Breast-Cancer-Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/devarshh08/Breast-Cancer-Classification/blob/main/Breast-Cancer-Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "lb_wA83FjuIA"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9ifi89PjuIC"
      },
      "source": [
        "Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "pAYquPogjuIE"
      },
      "outputs": [],
      "source": [
        "breast_cancer_dataset = sklearn.datasets.load_breast_cancer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ym0-SNg5juIF",
        "outputId": "83958ba6-1de9-42d2-a348-317484382f74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "       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",
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            "       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"
          ]
        }
      ],
      "source": [
        "print(breast_cancer_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCkBl_ZnjuIG"
      },
      "source": [
        "Tumors are classified as benign and malignant.\n",
        "\n",
        "Benign tumors do not spread to part of body and are not harmful.\n",
        "\n",
        "Malignant tumors are harmful."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "id": "a-omgmXvjuIH"
      },
      "outputs": [],
      "source": [
        "#loading data to a dataframe\n",
        "\n",
        "df = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "NFLAsADSjuIH",
        "outputId": "4c657b79-c9a0-4127-d71e-82dd5765cbc6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
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              "      <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>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
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              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
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              "      <th>worst concavity</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",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</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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              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
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              "        document.querySelector('#df-470a273f-61be-4771-ad11-4f7d92cfeebc button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-470a273f-61be-4771-ad11-4f7d92cfeebc');\n",
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              "          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",
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              "\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 155
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "5kum0uFljuII"
      },
      "outputs": [],
      "source": [
        "#adding the target column to our dataframe\n",
        "df[\"label\"] = breast_cancer_dataset.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "71pfYCj0juII",
        "outputId": "ed09c92b-9060-49a0-c1e4-c150ef84081d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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 texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   worst symmetry  worst fractal dimension  label  \n",
              "0          0.4601                  0.11890      0  \n",
              "1          0.2750                  0.08902      0  \n",
              "2          0.3613                  0.08758      0  \n",
              "3          0.6638                  0.17300      0  \n",
              "4          0.2364                  0.07678      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "        const element = document.querySelector('#df-1b9df65e-7689-4222-bdd7-5e757e35bb26');\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",
              "\n",
              "\n",
              "<div id=\"df-c552c4ba-803d-4967-8f84-3e49fec79b79\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c552c4ba-803d-4967-8f84-3e49fec79b79')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c552c4ba-803d-4967-8f84-3e49fec79b79 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 157
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siTXNgVOjuIJ",
        "outputId": "15f45ef6-74d3-458b-8309-04c65b766ad1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 31)\n"
          ]
        }
      ],
      "source": [
        "print(df.shape)\n",
        "#we can see that there are 31 columns, where 30 are inputs and label is the output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTkppa-GjuIK",
        "outputId": "dcdfebe9-380e-41b8-a9dd-74662f669799"
      },
      "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": 159
        }
      ],
      "source": [
        "#checking for any missing values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2p5wcSgpjuIL"
      },
      "source": [
        "There are no missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "npXhJgUtjuIM",
        "outputId": "489173f1-f2dd-4bf3-c1bc-1808a9c79259"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4dd3540e-b369-4a40-91fd-a83a3008478e\" 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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4dd3540e-b369-4a40-91fd-a83a3008478e')\"\n",
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              "    </button>\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4dd3540e-b369-4a40-91fd-a83a3008478e 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-4dd3540e-b369-4a40-91fd-a83a3008478e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a68f40b0-1069-44e7-a730-c46d1a7997b3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a68f40b0-1069-44e7-a730-c46d1a7997b3')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a68f40b0-1069-44e7-a730-c46d1a7997b3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 160
