Breast_Cancer_Classification_with_NN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPQxVrdM2O+lo0IJ6vGr+nN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MYoussef885/Breast_Cancer_Classification_using_NN/blob/main/Breast_Cancer_Classification_with_NN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the dependencies"
      ],
      "metadata": {
        "id": "5Jj9JNLTWd3Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2FU7t_anV8S_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Collection and Processing"
      ],
      "metadata": {
        "id": "kL7KLOlSW215"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the data from sklearn\n",
        "breast_cancer_dataset = sklearn.datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "9EXk9BgLW6Vz"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9raxkcyaXI18",
        "outputId": "85f608b0-72b3-4758-ca0f-394078e53732"
      },
      "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",
            "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
            "        8.902e-02],\n",
            "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
            "        8.758e-02],\n",
            "       ...,\n",
            "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
            "        7.820e-02],\n",
            "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
            "        1.240e-01],\n",
            "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
            "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
            "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
            "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
            "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
            "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
            "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
            "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
            "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
            "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
            "       'smoothness error', 'compactness error', 'concavity error',\n",
            "       'concave points error', 'symmetry error',\n",
            "       'fractal dimension error', 'worst radius', 'worst texture',\n",
            "       'worst perimeter', 'worst area', 'worst smoothness',\n",
            "       'worst compactness', 'worst concavity', 'worst concave points',\n",
            "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the data to a data frame\n",
        "data_frame = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)"
      ],
      "metadata": {
        "id": "sSs464ZLXMd2"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print the first five rows of the dataframe\n",
        "data_frame.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "tYSSEhONXpeQ",
        "outputId": "c7f2713f-230d-42f2-8549-4e118648cd4e"
      },
      "execution_count": 6,
      "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21367ca7-c556-4831-9b79-b569ae6f160c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21367ca7-c556-4831-9b79-b569ae6f160c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-21367ca7-c556-4831-9b79-b569ae6f160c 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-21367ca7-c556-4831-9b79-b569ae6f160c');\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-f60994be-6e9a-43fc-8a37-f86f252965f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f60994be-6e9a-43fc-8a37-f86f252965f4')\"\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-f60994be-6e9a-43fc-8a37-f86f252965f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adding the 'target' column to the data frame\n",
        "data_frame['label'] = breast_cancer_dataset.target"
      ],
      "metadata": {
        "id": "UdADrCqVYFVd"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# printing the last 5 rows\n",
        "data_frame.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "tzZ2ery_Yigk",
        "outputId": "57e66cdb-8ddf-4d1c-c869-33da24ef9ab2"
      },
      "execution_count": 8,
      "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-baf71d41-bb6f-4e00-b515-c91777c34525\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-baf71d41-bb6f-4e00-b515-c91777c34525')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-baf71d41-bb6f-4e00-b515-c91777c34525 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-baf71d41-bb6f-4e00-b515-c91777c34525');\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-1a2cac74-9f5b-4576-b7c3-a1f0129cfe5d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1a2cac74-9f5b-4576-b7c3-a1f0129cfe5d')\"\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-1a2cac74-9f5b-4576-b7c3-a1f0129cfe5d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q03quQCZYqBN",
