Breast_Cancer_Classification_with_NN.ipynb

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    "colab": {
      "name": " Breast Cancer Classification with NN.ipynb",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reenarao00/Data-Science./blob/main/Breast_Cancer_Classification_with_NN.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 a simple Neural Network (NN)**"
      ],
      "metadata": {
        "id": "RNC9RB0Non0k"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3wT8l6lfj--"
      },
      "source": [
        "**Importing the Dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqsQmOXGXXTe"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwJ9zLukg3Q_"
      },
      "source": [
        "Data Collection & Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6bMZMKUgz7L"
      },
      "source": [
        "# loading the data from sklearn\n",
        "breast_cancer_dataset = sklearn.datasets.load_breast_cancer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdY6i73KgkDG",
        "outputId": "d9768866-9f0f-4db9-83ca-38161a5012ef"
      },
      "source": [
        "print(breast_cancer_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
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            "       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": "code",
      "metadata": {
        "id": "yFamhVTThMdK"
      },
      "source": [
        "# loading the data to a data frame\n",
        "data_frame = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "xLiv1maYiGVH",
        "outputId": "64978de8-5903-4c69-c5e2-134ebeff769c"
      },
      "source": [
        "# print the first 5 rows of the dataframe\n",
        "data_frame.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>...</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",
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              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
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              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
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              "      <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",
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              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
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              "      <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>...</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",
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              "    <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",
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              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</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",
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              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\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-829a734d-c4bf-42c5-a800-19716b35aeff 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-829a734d-c4bf-42c5-a800-19716b35aeff');\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",
              "  "
            ],
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33           184.60   \n",
              "1                 0.05667  ...         24.99          23.41           158.80   \n",
              "2                 0.05999  ...         23.57          25.53           152.50   \n",
              "3                 0.09744  ...         14.91          26.50            98.87   \n",
              "4                 0.05883  ...         22.54          16.67           152.20   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEvD_aTDiNLF"
      },
      "source": [
        "# adding the 'target' column to the data frame\n",
        "data_frame['label'] = breast_cancer_dataset.target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "f_kmjEA5io2v",
        "outputId": "97057e6b-3e65-483c-83da-014259da2c5d"
      },
      "source": [
        "# print last 5 rows of the dataframe\n",
        "data_frame.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8dd16833-3fe1-4833-acc9-620b259b293c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</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",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dd16833-3fe1-4833-acc9-620b259b293c')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\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-8dd16833-3fe1-4833-acc9-620b259b293c 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-8dd16833-3fe1-4833-acc9-620b259b293c');\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",
              "  "
            ],
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  label  \n",
              "564                0.2216          0.2060                  0.07115      0  \n",
              "565                0.1628          0.2572                  0.06637      0  \n",
              "566                0.1418          0.2218                  0.07820      0  \n",
              "567                0.2650          0.4087                  0.12400      0  \n",
              "568                0.0000          0.2871                  0.07039      1  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sw3wjdK6iwK4",
        "outputId": "7958f26e-d774-4afd-ecb0-786bd4fa35d0"
      },
      "source": [
        "# number of rows and columns in the dataset\n",
        "data_frame.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWOjMuyBi77M",
        "outputId": "331edb2d-1dd9-40bd-87d2-1adace00be06"
      },
      "source": [
        "# getting some information about the data\n",
        "data_frame.info()"
      ],
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RuoVIUTYjLpk",