        }
      ],
      "source": [
        "#statistical measures about data\n",
        "\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GGLsIQMjuIN",
        "outputId": "2d5db4c6-3871-47f1-a6f9-ce3b6b7a109e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    357\n",
              "0    212\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ],
      "source": [
        "#checking value distribution for malignant and benign\n",
        "\n",
        "df['label'].value_counts()\n",
        "\n",
        "#we check this if there is a huge imbalance in the distribution, then we need to process the data by methods of upsampling/downsampling to make sure distribution is even"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geo4vW62juIO"
      },
      "source": [
        "### 1 : Benign (Not Dangerous)\n",
        "### 0 : Malignant (Dangerous)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "5_KK-EF6juIP",
        "outputId": "04208a66-246b-409d-c46a-ac6072c0863b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
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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",
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              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <td>17.462830</td>\n",
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              "      <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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              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-9f1c8015-5d65-4462-9444-fccf00e13ab8 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-9f1c8015-5d65-4462-9444-fccf00e13ab8');\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",
              "\n",
              "\n",
              "<div id=\"df-32120212-1c45-4799-84a0-b896a357c6d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32120212-1c45-4799-84a0-b896a357c6d9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-32120212-1c45-4799-84a0-b896a357c6d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 162
        }
      ],
      "source": [
        "#groups data points labelwise and gives mean value for each data point\n",
        "df.groupby('label').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "adYKV3iGjuIQ"
      },
      "outputs": [],
      "source": [
        "#splitting this data into features and targets\n",
        "\n",
        "X = df.drop(columns=\"label\", axis=1)\n",
        "Y = df[\"label\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IjtPGbVrjuIQ",
        "outputId": "9b836e90-064c-4161-fb8c-ac4bb6671c28"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LWEeNyyjuIQ",
        "outputId": "b640ff7b-8b8c-450a-a4db-aa27ffe8a571"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "56t18QNLjuIR"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standardize the data\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_std = scaler.fit_transform(X_train) #we fit only with the training data\n",
        "X_test_std = scaler.transform(X_test)       #here we do not fit, we only transform"
      ],
      "metadata": {
        "id": "Fhc0kyI-e54X"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fIDXQXajuIS",
        "outputId": "a0ed9b63-69a4-4d92-8f0b-d6bdf07cd902"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (455, 30) (114, 30)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_dVQBG5juIS"
      },
      "source": [
        "Building the Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf.random.set_seed(3) #we set a seed because when training a NN in keras, various weights and parameters are initialized, so each time you train your NN this is a random way of generating values so to counter that we set a random seed, to get a fixed set of values every time we train\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "fNnAFigJlGYV"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tensorflow is a DL library developed by Google.\n",
        "\n",
        "Keras is a wrapper of tensorflow, it used tf at its backend."
      ],
      "metadata": {
        "id": "OE0dkZ4nlPMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#setting up the layers of the NN\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape = (30,)), #input layer\n",
        "    keras.layers.Dense(units = 20, activation = \"ReLU\"), #hidden layer, all 20 units of hidden layer will have rectified linear activation function\n",
        "    keras.layers.Dense(units = 2, activation = \"sigmoid\") #output layer, all 2 units of output layer will have sigmoid activation function\n",
        "])"
      ],
      "metadata": {
        "id": "LvRViI1Ym22y"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compiling the Neural Network\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='SparseCategoricalCrossentropy',\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "#when using the target variable as 0, 1, 2, 3 etc, we have to use sparse categorical cross entropy,\n",
        "#and when you have one not encoded labels, you use categorical cross entropy"
      ],
      "metadata": {
        "id": "iQ4b7oWH2mTp"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Label Encoding and One not encoding:\n",
        "Say we want to build a Neural Network that will predict whether image is dog, or cat or horse\n",
        "\n",
        "say we label, (label encoding)\n",
        "\n",
        "dog - 0\n",
        "\n",
        "cat - 1\n",
        "\n",
