Project_Breast_Cancer_Detection.ipynb

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  "metadata": {
    "colab": {
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Breast Cancer Detection"
      ],
      "metadata": {
        "id": "i-yc-hl9UdnY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing the Libraries\n"
      ],
      "metadata": {
        "id": "tbynGBE3UgzI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "dRE-GpKnUNIm"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data collection and Preprocessing"
      ],
      "metadata": {
        "id": "UKzJ-swKWP5J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer as breast_cancer_dataset\n",
        "data = breast_cancer_dataset()"
      ],
      "metadata": {
        "id": "e9g7tKnYUrYX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tiCO3nnnY3Bj",
        "outputId": "44bb1d1f-ef68-47fc-eec7-35770e4ce2ec",
        "collapsed": true
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
            "        1.189e-01],\n",
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            "        1.240e-01],\n",
            "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
            "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
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            "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
            "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
            "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
            "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
            "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 569\\n\\n:Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n:Attribute Information:\\n    - radius (mean of distances from center to points on the perimeter)\\n    - texture (standard deviation of gray-scale values)\\n    - perimeter\\n    - area\\n    - smoothness (local variation in radius lengths)\\n    - compactness (perimeter^2 / area - 1.0)\\n    - concavity (severity of concave portions of the contour)\\n    - concave points (number of concave portions of the contour)\\n    - symmetry\\n    - fractal dimension (\"coastline approximation\" - 1)\\n\\n    The mean, standard error, and \"worst\" or largest (mean of the three\\n    worst/largest values) of these features were computed for each image,\\n    resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n    10 is Radius SE, field 20 is Worst Radius.\\n\\n    - class:\\n            - WDBC-Malignant\\n            - WDBC-Benign\\n\\n:Summary Statistics:\\n\\n===================================== ====== ======\\n                                        Min    Max\\n===================================== ====== ======\\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": [
        "# loading the dataframe into pandas for better view\n",
        "df = pd.DataFrame(data.data , columns= data.feature_names)"
      ],
      "metadata": {
        "id": "KS_9fdmnWjJg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "s0JbYsUwXL8O",
        "outputId": "d1bb77e2-8211-4f16-d8cb-1782d05f394a"
      },
      "execution_count": 5,
      "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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              "  <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",
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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",
              "    </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>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</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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              "    <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>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-532747ea-773b-4438-87c7-2709ad086cb3 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-532747ea-773b-4438-87c7-2709ad086cb3');\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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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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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",
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              "    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",
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              "    60% {\n",
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              "      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",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      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-021bcf6a-b15b-4c1f-99e5-d2af9ebc9df3 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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adding the target column\n",
        "df['label'] = data.target"
      ],
      "metadata": {
        "id": "iYM2gpIcaChv"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "bkQj-Tg5cr3s",
        "outputId": "43a7a90f-9cfe-4256-a4e4-78bad06ffbb2",
        "collapsed": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  label  \n",
              "564                0.2216          0.2060                  0.07115      0  \n",
              "565                0.1628          0.2572                  0.06637      0  \n",
              "566                0.1418          0.2218                  0.07820      0  \n",
              "567                0.2650          0.4087                  0.12400      0  \n",
              "568                0.0000          0.2871                  0.07039      1  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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",
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              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "        const element = document.querySelector('#df-92fd9057-3a12-4289-bbac-66cab1e24d51');\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-60a02c32-6d6b-4bc4-bf01-bc3f3d052989\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60a02c32-6d6b-4bc4-bf01-bc3f3d052989')\"\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-60a02c32-6d6b-4bc4-bf01-bc3f3d052989 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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# printing the number of rows and columns in the dataframe\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8IY0i-0Fcv-T",
        "outputId": "f907f58f-9fa1-4e35-b62e-681a2d6978d0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting the information about the data\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "kKn4aM7zdDGy",
