6.79756701e-02]\n",
            " [8.03013444e-02 9.22440231e-01]\n",
            " [7.66858876e-01 6.02774918e-02]\n",
            " [8.61950994e-01 2.08498418e-01]\n",
            " [2.38620877e-01 2.45253950e-01]\n",
            " [8.91853452e-01 4.85506356e-02]\n",
            " [8.07439566e-01 1.01397663e-01]\n",
            " [4.20483321e-01 4.84316409e-01]\n",
            " [6.48163557e-01 4.10355747e-01]\n",
            " [7.56402135e-01 1.97518408e-01]\n",
            " [9.12356734e-01 2.57394612e-02]\n",
            " [1.89111233e-01 9.30199742e-01]\n",
            " [6.29601955e-01 2.51604021e-01]\n",
            " [3.14373374e-02 9.52884614e-01]\n",
            " [5.68179607e-01 1.92618251e-01]\n",
            " [1.33410573e-01 9.42616582e-01]\n",
            " [5.02045453e-02 8.85035038e-01]\n",
            " [4.29343522e-01 6.59919977e-01]\n",
            " [3.51253778e-01 2.50329137e-01]\n",
            " [8.57866168e-01 7.45833516e-02]\n",
            " [6.15897477e-01 3.24038506e-01]\n",
            " [7.80464649e-01 3.28262448e-02]\n",
            " [2.97820091e-01 8.25631797e-01]\n",
            " [3.22329700e-01 9.15029883e-01]\n",
            " [5.12665212e-01 5.33478677e-01]\n",
            " [3.99112701e-02 7.48052359e-01]\n",
            " [6.61574304e-02 8.69753480e-01]\n",
            " [1.14416152e-01 6.16219997e-01]\n",
            " [8.98313165e-01 1.69061720e-02]\n",
            " [6.47959709e-02 8.64912689e-01]\n",
            " [3.39672744e-01 8.14571321e-01]\n",
            " [5.38001657e-02 7.56993353e-01]\n",
            " [8.31287622e-01 1.75130725e-01]\n",
            " [5.90439916e-01 3.28285158e-01]\n",
            " [1.26201808e-01 7.99193263e-01]\n",
            " [8.27804506e-01 5.27611971e-02]\n",
            " [8.52854133e-01 4.24982011e-02]\n",
            " [4.14670855e-01 8.74423802e-01]\n",
            " [6.40920103e-02 9.40127969e-01]\n",
            " [3.13404500e-02 9.51594710e-01]\n",
            " [4.71778274e-01 1.79165572e-01]\n",
            " [9.73144174e-01 1.58096850e-02]\n",
            " [9.36961472e-01 4.25356627e-03]\n",
            " [2.55101025e-01 8.89829278e-01]\n",
            " [5.93144298e-02 9.21470463e-01]\n",
            " [2.54574716e-02 7.91538715e-01]\n",
            " [2.15622216e-01 9.43121135e-01]\n",
            " [5.05408943e-02 9.95068669e-01]\n",
            " [4.32130247e-01 8.08572292e-01]\n",
            " [8.50104094e-01 7.60610104e-02]\n",
            " [8.94604981e-01 2.48076022e-02]\n",
            " [4.58159268e-01 3.21265638e-01]\n",
            " [7.20080793e-01 5.82030416e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting the prediction probability to class labels**"
      ],
      "metadata": {
        "id": "RtC4y0jLX6vm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_labels = [np.argmax(i) for i in Y_pred]\n",
        "print(Y_pred_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iJekkfcXs0P",
        "outputId": "93558c11-a685-4768-c7f4-306ced3c1dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building the predictive system"
      ],
      "metadata": {
        "id": "0Fa58OQkYQHV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (11.76,21.6,74.72,427.9,0.08637,0.04966,0.01657,0.01115,0.1495,0.05888,0.4062,1.21,2.635,28.47,0.005857,0.009758,0.01168,0.007445,0.02406,0.001769,12.98,25.72,82.98,516.5,0.1085,0.08615,0.05523,0.03715,0.2433,0.06563)\n",
        "\n",
        "#changing the input_data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "#reshaping the numpy array as we are predicting for one data point\n",
        "input_data_reshape = input_data_as_numpy_array.reshape(1, -1)\n",
        "\n",
        "#standardise the input data\n",
        "input_data_std = scalar.transform(input_data_reshape)\n",
        "\n",
        "prediction = model.predict(input_data_std)\n",
        "print(prediction)\n",
        "\n",
        "prediction_label = [np.argmax(prediction)]\n",
        "print(prediction_label)\n",
        "\n",
        "if(prediction_label[0] == 0):\n",
        "  print('The tumor is Malignant')\n",
        "else:\n",
        "  print('The tumor is Benign')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uc84ra6tYUV2",
        "outputId": "2e2cea30-3fa0-4fda-e59f-63e88e283e26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.10582107 0.8940957 ]]\n",
            "[1]\n",
            "The tumor is Benign\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    }
  ]
}