        "horse - 2\n",
        "\n",
        "so whenever using target variable as this kind of integer labels, we use sparse categorical cross entropy as loss function.\n",
        "\n",
        "And instead if you use one not encoding:\n",
        "\n",
        "dog - [1 0 0]\n",
        "\n",
        "cat - [0 1 0]\n",
        "\n",
        "horse - [0 0 1]\n",
        "\n",
        "in one not encoded labels, we use sparse categorical cross entropy and other loss functions available"
      ],
      "metadata": {
        "id": "GAi0C0JKso3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#training the neural network\n",
        "\n",
        "history = model.fit(X_train_std, Y_train, validation_split=0.1, epochs=10)  #X_train is features and Y_train is labels\n",
        "                                                                        #validation is like the train test we are doing, it is just like train_test_split\n",
        "                                                                        #epochs is like how many times your model has to go through the data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euiJ000Td5Wu",
        "outputId": "50d293dc-4816-405e-c2aa-20578e5ab920"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "13/13 [==============================] - 1s 19ms/step - loss: 0.5786 - accuracy: 0.7311 - val_loss: 0.3897 - val_accuracy: 0.9348\n",
            "Epoch 2/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.4083 - accuracy: 0.8533 - val_loss: 0.2811 - val_accuracy: 0.9348\n",
            "Epoch 3/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.3181 - accuracy: 0.9120 - val_loss: 0.2240 - val_accuracy: 0.9565\n",
            "Epoch 4/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.2654 - accuracy: 0.9193 - val_loss: 0.1884 - val_accuracy: 0.9783\n",
            "Epoch 5/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.2273 - accuracy: 0.9315 - val_loss: 0.1640 - val_accuracy: 0.9783\n",
            "Epoch 6/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.2001 - accuracy: 0.9438 - val_loss: 0.1468 - val_accuracy: 0.9783\n",
            "Epoch 7/10\n",
            "13/13 [==============================] - 0s 4ms/step - loss: 0.1796 - accuracy: 0.9487 - val_loss: 0.1333 - val_accuracy: 0.9783\n",
            "Epoch 8/10\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1625 - accuracy: 0.9511 - val_loss: 0.1233 - val_accuracy: 0.9783\n",
            "Epoch 9/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.1488 - accuracy: 0.9535 - val_loss: 0.1146 - val_accuracy: 0.9783\n",
            "Epoch 10/10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.1370 - accuracy: 0.9535 - val_loss: 0.1083 - val_accuracy: 0.9783\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing Accuracy"
      ],
      "metadata": {
        "id": "zrL65aL1iTOv"
      }
    },
    {
      "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('epochs')\n",
        "plt.legend(['training data', 'validation data'], loc=4) #can also use loc = 'lower right'\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MZIr66-Lelx_",
        "outputId": "f46cdd3f-09c9-4f5d-c2a4-a27b888d7024"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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c8yTEPASe3mZXJyIiZ6EAJFIatkJY9//gpxcgL8Oxr+2dEPsMBNU2tzYRETkvBSCRi7X/F8fVXanbHNsR0dD3Vah/lbl1iYjIBVMAErlQGUcc6/n8scCx7VcdrpsAHYaCh9XU0kRE5OIoAImcT0EurJoJv0yDghyweEDH++DaceBfw+zqRESkFBSARM7GMGDXYlg8xnEPL4D6XRyrONdua2ppIiJyaRSAREqStgcWP+W4aztAYG3oPQWib9MqziIiVYACkMiZ8k7Bz6/AqjfBXgAeXtDlEegxGnwCza5ORETKiAKQCDiGu7Z8CUsmwqkkx76mvaHPixDa1NzaRESkzCkAiSRtdlzWfmiVY7t6Q0fwad5Hw10iIlWUApC4r5zj8NPz8NscMOzg6Qc9/gVdHgUvX7OrExGRy0gBSNyP3QbrP4Afp8DpE459rQfA9c9BcD1TSxMRkfKhACTu5dBqWPQEJG92bNdq5bhpaaMe5tYlIiLlSgFI3MOpZMcE583zHNs+wXDt09DpH2DVfwYiIu5G//JL1VaYD2veguUvQ34WYIH290CvSRAQZnZ1IiIuCm12MnMLyThdUOyRebqArLxCs0ssM1c1rsk1zc37d1gBSKquPUvh+zGQvtuxXbcD9H3F8aeIyGVSYLOTeZYAU1KwyThd6HyuKgWc87GAewegN954g1deeYXk5GTatWvHzJkz6dy5c4ltCwoKmDp1Kh9++CGJiYlERUXx0ksv0adPH2ebyZMn88wzz7i8Lioqih07dlzW85AK5MQBWPw07Fzo2K4WBrGTod0g8PAwszIRqSTyC+3Fwktm7p/bOSUHmaIQk51vu+T3r+ZtJdjPiyA/L4L/fAT5eRHg44lHFVmeo2PD6qa+v6kBaN68ecTHxzN79mxiYmKYPn06cXFx7Ny5k1q1ahVrP378eD755BPeffddWrRowQ8//MCAAQP49ddfad++vbNd69atWbp0qXPb09P0nCflIT8HVvwbVs4AWx5YrBDzT+g5BnyDza5ORMpZXqGt5J6XHEevy7l6Z04XXHqICfDxPCPEeDqDTPDfQk1J+7ys+p+1y81iGIZh1pvHxMTQqVMnZs2aBYDdbicyMpJHH32UMWPGFGtfp04dxo0bxyOPPOLcd+utt+Ln58cnn3wCOHqAvvnmGzZu3FjqujIzMwkODiYjI4OgoKBSH0fKiWHAtv/A/8ZDxmHHvkY9HDctrdXS3NpE5JLkFtjOMXR07uGl3AL7Jb9/oI9niSEl2P/sASbYz4sgX088FWLK3cV8f5vWNZKfn8/69esZO3asc5+HhwexsbGsWrWqxNfk5eXh6+u6QJ2fnx8rVqxw2bd7927q1KmDr68vXbp0YerUqdSvX/+steTl5ZGXl+fczszMLM0piRlSdzhWcd6/3LEdHOlYz6dVf63iLFJB5BbYSuiBubD5MXmFlxZiLBZHiCkKLH/vaQnyLTnAFD1v9dC/I1WVaQEoLS0Nm81GeHi4y/7w8PCzzteJi4tj2rRp9OjRgyZNmpCQkMD8+fOx2f7qqoyJieGDDz4gKiqKpKQknnnmGa6++mq2bt1KYGDJN7OcOnVqsXlDUsHlZsCyF2HN22DYwOoD3UZC91Hg7W92dSJVimEY5BbYL7oHpuiRXwYhpqSgcrbelzMfAb6eCjFSoko1OWbGjBkMHz6cFi1aYLFYaNKkCcOGDWPOnDnONjfccIPz723btiUmJoYGDRrwxRdfcP/995d43LFjxxIfH+/czszMJDIy8vKdiJSe3Q6bPoWlkyH7mGNf1I0Q9zzUaGRqaSKVVYHNztGTpzl0PMf5OPznn8kZeWSeLiDfdmkhxsNCscByIQEmyM+LQB9PPBRipIyZFoBCQ0OxWq2kpKS47E9JSSEiIqLE14SFhfHNN9+Qm5tLeno6derUYcyYMTRu3Pis7xMSEkLz5s3Zs2fPWdv4+Pjg4+NTuhOR8pO4HhY9CYm/ObZrNnWs4tw01ty6RCo4wzA4kVPgEm4OnxF2jp48jf0CZoNaPSxnCS9/Tvb1PUvA8fciwFshRioW0wKQt7c3HTp0ICEhgZtvvhlwTIJOSEhgxIgR53ytr68vdevWpaCggK+//po77rjjrG2zsrLYu3cv9957b1mWL+UpO83R4/P7J4AB3gFwzZMQ8xB4eptdnUiFkFdoI/HEaZfeG8fjNIeP55x3fRkfTw/q1/Cnfg1/Iv/8s34Nf2qH+BLi702wnxfVvK1YNLdOqghTh8Di4+MZMmQIHTt2pHPnzkyfPp3s7GyGDRsGwODBg6lbty5Tp04FYM2aNSQmJnLFFVeQmJjI5MmTsdvtPPnkk85jjh49mn79+tGgQQOOHj3KpEmTsFqtDBw40JRzlEtgK4R1/w9+egHyMhz72t4Jsc9AUG1zaxMpZ4ZhkJ6d/1fASXcdrkrKzOV