        "outputId": "917857df-8f6a-4058-8d5d-3265f26cf4e8",
        "collapsed": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 31 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   mean radius              569 non-null    float64\n",
            " 1   mean texture             569 non-null    float64\n",
            " 2   mean perimeter           569 non-null    float64\n",
            " 3   mean area                569 non-null    float64\n",
            " 4   mean smoothness          569 non-null    float64\n",
            " 5   mean compactness         569 non-null    float64\n",
            " 6   mean concavity           569 non-null    float64\n",
            " 7   mean concave points      569 non-null    float64\n",
            " 8   mean symmetry            569 non-null    float64\n",
            " 9   mean fractal dimension   569 non-null    float64\n",
            " 10  radius error             569 non-null    float64\n",
            " 11  texture error            569 non-null    float64\n",
            " 12  perimeter error          569 non-null    float64\n",
            " 13  area error               569 non-null    float64\n",
            " 14  smoothness error         569 non-null    float64\n",
            " 15  compactness error        569 non-null    float64\n",
            " 16  concavity error          569 non-null    float64\n",
            " 17  concave points error     569 non-null    float64\n",
            " 18  symmetry error           569 non-null    float64\n",
            " 19  fractal dimension error  569 non-null    float64\n",
            " 20  worst radius             569 non-null    float64\n",
            " 21  worst texture            569 non-null    float64\n",
            " 22  worst perimeter          569 non-null    float64\n",
            " 23  worst area               569 non-null    float64\n",
            " 24  worst smoothness         569 non-null    float64\n",
            " 25  worst compactness        569 non-null    float64\n",
            " 26  worst concavity          569 non-null    float64\n",
            " 27  worst concave points     569 non-null    float64\n",
            " 28  worst symmetry           569 non-null    float64\n",
            " 29  worst fractal dimension  569 non-null    float64\n",
            " 30  label                    569 non-null    int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 137.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# method to check for missing values.\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1056
        },
        "id": "03-cY8vSdS2f",
        "outputId": "26098acc-f545-49d0-aa74-27b6b313ebbd",
        "collapsed": true
      },
      "execution_count": 10,
      "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"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>0</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean radius</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>mean area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean smoothness</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean compactness</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>mean concavity</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>mean concave points</th>\n",
              "      <td>0</td>\n",
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              "      <th>mean symmetry</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal dimension error</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst radius</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst texture</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst perimeter</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst smoothness</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst compactness</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst concavity</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst concave points</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst symmetry</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# statistical measures about the data\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "irWMOzzPdx0t",
        "outputId": "7c9f6f38-70bc-485f-afb3-e1d115cbe36e",
        "collapsed": true
      },
      "execution_count": 11,
      "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]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6263c61d-d4a2-4508-a4c1-02e2c2a6d07d');\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",
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              "          + ' 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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              "\n",
              "\n",
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              "    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",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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              "    }\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",
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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",
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              "    (() => {\n",
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              "        document.querySelector('#df-718cb9e3-53dc-493a-b861-b36cc252b241 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the distribution of the target variable\n",
        "df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "x3VP5o4YeBP7",
        "outputId": "f5f4a74e-269d-4c45-d4c0-ed241c5880a0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "1    357\n",
              "0    212\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### here in the above checkings\n",
        "0 -> Benign Cancer Type (More dangerous)\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "1 -> Malignant Cancer Type"
      ],
      "metadata": {
        "id": "SgCCKXXFentm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('label').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "npBY5gWzeYsn",
        "outputId": "91ae2390-be67-4367-f820-7f9837ade838",
        "collapsed": true
      },
      "execution_count": 13,
      "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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              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
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              "      <td>21.134811</td>\n",
              "      <td>29.318208</td>\n",