        "outputId": "af6c56a0-c899-4fb3-e974-f8eff23ad720"
      },
      "source": [
        "# checking for missing values\n",
        "data_frame.isnull().sum()"
      ],
      "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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "oLMuXI33jlkq",
        "outputId": "7f7030d3-7869-40dc-e56d-e7e9fb568971"
      },
      "source": [
        "# statistical measures about the data\n",
        "data_frame.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-53eaa542-1184-4254-8b6a-62337d6dc094\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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>...</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>...</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>...</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>...</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>...</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>...</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>...</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>...</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53eaa542-1184-4254-8b6a-62337d6dc094')\"\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",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\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-53eaa542-1184-4254-8b6a-62337d6dc094 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-53eaa542-1184-4254-8b6a-62337d6dc094');\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",
              "  "
            ],
            "text/plain": [
              "       mean radius  mean texture  mean perimeter    mean area  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
              "count     569.000000              569.000000  ...     569.000000   \n",
              "mean        0.181162                0.062798  ...      25.677223   \n",
              "std         0.027414                0.007060  ...       6.146258   \n",
              "min         0.106000                0.049960  ...      12.020000   \n",
              "25%         0.161900                0.057700  ...      21.080000   \n",
              "50%         0.179200                0.061540  ...      25.410000   \n",
              "75%         0.195700                0.066120  ...      29.720000   \n",
              "max         0.304000                0.097440  ...      49.540000   \n",
              "\n",
              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
              "count       569.000000   569.000000        569.000000         569.000000   \n",
              "mean        107.261213   880.583128          0.132369           0.254265   \n",
              "std          33.602542   569.356993          0.022832           0.157336   \n",
              "min          50.410000   185.200000          0.071170           0.027290   \n",
              "25%          84.110000   515.300000          0.116600           0.147200   \n",
              "50%          97.660000   686.500000          0.131300           0.211900   \n",
              "75%         125.400000  1084.000000          0.146000           0.339100   \n",
              "max         251.200000  4254.000000          0.222600           1.058000   \n",
              "\n",
              "       worst concavity  worst concave points  worst symmetry  \\\n",
              "count       569.000000            569.000000      569.000000   \n",
              "mean          0.272188              0.114606        0.290076   \n",
              "std           0.208624              0.065732        0.061867   \n",
              "min           0.000000              0.000000        0.156500   \n",
              "25%           0.114500              0.064930        0.250400   \n",
              "50%           0.226700              0.099930        0.282200   \n",
              "75%           0.382900              0.161400        0.317900   \n",
              "max           1.252000              0.291000        0.663800   \n",
              "\n",
              "       worst fractal dimension       label  \n",
              "count               569.000000  569.000000  \n",
              "mean                  0.083946    0.627417  \n",
              "std                   0.018061    0.483918  \n",
              "min                   0.055040    0.000000  \n",
              "25%                   0.071460    0.000000  \n",
              "50%                   0.080040    1.000000  \n",
              "75%                   0.092080    1.000000  \n",
              "max                   0.207500    1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tC8Yii4Yjzer",
        "outputId": "0d5589af-9af8-44ed-9d07-38e7ff360e21"
      },
      "source": [
        "# checking the distribution of Target Varibale\n",
        "data_frame['label'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    357\n",
              "0    212\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbbkzWeFkjqc"
      },
      "source": [
        "1 --> Benign\n",
        "\n",
        "0 --> Malignant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "YGWHjrVSkN5c",
        "outputId": "e8c49a34-7c21-489b-94d1-ff2ce146fca7"
      },
      "source": [
        "data_frame.groupby('label').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
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              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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",
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              "      <th>label</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "    <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>...</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>...</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",