81/RGBPkSWcPPJeAUPcICfRRuxK2YGoDuvPNOjh07xsSJE0lOTuaKK65g8eLFzonRhw4dwuOMhetyc3MZP348+/btIyAggL59+/Lxxx8TEhLibHPkyBEGDhxIeno6YWFhdO/endWrVxMWptseVCr7f3Fc3ZW6zbEdEQ19X4X6V5lbl8hllFtg48iJP4NNuqP3xjlkdSKHnPMssOfvbS3Wg1O0Xa+6H75e1nI6E5GKz9R1gCoqrQNkoowjjvV8/ljg2ParDtdNgA5DwUP/eEvlZhgGx07luUw2PnPIKiUz75yvt1igdpCva8Cp+VfgqVnNW7044tYqxTpAIi4KcmHVTPhlGhTkgMUDOt4H144D/xpmVydyUVIyc9lyJKNYwDl8Iue8i/MF+Hj+GWj8is3JqVvdDx9P/Y+ASFlQABLz7VwMi8fAif2O7fpdHKs4125rbl0iFyEp4zSLtiSzaEsS6w+eOGs7DwvUCXENN2f26FT391Ivjkg5UAAS86TvdQSf3f9zbAfWht5TIPo2reIslcLRk6dZtCWJRVuS2HDopMtzLWsH0Si0+HycOiF+us+TSAWgACTlLy8LfnkVVr0Btnzw8IIuj0CP0eBT8mrdIhXFkRM5fL8lmYVbkth4+KRzv8UCHRtUp290bW5oU5uIYN+zH0RETKcAJOXHMGDr1/C/CXDqqGNf097Q50UIbWpubSLncPh4Dt9vTWLhlmQ2/S30dGpYgxuja9OnTQThQQo9IpWFApCUj+QtjlWcD/3q2K7e0BF8mvfRcJdUSIeP5ziHtzYdyXDut1igc8Ma3Ni2Nn1aR1BLoUekUlIAkssr5zj89Dz8NgcMO3j6QY9/QZdHwUtfHFKxHErPYeGfoWdL4l+hx8MCnRs5enri2kRQK1C/uyKVnQKQXB52G2z4EBKmwOnjjn2tB8D1z0FwPXNrEznDwfRsZ+jZmpjp3O9hgasa16RvdG3iWkcQFqj7BYpUJQpAUvYOrYHvn4CkTY7tWq0cNy1t1MPcukT+dCDtr9Dzx1HX0NOlyV+hJzRAoUekqlIAkrJzKhmWTILNnzu2fYLh2qeh0z/Aql81Mde+Y1ks2uKYyLw96a/QY/Ww0MXZ0xNOTYUeEbegbyW5dIX5sGY2LH8Z8k8BFmh/D/SaBAG6B5uYZ++xLBZtTmLhliR2JJ9y7rd6WOjapCY3Rtfm+tYR1KjmbWKVImIGBSC5NHsS4PunIH23Y7tuB+j7iuNPERPsST3Fws2OFZl3pvwVejw9LHRtGsqN0RFc3yqC6go9Im5NAag82W2OR1WQecSxns+O7xzb1cIgdjK0GwQeWuVWytfulFPOOT27UrKc+z09LHRvFkrf6Npc3yqcEH+FHhFxUAAqTytnQMIzZldRtixWiPkn9BwDvsFmVyNuZFfKKRZudoSe3al/hR4vq4XuTYtCTwTB/l4mVikiFZUCkJReo2scV3fVaml2JeIGDMNgZ8opFm1OYtHWZPb8LfRc3SyMvtG16d0yXKFHRM5LAag8XfUwdLrf7CrKhsUKPgFmVyFVnGEY7Eg+9efVW0nsO5btfM7b6kGP5o6enl4twwn2U+gRkQunAFSevHy1+rHIeRiGwfakU87bUOxL+3voCePGthH0ahlOkK9Cj4iUjgKQiJjOMAz+OJrJ91uTWLQlmf1nhh5PD65pHsaN0bXp1bIWgQo9IlIGFIBExBRFoWfhliS+35LEgfQc53M+nh70jHLM6bmuhUKPiJQ9BSARKTeGYbA18c/QszWJg38LPddG1aJvW0foCfDRP08icvnoXxgRuawMw2BLYsafPT3JHDr+V+jx9foz9PzZ01NNoUdEyon+tRGRMmcYBpuOZDgnMh85cdr5nK+XB9e1+Cv0+HvrnyERKX/6l0dEyoRhGGw8fPLP0JNM4sm/Qo+fl5XrWtaib5vaXNsiTKFHREynf4VEpNQMw+D3wydZtDmJ77e6hh5/byvXtajFjdG16RlVCz9vq4mVioi4UgASkYtit/8Zev68eutoRq7zOX9vK71ahnNjdATXNFfoEZGKSwFIRM7LbjfYcOgEC7cksXhrMklnhJ5q3lZiW4VzQ5va9IwKw9dLoUdEKj4FIBEpkd1usP7QCRZudoSe5My/Qk+AjyexLR0TmXs0V+gRkcpHAUhEnOx2g98OnnAMb21NIiUzz/lcoI8nsa3C6Rtdm6ubhSr0iEilpgAk4uZsdoPfDhz/M/Qkk3rKNfT0Lgo9zUPx8VToEZGqQQFIxA3Z7AZr9ztCz+I/kjl2Zujx9eT6VhH0jY6gezOFHhGpmhSARNyEzW6wZn+6I/RsTSEt66/QE+TryfWtI7gxujbdmobi7elhYqUiIpefApBIFVZos7N2/3EWbknihz+SScvKdz4X7OfF9a3C6du2Nt2aKPSIiHtRABKpYgptdlbvO86irUn8sDWZ9Oy/Qk+IvxdxrSK4ITqCbk1D8bIq9IiIe1IAEqkCCm12Vu1zDG/98EcKx88IPdX9vYhrHUHf6Np0aVJToUdEBAUgkUrHbjfYl5bN1sQMtiRmsDUxgz+OZpKVV+hsU6OaN3GtHVdvXdVYoUdE5O8UgEQqMJvdYH9aFlsSM9hyJPPPsJNBdr6tWFtH6HFMZL6qcQ08FXpERM5KAUikgrDZDfYd+zPsnNGzk1NC2PH18qBV7SCi6wbTpm4w0fWCaRoWoNAjInKBFIBETFBos7P3WLYz6GxNzGBbUslhx8/LSqs6Z4SdusE0CaumsCMicgkUgEQus0KbnT3HsthyJMM5b2dbUia5BfZibf29rbSuE+QMOm3qBtMkLACrh8WEykVEqi4FIJEyVGizszs1y9mzsyUxg+1nCTvVvK20rlM0hOXo4WkUqrAjIlIeFIBESqnAZmdXyin+SMx0ztvZnpRJXmHxsBPg4+kcxirq2WkcWg0PhR0REVMoAIlcgPxCR9g589Lz7cmnyC8h7AT6eNK67l9zdtrUDaZRTYUdEZGKRAFI5G+Kws6ZV2PtSDpFvq2EsOPrSZs6jquwiubtNKjhr7AjIlLBKQCJW8srtLEr2fXS853JJYedIF9Pl8nJ0XWDqa+wIyJSKSkAidvak5rFoHdXk3oqr9hzwX5eLkEnum4wkTX8sFgUdkREqgIFIHFLdrvB2PmbST2VR5CvJ+0iQ1zCTr3qCjsiIlWZApC4pXm/HWbdgRP4e1v5/vEe1A3xM7skEREpR1pKVtxO6qlcpi7aDsC/ro9S+BERcUMKQOJ2pny3nczcQqLrBjO0a0OzyxERERMoAIlbWbYzlf9uOoqHBabeEq1Vl0VE3JQCkLiN0/k2xn+zFYBh3RrRpm6wyRWJiIhZFIDEbUxP2MWRE6epG+JHfO/mZpcjIiImUgASt7DtaCb/75f9ADzbvzXVfHQBpIiIO1MAkirPZjcYu2ALNrtB3+gIerUMN7skERExmQKQVHmfrD7IpsMnCfTxZFK/1maXIyIiFYACkFRpyRm5vPLDTgCe7BNFeJCvyRWJiEhFoAAkVdrkb/8gK6+Q9vVDuDumgdnliIhIBWF6AHrjjTdo2LAhvr6+xMTEsHbt2rO2LSgo4Nlnn6VJkyb4+vrSrl07Fi9efEnHlKprybYUFv+RjKeHham3ROuu7SIi4mRqAJo3bx7x8fFMmjSJDRs20K5dO+Li4khNTS2x/fjx43n77beZOXMm27Zt48EHH2TAgAH8/vvvpT6mVE1ZeYVM/I9jzZ/hPRrTIiLI5IpERKQisRiGYZj15jExMXTq1IlZs2YBYLfbiYyM5NFHH2XMmDHF2tepU4dx48bxyCOPOPfdeuut+Pn58cknn5TqmCXJzMwkODiYjIwMgoL0xVkZPfPfP3h/5QEia/jxv8evwc/banZJIiJymV3M97dpPUD5+fmsX7+e2NjYv4rx8CA2NpZVq1aV+Jq8vDx8fV0nsfr5+bFixYpSH7PouJmZmS4Pqbw2HznJh78eAOD5m6MVfkREpBjTAlBaWho2m43wcNc1WcLDw0lOTi7xNXFxcUybNo3du3djt9tZsmQJ8+fPJykpqdTHBJg6dSrBwcHOR2Rk5CWenZil0GZn7Pwt2A3of0UdejQPM7skERGpgEyfBH0xZsyYQbNmzWjRogXe3t6MGDGCYcOG4eFxaacxduxYMjIynI/Dhw+XUcVS3j749QB/HM0k2M+LCTe1MrscERGpoEwLQKGhoVitVlJSUlz2p6SkEBERUeJrwsLC+Oabb8jOzubgwYPs2LGDgIAAGjduXOpjAvj4+BAUFOTykMrnyIkcXvvfLgCe7tuC0AAfkysSEZGKyrQA5O3tTYcOHUhISHDus9vtJCQk0KVLl3O+1tfXl7p161JYWMjXX39N//79L/mYUrkZhsHE//zB6QIbnRvW4PYOGsYUEZGzM/WOkPHx8QwZMoSOHTvSuXNnpk+fTnZ2NsOGDQNg8ODB1K1bl6lTpwKwZs0aEhMTueKKK0hMTGTy5MnY7XaefPLJCz6mVE3fb03mxx2peFktvHBLG635IyIi52RqALrzzjs5duwYEydOJDk5mSuuuILFixc7JzEfOnTIZX5Pbm4u48ePZ9++fQQEBNC3b18+/vhjQkJCLviYUvVk5hYw+ds/AHioZ1Oa1go0uSIREanoTF0HqKLSOkCVy/hvtvDJ6kM0Dq3GopFX4+uly95FRNxRpVgHSKQsrD94grlrDgHw/IBohR8REbkgCkBSaRXY7Dw9fwuGAbd1qEeXJjXNLklERCoJBSCptN75eR87U05Ro5o34/q2NLscERGpRBSApFI6mJ7N6wm7ARh/Y0uqV/M2uSIREalMFICk0jEMg/HfbCWv0E63pjUZ0L6u2SWJiEglowAklc5/Nh7ll91peHt68PzN0VgsWvNHREQujgKQVConc/KZ8t02AB67rikNQ6uZXJGIiFRGCkBSqbywaDvp2fk0Dw/ggR5NzC5HREQqKQUgqTRW70vni9+OAPDCgGi8PfXrKyIipaNvEKkU8gptPL1gCwCDYurTsWENkysSEZHKTAFIKoW3lu1l37FsQgN8eKpPC7PLERGRSk4BSCq8PalZvPnTXgAm9WtFsJ+XyRWJiEhlpwAkFZphGIxbsIV8m52eUWHc1La22SWJiEgVoAAkFdqX64+wZv9x/LysTOnfRmv+iIhImVAAkgorPSuPFxZtB2BU72ZE1vA3uSIREakqFICkwnpu4XZO5hTQsnYQ93VrZHY5IiJShSgASYX0y+5jLPg9EYsFpt4SjadVv6oiIlJ29K0iFU5ugY3x32wFYEiXhlwRGWJuQSIiUuUoAEmF83rCbg6m5xAR5Mu/rm9udjkiIlIFKQBJhbIz+RTv/LwPgGf6tybQV2v+iIhI2VMAkgrDbjd4esEWCu0G17cKJ651hNkliYhIFaUAJBXGp2sPsf7gCap5W3mmf2uzyxERkSpMAUgqhNTMXF5avAOA0XFR1A72M7kiERGpyhSApEJ45r/bOJVbSLt6wQzu0tDsckREpIpTABLT/bgjhYVbkrB6WHjhlmisHrrdhYiIXF4KQGKqnPxCJnzzBwD3d29E6zrBJlckIiLuoFQB6KeffirrOsRN/XvJLhJPnqZuiB+PxzYzuxwREXETpQpAffr0oUmTJjz33HMcPny4rGsSN7E1MYM5Kw8A8NzNbfD39jS3IBERcRulCkCJiYmMGDGCr776isaNGxMXF8cXX3xBfn5+WdcnVZTtzzV/bHaDG9vW5toWtcwuSURE3EipAlBoaCijRo1i48aNrFmzhubNm/Pwww9Tp04dHnvsMTZt2lTWdUoV89GqA2w+kkGgryeT+rUyuxwREXEzlzwJ+sorr2Ts2LGMGDGCrKws5syZQ4cOHbj66qv5448/yqJGqWKOnjzNqz/sBGDMDS2oFehrckUiIuJuSh2ACgoK+Oqrr+jbty8NGjTghx9+YNasWaSkpLBnzx4aNGjA7bffXpa1ShUx6ds/yM630aFBdQZ2qm92OSIi4oZKNev00Ucf5bPPPsMwDO69915efvll2rRp43y+WrVqvPrqq9SpU6fMCpWqYfHWZJZsS8HTw8ILA6Lx0Jo/IiJiglIFoG3btjFz5kxuueUWfHx8SmwTGhqqy+XFxancAiZ/6xgW/ec1jYmKCDS5IhERcVelCkAJCQnnP7CnJ9dcc01pDi9V1Ks/7CQ5M5eGNf159Dqt+SMiIuYp1RygqVOnMmfOnGL758yZw0svvXTJRUnVs/HwST5afRCA5wdE4+tlNbkiERFxZ6UKQG+//TYtWrQotr9169bMnj37kouSqqXAZmfs/C0YBtzSvi7dmoaaXZKIiLi5UgWg5ORkateuXWx/WFgYSUlJl1yUVC1zVuxne1ImIf5ejLuxpdnliIiIlC4ARUZGsnLlymL7V65cqSu/xMXh4zn8e+kuAJ7u25KaASVPmhcRESlPpZoEPXz4cB5//HEKCgq47rrrAMfE6CeffJJ//etfZVqgVF6GYTD+m63kFti5qnENbu9Qz+ySREREgFIGoCeeeIL09HQefvhh5/2/fH19eeqppxg7dmyZFiiV13ebk1i+6xjeVg+eHxCNxaI1f0REpGKwGIZhlPbFWVlZbN++HT8/P5o1a3bWNYEqm8zMTIKDg8nIyCAoKMjsciqljJwCek1bTlpWHqNimzMyVpe9i4jI5XUx39+l6gEqEhAQQKdOnS7lEFJFvbh4B2lZeTQJq8aDPRubXY6IiIiLUgeg3377jS+++IJDhw45h8GKzJ8//5ILk8pr3YHjfLb2EAAvDIjGx1Nr/oiISMVSqqvAPv/8c7p27cr27dtZsGABBQUF/PHHH/z4448EBweXdY1SieQX2nl6/hYA7uwYSUzjmiZXJCIiUlypAtALL7zAv//9b/773//i7e3NjBkz2LFjB3fccQf16+vu3u7snZ/3sjs1i9AAb8b2Lb5YpoiISEVQqgC0d+9ebrzxRgC8vb3Jzs7GYrEwatQo3nnnnTItUCqP/WnZvP7jHgAm3NSKEH9vkysSEREpWakCUPXq1Tl16hQAdevWZevWrQCcPHmSnJycsqtOKg3DMBi3YAv5hXaubhbK/7XTgpgiIlJxlWoSdI8ePViyZAnR0dHcfvvtjBw5kh9//JElS5bQq1evsq5RKoH5GxL5dW86Pp4ePHdzG635IyIiFVqpAtCsWbPIzc0FYNy4cXh5efHrr79y6623Mn78+DItUCq+49n5PLdwGwAjY5vRoGY1kysSERE5t4sOQIWFhXz33XfExcUB4OHhwZgxY8q8MKk8nl+4nRM5BbSICGT41VrzR0REKr6LngPk6enJgw8+6OwBEvf26940vt5wBIsFnh8QjZe1VNPKREREylWpvq06d+7Mxo0by7gUqWxyC2yMW+CYAH93TH06NKhuckUiIiIXplRzgB5++GHi4+M5fPgwHTp0oFo11zkfbdu2LZPipGJ786c97E/LplagD0/20Zo/IiJSeZTqZqgeHsU7jiwWC4ZhYLFYsNlsZVKcWXQz1PPbnXKKvq//QoHN4M27r6RvdG2zSxIRETd32W+Gun///lIVJlWD3W7w9IItFNgMerWoxQ1tIswuSURE5KKUKgA1aNCgrOuQSuR/21JYd+AE/t5WntWaPyIiUgmVKgB99NFH53x+8ODBF3ysN954g1deeYXk5GTatWvHzJkz6dy581nbT58+nbfeeotDhw4RGhrKbbfdxtSpU/H19QVg8uTJPPPMMy6viYqKYseOHRdck5zb4q1JAAzqXJ+6IX4mVyMiInLxShWARo4c6bJdUFBATk4O3t7e+Pv7X3AAmjdvHvHx8cyePZuYmBimT59OXFwcO3fupFatWsXaf/rpp4wZM4Y5c+bQtWtXdu3axdChQ7FYLEybNs3ZrnXr1ixduvSvk/Qs1WlKCQptdn7aeQyA61tr6EtERCqnUl0Gf+LECZdHVlYWO3fupHv37nz22WcXfJxp06YxfPhwhg0bRqtWrZg9ezb+/v7MmTOnxPa//vor3bp1Y9CgQTRs2JDrr7+egQMHsnbtWpd2np6eREREOB+hoaGlOU0pwfqDJ8g4XUCIvxdX1g8xuxwREZFSKbNV65o1a8aLL75YrHfobPLz81m/fj2xsbF/FePhQWxsLKtWrSrxNV27dmX9+vXOwLNv3z4WLVpE3759Xdrt3r2bOnXq0LhxY+6++24OHTp0zlry8vLIzMx0eUjJEnakAnBtVC08teihiIhUUmU6NuTp6cnRo0cvqG1aWho2m43w8HCX/eHh4WedrzNo0CDS0tLo3r07hmFQWFjIgw8+yNNPP+1sExMTwwcffEBUVBRJSUk888wzXH311WzdupXAwMASjzt16tRi84akZEu3pQDQq2XxIUoREZHKolQB6Ntvv3XZNgyDpKQkZs2aRbdu3cqksJIsW7aMF154gTfffJOYmBj27NnDyJEjmTJlChMmTADghhtucLZv27YtMTExNGjQgC+++IL777+/xOOOHTuW+Ph453ZmZiaRkZGX7Twqq33HstiXlo2nh4UezcPMLkdERKTUShWAbr75Zpdti8VCWFgY1113Ha+99toFHSM0NBSr1UpKSorL/pSUFCIiSp5cO2HCBO69917+8Y9/ABAdHU12djYPPPAA48aNK3GBxpCQEJo3b86ePXvOWouPjw8+Pj4XVLc7S9juGP6KaVyDIF8vk6sREREpvVJN4rDb7S4Pm81GcnIyn376KbVrX9iKwN7e3nTo0IGEhASX4yYkJNClS5cSX5OTk1Ms5FitVsDRC1WSrKws9u7de8F1ydkt3e4Iq7Etw8/TUkREpGIz9frw+Ph4hgwZQseOHencuTPTp08nOzubYcOGAY71hOrWrcvUqVMB6NevH9OmTaN9+/bOIbAJEybQr18/ZxAaPXo0/fr1o0GDBhw9epRJkyZhtVoZOHCgaedZFZzMyee3gycABSAREan8ShWAbr31Vjp37sxTTz3lsv/ll19m3bp1fPnllxd0nDvvvJNjx44xceJEkpOTueKKK1i8eLFzYvShQ4dcenzGjx+PxWJh/PjxJCYmEhYWRr9+/Xj++eedbY4cOcLAgQNJT08nLCyM7t27s3r1asLCNGflUizbeQyb3aB5eACRNfzNLkdEROSSlOpmqGFhYfz4449ER0e77N+yZQuxsbHF5vVUNroZanEjPt3Ad5uTeKhnE57Snd9FRKQCupjv71LNAcrKysLb27vYfi8vL62hUwUV2Ows3+VY/TlWl7+LiEgVUKoAFB0dzbx584rt//zzz2nVqtUlFyUVy7r9xzmVW0jNat5cEVnd7HJEREQuWanmAE2YMIFbbrmFvXv3ct111wGQkJDAZ599dsHzf6TyWPrn5e/XtqiF1UN3fhcRkcqvVAGoX79+fPPNN7zwwgt89dVX+Pn50bZtW5YuXco111xT1jWKiQzDOOPydw1/iYhI1VDqy+BvvPFGbrzxxrKsRSqgPalZHDqeg7fVg6ub6Uo6ERGpGko1B2jdunWsWbOm2P41a9bw22+/XXJRUnEUDX9d1aQm1XxMXTZKRESkzJQqAD3yyCMcPny42P7ExEQeeeSRSy5KKo6EP4e/emv4S0REqpBSBaBt27Zx5ZVXFtvfvn17tm3bdslFScVwPDufDYccqz9fp9WfRUSkCilVAPLx8SlxscOkpCQ8PTVMUlX8tCMVuwEtawdRN8TP7HJERETKTKkC0PXXX8/YsWPJyMhw7jt58iRPP/00vXv3LrPixFy6+ktERKqqUnXXvPrqq/To0YMGDRrQvn17ADZu3Eh4eDgff/xxmRYo5sgrtPHzn6s/99Lwl4iIVDGlCkB169Zl8+bNzJ07l02bNuHn58ewYcMYOHAgXl5eZV2jmGDNvuNk59sIC/Shbd1gs8sREREpU6WesFOtWjW6d+9O/fr1yc/PB+D7778H4P/+7//KpjoxTdHVX71a1MJDqz+LiEgVU6oAtG/fPgYMGMCWLVuwWCwYhoHF8teXpM1mK7MCpfw5Vn92rP+j4S8REamKSjUJeuTIkTRq1IjU1FT8/f3ZunUry5cvp2PHjixbtqyMS5TytiP5FIknT+Pj6UH3pqFmlyMiIlLmStUDtGrVKn788UdCQ0Px8PDAarXSvXt3pk6dymOPPcbvv/9e1nVKOSoa/urWNBQ/b6vJ1YiIiJS9UvUA2Ww2AgMDAQgNDeXo0aMANGjQgJ07d5ZddWKKouGvWA1/iYhIFVWqHqA2bdqwadMmGjVqRExMDC+//DLe3t688847NG7cuKxrlHJ07FQem46cBKCX1v8REZEqqlQBaPz48WRnZwPw7LPPctNNN3H11VdTs2ZN5s2bV6YFSvn6aUcqhgHRdYMJD/I1uxwREZHLolQBKC4uzvn3pk2bsmPHDo4fP0716tVdrgaTymdJ0eXv6v0REZEqrMxu3FWjRo2yOpSYJLfAxordaYDm/4iISNVWqknQUjWt2pvO6QIbEUG+tK4TZHY5IiIil40CkDgtPWP4S0OZIiJSlSkACeBY/TlBl7+LiIibUAASAP44mklyZi5+Xla6NKlpdjkiIiKXlQKQAH8Nf3VvFoqvl1Z/FhGRqk0BSACcw1+9NfwlIiJuQAFISMnMZUtiBhYLXNtC6/+IiEjVpwAkzt6fdvVCCAv0MbkaERGRy08BSJzzf2K1+rOIiLgJBSA3dzrfxso9jtWfe2n+j4iIuAkFIDe3Yk8aeYV26ob40SIi0OxyREREyoUCkJtLOGP4S6s/i4iIu1AAcmN2u0HCDscEaA1/iYiIO1EAcmNbEjM4diqPat5WYhrXMLscERGRcqMA5MaKrv7q0TwMH0+t/iwiIu5DAciNLd2u4S8REXFPCkBuKvHkabYnZeJhgWujwswuR0REpFwpALmpH/8c/rqyfnVqBmj1ZxERcS8KQG5Kw18iIuLOFIDcUFZeIav2pgO6/YWIiLgnBSA3tGL3MfJtdurX8KdprQCzyxERESl3CkBuqGj4K7ZluFZ/FhERt6QA5GZsdoOfdhQFIA1/iYiIe1IAcjMbD58kPTufQF9POjXS6s8iIuKeFIDcTNHqz9c0D8PLqh+/iIi4J30Dupm/7v6uy99FRMR9KQC5kcPHc9iVkoXVw0JPrf4sIiJuTAHIjRQNf3VsUJ0Qf2+TqxERETGPApAbSTjj8ncRERF3pgDkJjJzC1i9z7H6cy9d/i4iIm5OAchN/LzrGIV2g8ah1WgcptWfRUTEvSkAuQnn8FcrDX+JiIgoALmBQpudn3b+eff3Fhr+EhERUQByAxsOneRkTgHBfl50aFDd7HJERERMpwDkBoouf782KgxPrf4sIiKiAOQOigJQL13+LiIiAlSAAPTGG2/QsGFDfH19iYmJYe3atedsP336dKKiovDz8yMyMpJRo0aRm5t7ScesyvanZbPvWDaeHhau0erPIiIigMkBaN68ecTHxzNp0iQ2