        "outputId": "6849dfc8-d167-4290-ac4c-1831d09934db"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAYXvqV6YtPf",
        "outputId": "26c38518-21c6-4f8a-8269-12f26525f1a1"
      },
      "execution_count": 10,
      "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": [
        "# checking for missing values\n",
        "data_frame.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKSW2WrQYzNr",
        "outputId": "9aed1bd6-267f-4dbc-9c7c-ff559ca2582d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean radius                0\n",
              "mean texture               0\n",
              "mean perimeter             0\n",
              "mean area                  0\n",
              "mean smoothness            0\n",
              "mean compactness           0\n",
              "mean concavity             0\n",
              "mean concave points        0\n",
              "mean symmetry              0\n",
              "mean fractal dimension     0\n",
              "radius error               0\n",
              "texture error              0\n",
              "perimeter error            0\n",
              "area error                 0\n",
              "smoothness error           0\n",
              "compactness error          0\n",
              "concavity error            0\n",
              "concave points error       0\n",
              "symmetry error             0\n",
              "fractal dimension error    0\n",
              "worst radius               0\n",
              "worst texture              0\n",
              "worst perimeter            0\n",
              "worst area                 0\n",
              "worst smoothness           0\n",
              "worst compactness          0\n",
              "worst concavity            0\n",
              "worst concave points       0\n",
              "worst symmetry             0\n",
              "worst fractal dimension    0\n",
              "label                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# statistical measures about the data\n",
        "data_frame.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "Hx5EiWHCZM8S",
        "outputId": "a4043898-6372-430d-dd59-f42fa89f9034"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean radius  mean texture  mean perimeter    mean area  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
              "count     569.000000              569.000000  ...     569.000000   \n",
              "mean        0.181162                0.062798  ...      25.677223   \n",
              "std         0.027414                0.007060  ...       6.146258   \n",
              "min         0.106000                0.049960  ...      12.020000   \n",
              "25%         0.161900                0.057700  ...      21.080000   \n",
              "50%         0.179200                0.061540  ...      25.410000   \n",
              "75%         0.195700                0.066120  ...      29.720000   \n",
              "max         0.304000                0.097440  ...      49.540000   \n",
              "\n",
              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
              "count       569.000000   569.000000        569.000000         569.000000   \n",
              "mean        107.261213   880.583128          0.132369           0.254265   \n",
              "std          33.602542   569.356993          0.022832           0.157336   \n",
              "min          50.410000   185.200000          0.071170           0.027290   \n",
              "25%          84.110000   515.300000          0.116600           0.147200   \n",
              "50%          97.660000   686.500000          0.131300           0.211900   \n",
              "75%         125.400000  1084.000000          0.146000           0.339100   \n",
              "max         251.200000  4254.000000          0.222600           1.058000   \n",
              "\n",
              "       worst concavity  worst concave points  worst symmetry  \\\n",
              "count       569.000000            569.000000      569.000000   \n",
              "mean          0.272188              0.114606        0.290076   \n",
              "std           0.208624              0.065732        0.061867   \n",
              "min           0.000000              0.000000        0.156500   \n",
              "25%           0.114500              0.064930        0.250400   \n",
              "50%           0.226700              0.099930        0.282200   \n",
              "75%           0.382900              0.161400        0.317900   \n",
              "max           1.252000              0.291000        0.663800   \n",
              "\n",
              "       worst fractal dimension       label  \n",
              "count               569.000000  569.000000  \n",
              "mean                  0.083946    0.627417  \n",