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              "      <td>0.062867</td>\n",
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              "      <td>558.899440</td>\n",
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      "cell_type": "markdown",
      "source": [
        "#Splitting the data by using the sci-kit learn library"
      ],
      "metadata": {
        "id": "pjUeuM15fT-i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "RPL8FyUie5fr"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
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          "height": 0
        },
        "outputId": "0c397f7e-3f37-45c0-f0f1-f79d1888ac2f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "        1.189e-01],\n",
              "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "        8.902e-02],\n",
              "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "        8.758e-02],\n",
              "       ...,\n",
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              "        7.820e-02],\n",
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        "collapsed": true
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      "execution_count": 16,
      "outputs": [
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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",
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              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
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          "metadata": {},
          "execution_count": 16
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      "cell_type": "markdown",
      "source": [
        "#Splitting the data into training data and test data"
      ],
      "metadata": {
        "id": "VSOaoMwcf0Ni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.2, random_state = 2)"
      ],
      "metadata": {
        "id": "25xt6PU-fkYz"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "OW4GmBTWgGcy",
        "outputId": "f73a7cc0-346a-4c6c-f7e7-ced6d81d0008"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (455, 30) (114, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Scailing"
      ],
      "metadata": {
        "id": "MaiNPQF6927M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "nAIbe05y94hz"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Training"
      ],
      "metadata": {
        "id": "cfB3nPQqgrGd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic Regression"
      ],
      "metadata": {
        "id": "KdGQRNNjguEh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "LUL5n-A0gk9o",
        "outputId": "89393b55-bf66-4e3d-e0cd-601119e31019"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluating the model"
      ],
      "metadata": {
        "id": "dAqv4tS4hFhV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Accuracy Score\n"
      ],
      "metadata": {
        "id": "l2bpLOilhH6m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pred = model.predict(X_train)"
      ],
      "metadata": {
        "id": "IwWzTljzg6yz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#comparing the y_train and X_train\n",
        "training_data_accuracy = accuracy_score(y_train, X_train_pred)"
      ],
      "metadata": {
        "id": "1o7QMOv_hU9Q"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MeHf9AS8hhre",
        "outputId": "f698d70a-392e-4b87-949c-d56774aeaef9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.989010989010989\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "7DOhoae0hmMw"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_accuracy_test = accuracy_score(y_test, X_test_pred)"
      ],
      "metadata": {
        "id": "L8w_D5YZiYRk"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(training_data_accuracy_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "D-NZ7Kmriask",
        "outputId": "5d47235c-c81e-42a3-e54f-ecb9842dc70d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9736842105263158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Building a predictive system"
      ],
      "metadata": {
        "id": "uxEHQx5WixuR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (13.54,14.36,87.46,566.3,0.09779,0.08129,0.06664,0.04781,0.1885,0.05766,0.2699,0.7886,2.058,23.56,0.008462,0.0146,0.02387,0.01315,0.0198,0.0023,15.11,19.26,99.7,711.2,0.144,0.1773,0.239,0.1288,0.2977,0.07259)\n",
        "\n",
        "# Change the input data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "# reshaping the model for one data point\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "if(prediction[0] == 0):\n",
        "  print('The Breast cancer is Malignant')\n",
        "\n",
        "else:\n",
        "  print('The Breast Cancer is Benign')"
      ],
      "metadata": {
        "id": "olY9Vw-ziiT1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1c98b240-3368-4011-f05c-a9f657aaff0f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Breast cancer is Malignant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualization"
      ],
      "metadata": {
        "id": "UZ0DLQd2lpMA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create bar plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(['Training Accuracy', 'Test Accuracy'], [training_data_accuracy, training_data_accuracy_test], color=['blue', 'green'])\n",
        "plt.ylim([0, 1])  # Set y-axis limits from 0 to 1\n",
        "plt.title('Model Accuracy Comparison')\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "# Add text labels on top of each bar\n",
        "for i, v in enumerate([training_data_accuracy, training_data_accuracy_test]):\n",
        "    plt.text(i, v + 0.01, f'{v:.4f}', ha='center')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "i8C6dlLtlox-",
        "outputId": "ce125546-2131-4ba0-f977-26ac94257856"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "importances = model.coef_[0]\n",
        "\n",
        "# Sort features by importance\n",
        "indices = np.argsort(np.abs(importances))\n",
        "top_k = 10  # Show top 10 features\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title(f\"Top {top_k} Feature Importances\")\n",
        "plt.bar(range(top_k), np.abs(importances[indices[-top_k:]]))\n",
        "plt.xticks(range(top_k), [f\"Feature {i}\" for i in indices[-top_k:]], rotation=45, ha='right')\n",
        "plt.xlabel(\"Features\")\n",
        "plt.ylabel(\"Absolute Importance\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "drI9vwN3mohg",
        "outputId": "00df649f-bd92-4816-dc38-56457587e0e2"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}