bNhAu3btiIuLIzU1tcT2n376KWPGjGHSpEls376d9957j3nz5vH000+X+phVXdG9v2Ia1yDI18vkakRERCoGUwPQtGnTGD58OMOGDaNVq1bMnj0bf39/5syZU2L7X3/9lW7dujFo0CAaNmzI9ddfz8CBA116eC72mFWdc/irhYa/REREipgWgPLz81m/fj2xsbF/FePhQWxsLKtWrSrxNV27dmX9+vXOwLNv3z4WLVpE3759S31MgLy8PDIzM10eVUFGTgHrDpwAdPsLERGRM3ma9cZpaWnYbDbCw12/mMPDw9mxY0eJrxk0aBBpaWl0794dwzAoLCzkwQcfdA6BleaYAFOnTuWZZ565xDOqeJbtSsVmN2hWK4D6Nf3NLkdERKTCMH0S9MVYtmwZL7zwAm+++SYbNmxg/vz5LFy4kClTplzScceOHUtGRobzcfjw4TKq2FxLtfqziIhIiUzrAQoNDcVqtZKSkuKyPyUlhYiIiBJfM2HCBO69917+8Y9/ABAdHU12djYPPPAA48aNK9UxAXx8fPDx8bnEM6pYCmx2lu0suvu7Vn8WERE5k2k9QN7e3nTo0IGEhATnPrvdTkJCAl26dCnxNTk5OXh4uJZstVoBMAyjVMesqtYdOM6p3EJqVPPmikit/iwiInIm03qAAOLj4xkyZAgdO3akc+fOTJ8+nezsbIYNGwbA4MGDqVu3LlOnTgWgX79+TJs2jfbt2xMTE8OePXuYMGEC/fr1cwah8x3TXRTd/PTaqFpYPSwmVyMiIlKxmBqA7rzzTo4dO8bEiRNJTk7miiuuYPHixc5JzIcOHXLp8Rk/fjwWi4Xx48eTmJhIWFgY/fr14/nnn7/gY7oDwzCcl79r+EtERKQ4i2EYhtlFVDSZmZkEBweTkZFBUFCQ2eVctD2pp4id9jPeVg82TOxNgI+pOVdERKRcXMz3d6W6CkwuTNHVX1c1qanwIyIiUgIFoCooQcNfIiIi56QAVMUcz85n/UHH6s/XtVAAEhERKYkCUBXz045U7Aa0iAikXnWt/iwiIlISBaAqJmFH0fCX+1z1JiIicrEUgKqQ/EI7P+9KA3T7CxERkXNRAKpC1uxPJyuvkLBAH9rWDTa7HBERkQpLAagKKVr9+bqoWnho9WcREZGzUgCqIgzDYMk2x/yfXrr8XURE5JwUgKqInSmnSDx5Gm9PD7o3CzW7HBERkQpNAaiKKBr+6t40FH9vrf4sIiJyLgpAVUTRzU81/CUiInJ+CkBVwLFTeWw8fBKAXi10+buIiMj5KABVAT/tSMUwoE3dICKCfc0uR0REpMJTAKoCnMNf6v0RERG5IApAlVxugY1fdjtWf+6t1Z9FREQuiAJQJbdqXzqnC2xEBPnSuk6Q2eWIiIhUCgpAlVzCn8Nf17WshcWi1Z9FREQuhAJQJWYYhnP9n1hd/i4iInLBFIAqsT+OZpKUkYuvlwddm2j1ZxERkQulAFSJFfX+XN0sDF8vq8nViIiIVB4KQJVYwg7H/B8Nf4mIiFwcBaBKKiUzl81HMgC4toUCkIiIyMVQAKqkioa/2kWGUCtQqz+LiIhcDAWgSqro8vdY9f6IiIhcNAWgSuh0vo0VexyrP8dq9WcREZGLpgBUCa3ck0ZeoZ26IX60iAg0uxwREZFKRwGoEiq6+quXVn8WEREpFQWgSsZu/2v1514tNfwlIiJSGgpAlcyWxAxST+VRzdvKVY1rmF2OiIhIpaQAVMkUXf3Vo3kYPp5a/VlERKQ0FIAqmaUa/hIREblkCkCVyNGTp9mWlInFAtdGhZldjoiISKWlAFSJJOxw9P5cWb86NQN8TK5GRESk8lIAqkSWbvvr8ncREREpPQWgSiI7r5BVe9MBiNX8HxERkUuiAFRJ/LI7jXybnfo1/GlWK8DsckRERCo1BaBKoujyd63+LCIicukUgCoBm93gxz8nQGv4S0RE5NIpAFUCGw+fJD07n0AfTzo11OrPIiIil0oBqBJwrv4cFYa3p35kIiIil0rfppVA0c1Pe2v4S0REpEwoAFVwh4/nsDPlFFYPCz21+rOIiEiZUACq4IqGvzo0qE6Iv7fJ1YiIiFQNCkAVXNHNT2O1+rOIiEiZUQCqwE7lFrBmv2P1Z939XUREpOwoAFVgP+9Ko8Bm0Di0Gk3CtPqziIhIWVEAqsDOXP1ZREREyo4CUAVVaLPz007H/B8Nf4mIiJQtBaAKasOhk5zIKSDYz4uODaqbXY6IiEiV4ml2AVKyouGvnlFheFqVU0Wk8rDZbBQUFJhdhlRBXl5eWK3WMjmWAlAFtfTPAKSbn4pIZWEYBsnJyZw8edLsUqQKCwkJISIiAovFcknHUQCqgA6kZbP3WDaeHhau0erPIlJJFIWfWrVq4e/vf8lfUCJnMgyDnJwcUlMd82Nr1659ScdTAKqAinp/OjeqQZCvl8nViIicn81mc4afmjVrml2OVFF+fn4ApKamUqtWrUsaDtPkkgqo6OanuvpLRCqLojk//v7+JlciVV3R79ilzjNTAKpgMnIKWHvgOKDbX4hI5aNhL7ncyup3TAGoglm2KxWb3aBZrQAa1KxmdjkiInKRGjZsyPTp0y+4/bJly7BYLKZMHv/ggw8ICQkp9/etCCpEAHrjjTdo2LAhvr6+xMTEsHbt2rO27dmzJxaLpdjjxhtvdLYZOnRosef79OlTHqdyyTT8JSJSvnr27Mnjjz9eZsdbt24dDzzwwAW379q1K0lJSQQHB5dZDZfTxQa8isr0SdDz5s0jPj6e2bNnExMTw/Tp04mLi2Pnzp3UqlV8CGj+/Pnk5+c7t9PT02nXrh233367S7s+ffrw/vvvO7d9fHwu30mUkQKbnWU7dfd3EZGKxjAMbDYbnp7n/9oMC7u4q3e9vb2JiIgobWlSSqb3AE2bNo3hw4czbNgwWrVqxezZs/H392fOnDkltq9RowYRERHOx5IlS/D39y8WgHx8fFzaVa9e8VdT/u3ACTJzC6lRzZv29St+vSIild3QoUNZvnw5M2bMcI4YHDhwwDks9f3339OhQwd8fHxYsWIFe/fupX///oSHhxMQEECnTp1YunSpyzH/3kNisVj4f//v/zFgwAD8/f1p1qwZ3377rfP5vw+BFQ1L/fDDD7Rs2ZKAgAD69OlDUlKS8zWFhYU89thjhISEULNmTZ566imGDBnCzTfffM7z/eCDD6hfvz7+/v4MGDCA9PR0l+fPd349e/bk4MGDjBo1yvl5gaMzYuDAgdStWxd/f3+io6P57LPPLuZHUe5MDUD5+fmsX7+e2NhY5z4PDw9iY2NZtWrVBR3jvffe46677qJaNdf5MsuWLaNWrVpERUXx0EMPFfshnykvL4/MzEyXhxmWnrH6s9VDEwlFpHIzDIOc/EJTHoZhXFCNM2bMoEuXLgwfPpykpCSSkpKIjIx0Pj9mzBhefPFFtm/fTtu2bcnKyqJv374kJCTw+++/06dPH/r168ehQ4fO+T7PPPMMd9xxB5s3b6Zv377cfffdHD9+/Kztc3JyePXVV/n444/5+eefOXToEKNHj3Y+/9JLLzF37lzef/99Vq5cSWZmJt988805a1izZg33338/I0aMYOPGjVx77bU899xzLm3Od37z58+nXr16PPvss87PCyA3N5cOHTqwcOFCtm7dygMPPMC99957ziktZjN1CCwtLQ2bzUZ4uOt8l/DwcHbs2HHe169du5atW7fy3nvvuezv06cPt9xyC40aNWLv3r08/fTT3HDDDaxatarENQOmTp3KM888c2knc4kMw3De/qK35v+ISBVwusBGq4k/mPLe256Nw9/7/F9xwcHBeHt74+/vX+Iw1LPPPkvv3r2d2zVq1KBdu3bO7SlTprBgwQK+/fZbRowYcdb3GTp0KAMHDgTghRde4PXXX2ft2rVnnZ9aUFDA7NmzadKkCQAjRozg2WefdT4/c+ZMxo4dy4ABAwCYNWsWixYtOue5zpgxgz59+vDkk08C0Lx5c3799VcWL17sbNOuXbtznl+NGjWwWq0EBga6fF5169Z1CWiPPvooP/zwA1988QWdO3c+Z11mMX0I7FK89957REdHF/tw77rrLv7v//6P6Ohobr75Zr777jvWrVvHsmXLSjzO2LFjycjIcD4OHz5cDtW72nssmwPpOXhbPbi6uVZ/FhGpCDp27OiynZWVxejRo2nZsiUhISEEBASwffv28/YAtW3b1vn3atWqERQU5FzRuCT+/v7O8AOOVY+L2mdkZJCSkuLy3We1WunQocM5a9i+fTsxMTEu+7p06VIm52ez2ZgyZQrR0dHUqFGDgIAAfvjhh/O+zkym9gCFhoZitVpJSUlx2Z+SknLeCWHZ2dl8/vnnLon4bBo3bkxoaCh79uyhV69exZ738fExfZJ0Ue9PTOMaBPiYPjddROSS+XlZ2fZsnGnvXRb+Pr1i9OjRLFmyhFdffZWmTZvi5+fHbbfd5nJxTkm8vFxX9bdYLNjt9otqf6HDepeitOf3yiuvMGPGDKZPn050dDTVqlXj8ccfP+/rzGTqN623tzcdOnQgISHBOXHLbreTkJBwzq5EgC+//JK8vDzuueee877PkSNHSE9Pv+T7hlxORZe/6+anIlJVWCyWCxqGMpu3tzc2m+2C2q5cuZKhQ4c6h56ysrI4cODAZayuuODgYMLDw1m3bh09evQAHD0wGzZs4Iorrjjr61q2bMmaNWtc9q1evdpl+0LOr6TPa+XKlfTv39/5nWy329m1axetWrUqzSmWC9OHwOLj43n33Xf58MMP2b59Ow899BDZ2dkMGzYMgMGDBzN27Nhir3vvvfe4+eabi91zJisriyeeeILVq1dz4MABEhIS6N+/P02bNiUuzpz/EzmfE9n5/HbQMRmuly5/FxEpVw0bNmTNmjUcOHCAtLS0c/bMNGvWjPnz57Nx40Y2bdrEoEGDztn+cnn00UeZOnUq//nPf9i5cycjR47kxIkT51wl+bHHHmPx4sW8+uqr7N69m1mzZrnM/4ELO7+GDRvy888/k5iYSFpamvN1S5Ys4ddff2X79u3885//LDa6U9GYHoDuvPNOXn31VSZOnMgVV1zBxo0bWbx4sXNi9KFDh1wu/QPYuXMnK1as4P777y92PKvVyubNm/m///s/mjdvzv3330+HDh345ZdfTB/mOpufdqZiN6BFRCD1qus+OiIi5Wn06NFYrVZatWpFWFjYOeetTJs2jerVq9O1a1f69etHXFwcV155ZTlW6/DUU08xcOBABg8eTJcuXQgICCAuLg5fX9+zvuaqq67i3XffZcaMGbRr147//e9/jB8/3qXNhZzfs88+y4EDB2jSpIlzzaPx48dz5ZVXEhcXR8+ePYmIiDjvJflmsxjlMahYyWRmZhIcHExGRgZBQUGX/f0embuBhVuSGHFtU0bHRV329xMRKWu5ubns37+fRo0anfNLWC4Pu91Oy5YtueOOO5gyZYrZ5VxW5/pdu5jv74o/OFvF5RfaWb7rGKDhLxERuTAHDx7kf//7H9dccw15eXnMmjWL/fv3M2jQILNLqzRMHwJzd2v3Hycrr5DQAB/a1QsxuxwREakEPDw8+OCDD+jUqRPdunVjy5YtLF26lJYtW5pdWqWhHiCTFa3+fF2LMDy0+rOIiFyAyMhIVq5caXYZlZp6gExkGIYzAOnydxERkfKjAGSiXSlZHDlxGm9PD7o3CzW7HBEREbehAGSiot