              "<p>2 rows × 30 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-61269f98-004c-4e99-b1ef-99841fb68d3f');\n",
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            ],
            "text/plain": [
              "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
              "label                                                                           \n",
              "0        17.462830     21.604906      115.365377  978.376415         0.102898   \n",
              "1        12.146524     17.914762       78.075406  462.790196         0.092478   \n",
              "\n",
              "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "label                                                                         \n",
              "0              0.145188        0.160775             0.087990       0.192909   \n",
              "1              0.080085        0.046058             0.025717       0.174186   \n",
              "\n",
              "       mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "label                          ...                                \n",
              "0                    0.062680  ...     21.134811      29.318208   \n",
              "1                    0.062867  ...     13.379801      23.515070   \n",
              "\n",
              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
              "label                                                                      \n",
              "0           141.370330  1422.286321          0.144845           0.374824   \n",
              "1            87.005938   558.899440          0.124959           0.182673   \n",
              "\n",
              "       worst concavity  worst concave points  worst symmetry  \\\n",
              "label                                                          \n",
              "0             0.450606              0.182237        0.323468   \n",
              "1             0.166238              0.074444        0.270246   \n",
              "\n",
              "       worst fractal dimension  \n",
              "label                           \n",
              "0                     0.091530  \n",
              "1                     0.079442  \n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUPYps4DlVFR"
      },
      "source": [
        "Separating the features and target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5pD8rP5kzKD"
      },
      "source": [
        "X = data_frame.drop(columns='label', axis=1)\n",
        "Y = data_frame['label']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-GWJHpAlpWJ",
        "outputId": "79222686-492d-4d6d-dd39-b69285477b81"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
            "0          17.99         10.38          122.80     1001.0          0.11840   \n",
            "1          20.57         17.77          132.90     1326.0          0.08474   \n",
            "2          19.69         21.25          130.00     1203.0          0.10960   \n",
            "3          11.42         20.38           77.58      386.1          0.14250   \n",
            "4          20.29         14.34          135.10     1297.0          0.10030   \n",
            "..           ...           ...             ...        ...              ...   \n",
            "564        21.56         22.39          142.00     1479.0          0.11100   \n",
            "565        20.13         28.25          131.20     1261.0          0.09780   \n",
            "566        16.60         28.08          108.30      858.1          0.08455   \n",
            "567        20.60         29.33          140.10     1265.0          0.11780   \n",
            "568         7.76         24.54           47.92      181.0          0.05263   \n",
            "\n",
            "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
            "0             0.27760         0.30010              0.14710         0.2419   \n",
            "1             0.07864         0.08690              0.07017         0.1812   \n",
            "2             0.15990         0.19740              0.12790         0.2069   \n",
            "3             0.28390         0.24140              0.10520         0.2597   \n",
            "4             0.13280         0.19800              0.10430         0.1809   \n",
            "..                ...             ...                  ...            ...   \n",
            "564           0.11590         0.24390              0.13890         0.1726   \n",
            "565           0.10340         0.14400              0.09791         0.1752   \n",
            "566           0.10230         0.09251              0.05302         0.1590   \n",
            "567           0.27700         0.35140              0.15200         0.2397   \n",
            "568           0.04362         0.00000              0.00000         0.1587   \n",
            "\n",
            "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
            "0                   0.07871  ...        25.380          17.33   \n",
            "1                   0.05667  ...        24.990          23.41   \n",
            "2                   0.05999  ...        23.570          25.53   \n",
            "3                   0.09744  ...        14.910          26.50   \n",
            "4                   0.05883  ...        22.540          16.67   \n",
            "..                      ...  ...           ...            ...   \n",
            "564                 0.05623  ...        25.450          26.40   \n",
            "565                 0.05533  ...        23.690          38.25   \n",
            "566                 0.05648  ...        18.980          34.12   \n",
            "567                 0.07016  ...        25.740          39.42   \n",