              "std                   0.018061    0.483918  \n",
              "min                   0.055040    0.000000  \n",
              "25%                   0.071460    0.000000  \n",
              "50%                   0.080040    1.000000  \n",
              "75%                   0.092080    1.000000  \n",
              "max                   0.207500    1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-81158423-021c-4f7a-801d-ac46cde24fee\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>0.627417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>0.483918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81158423-021c-4f7a-801d-ac46cde24fee')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-81158423-021c-4f7a-801d-ac46cde24fee 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-81158423-021c-4f7a-801d-ac46cde24fee');\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-c334a9ba-ba7e-41ad-90df-dd1b0d66aa55\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c334a9ba-ba7e-41ad-90df-dd1b0d66aa55')\"\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-c334a9ba-ba7e-41ad-90df-dd1b0d66aa55 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distribution of target variable\n",
        "data_frame['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GNk_7EZ6ZTtJ",
        "outputId": "ac42e64d-8a52-4fba-f292-4de64b1e687f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    357\n",
              "0    212\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 --> Benign\n",
        "\n",
        "0 --> Malignant"
      ],
      "metadata": {
        "id": "tMg--OIKZqPC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_frame.groupby('label').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "YL7TFJnKZmsd",
        "outputId": "50706d3f-cc79-4b48-aa5f-35aca08f0e92"
      },
      "execution_count": 14,
      "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8968ae9d-30bb-4f4e-afe2-c62be273e308\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
              "      <td>...</td>\n",
              "      <td>21.134811</td>\n",
              "      <td>29.318208</td>\n",
              "      <td>141.370330</td>\n",
              "      <td>1422.286321</td>\n",
              "      <td>0.144845</td>\n",
              "      <td>0.374824</td>\n",
              "      <td>0.450606</td>\n",
              "      <td>0.182237</td>\n",
              "      <td>0.323468</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.146524</td>\n",
              "      <td>17.914762</td>\n",
              "      <td>78.075406</td>\n",
              "      <td>462.790196</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.080085</td>\n",
              "      <td>0.046058</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.174186</td>\n",
              "      <td>0.062867</td>\n",
              "      <td>...</td>\n",
              "      <td>13.379801</td>\n",
              "      <td>23.515070</td>\n",
              "      <td>87.005938</td>\n",
              "      <td>558.899440</td>\n",
              "      <td>0.124959</td>\n",
              "      <td>0.182673</td>\n",
              "      <td>0.166238</td>\n",
              "      <td>0.074444</td>\n",
              "      <td>0.270246</td>\n",
              "      <td>0.079442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8968ae9d-30bb-4f4e-afe2-c62be273e308')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8968ae9d-30bb-4f4e-afe2-c62be273e308 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-8968ae9d-30bb-4f4e-afe2-c62be273e308');\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-8d3d74f4-1c84-49f8-8987-143c3f81ad28\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d3d74f4-1c84-49f8-8987-143c3f81ad28')\"\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-8d3d74f4-1c84-49f8-8987-143c3f81ad28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seperating the features and target"
      ],
      "metadata": {
        "id": "rcAoUeDiaLVn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_frame.drop(columns='label', axis=1)\n",
        "Y = data_frame['label']"
      ],
      "metadata": {
        "id": "p246HADNZ7fa"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ARdnOEwaZWf",
        "outputId": "4af1227d-1e78-4b87-d209-d95f51c7a71b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
            "0          17.99         10.38          122.80     1001.0          0.11840   \n",
            "1          20.57         17.77          132.90     1326.0          0.08474   \n",
            "2          19.69         21.25          130.00     1203.0          0.10960   \n",
            "3          11.42         20.38           77.58      386.1          0.14250   \n",
            "4          20.29         14.34          135.10     1297.0          0.10030   \n",
