Breast_cancer_classification.ipynb

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      "authorship_tag": "ABX9TyNUylti1/5OnXkLT2sZk7QU",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/faisal-rasheed-lone/Machine_Learning/blob/main/Breast_cancer_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Section 1**\n",
        "Loading the data and performing *EDA*"
      ],
      "metadata": {
        "id": "iY-eSJM7DKZo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NmCld7RVs8ZZ"
      },
      "outputs": [],
      "source": [
        "import sklearn.datasets\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer = sklearn.datasets.load_breast_cancer()\n"
      ],
      "metadata": {
        "id": "XqtJqKpU_i66"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3gxmuEB_x6N",
        "outputId": "86901479-49cd-426c-e595-a01ade6edefa"
      },
      "execution_count": 3,
      "outputs": [
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            "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
            "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
            "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
            "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 569\\n\\n:Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n:Attribute Information:\\n    - radius (mean of distances from center to points on the perimeter)\\n    - texture (standard deviation of gray-scale values)\\n    - perimeter\\n    - area\\n    - smoothness (local variation in radius lengths)\\n    - compactness (perimeter^2 / area - 1.0)\\n    - concavity (severity of concave portions of the contour)\\n    - concave points (number of concave portions of the contour)\\n    - symmetry\\n    - fractal dimension (\"coastline approximation\" - 1)\\n\\n    The mean, standard error, and \"worst\" or largest (mean of the three\\n    worst/largest values) of these features were computed for each image,\\n    resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n    10 is Radius SE, field 20 is Worst Radius.\\n\\n    - class:\\n            - WDBC-Malignant\\n            - WDBC-Benign\\n\\n:Summary Statistics:\\n\\n===================================== ====== ======\\n                                        Min    Max\\n===================================== ====== ======\\nradius (mean):                        6.981  28.11\\ntexture (mean):                       9.71   39.28\\nperimeter (mean):                     43.79  188.5\\narea (mean):                          143.5  2501.0\\nsmoothness (mean):                    0.053  0.163\\ncompactness (mean):                   0.019  0.345\\nconcavity (mean):                     0.0    0.427\\nconcave points (mean):                0.0    0.201\\nsymmetry (mean):                      0.106  0.304\\nfractal dimension (mean):             0.05   0.097\\nradius (standard error):              0.112  2.873\\ntexture (standard error):             0.36   4.885\\nperimeter (standard error):           0.757  21.98\\narea (standard error):                6.802  542.2\\nsmoothness (standard error):          0.002  0.031\\ncompactness (standard error):         0.002  0.135\\nconcavity (standard error):           0.0    0.396\\nconcave points (standard error):      0.0    0.053\\nsymmetry (standard error):            0.008  0.079\\nfractal dimension (standard error):   0.001  0.03\\nradius (worst):                       7.93   36.04\\ntexture (worst):                      12.02  49.54\\nperimeter (worst):                    50.41  251.2\\narea (worst):                         185.2  4254.0\\nsmoothness (worst):                   0.071  0.223\\ncompactness (worst):                  0.027  1.058\\nconcavity (worst):                    0.0    1.252\\nconcave points (worst):               0.0    0.291\\nsymmetry (worst):                     0.156  0.664\\nfractal 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.. dropdown:: References\\n\\n  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\\n    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\\n    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n    San Jose, CA, 1993.\\n  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\\n    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\\n    July-August 1995.\\n  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\\n    163-171.\\n', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
            "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
            "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
            "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
            "       'smoothness error', 'compactness error', 'concavity error',\n",
            "       'concave points error', 'symmetry error',\n",
            "       'fractal dimension error', 'worst radius', 'worst texture',\n",
            "       'worst perimeter', 'worst area', 'worst smoothness',\n",
            "       'worst compactness', 'worst concavity', 'worst concave points',\n",
            "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = breast_cancer.data\n",
        "y = breast_cancer.target\n",
        "print(X)\n",
        "print(y)"
      ],
      "metadata": {
        "id": "mzMHsufT_4Vr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e690abf-ea9c-44b9-9bda-c6d24b723326"
      },
      "execution_count": 4,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
            " ...\n",
            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
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            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEaO5rzgAj1j",
        "outputId": "4024bdb4-9408-40d1-9445-15d66d434f9b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (569,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "D9eX3_0HBCrQ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame(breast_cancer.data, columns =breast_cancer.feature_names)"
      ],
      "metadata": {
        "id": "-A6L8TEIAnxo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['class'] = breast_cancer.target"
      ],
      "metadata": {
        "id": "w8kkm5X_BTC3"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "fcLQqT7EBNyv",
        "outputId": "dfd6cee7-6d7b-4fe7-f75d-5c9a96061e68"
      },
      "execution_count": 9,