            "568                 0.05884  ...         9.456          30.37   \n",
            "\n",
            "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
            "0             184.60      2019.0           0.16220            0.66560   \n",
            "1             158.80      1956.0           0.12380            0.18660   \n",
            "2             152.50      1709.0           0.14440            0.42450   \n",
            "3              98.87       567.7           0.20980            0.86630   \n",
            "4             152.20      1575.0           0.13740            0.20500   \n",
            "..               ...         ...               ...                ...   \n",
            "564           166.10      2027.0           0.14100            0.21130   \n",
            "565           155.00      1731.0           0.11660            0.19220   \n",
            "566           126.70      1124.0           0.11390            0.30940   \n",
            "567           184.60      1821.0           0.16500            0.86810   \n",
            "568            59.16       268.6           0.08996            0.06444   \n",
            "\n",
            "     worst concavity  worst concave points  worst symmetry  \\\n",
            "0             0.7119                0.2654          0.4601   \n",
            "1             0.2416                0.1860          0.2750   \n",
            "2             0.4504                0.2430          0.3613   \n",
            "3             0.6869                0.2575          0.6638   \n",
            "4             0.4000                0.1625          0.2364   \n",
            "..               ...                   ...             ...   \n",
            "564           0.4107                0.2216          0.2060   \n",
            "565           0.3215                0.1628          0.2572   \n",
            "566           0.3403                0.1418          0.2218   \n",
            "567           0.9387                0.2650          0.4087   \n",
            "568           0.0000                0.0000          0.2871   \n",
            "\n",
            "     worst fractal dimension  \n",
            "0                    0.11890  \n",
            "1                    0.08902  \n",
            "2                    0.08758  \n",
            "3                    0.17300  \n",
            "4                    0.07678  \n",
            "..                       ...  \n",
            "564                  0.07115  \n",
            "565                  0.06637  \n",
            "566                  0.07820  \n",
            "567                  0.12400  \n",
            "568                  0.07039  \n",
            "\n",
            "[569 rows x 30 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5rs8pColqsn",
        "outputId": "ac5f9e59-3aa1-43b8-91f9-e97e761a117f"
      },
      "source": [
        "print(Y)"
      ],
      "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",
      "metadata": {
        "id": "B5yk9d-Nl4VV"
      },
      "source": [
        "Splitting the data into training data & Testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZtU30bPluG_"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWaeuX3amqYH",
        "outputId": "5f56841f-272a-4494-f6b0-f269f2d35250"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (455, 30) (114, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardize the data"
      ],
      "metadata": {
        "id": "0g1NEndK7UB6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "C5EP9f_B7UXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_std = scaler.fit_transform(X_train)\n",
        "\n",
        "X_test_std = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "sCGC8dku7cge"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Building the Neural Network**"
      ],
      "metadata": {
        "id": "VxyxgeK8qkDj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "sC4KsFDyqyvN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing tensorflow and Keras\n",
        "import tensorflow as tf \n",
        "tf.random.set_seed(3)\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "1WX_ChHfqrV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# setting up the layers of Neural Network\n",
        "\n",
        "model = keras.Sequential([\n",
        "                          keras.layers.Flatten(input_shape=(30,)),\n",
        "                          keras.layers.Dense(20, activation='relu'),\n",
        "                          keras.layers.Dense(2, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "rL0jvBLY3Lkq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compiling the Neural Network\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "DrqX3ZGv48Ef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the Meural Network\n",
        "\n",
        "history = model.fit(X_train_std, Y_train, validation_split=0.1, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4aullV3v584B",
        "outputId": "23f9f3a9-006a-439c-a8e8-cd301f5d4301"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "13/13 [==============================] - 1s 14ms/step - loss: 0.7669 - accuracy: 0.4474 - val_loss: 0.5547 - val_accuracy: 0.7174\n",
            "Epoch 2/10\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.5273 - accuracy: 0.7824 - val_loss: 0.3855 - val_accuracy: 0.9348\n",
            "Epoch 3/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.3908 - accuracy: 0.8949 - val_loss: 0.2902 - val_accuracy: 0.9565\n",
            "Epoch 4/10\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.3127 - accuracy: 0.9169 - val_loss: 0.2331 - val_accuracy: 0.9565\n",
            "Epoch 5/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.2630 - accuracy: 0.9340 - val_loss: 0.1975 - val_accuracy: 0.9565\n",