            "..           ...           ...             ...        ...              ...   \n",
            "564        21.56         22.39          142.00     1479.0          0.11100   \n",
            "565        20.13         28.25          131.20     1261.0          0.09780   \n",
            "566        16.60         28.08          108.30      858.1          0.08455   \n",
            "567        20.60         29.33          140.10     1265.0          0.11780   \n",
            "568         7.76         24.54           47.92      181.0          0.05263   \n",
            "\n",
            "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
            "0             0.27760         0.30010              0.14710         0.2419   \n",
            "1             0.07864         0.08690              0.07017         0.1812   \n",
            "2             0.15990         0.19740              0.12790         0.2069   \n",
            "3             0.28390         0.24140              0.10520         0.2597   \n",
            "4             0.13280         0.19800              0.10430         0.1809   \n",
            "..                ...             ...                  ...            ...   \n",
            "564           0.11590         0.24390              0.13890         0.1726   \n",
            "565           0.10340         0.14400              0.09791         0.1752   \n",
            "566           0.10230         0.09251              0.05302         0.1590   \n",
            "567           0.27700         0.35140              0.15200         0.2397   \n",
            "568           0.04362         0.00000              0.00000         0.1587   \n",
            "\n",
            "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
            "0                   0.07871  ...        25.380          17.33   \n",
            "1                   0.05667  ...        24.990          23.41   \n",
            "2                   0.05999  ...        23.570          25.53   \n",
            "3                   0.09744  ...        14.910          26.50   \n",
            "4                   0.05883  ...        22.540          16.67   \n",
            "..                      ...  ...           ...            ...   \n",
            "564                 0.05623  ...        25.450          26.40   \n",
            "565                 0.05533  ...        23.690          38.25   \n",
            "566                 0.05648  ...        18.980          34.12   \n",
            "567                 0.07016  ...        25.740          39.42   \n",
            "568                 0.05884  ...         9.456          30.37   \n",
            "\n",
            "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
            "0             184.60      2019.0           0.16220            0.66560   \n",
            "1             158.80      1956.0           0.12380            0.18660   \n",
            "2             152.50      1709.0           0.14440            0.42450   \n",
            "3              98.87       567.7           0.20980            0.86630   \n",
            "4             152.20      1575.0           0.13740            0.20500   \n",
            "..               ...         ...               ...                ...   \n",
            "564           166.10      2027.0           0.14100            0.21130   \n",
            "565           155.00      1731.0           0.11660            0.19220   \n",
            "566           126.70      1124.0           0.11390            0.30940   \n",
            "567           184.60      1821.0           0.16500            0.86810   \n",
            "568            59.16       268.6           0.08996            0.06444   \n",
            "\n",
            "     worst concavity  worst concave points  worst symmetry  \\\n",
            "0             0.7119                0.2654          0.4601   \n",
            "1             0.2416                0.1860          0.2750   \n",
            "2             0.4504                0.2430          0.3613   \n",
            "3             0.6869                0.2575          0.6638   \n",
            "4             0.4000                0.1625          0.2364   \n",
            "..               ...                   ...             ...   \n",
            "564           0.4107                0.2216          0.2060   \n",
            "565           0.3215                0.1628          0.2572   \n",
            "566           0.3403                0.1418          0.2218   \n",
            "567           0.9387                0.2650          0.4087   \n",
            "568           0.0000                0.0000          0.2871   \n",
            "\n",
            "     worst fractal dimension  \n",
            "0                    0.11890  \n",
            "1                    0.08902  \n",