6fbk1qVorFwkRERKoKBSATFd3+opeGv0RERMqVApBJ0rLy+P3wSUCXv4uIiJQ3BSCT/LgjFcOA1nWCqB3sZ3Y5IiIibkUByCQJuvpLRKTKaNiwIdOnT3duWywWvvnmm7O2P3DgABaLhY0bN17S+5bVcUpj6NChFf52F+eiAGSC3AIbv+x23EBOAUhEpOpJSkrihhtuKNNjlhQ4IiMjSUpKok2bNmX6XpeDmWGtJLr0yASr96WTk28jPMiHNnUv/73GRESkfEVERJTL+1it1nJ7r6pGPUAm+Gv153AsFq3+LCJilnfeeYc6depgt9td9vfv35/77rsPgL1799K/f3/Cw8MJCAigU6dOLF269JzH/fsQ2Nq1a2nfvj2+vr507NiR33//3aW9zWbj/vvvp1GjRvj5+REVFcWMGTOcz0+ePJkPP/yQ//znP1gsFiwWC8uWLSuxV2X58uV07twZHx8fateuzZgxYygsLHQ+37NnTx577DGefPJJatSoQUREBJMnTz7n+dhsNuLj4wkJCaFmzZo8+eST/P1e6osXL6Z79+7ONjfddBN79+51Pt+oUSMA2rdvj8VioWfPngCsW7eO3r17ExoaSnBwMNdccw0bNmw4Zz1lQQGonBmGwY/bUwGI1dVfIlKVGQbkZ5vz+NuX89ncfvvtpKen89NPPzn3HT9+nMWLF3P33XcDkJWVRd++fUlISOD333+nT58+9OvXj0OHDl3Qe2RlZXHTTTfRqlUr1q9fz+TJkxk9erRLG7vdTr169fjyyy/Ztm0bEydO5Omnn+aLL74AYPTo0dxxxx306dOHpKQkkpKS6Nq1a7H3SkxMpG/fvnTq1IlNmzbx1ltv8d577/Hcc8+5tPvwww+pVq0aa9as4eWXX+bZZ59lyZIlZz2H1157jQ8++IA5c+awYsUKjh8/zoIFC1zaZGdnEx8fz2+//UZCQgIeHh4MGDDAGS7Xrl0LwNKlS0lKSmL+/PkAnDp1iiFDhrBixQpWr15Ns2bN6Nu3L6dOnbqgz7e0NARWzrYlZXI0IxdfLw+6NdXqzyJShRXkwAt1zHnvp4+Cd7XzNqtevTo33HADn376Kb169QLgq6++IjQ0lGuvvRaAdu3a0a5dO+drpkyZwoIFC/j2228ZMWLEed/j008/xW6389577+Hr60vr1q05cuQIDz30kLONl5cXzzzzjHO7UaNGrFq1ii+++II77riDgIAA/Pz8yMvLO+eQ15tvvklkZCSzZs3CYrHQokULjh49ylNPPcXEiRPx8HD0e7Rt25ZJkyYB0KxZM2bNmkVCQgK9e/cu8bjTp09n7Nix3HLLLQDMnj2bH374waXNrbfe6rI9Z84cwsLC2LZtG23atCEsLAyAmjVrupzDdddd5/K6d955h5CQEJYvX85NN9101nO9VOoBKmcJf/b+dG8ahq+X1eRqRETk7rvv5uuvvyYvLw+AuXPnctdddznDQlZWFqNHj6Zly5aEhIQQEBDA9u3bL7gHaPv27bRt2xZfX1/nvi5duhRr98Ybb9ChQwfCwsIICAjgnXfeueD3OPO9unTp4jK9olu3bmRlZXHkyBHnvrZt27q8rnbt2qSmppZ4zIyMDJKSkoiJiXHu8/T0pGPHji7tdu/ezcCBA2ncuDFBQUE0bNgQ4LznkJKSwvDhw2nWrBnBwcEEBQWRlZV10ed+sdQDVM7+uvxdw18iUsV5+Tt6Ysx67wvUr18/DMNg4cKFdOrUiV9++YV///vfzudHjx7NkiVLePXVV2natCl+fn7cdttt5Ofnl1m5n3/+OaNHj+a1116jS5cuBAYG8sorr7BmzZoye48zeXl5uWxbLJZi86AuVr9+/WjQoAHvvvuuc15VmzZtzvs5DRkyhPT0dGbMmEGDBg3w8fGhS5cuZfr5lkQBqBylZuay6UgGANe1UAASkSrOYrmgYSiz+fr6cssttzB37lz27NlDVFQUV155pfP5lStXMnToUAYMGAA4eoQOHDhwwcdv2bIlH3/8Mbm5uc5eoNWrV7u0WblyJV27duXhhx927jtzAjGAt7c3NpvtvO/19ddfYxiGsxdo5cqVBAYGUq9evQuu+UzBwcHUrl2bNWvW0KNHDwAKCwtZv36983NKT09n586dvPvuu1x99dUArFixolj9QLFzWLlyJW+++SZ9+/YF4PDhw6SlpZWq1ouhIbBylLDD0b3Yrl4wtYJ8z9NaRETKy913383ChQuZM2eOc/JzkWbNmjF//nw2btzIpk2bGDRo0EX1lgwaNAiLxcLw4cPZtm0bixYt4tVXXy32Hr/99hs//PADu3btYsKECaxbt86lTcOGDdm8eTM7d+4kLS2NgoKCYu/18MMPc/jwYR599FF27NjBf/7zHyZNmkR8fLxzSK80Ro4cyYsvvsg333zDjh07ePjhhzl58qTz+erVq1OzZk3eeecd9uzZw48//kh8fLzLMWrVqoWfnx+LFy8mJSWFjIwM57l//PHHbN++nTVr1nD33Xfj53f575CgAFSOjmfn4+dl1eKHIiIVzHXXXUeNGjXYuXMngwYNcnlu2rRpVK9ena5du9KvXz/i4uJceojOJyAggP/+979s2bKF9u3bM27cOF566SWXNv/85z+55ZZbuPPOO4mJiSE9Pd2lNwhg+PDhREVF0bFjR8LCwli5cmWx96pbty6LFi1i7dq1tGvXjgcffJD777+f8ePHX8SnUdy//vUv7r33XoYMGeIcoivqEQPw8PDg888/Z/369bRp04ZRo0bxyiuvuBzD09OT119/nbfffps6derQv39/AN577z1OnDjBlVdeyb333stjjz1GrVqXf5TEYvz9Qn4hMzOT4OBgMjIyCAoq24UKcwts5NvsBPl6nb+xiEglkZuby/79+2nUqJHLZF+Rsnau37WL+f7WHKBy5utl1dVfIiIiJtMQmIiIiLgdBSARERFxOwpAIiIi4nYUgERERMTtKACJiEiZ0YXFcrmV1e+YApCIiFyyolsr5OTkmFyJVHVFv2N/v53HxdJl8CIicsmsVishISHOG2r6+/u73JBT5FIZhkFOTg6pqamEhIRgtV7akjIKQCIiUiYiIiIAznpXcZGyEBIS4vxduxQKQCIiUiYsFgu1a9emVq1aJd6nSuRSeXl5XXLPTxEFIBERKVNWq7XMvqRELhdNghYRERG3owAkIiIibkcBSERERNyO5gCVoGiRpczMTJMrERERkQtV9L19IYslKgCV4NSpUwBERkaaXImIiIhcrFOnThEcHHzONhZD65YXY7fbOXr0KIGBgWW+kFdmZiaRkZEcPnyYoKCgMj22XDz9PCoW/TwqFv08Khb9PM7PMAxOnTpFnTp18PA49ywf9QCVwMPDg3r16l3W9wgKCtIvcAWin0fFop9HxaKfR8Win8e5na/np4gmQYuIiIjbUQASERERt6MAVM58fHyYNGkSPj4+Zpci6OdR0ejnUbHo51Gx6OdRtjQJWkRERNyOeoBERETE7SgAiYiIiNtRABIRERG3owAkIiIibkcBqBy98cYbNGzYEF9fX2JiYli7dq3ZJbmlqVOn0qlTJwIDA6lVqxY333wzO3fuNLss+dOLL76IxWLh8ccfN7sUt5aYmMg999xDzZo18fPzIzo6mt9++83sstySzWZjwoQJNGrUCD8/P5o0acKUKVMu6H5XcnYKQOVk3rx5xMfHM2nSJDZs2EC7du2Ii4sjNTXV7NLczvLly3nkkUdYvXo1S5YsoaCggOuvv57s7GyzS3N769at4+2336Zt27Zml+LWTpw4Qbdu3fDy8uL7779n27ZtvPbaa1SvXt3s0tzSSy+9xFtvvcWsWbPYvn07L730Ei+//DIzZ840u7RKTZfBl5OYmBg6derErFmzAMf9xiIjI3n00UcZM2aMydW5t2PHjlGrVi2WL19Ojx49zC7HbWVlZXHllVfy5ptv8txzz3HFFVcwffp0s8tyS2PGjGHlypX88ssvZpciwE033UR4eDjvvfeec9+tt96Kn58fn3zyiYmVVW7qASoH+fn5rF+/ntjYWOc+Dw8PYmNjWbVqlYmVCUBGRgYANWrUMLkS9/bII49w4403uvx3Iub49ttv6dixI7fffju1atWiffv2vPvuu2aX5ba6du1KQkICu3btAmDTpk2sWLGCG264weTKKjfdDLUcpKWlYbPZCA8Pd9kfHh7Ojh07TKpKwNET9/jjj9OtWzfatGljdjlu6/PPP2fDhg2sW7fO7FIE2LdvH2+99Rbx8fE8/fTTrFu3jsceewxvb2+GDBlidnluZ8yYMWRmZtKiRQusVis2m43nn3+eu+++2+zSKjUFIHFrjzzyCFu3bmXFihVml+K2Dh8+zMiRI1myZAm+vr5mlyM4/segY8eOvPDCCwC0b9+erVu3Mnv2bAUgE3zxxRfMnTuXTz/9lNatW7Nx40Yef/xx6tSpo5/HJVAAKgehoaFYrVZSUlJc9qekpBAREWFSVTJixAi+++47fv75Z+rVq2d2OW5r/fr1pKamcuWVVzr32Ww2fv75Z2bNmkVeXh5Wq9XECt1P7dq1adWqlcu+li1b8vXXX5tUkXt74oknGDNmDHfddRcA0dHRHDx4kKlTpyoAXQLNASoH3t7edOjQgYSEBOc+u91OQkICXbp0MbEy92QYBiNGjGDBggX8+OOPNGrUyOyS3FqvXr3YsmULGzdudD46duzI3XffzcaNGxV+TNCtW7diS0Ps2rWLBg0amFSRe8vJycHDw/Xr2mq1YrfbTaqoalAPUDmJj49nyJAhdOzYkc6dOzN9+nSys7MZNmyY2aW5nUceeYRPP/2U//znPwQGBpKcnAxAcHAwfn5+JlfnfgIDA4vNv6pWrRo1a9bUvCyTjBo1iq5du/LCCy9wxx13sHbtWt555x3eeecds0tzS/369eP555+nfv36tG7dmt9//51p06Zx3333mV1apabL4MvRrFmzeOWVV0hOTuaKK67g9ddfJyYmxuyy3I7FYilx//vvv8/QoUPLtxgpUc+ePXUZvMm+++47xo4dy+7du2nUqBHx8fEMHz7c7LLc0qlTp5gwYQILFiwgNTWVOnXqMHDgQCZOnIi3t7fZ5VVaCkAiIiLidjQHSERERNyOApCIiIi4HQUgERERcTsKQCIiIuJ2FIBERETE7SgAiYiIiNtRABIRERG3owAkIlKCZcuWYbFYOHnypNmliMhloAAkIiIibkcBSERERNyOApCIVEh2u52pU6fSqFEj/Pz8aNeuHV999RXw1/DUwoULadu2Lb6+vlx11VVs3brV5Rhff/01rVu3xsfHh4YNG/Laa6+5PJ+Xl8dTTz1FZGQkPj4+NG3alPfee8+lzfr16+nYsSP+/v507drV5S7pmzZt4tprryUwMJCgoCA6dOjAb7/9dpk+EREpSwpAIlIhTZ06lY8++ojZs2fzxx9/MGrUKO655x6WL1/ubPPEE0/w2muvsW7dOsLCwujXrx8FBQWAI7jccccd3HXXXWzZsoXJkyczYcIEPvjgA+frBw8ezGeffcbrr7/O9u3befvttwkICHCpY9y4cbz22mv89ttveHp6utyB++6776ZevXqsW7eO9evXM2bMGLy8vC7vByMiZcMQEalgcnNzDX9/f+PXX3912X///fcbAwcONH766ScDMD7//HPnc+np6Yafn58xb948wzAMY9CgQUbv3r1dXv/EE08YrVq1MgzDMHbu3