            "Epoch 6/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.2302 - accuracy: 0.9364 - val_loss: 0.1726 - val_accuracy: 0.9783\n",
            "Epoch 7/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.2047 - accuracy: 0.9413 - val_loss: 0.1544 - val_accuracy: 0.9783\n",
            "Epoch 8/10\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.1853 - accuracy: 0.9413 - val_loss: 0.1409 - val_accuracy: 0.9565\n",
            "Epoch 9/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1697 - accuracy: 0.9462 - val_loss: 0.1298 - val_accuracy: 0.9565\n",
            "Epoch 10/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1556 - accuracy: 0.9584 - val_loss: 0.1221 - val_accuracy: 0.9565\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing accuracy and loss"
      ],
      "metadata": {
        "id": "QZCz8JJ98guH"
      }
    },
    {
      "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": "pNism-Z55zmw",
        "outputId": "4bd981d6-e52b-42cc-f40d-078df36ca4f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fcf3ff52450>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3xmeWCLyW/yw0NcCZ3/Q7Es/UNzbx6e6KloP+yu2lHHLb9oem9uHcU/oz1T3wZ2cm2Th7Y0KMp4lARGYDj+I8X+cpVX2ozfyRwNNAf6AUuElVC72MqUc11kP+M84dxBmj/I6m29TUN7JmV3nLgT9/RxmH3SJlJ/VP4rLxg5manc6ZWekMS4uMqyBjwplniUBEooHHgYuBQmCliLypqhsDFnsYeE5VnxWRC4BfADd7FVOP++x/4dBeuOy//Y7khFTWNpC/o6ylfX/trgrqGpsQgTGDUphz5vCWA7+N4jEm/Hh5RTAV2KyqWwFE5CXgSiAwEYwD/s19vxh4w8N4et7Kp6DfCBj9Jb8j6ZSyqjpWbnfb97eXsn53BU3q3J17xtB+3Dozi6nZ6eSMTKdfoj3M3Zhw52UiGArsCvhcCExrs8xa4Gqc5qOvAMkikqGqJYELicg8YB7AiBEjPAu4Wx3YBNtz4aIHICq072jdf7CmVcfu5/sPAU5N90nDU7nj/JOZmp3BpBGpJMVbt5IxvY3ff9X/AfyPiMwFPgJ2A0c9EUNVnwSeBMjJyemmGpEeW/kURMfDpFv8jqRdy7aU8PrqQlZsK2V7STXg1KSfkpXOFROHMDU7nfHD+lmFS2MigJeJYDcwPODzMHdaC1Xdg3NFgIj0Bb6qquUextQzag7C2pfg9KshKcPvaI6Sv6OUW55eTmJcDFOz07lp+kimZqczbnCKjd83JgJ5mQhWAqNFJBsnAVwP3Bi4gIhkAqWq2gT8CGcEUfhb9zLUVYZkXaF9FTXc9vwqhqb24a+3n21t/MYYPDv9U9UG4A7gHWATsFBVN4jIgyJyhbvYecDnIvIFMBD4uVfx9BhVWLEAhkyCYVP8jqaVmvpGvvN8PtW1DTx5S44lAWMM4HEfgaq+DbzdZtp9Ae8XAYu8jKHHbc+F4s/hyt/5HUkrqsp/vbGetbvKeeLmKZwyMNnvkIwxIcIahLvbigXQJ83pHwghf1q6nUX5hdx14Wj+z2mD/A7HGBNCLBF0p4rdzk1kk26G2D5+R9Ni6ZZifva/m7h43EDuunC03+EYY0KMJYLulP8n0CbI+YbfkbTYVVrN7S+sIjsziUeum2AF3owxR7FE0F0a6pxEMPpiSM/2OxoAqusamPfnfBqblAW35JCcYJ3Dxpij+X1DWe+x6U2oOhAyQ0ZVlXsWrePzfQd5eu6ZZGcm+R2SMSZE2RVBd1n5FKRlwckX+R0JAH/4cCt/W7eXe2aP4bxTB/gdjjEmhFki6A771sPOZZDzTYjy/ydd/PkBfvXOZ1w+YQjfOeckv8MxxoQ4/49avcHKpyAmASbd5HckbC2q5M4XVzN2UAq/+up4ewiMMea4LBGcqJoKWLcQTr8GEtN9DeVQTT3z/pxPbHQUT94yhT5xVjDOGHN81ll8ota8CPVVMPVbvobR1KR8/+W1bCuu4vlvTrMngxljgmZXBCdC1WkWGprj1Bby0W/+WcD7m/bzk0vHMmNU6FU8NcaELksEJ2LrB1BSAFP9HTL6j/V7eeyfBVw7ZRhfPyvL11iMMeHHEsGJWPkUJGbAuKt8C+HzfYf4t4VrmTA8lf971enWOWyM6TRLBF1Vvgs+fxsm3wKxCf6EUF3HvD/nkRQfw5M3TyEh1jqHjTGdZ4mgq/Kfcf7rU12hhsYmvvfiavaW1/CHm6YwMMWfZGSMCX82aqgrGmoh/1k4ZTakjvAlhF+/8zm5BcU8dPUZTBmZ5ksMxpjewa4IumLjX6G6GM70Z8joX9fs5omPtnLz9JFcP9WfRGSM6T0sEXTFigWQPgpOOr/Hd71+dwX3LFrH1Ox07rt8XI/v3xjT+1gi6Ky9a6FwhXM10MN1hYora5n3XB4ZSXH87muTiY22fz5jzImzPoLOWrEAYhNh4o09utv6xia++8IqSqrqWHTbWWT2je/R/Rtjei87peyMw2Xw6SI441rok9qju/6/f9vIim2l/Oqa8ZwxrF+P7tsY07tZIuiM1S9Aw+Ee7yR+eeVOnlu2g3nnnMSVE4f26L6NMb2fJYJgNTVB3h9h+DQYPL7Hdpu/o4yfvLGBWaMzuef/nNpj+zXGRA5LBMHa+i8o3dqjj6Lcf7CG+c/nM6hfAr+9YRIx1jlsjPGAHVmCteIpSOoP467okd3V1DfynT/nU1nbwIJbckhNjOuR/RpjIo8lgmCU7YAv/gGTvw4x3o/WUVV+8sZ61uwq55HrJnDqoGTP92mMiVyWCIKR9zSIQM6tPbK755bt4JX8Qu684GRmnz64R/ZpjIlclgiOp74GVj0Hp34Z+g3zfHfLtpTw4N82ctHYgdx90Sme788YYzxNBCIyW0Q+F5HNInJvO/NHiMhiEVktIutE5MtextMlG16Hw6U98vCZwrJqbv/LKrIyEvnvOROIirJnCxhjvOdZIhCRaOBx4BJgHHCDiLQtjvNfwEJVnQRcD/zOq3i6bOUCyDwFss/1dDeH6xqZ91w+9Y1NLLglh+SEWE/3Z4wxzby8IpgKbFbVrapaB7wEXNlmGQVS3Pf9gD0extN5u1fB7nznBjIPn/ylqtzz6jo27TvIY9dP4qT+fT3blzHGtOVlraGhwK6Az4XAtDbLPAC8KyLfA5KAizyMp/NWPgWxSTDhek9388RHW3lr7R7umX0q548Z4Om+jDGmLb87i28A/qSqw4AvA38WkaNiEpF5IpInInlFRUU9E1l1Kax/FSbMgQTvavt88PkBfvmPz7h0/GDmnzvKs/0YY0xHvEwEu4HhAZ+HudMCfRNYCKCqy4AEILPthlT1SVXNUdWc/v37exRuG6v/DA01nt5JvK24ijtfXM2YQSn8+prx9uB5Y4wvvEwEK4HRIpItInE4ncFvtllmJ3AhgIiMxUkEPXTKfwxNjbDyjzByJgz05uEvlbUNzHsuj+go4cmbp5AYZxXBjTH+8CwRqGoDcAfwDrAJZ3TQBhF5UESa6zT8O/BtEVkLvAjMVVX1KqagbX4fynd4VmW0qUn5/str2FpcxeM3TmZ4eqIn+zHGmGB4ehqqqm8Db7eZdl/A+43ATC9j6JIVC6DvIBh7uSebf/SfBby3cT/3XTaOs04+qiXMGGN6lN+dxaGndKtzRTBlLkR3/1j+9zfu59F/FvDVycO4dWZWt2/fGGM6yxJBWyv/CFHRTiLwwJMfbeWkzCR+/pXTrXPYGBMSLBEEqj8Mq5+HMZdBSvcXe6usbWDVzjK+dNogEmKju337xhjTFZYIAq1/FWrKPeskXr61hIYm5ZzR1i9gjAkdlgiaqTqdxP3HQtbZnuwit6CYhNgopmSlebJ9Y4zpCksEzXbnw941cOY3PasrlFtQxLTsDOJjrFnIGBM6LBE0W7EA4pI9qyu0p/wwW4qqmGXNQsaYEBNUIhCR10Tk0vbqAPUKVcWw4TUnCcR781jIJQXFAJxticAYE2KCPbD/DrgRKBCRh0TkVA9j6nmrnoPGOs86iQFyNxfTPzmeUwfa84eNMaElqESgqu+r6teAycB24H0RWSoit4pIeD9BpakR8p6BrFkwYIw3u2hSPt5czKyTM+3eAWNMyAm6qUdEMoC5wLeA1cCjOInhPU8i6ylfvAMVOz19FOXGvQcprapj1inWLGSMCT1B1RoSkdeBU4E/A5er6l531ssikudVcD1i5QJIHgKnXurZLj4qcAqqzrS6QsaYEBRs0bnHVHVxezNUNacb4+lZxZthy7/g/P+EaO/q7y0pKGbMoGQGJCd4tg9jjOmqYJuGxolIavMHEUkTke96FFPPyfsjRMXC5K97tovDdY3kbS+zYaPGmJAVbCL4tqqWN39Q1TLAu0b1nlBXBatfgHFXQPJAz3azfFsJdY1NnD26h56sZowxnRRsIoiWgOEuIhINxHkTUg/59BWorfD0UZTgNAvFxUQxNSvd0/0YY0xXBdsw/g+cjuEn3M/fcaeFJ1VY8RQMPB1GTPd0V7kFxZyZlUafOCsrYYwJTcFeEfwQWAzMd1//BO7xKijP7VoO+z91biDzcFz/gYM1fL7/ELOsWcgYE8KCuiJQ1Sbg9+4r/K18CuL7wfjrPN3Nks1uWQkbNmqMCWHB3kcwGvgFMA5oGQOpqid5FJd3Kg/Ahjecq4G4JE93lVtQTEZSHOMGp3i6H2OMORHBNg09g3M10ACcDzwHPO9VUJ5a9Sw01Tvlpj2kquQWFDPz5EyioqyshDEmdAWbCPqo6j8BUdUdqvoA4N2tuF5pbHDqCp10HmSO9nRXn+07RHFlrVUbNcaEvGBHDdW6JagLROQOYDfQ17uwPPLF3+HgbrjkV57vqrnstN1IZowJdcFeEdwFJAJ3AlOAmwDvbsf1yooFkDIMTpnt+a5yNxdz8oC+DO7Xx/N9GWPMiThuInBvHpujqpWqWqiqt6rqV1X1kx6Ir/sUfQHbPoScWz2tKwRQU9/I8q0lNlrIGBMWjpsIVLUR8OZp7j3p01cgOs7TukLN8neUUdvQxDlWdtoYEwaCPTVeLSJvAq8AVc0TVfU1T6Lywnk/cuoK9fX+5q6PCoqIjRamZWd4vi9jjDlRwSaCBKAEuCBgmgLhkwiiomDQGT2yqyUFxUwekUZSvLdNUMYY0x2CvbP4Vq8D6S1KKmvZsOcg//GlU/wOxRhjghLsncXP4FwBtKKq3zjOerNxHmkZDTylqg+1mf/fODeogTMqaYCqphLGWspKWH0hY0yYCLbt4m8B7xOArwB7jrWCO9roceBioBBYKSJvqurG5mVU9fsBy38PmBRkPCFrSUEx/frEcsbQfn6HYowxQQm2aejVwM8i8iKw5DirTQU2q+pWd52XgCuBjR0sfwNwfzDxhKojZSUyiLayEsaYMBHsDWVtjQYGHGeZocCugM+F7rSjiMhIIBv4Vwfz54lInojkFRUVdSHcnrGlqJJ9B2us7LQxJqwE20dwiNZ9BPtwnlHQXa4HFrn3LBxFVZ8EngTIyck5qq8iVOQWWNlpY0z4CbZpKLkL294NDA/4PMyd1p7rgdu7sI+QkltQTFZGIsPTE/0OxRhjghZU05CIfEVE+gV8ThWRq46z2kpgtIhki0gczsH+zXa2PQZIA5YFH3boqWto4pOtJdYsZIwJO8H2EdyvqhXNH1S1nON07KpqA3AH8A6wCVioqhtE5EERuSJg0euBl1Q1ZJt8grFqZxnVdY1WdtoYE3aCHT7aXsI47rqq+jbwdptp97X5/ECQMYS0JQXFREcJM0ZZWQljTHgJ9oogT0QeEZFR7usRIN/LwMJN7uZiJg5PJSUh1u9QjDGmU4JNBN8D6oCXgZeAGnpB5253Ka+uY11huY0WMsaEpWBHDVUB93ocS9hauqUEVazstDEmLAU7aug9EUkN+JwmIu94F1Z4yS0oIjk+hgnDwrpMkjEmQgXbNJTpjhQCQFXLOP6dxRGhuazEjFEZxER39UZtY4zxT7BHriYRGdH8QUSyaKcaaSTaUVJNYdlhe0i9MSZsBTt89D+BJSLyISDALGCeZ1GFkdwCp/aR3UhmjAlXwXYW/0NEcnAO/quBN4DDXgYWLnILihmW1oeRGVZWwhgTnoItOvct4C6cekFrgOk4JSEuONZ6vV1DYxPLtpRw2YTBiFjZaWNMeAq2j+Au4Exgh6qej/MAmfJjr9L7rS0s51BtgzULGWPCWrCJoEZVawBEJF5VPwNO9S6s8JBbUIwInGVlJYwxYSzYzuJC9z6CN4D3RKQM2OFdWOEht6CY8cNSSU2M8zsUY4zpsmA7i7/ivn1ARBYD/YB/eBZVGDhYU8+aXeXMP3eU36EYY8wJCfaKoIWqfuhFIOFm2ZYSGpvUyk4bY8Ke3QrbRUsKikmMi2byiDS/QzHGmBNiiaCLlmwuZvpJGcTF2E9ojAlvdhTrgl2l1WwrrrKyEsaYXsESQRcs2VwMYInAGNMrWCLogtyCIgalJDCqf1+/QzHGmBNmiaCTGpuUjzeXMGt0ppWVMMb0CpYIOmn97goqDtfbsFFjTK9hiaCTmstO2/OJjTG9hSWCTsotKOa0ISlk9I33OxRjjOkWlgg6oaq2gVU7y6xZyBjTq1gi6ITl20qob1TOsbLTxphexBJBJ+QWFBMfE8WUkVZWwhjTe1gi6ITcgoSmBYQAABLVSURBVGKmnZRBQmy036EYY0y3sUQQpL0Vh9l8oJJZNlrIGNPLeJoIRGS2iHwuIptF5N4OlrlORDaKyAYR+YuX8ZyI3AKnrIR1FBtjeptOP48gWCISDTwOXAwUAitF5E1V3RiwzGjgR8BMVS0TkQFexXOilhQUk9k3njGDkv0OxRhjupWXVwRTgc2qulVV64CXgCvbLPNt4HFVLQNQ1QMextNlTU3Kx5uLrayEMaZX8jIRDAV2BXwudKcFOgU4RUQ+FpFPRGR2exsSkXkikicieUVFRR6F27GNew9SUlVn1UaNMb2S353FMcBo4DzgBmCBiKS2XUhVn1TVHFXN6d+/58fwN5edtrISxpjeyMtEsBsYHvB5mDstUCHwpqrWq+o24AucxBBScguKGDMomQEpCX6HYowx3c7LRLASGC0i2SISB1wPvNlmmTdwrgYQkUycpqKtHsbUaTX1jazcXmZXA8aYXsuzRKCqDcAdwDvAJmChqm4QkQdF5Ap3sXeAEhHZCCwGfqCqJV7F1BUrtpVS19Bkw0aNMb2WZ8NHAVT1beDtNtPuC3ivwL+5r5CUW1BEXHQU07Iz/A7FGGM84XdnccjLLSgmJyuNPnFWVsIY0ztZIjiGA4dq+GzfIWZZtVFjTC9mieAYPnaHjdr9A8aY3swSwTHkFhSTnhTHuMEpfodijDGesUTQAVVlSUExM0/OJCrKykoYY3ovSwQd+GJ/JQcO1VrZaWNMr2eJoAO5BU5NI7t/wBjT21ki6EBuQTGj+icxJLWP36EYY4ynLBG0o7ahkeXbSmzYqDEmIlgiaEf+9jJq6pts2KgxJiJYImhH7uZiYqKEaSdZWQljTO9niaAduQVFTB6ZRt94T0sxGWNMSLBE0EZpVR0b9hy0YaPGmIhhiaCNjzcXo2rDRo0xkcMSQRu5BUWkJMQwfthRT8w0xpheyRJBgMCyEtFWVsIYEyEsEQTYUlTFnooau3/AGBNRLBEEWOKWlbD7B4wxkcQSQYAlm4sZmZHI8PREv0MxxpgeY4nAVd/YxLItJXY1YIyJOJYIXKt3llNV18jZJ1v/gDEmslgicOUWFBEdJcwYZWUljDGRxRKBK7egmAnD+tGvT6zfoRhjTI+yYjpARXU96wrLueOC0X6HYkzIqK+vp7CwkJqaGr9DMZ2QkJDAsGHDiI0N/qTWEgGwdEsxTQrnWEexMS0KCwtJTk4mKysLEbvBMhyoKiUlJRQWFpKdnR30etY0hFN2um98DBOGW1kJY5rV1NSQkZFhSSCMiAgZGRmdvoqzRIDTUTxjVAax0fZzGBPIkkD46cq/WcQf+XaUVLGr9LDdP2CMiVieJgIRmS0in4vIZhG5t535c0WkSETWuK9veRlPe3ILigGsvpAxIaa8vJzf/e53XVr3y1/+MuXl5cdc5r777uP999/v0vaP5U9/+hN33HHHMZf54IMPWLp0abfvu6s8SwQiEg08DlwCjANuEJFx7Sz6sqpOdF9PeRVPR3ILihia2oesDCsrYUwoOVYiaGhoOOa6b7/9Nqmpx+7ze/DBB7nooou6HN+JCLVE4OWooanAZlXdCiAiLwFXAhs93GenNDQ2sXRLCZeeMdjaQo05hp++tYGNew526zbHDUnh/stP63D+vffey5YtW5g4cSIXX3wxl156KT/5yU9IS0vjs88+44svvuCqq65i165d1NTUcNdddzFv3jwAsrKyyMvLo7KykksuuYSzzz6bpUuXMnToUP7617/Sp08f5s6dy2WXXcY111xDVlYWX//613nrrbeor6/nlVdeYcyYMRQVFXHjjTeyZ88eZsyYwXvvvUd+fj6Zma2bkp955hl+8YtfkJqayoQJE4iPjwfgrbfe4mc/+xl1dXVkZGTwwgsvcPjwYf7whz8QHR3N888/z29/+1vKy8uPWm7gwIHd+nsfi5dNQ0OBXQGfC91pbX1VRNaJyCIRGd7ehkRknojkiUheUVFRtwW4trCCQzUN1ixkTAh66KGHGDVqFGvWrOHXv/41AKtWreLRRx/liy++AODpp58mPz+fvLw8HnvsMUpKSo7aTkFBAbfffjsbNmwgNTWVV199td39ZWZmsmrVKubPn8/DDz8MwE9/+lMuuOACNmzYwDXXXMPOnTuPWm/v3r3cf//9fPzxxyxZsoSNG4+c65599tl88sknrF69muuvv55f/epXZGVlcdttt/H973+fNWvWMGvWrHaX60l+30fwFvCiqtaKyHeAZ4EL2i6kqk8CTwLk5ORod+18SUExInCWlZUw5piOdebek6ZOndpqfPxjjz3G66+/DsCuXbsoKCggI6P133N2djYTJ04EYMqUKWzfvr3dbV999dUty7z22msALFmypGX7s2fPJi0t7aj1li9fznnnnUf//s4J5Zw5c1oSVWFhIXPmzGHv3r3U1dV1OLY/2OW84uUVwW4g8Ax/mDuthaqWqGqt+/EpYIqH8RxlyeYixg/tR1pSXE/u1hjTRUlJSS3vP/jgA95//32WLVvG2rVrmTRpUrvj55ubaQCio6M77F9oXu5Yy3TW9773Pe644w4+/fRTnnjiiQ7H9we7nFe8TAQrgdEiki0iccD1wJuBC4jI4ICPVwCbPIynlUM19azaWW4PqTcmRCUnJ3Po0KEO51dUVJCWlkZiYiKfffYZn3zySbfHMHPmTBYuXAjAu+++S1lZ2VHLTJs2jQ8//JCSkpKW/oXAGIcOdVrEn3322Zbpbb9bR8v1FM8Sgao2AHcA7+Ac4Beq6gYReVBErnAXu1NENojIWuBOYK5X8bT1ydZSGpvUyk4bE6IyMjKYOXMmp59+Oj/4wQ+Omj979mwaGhoYO3Ys9957L9OnT+/2GO6//37effddTj/9dF555RUGDRpEcnJyq2UGDx7MAw88wIwZM5g5cyZjx45tmffAAw9w7bXXMmXKlFYdzJdffjmvv/46EydOJDc3t8PleoqodluTe4/IycnRvLy8E97OfX9dz6L8QlbfdzHxMdHdEJkxvcumTZtaHdQiUW1tLdHR0cTExLBs2TLmz5/PmjVr/A7ruNr7txORfFXNaW95vzuLfbOkoJhp2emWBIwxHdq5cyfXXXcdTU1NxMXFsWDBAr9D8kREJoLCsmq2Fldx0/SRfodijAlho0ePZvXq1X6H4bmIrDW0pKWshHUUG2NMRCaC3M3FDEyJ5+QBff0OxRhjfBdxiaCxSfl4czGzRve3shLGGEMEJoINeyoor663ZiFjjHFFXCJoLjs982RLBMb0Nn37Os29e/bs4Zprrml3mfPOO4/jDUH/zW9+Q3V1dcvnYMpad0VzvB05kVLcnRGBiaCIcYNTyOwbf/yFjTFhaciQISxatKjL67dNBMGUtfZCTyWCiBo+Wl3XQP6OMr4xs2cLOhkT9v5+L+z7tHu3OegMuOShDmffe++9DB8+nNtvvx1w7tLt27cvt912G1deeSVlZWXU19fzs5/9jCuvvLLVutu3b+eyyy5j/fr1HD58mFtvvZW1a9cyZswYDh8+3LLc/PnzWblyJYcPH+aaa67hpz/9KY899hh79uzh/PPPJzMzk8WLF7eUtc7MzOSRRx7h6aefBuBb3/oWd999N9u3b++w3HWgbdu2ceONN1JZWdkq5ubPbb9T21Lc999//3G/e1dEVCJYvrWU+ka1stPGhIE5c+Zw9913tySChQsX8s4775CQkMDrr79OSkoKxcXFTJ8+nSuuuKLDwR+///3vSUxMZNOmTaxbt47Jkye3zPv5z39Oeno6jY2NXHjhhaxbt44777yTRx55hMWLFx9V7iE/P59nnnmG5cuXo6pMmzaNc889l7S0NAoKCnjxxRdZsGAB1113Ha+++io33XRTq/Xvuusu5s+fzy233MLjjz/eMr2j7/TQQw+xfv36lruZGxoaOvXdgxVRiSC3oJj4mChyso4uJWuMOYZjnLl7ZdKkSRw4cIA9e/ZQVFREWloaw4cPp76+nh//+Md89NFHREVFsXv3bvbv38+gQYPa3c5HH33EnXfeCcD48eMZP358y7yFCxfy5JNP0tDQwN69e9m4cWOr+W0tWbKEr3zlKy1VUK+++mpyc3O54oorgip3/fHHH7c8D+Hmm2/mhz/8IQCq2u53aquj5Tr67sGKqESwZHMRU7PTSYi1shLGhINrr72WRYsWsW/fPubMmQPACy+8QFFREfn5+cTGxpKVldWlss3btm3j4YcfZuXKlaSlpTF37twTKv/cttx1YBNUoPbO3oP9Tt313duKmM7ifRU1fLG/0oaNGhNG5syZw0svvcSiRYu49tprAadk84ABA4iNjWXx4sXs2LHjmNs455xz+Mtf/gLA+vXrWbduHQAHDx4kKSmJfv36sX//fv7+97+3rNNRCexZs2bxxhtvUF1dTVVVFa+//jqzZs0K+vvMnDmTl156CXAO6s06+k7tlavuzHcPVsRcESzZ7AwbtbLTxoSP0047jUOHDjF06FAGD3YeX/K1r32Nyy+/nDPOOIOcnBzGjBlzzG3Mnz+fW2+9lbFjxzJ27FimTHGefzVhwgQmTZrEmDFjGD58ODNnzmxZZ968ecyePZshQ4awePHilumTJ09m7ty5TJ06FXA6iydNmtThU8/aevTRR7nxxhv55S9/2aqTt6PvFFiK+5JLLuGHP/xhp757sCKmDPW7G/bxSn4hT9w0hagou6PYmOOxMtThy8pQd+BLpw3iS6edWIeKMcb0RhHTR2CMMaZ9lgiMMR0Kt6Zj07V/M0sExph2JSQkUFJSYskgjKgqJSUlJCQkdGq9iOkjMMZ0zrBhwygsLKSoqMjvUEwnJCQkMGzYsE6tY4nAGNOu2NhYsrOtLlcksKYhY4yJcJYIjDEmwlkiMMaYCBd2dxaLSBHQ1QIbmUBxN4YT7uz3aM1+jyPst2itN/weI1W13Ro7YZcIToSI5HV0i3Ukst+jNfs9jrDforXe/ntY05AxxkQ4SwTGGBPhIi0RPOl3ACHGfo/W7Pc4wn6L1nr17xFRfQTGGGOOFmlXBMYYY9qwRGCMMREuYhKBiMwWkc9FZLOI3Ot3PH4RkeEislhENorIBhG5y++YQoGIRIvIahH5m9+x+E1EUkVkkYh8JiKbRGSG3zH5RUS+7/6drBeRF0Wkc2U9w0REJAIRiQYeBy4BxgE3iMg4f6PyTQPw76o6DpgO3B7Bv0Wgu4BNfgcRIh4F/qGqY4AJROjvIiJDgTuBHFU9HYgGrvc3Km9ERCIApgKbVXWrqtYBLwFXHmedXklV96rqKvf9IZw/8qH+RuUvERkGXAo85XcsfhORfsA5wB8BVLVOVcv9jcpXMUAfEYkBEoE9PsfjiUhJBEOBXQGfC4nwgx+AiGQBk4Dl/kbiu98A9wBNfgcSArKBIuAZt6nsKRFJ8jsoP6jqbuBhYCewF6hQ1Xf9jcobkZIITBsi0hd4FbhbVQ/6HY9fROQy4ICq5vsdS4iIASYDv1fVSUAVEJF9aiKShtNykA0MAZJE5CZ/o/JGpCSC3cDwgM/D3GkRSURicZLAC6r6mt/x+GwmcIWIbMdpMrxARJ73NyRfFQKFqtp8lbgIJzFEoouAbapapKr1wGvAWT7H5IlISQQrgdEiki0icTgdPm/6HJMvRERw2n83qeojfsfjN1X9kaoOU9UsnP8v/qWqvfKsLxiqug/YJSKnupMuBDb6GJKfdgLTRSTR/bu5kF7acR4Rj6pU1QYRuQN4B6fn/2lV3eBzWH6ZCdwMfCoia9xpP1bVt32MyYSW7wEvuCdNW4FbfY7HF6q6XEQWAatwRtutppeWmrASE8YYE+EipWnIGGNMBywRGGNMhLNEYIwxEc4SgTHGRDhLBMYYE+EsERjTg0TkPKtwakKNJQJjjIlwlgiMaYeI3CQiK0RkjYg84T6voFJE/tutT/9PEenvLjtRRD4RkXUi8rpbowYROVlE3heRtSKySkRGuZvvG1Dv/wX3rlVjfGOJwJg2RGQsMAeYqaoTgUbga0ASkKeqpwEfAve7qzwH/FBVxwOfBkx/AXhcVSfg1KjZ606fBNyN82yMk3Du9jbGNxFRYsKYTroQmAKsdE/W+wAHcMpUv+wu8zzwmlu/P1VVP3SnPwu8IiLJwFBVfR1AVWsA3O2tUNVC9/MaIAtY4v3XMqZ9lgiMOZoAz6rqj1pNFPlJm+W6Wp+lNuB9I/Z3aHxmTUPGHO2fwDUiMgBARNJFZCTO38s17jI3AktUtQIoE5FZ7vSbgQ/dp78VishV7jbiRSSxR7+FMUGyMxFj2lDVjSLyX8C7IhIF1AO34zykZao77wBOPwLA14E/uAf6wGqdNwNPiMiD7jau7cGvYUzQrPqoMUESkUpV7et3HMZ0N2saMsaYCGdXBMYYE+HsisAYYyKcJQJjjIlwlgiMMSbCWSIwxpgIZ4nAGGMi3P8PRGAZD90n6s4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "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": "ZrgfNlbk9EZK",
        "outputId": "fd085a2b-1a9a-46b2-a03a-7713d31a0e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fcf409fbcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy of the model on test data"
      ],
      "metadata": {
        "id": "ycbLKSDl-SJX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test_std, Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3RgBzQF-B8W",
        "outputId": "a4b9d5b1-c9d7-41fe-f0f2-9ee230b4b67b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 5ms/step - loss: 0.1693 - accuracy: 0.9386\n",
            "0.9385964870452881\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std.shape)\n",
        "print(X_test_std[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWyJPf9O-oJT",
        "outputId": "b64b80a8-5327-4e6f-96f2-bd2b78fa6a0f"
      },
      "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": "XWAZk1SK-3qf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred.shape)\n",
        "print(Y_pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umNl8qUX_GeO",
        "outputId": "96a03dc1-a768-4d5e-9fbf-0b59abf44751"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(114, 2)\n",
            "[0.24822891 0.537705  ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lsaXHZr_mTT",
        "outputId": "f02bd988-b222-435f-8524-c2e4cd5caade"
      },
      "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": "code",
      "source": [
        "print(Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmaeRBc7_M59",
        "outputId": "61de1d7b-54d9-48e1-f2bb-e37fcdf3a211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.48228908e-01 5.37705004e-01]\n",
            " [5.04756272e-01 5.95336974e-01]\n",
            " [3.67398351e-01 9.86996770e-01]\n",
            " [9.29620504e-01 2.29299068e-04]\n",
            " [5.93764186e-01 4.93302941e-01]\n",
            " [8.53436947e-01 1.02660358e-02]\n",
            " [2.81091124e-01 6.12119973e-01]\n",
            " [4.79383349e-01 9.91485417e-01]\n",
            " [4.34603453e-01 9.51259434e-01]\n",
            " [6.34022474e-01 9.53936636e-01]\n",
            " [4.83262986e-01 6.63963675e-01]\n",
            " [3.86232853e-01 9.23828781e-01]\n",
            " [2.77723640e-01 7.82129407e-01]\n",
            " [3.78485560e-01 8.21216226e-01]\n",
            " [4.91592646e-01 9.53726530e-01]\n",
            " [7.36154735e-01 1.57864958e-01]\n",
            " [4.71651524e-01 9.74555612e-01]\n",
            " [2.54375935e-01 8.85443091e-01]\n",
            " [5.75661778e-01 9.49565291e-01]\n",
            " [8.55701387e-01 1.94370747e-02]\n",
            " [1.90429598e-01 1.22809112e-02]\n",
            " [4.47704107e-01 9.82434034e-01]\n",
            " [5.11266708e-01 9.58328366e-01]\n",
            " [5.38256645e-01 9.74644005e-01]\n",
            " [5.29228508e-01 8.16611886e-01]\n",
            " [7.13969886e-01 6.25776052e-02]\n",
            " [4.54092264e-01 9.09015238e-01]\n",
            " [5.10061681e-01 7.40805507e-01]\n",
            " [6.70265079e-01 1.68987542e-01]\n",
            " [6.56581044e-01 1.18384689e-01]\n",
            " [5.91236949e-01 9.38910842e-01]\n",
            " [4.20632422e-01 9.19608235e-01]\n",
            " [5.11391044e-01 9.51588750e-01]\n",
            " [8.78562212e-01 1.91476941e-03]\n",
            " [8.40861440e-01 3.40589881e-02]\n",
            " [5.59490383e-01 7.92683899e-01]\n",
            " [4.08323050e-01 9.93038535e-01]\n",
            " [5.17861009e-01 8.50202918e-01]\n",
            " [3.98939788e-01 9.78648603e-01]\n",
            " [3.16001832e-01 9.14353132e-01]\n",
            " [9.31815386e-01 1.04099512e-03]\n",