            "2                    0.08758  \n",
            "3                    0.17300  \n",
            "4                    0.07678  \n",
            "..                       ...  \n",
            "564                  0.07115  \n",
            "565                  0.06637  \n",
            "566                  0.07820  \n",
            "567                  0.12400  \n",
            "568                  0.07039  \n",
            "\n",
            "[569 rows x 30 columns] 0      0\n",
            "1      0\n",
            "2      0\n",
            "3      0\n",
            "4      0\n",
            "      ..\n",
            "564    0\n",
            "565    0\n",
            "566    0\n",
            "567    0\n",
            "568    1\n",
            "Name: label, Length: 569, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the data into training data and test data"
      ],
      "metadata": {
        "id": "PhHGot21agvu"
      }
    },
    {
      "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": "aj43Lt11aa4-"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ckfn_YgSaxRh",
        "outputId": "6b71300c-f648-4f32-a7b8-0753b89884ef"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (455, 30) (114, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardize the data"
      ],
      "metadata": {
        "id": "C09b3gD4hzSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "RJQ0Jl71h38f"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_std = scaler.fit_transform(X_train)\n",
        "\n",
        "X_test_std = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "eNrQb88Gh_v8"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bp47xsFEipEi",
        "outputId": "411a2ec1-9c06-47bb-8592-62fe84c38568"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.01330339  1.7757658  -0.01491962 ... -0.13236958 -1.08014517\n",
            "  -0.03527943]\n",
            " [-0.8448276  -0.6284278  -0.87702746 ... -1.11552632 -0.85773964\n",
            "  -0.72098905]\n",
            " [ 1.44755936  0.71180168  1.47428816 ...  0.87583964  0.4967602\n",
            "   0.46321706]\n",
            " ...\n",
            " [-0.46608541 -1.49375484 -0.53234924 ... -1.32388956 -1.02997851\n",
            "  -0.75145272]\n",
            " [-0.50025764 -1.62161319 -0.527814   ... -0.0987626   0.35796577\n",
            "  -0.43906159]\n",
            " [ 0.96060511  1.21181916  1.00427242 ...  0.8956983  -1.23064515\n",
            "   0.50697397]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the Neural Network"
      ],
      "metadata": {
        "id": "ffLMa9_Fa_0w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing tensorflow and keras\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(3)\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "FpQvtqsLa-MW"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# setting up the layers of the neural network\n",
        "model = keras.Sequential([\n",
        "                          keras.layers.Flatten(input_shape=(30, )),\n",
        "                          keras.layers.Dense(20, activation='relu'),\n",
        "                          keras.layers.Dense(2, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "jkMppLbTc_rz"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compiling the neural network\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "9Kq-FJRoe-eW"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the neural network\n",
        "history = model.fit(X_train_std, Y_train, validation_split=0.1, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYk6Ttqhgg6v",
        "outputId": "cf5424a9-ea7d-4880-bbce-b9a4dc18a0ea"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "13/13 [==============================] - 1s 19ms/step - loss: 0.5753 - accuracy: 0.7115 - val_loss: 0.4565 - val_accuracy: 0.7826\n",
            "Epoch 2/10\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.4184 - accuracy: 0.8460 - val_loss: 0.3356 - val_accuracy: 0.9565\n",
            "Epoch 3/10\n",
            "13/13 [==============================] - 0s 9ms/step - loss: 0.3195 - accuracy: 0.9193 - val_loss: 0.2618 - val_accuracy: 0.9565\n",
            "Epoch 4/10\n",
            "13/13 [==============================] - 0s 9ms/step - loss: 0.2578 - accuracy: 0.9267 - val_loss: 0.2149 - val_accuracy: 0.9565\n",
            "Epoch 5/10\n",
            "13/13 [==============================] - 0s 8ms/step - loss: 0.2148 - accuracy: 0.9462 - val_loss: 0.1842 - val_accuracy: 0.9783\n",
            "Epoch 6/10\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1843 - accuracy: 0.9511 - val_loss: 0.1631 - val_accuracy: 0.9783\n",
            "Epoch 7/10\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1634 - accuracy: 0.9584 - val_loss: 0.1471 - val_accuracy: 0.9783\n",