GkAxpIlS0qsoeg9li5d6ty3cOFCAzBOnz5tGIZhBAYGGh988MGln7CIlDv1AIlIhbNnzx5ycnLo3bs3AQEBzsdHH33E3r17ne26dOni/HuNGjWIiopi+/btAGzfvp1u3bq5HLdbt27s3r0bm83Gxo0bsVqtXHPNNeespW3bts6/165dG4DU1FQA4uPj+cc//kFsbCwvvviiS20iUrEpAIlIhZOVlQXAwoUL2bhxo/Oxbds25zygS+Xn53dB7c4c0rJYLIBjfhLA5MmT+eOPP7jxxhv58ccfadWqFQsWLCiT+kTk8lIAEpEKp1WrVvj4+HDo0CGaNm3q8oiMjHS2W716tfPvJ06cYNeuXbRs2RKAli1bsnLlSpfjrly5kubNm2O1WomOjsZut7vMKSqN5s2bM2rUKP73v/9xyy238P7771/S8USkfHiaXYCIyN8FBgYyevRoRo0ahd1up3v37mRkZLBy5UqCgoJo0KABAM8++yw1a9YkPDyccePGERoays033wzAv/71Lzp16sSUKVO48847WbVqFbNmzeLNN98EoGHDhgwZMoT77ruP119/nXbt2nHw4EFSU1O54447zlvj6dOneeKJJ7jtttto1KgRR44cYd26ddx6662X7XMRkTJk9iQkEZGS2O12Y/r06UZUVJTh5eVlhIWFGXFxccby5cudE5T/+9//Gq1btza8vb2Nzp07G5s2bXI5xldffWW0atXK8PLyMurXr2+88sorLs+fPn3aGDVqlFG7dm3D29vbaNq0qTFnzhzDMP6aBH3ixAln+99//90AjP379xt5eXnGXXfdZURGRhre3t5GnTp1jBEjRjgnSItIxWYxDMMwOYOJiFyUZcuWce2113LixAlCQkLMLkdEKiHNARIRERG3owAkIiIibkdDYCIiIuJ21AMkIiIibkcBSERERNyOApCIiIi4HQUgERERcTsKQCIiIuJ2FIBERETE7SgAiYiIiNtRABIRERG3owAkIiIibuf/AxSoc9grXhp3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing loss\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.xlabel('loss')\n",
        "plt.ylabel('epochs')\n",
        "\n",
        "plt.legend(['training data', 'validation data'], loc=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "EzwZ129MiWVq",
        "outputId": "ad9e0bd6-7541-4ea7-c6a7-eb2a2f4658e2"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7980fc299c90>"
            ]
          },
          "metadata": {},
          "execution_count": 174
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have created a NN, now we have to test it.\n",
        "\n",
        "So to get accuracy of model on test data:"
      ],
      "metadata": {
        "id": "GUeYNfFMjA6w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test_std, Y_test) #model takes X_test standardized data and takes the output which is Y_test and calculates it, then it gives two values loss and accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zwtnn3-AjHw-",
        "outputId": "c082af73-bb1c-417d-842e-ce2a66133d4a"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1443 - accuracy: 0.9561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now building a predictive system based on this Neural Network"
      ],
      "metadata": {
        "id": "S4hD5G06jhJu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std.shape)\n",
        "print(X_test_std[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqen2YO3joee",
        "outputId": "8c4d321e-17da-430f-db8d-d86b37d392f3"
      },
      "execution_count": 176,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMkA9efsj4bd",
        "outputId": "0b91c2fc-4b32-464c-93d2-f3c4a43f7eac"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred.shape)\n",
        "print(Y_pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_YbzK0Ij-TW",
        "outputId": "7530c852-22ff-4d82-f789-37144e4e6125"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(114, 2)\n",
            "[0.11546424 0.7135798 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First value represents probability of label 0 : malignant and second value represents probability of label 1 : benign"
      ],
      "metadata": {
        "id": "bmlRvMsJkKrl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "So we simply have to compare the first and second value, and we convert the probability value to labels using the np.rmax function"
      ],
      "metadata": {
        "id": "rmWVXc8OkV5H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "model.predict() gives the prediction probability of each class for the data point"
      ],
      "metadata": {
        "id": "ya5aGmNGkk_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#converting the prediction probability to class labels\n",
        "\n",
        "Y_pred_label = [np.argmax(i) for i in Y_pred]\n",
        "print(Y_pred_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lko1B64Bku-c",
        "outputId": "1dc70c56-0b5a-4c9d-8cd7-ec9ff44bee9c"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 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": "code",
      "source": [
        "#building the predictive system\n",
        "input_data = (17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189)\n",
        "#here we enter the data from the csv file, sample data already entered\n",
        "\n",
        "#change the input data to np array\n",
        "input_data_as_np_array = np.asarray(input_data)\n",
        "\n",
        "#reshaping the np array as we are predicting for one data point\n",
        "input_data_reshaped = input_data_as_np_array.reshape(1, -1)\n",
        "\n",
        "#standardizing the input data\n",
        "input_data_standardized = scaler.transform(input_data_reshaped)\n",
        "\n",
        "#prediction\n",
        "prediction = model.predict(input_data_standardized)\n",
        "print(f\"Prediction: {prediction}\")\n",
        "\n",
        "prediction_label = [np.argmax(prediction)]\n",
        "print(f\"Prediction Label: {prediction_label}\")\n",
        "\n",
        "if prediction_label == [0]:\n",
        "  print(\"The tumor is Malignant\")\n",
        "elif prediction_label == [1]:\n",
        "  print(\"The tumor is Benign\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtRDbuv9k_d1",
        "outputId": "f8ae63d5-b00c-4a77-f266-1cc23063bd67"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 31ms/step\n",
            "Prediction: [[0.82543004 0.00719415]]\n",
            "Prediction Label: [0]\n",
            "The tumor is Malignant\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    },
    "colab": {
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}