      "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  class  \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>class</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>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",
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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>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",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\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",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
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              "        document.querySelector('#df-e2d2cf20-08c4-4431-b13b-44c0e33ca970 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-e2d2cf20-08c4-4431-b13b-44c0e33ca970');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e7c00f4-2eef-4e91-8ff8-072d67c18cf7')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </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",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\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",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      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",
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              "    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-2e7c00f4-2eef-4e91-8ff8-072d67c18cf7 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": "data"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "86L4KVMOBerC",
        "outputId": "8ef37734-b889-47c9-feb9-c1b5be170200"
      },
      "execution_count": 10,
      "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       class  \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-1236259f-6609-45b1-9fa8-8cc311a2959c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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>class</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",
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              "\n",
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              "  </style>\n",
              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3bc50ea8-a2e9-43f3-a417-23fbf32e946c button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gettng the value count of class - i.e how many are having cancer and how mant are safe\n",
        "\n",
        "print(data['class'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X91JEQfxBvSj",
        "outputId": "5f7708ad-cb8b-467f-b0b8-a25e6c284f16"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "1    357\n",
            "0    212\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer.target_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niVuLJPZCJEi",
        "outputId": "b9034bc9-9298-4ca3-fb33-09d3b68d9b4e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['malignant' 'benign']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "SQ-Pkl1-CTnO",
        "outputId": "f3becc50-401a-47ba-bb0d-72622e5f5ad3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
              "class                                                                           \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",
              "class                                                                         \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",
              "class                          ...                                \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",
              "class                                                                      \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",
              "class                                                          \n",
              "0             0.450606              0.182237        0.323468   \n",
              "1             0.166238              0.074444        0.270246   \n",
              "\n",
              "       worst fractal dimension  \n",
              "class                           \n",
              "0                     0.091530  \n",
              "1                     0.079442  \n",
              "\n",
              "[2 rows x 30 columns]"
            ],
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              "\n",
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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 radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
              "      <td>...</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",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65874e98-180a-4e5b-83e1-00a20b81c69c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-65874e98-180a-4e5b-83e1-00a20b81c69c 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-65874e98-180a-4e5b-83e1-00a20b81c69c');\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-557e1d32-04f1-46b1-a54e-d68f6ec63737\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-557e1d32-04f1-46b1-a54e-d68f6ec63737')\"\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-557e1d32-04f1-46b1-a54e-d68f6ec63737 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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 2 : Training and Testing the data**"
      ],
      "metadata": {
        "id": "5Qmp93QVDdC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "g698YhRqDmPX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('class', axis = 1) # Axis = 1 means droping the column\n",
        "y = data['class'] # storing Traget variable in Y"
      ],
      "metadata": {
        "id": "hE0mfUGWEICi"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, stratify=y, random_state=40)\n"
      ],
      "metadata": {
        "id": "7okXm3TnEs1c"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2jUh8AnE3Rz",
        "outputId": "0b612bc4-ed6d-47b1-c37b-8f85aef21ecd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (381, 30) (188, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "1eTiqCXjMlF1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train.T, '')\n",
        "plt.xticks(rotation ='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "vavEBdQ8Mqqx",
        "outputId": "17deed50-426d-4de7-baa7-fbab5d0e5ff8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VcKDrN2HUUFn"
      },
      "execution_count": 19,
      "outputs": []
    }
  ]
}