            "Epoch 8/10\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1468 - accuracy: 0.9609 - val_loss: 0.1349 - val_accuracy: 0.9783\n",
            "Epoch 9/10\n",
            "13/13 [==============================] - 0s 6ms/step - loss: 0.1343 - accuracy: 0.9633 - val_loss: 0.1247 - val_accuracy: 0.9783\n",
            "Epoch 10/10\n",
            "13/13 [==============================] - 0s 7ms/step - loss: 0.1228 - accuracy: 0.9682 - val_loss: 0.1176 - val_accuracy: 0.9783\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing accuracy and loss"
      ],
      "metadata": {
        "id": "4bpcpWKikCoT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['training data', 'validation data'], loc ='lower right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "i4DIrOMIg0V7",
        "outputId": "a85401bf-bb01-4c4e-9d42-5700018dc3ec"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7b016349db40>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['training data', 'validation data'], loc ='upper right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "HQaIx_rZk3fr",
        "outputId": "75e91130-2c6b-489f-9f79-d390b583d74e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7b0163801a20>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy of the model on test data"
      ],
      "metadata": {
        "id": "UOmaDwSkmX8g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test_std, Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFvlOCpSmE9S",
        "outputId": "3d28626e-7a4f-43c6-a878-9d4829d36834"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1199 - accuracy: 0.9737\n",
            "0.9736841917037964\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std.shape)\n",
        "print(X_test_std[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWZwijw7muyb",
        "outputId": "bcd32cd6-6023-431e-d44f-fd4f6d9bc610"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(114, 30)\n",
            "[-0.04462793 -1.41612656 -0.05903514 -0.16234067  2.0202457  -0.11323672\n",
            "  0.18500609  0.47102419  0.63336386  0.26335737  0.53209124  2.62763999\n",
            "  0.62351167  0.11405261  1.01246781  0.41126289  0.63848593  2.88971815\n",
            " -0.41675911  0.74270853 -0.32983699 -1.67435595 -0.36854552 -0.38767294\n",
            "  0.32655007 -0.74858917 -0.54689089 -0.18278004 -1.23064515 -0.6268286 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test_std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adGhOo5qm-wR",
        "outputId": "f6a3850a-1c7f-47b6-f467-119a3ff18962"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred.shape)\n",
        "print(Y_pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PRPGpOgnNjD",
        "outputId": "74fb98af-70bc-44c9-dcc0-cd6495a37da7"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(114, 2)\n",
            "[0.2536318  0.64089036]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_std)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kp91LfPLoSOV",
        "outputId": "b1592854-e220-41a3-a350-8d8b17ceec7c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.04462793 -1.41612656 -0.05903514 ... -0.18278004 -1.23064515\n",
            "  -0.6268286 ]\n",
            " [ 0.24583601 -0.06219797  0.21802678 ...  0.54129749  0.11047691\n",
            "   0.0483572 ]\n",
            " [-1.26115925 -0.29051645 -1.26499659 ... -1.35138617  0.269338\n",
            "  -0.28231213]\n",
            " ...\n",
            " [ 0.72709489  0.45836817  0.75277276 ...  1.46701686  1.19909344\n",
            "   0.65319961]\n",
            " [ 0.25437907  1.33054477  0.15659489 ... -1.29043534 -2.22561725\n",
            "  -1.59557344]\n",
            " [ 0.84100232 -0.06676434  0.8929529  ...  2.15137705  0.35629355\n",
            "   0.37459546]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVCSt6QfnaIN",
        "outputId": "021b4c63-ba2a-4e44-e54b-1e24f4755b75"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.2536318  0.64089036]\n",
            " [0.3517331  0.4965059 ]\n",
            " [0.00517833 0.39737698]\n",
            " [0.9999989  0.18472013]\n",
            " [0.5013527  0.58710146]\n",
            " [0.999769   0.18516158]\n",
            " [0.26049542 0.64775795]\n",
            " [0.00479085 0.4236998 ]\n",
            " [0.0379519  0.4803492 ]\n",
            " [0.02004892 0.40808043]\n",
            " [0.29021084 0.42711857]\n",
            " [0.04097691 0.3747962 ]\n",
            " [0.11870839 0.44508114]\n",
            " [0.1646286  0.47982165]\n",
            " [0.01718585 0.4771732 ]\n",
            " [0.99684393 0.38879725]\n",
            " [0.00765586 0.38955745]\n",
            " [0.08536224 0.51208365]\n",
            " [0.05415692 0.66913605]\n",
            " [0.9989751  0.4247187 ]\n",
            " [0.784371   0.9963897 ]\n",
            " [0.01658421 0.4810623 ]\n",