            " [5.36973536e-01 2.56363392e-01]\n",
            " [3.96728277e-01 9.79030609e-01]\n",
            " [2.11794227e-01 8.85924697e-01]\n",
            " [7.01928675e-01 1.99133068e-01]\n",
            " [4.22424793e-01 9.64499712e-01]\n",
            " [2.93639600e-01 9.86928105e-01]\n",
            " [4.57987458e-01 9.43145216e-01]\n",
            " [8.21546316e-01 1.38805807e-02]\n",
            " [7.11578608e-01 1.09298050e-01]\n",
            " [6.50350690e-01 9.58284259e-01]\n",
            " [6.42665863e-01 3.17812234e-01]\n",
            " [5.54926872e-01 6.52379811e-01]\n",
            " [4.63142991e-01 9.63462353e-01]\n",
            " [3.21405470e-01 9.79116678e-01]\n",
            " [5.65109372e-01 6.47464037e-01]\n",
            " [4.16883409e-01 8.96000743e-01]\n",
            " [2.26916492e-01 9.79369640e-01]\n",
            " [8.66908669e-01 3.34404111e-02]\n",
            " [3.27705920e-01 8.99716973e-01]\n",
            " [4.26270485e-01 7.30707526e-01]\n",
            " [7.14055181e-01 4.19219434e-02]\n",
            " [4.55140263e-01 9.68491018e-01]\n",
            " [7.43423223e-01 4.48367894e-02]\n",
            " [7.25335717e-01 2.34054148e-01]\n",
            " [3.37363720e-01 1.61187023e-01]\n",
            " [7.93728828e-01 2.18253434e-02]\n",
            " [5.88501632e-01 9.55429971e-02]\n",
            " [6.28320396e-01 8.95084023e-01]\n",
            " [8.62212181e-01 3.93467546e-01]\n",
            " [6.17529809e-01 1.45455003e-01]\n",
            " [7.73986042e-01 2.57445574e-02]\n",
            " [4.81976897e-01 9.11388099e-01]\n",
            " [6.94413900e-01 1.66525394e-01]\n",
            " [3.52115363e-01 9.90811825e-01]\n",
            " [7.20340490e-01 2.14831024e-01]\n",
            " [3.15220028e-01 9.67319369e-01]\n",
            " [4.59937215e-01 9.61403847e-01]\n",
            " [6.54601097e-01 7.08399773e-01]\n",
            " [6.76424980e-01 3.06733221e-01]\n",
            " [6.36298954e-01 4.24867868e-02]\n",
            " [5.38406253e-01 1.78474665e-01]\n",
            " [8.75681341e-01 5.56172132e-02]\n",
            " [5.00144899e-01 7.96875000e-01]\n",
            " [3.75306785e-01 8.99501085e-01]\n",
            " [4.96103704e-01 5.04535496e-01]\n",
            " [6.86801672e-02 8.15004587e-01]\n",
            " [4.70373780e-01 9.27606702e-01]\n",
            " [3.57609630e-01 7.31807709e-01]\n",
            " [4.86540377e-01 9.89374518e-03]\n",
            " [3.63103151e-01 9.35027659e-01]\n",
            " [5.54192960e-01 9.04821754e-01]\n",
            " [1.24884248e-01 9.30378377e-01]\n",
            " [5.36735773e-01 5.64434230e-02]\n",
            " [6.35069132e-01 2.68243432e-01]\n",
            " [5.31905234e-01 8.88440967e-01]\n",
            " [7.59771287e-01 2.76531875e-02]\n",
            " [8.48565996e-01 1.16404355e-01]\n",
            " [4.62344438e-01 8.70560169e-01]\n",
            " [4.66147751e-01 9.91854131e-01]\n",
            " [4.39054519e-01 9.95146155e-01]\n",
            " [4.87376779e-01 2.95026422e-01]\n",
            " [8.58204126e-01 5.64253330e-03]\n",
            " [8.87171805e-01 6.52572513e-03]\n",
            " [4.50704664e-01 9.56563294e-01]\n",
            " [3.15652668e-01 9.76885676e-01]\n",
            " [3.69175375e-01 9.64297891e-01]\n",
            " [1.64411336e-01 9.82585311e-01]\n",
            " [1.48511946e-01 9.84461486e-01]\n",
            " [2.75414258e-01 7.90414095e-01]\n",
            " [7.28302896e-01 3.56283486e-02]\n",
            " [8.83430898e-01 1.98226571e-02]\n",
            " [5.14329910e-01 5.95070362e-01]\n",
            " [7.52037287e-01 6.70901835e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "model.predict() gives the prediction probability of each class for that data point"
      ],
      "metadata": {
        "id": "dx5ZqN_W_6U-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  argmax function\n",
        "\n",
        "my_list = [0.25, 0.56]\n",
        "\n",
        "index_of_max_value = np.argmax(my_list)\n",
        "print(my_list)\n",
        "print(index_of_max_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPF0aJ0sAbML",
        "outputId": "bf537283-6540-4140-9862-50d8a6a52634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.25, 0.56]\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the prediction probability to class labels\n",
        "\n",
        "Y_pred_labels = [np.argmax(i) for i in Y_pred]\n",
        "print(Y_pred_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67EUrfSf_rZi",
        "outputId": "1b0e9ad6-04fb-4577-b940-9626b87b7d1f"
      },
      "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, 0, 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, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 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, 1, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Building the predictive system**"
      ],
      "metadata": {
        "id": "PBvyhDoEBVk8"
      }
    },
    {
      "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",
        "# change the input_data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the numpy array as we are predicting for one data point\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "# standardizing the input data\n",
        "input_data_std = scaler.transform(input_data_reshaped)\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",
        "\n",
        "else:\n",
        "  print('The tumor is Benign')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXQi6eStBJxb",
        "outputId": "3ea1b205-dfef-4613-d2e2-e569cced7254"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.42537233 0.9805447 ]]\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "W0jvu0aNDDP3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}