            " [0.01832598 0.42358083]\n",
            " [0.00932397 0.3136231 ]\n",
            " [0.11299942 0.45801312]\n",
            " [0.97798896 0.29450905]\n",
            " [0.08650792 0.45943856]\n",
            " [0.20133226 0.49956143]\n",
            " [0.96605974 0.3492099 ]\n",
            " [0.98039716 0.24903807]\n",
            " [0.04103222 0.38587645]\n",
            " [0.08997045 0.49946892]\n",
            " [0.05124791 0.5978891 ]\n",
            " [0.9999743  0.09066447]\n",
            " [0.99740624 0.21124008]\n",
            " [0.2042913  0.7289674 ]\n",
            " [0.00198401 0.48284775]\n",
            " [0.05108488 0.47285458]\n",
            " [0.01077524 0.3289357 ]\n",
            " [0.07713641 0.48468077]\n",
            " [0.99999547 0.22363564]\n",
            " [0.8732409  0.3779887 ]\n",
            " [0.01760434 0.39518118]\n",
            " [0.07547463 0.7520422 ]\n",
            " [0.9625015  0.70661867]\n",
            " [0.01048144 0.4163365 ]\n",
            " [0.0075912  0.3869219 ]\n",
            " [0.02617386 0.4316894 ]\n",
            " [0.99964577 0.07487389]\n",
            " [0.9750095  0.30747995]\n",
            " [0.02404912 0.45163646]\n",
            " [0.66605496 0.3434669 ]\n",
            " [0.24493858 0.42062423]\n",
            " [0.01969335 0.44812134]\n",
            " [0.02089752 0.48308933]\n",
            " [0.3401028  0.46384865]\n",
            " [0.06902492 0.41292322]\n",
            " [0.0770306  0.76819104]\n",
            " [0.99995714 0.9778738 ]\n",
            " [0.11547692 0.7091813 ]\n",
            " [0.24362229 0.51188016]\n",
            " [0.98210365 0.6919844 ]\n",
            " [0.01308035 0.3861836 ]\n",
            " [0.99659085 0.31374618]\n",
            " [0.9672753  0.1974808 ]\n",
            " [0.84878665 0.9634454 ]\n",
            " [0.9983337  0.15091303]\n",
            " [0.99312484 0.272928  ]\n",
            " [0.11027874 0.4277115 ]\n",
            " [0.99155873 0.9494804 ]\n",
            " [0.9454574  0.6087366 ]\n",
            " [0.999543   0.48515853]\n",
            " [0.05710577 0.4962918 ]\n",
            " [0.8848617  0.39930704]\n",
            " [0.00479974 0.3512536 ]\n",
            " [0.9591905  0.5345042 ]\n",
            " [0.05932907 0.48390818]\n",
            " [0.01296115 0.3514207 ]\n",
            " [0.2912114  0.37684438]\n",
            " [0.8187235  0.6284623 ]\n",
            " [0.99030876 0.37303534]\n",
            " [0.907516   0.509715  ]\n",
            " [0.99724436 0.4072346 ]\n",
            " [0.2559125  0.7039162 ]\n",
            " [0.07363797 0.4939485 ]\n",
            " [0.49089146 0.5657067 ]\n",
            " [0.2678786  0.97546405]\n",
            " [0.06524153 0.5890958 ]\n",
            " [0.2448281  0.6452677 ]\n",
            " [0.99967635 0.48554164]\n",
            " [0.07476    0.4911603 ]\n",
            " [0.05259195 0.377376  ]\n",
            " [0.20465517 0.9340104 ]\n",
            " [0.9792016  0.6087827 ]\n",
            " [0.87758327 0.46063417]\n",
            " [0.08874669 0.4589154 ]\n",
            " [0.99693614 0.35454386]\n",
            " [0.9977261  0.59814936]\n",
            " [0.03707178 0.43151757]\n",
            " [0.00397829 0.5045027 ]\n",
            " [0.00157809 0.40121698]\n",
            " [0.88373154 0.6431726 ]\n",
            " [0.99998057 0.10232426]\n",
            " [0.9999438  0.21035883]\n",
            " [0.02250311 0.4244779 ]\n",
            " [0.0165594  0.5214333 ]\n",
            " [0.02478646 0.7381035 ]\n",
            " [0.02774139 0.61508375]\n",
            " [0.01146051 0.59555566]\n",
            " [0.16206974 0.6607161 ]\n",
            " [0.9949726  0.24295928]\n",
            " [0.99888915 0.24757461]\n",
            " [0.58585984 0.54674363]\n",
            " [0.97918075 0.42687643]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***model.predict()*** gives the prediction probability of each class for that data point"
      ],
      "metadata": {
        "id": "vSb61xEMowos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# argmax function example\n",
        "\n",
        "my_list = [0.56,0.25]\n",
        "\n",
        "index_of_max_value = np.argmax(my_list)\n",
        "print(my_list)\n",
        "print(index_of_max_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzwwX9DhpUjI",
        "outputId": "409e8a2c-ee66-4d8c-ff5c-f912be77d119"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.56, 0.25]\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting the prediction probability to class labels\n",
        "Y_pred_labels = [np.argmax(i) for i in Y_pred]\n",
        "print(Y_pred_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K148AyAjoZdH",
        "outputId": "8ef25ca6-d77a-46b8-aaaf-2eeeeea98238"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the predictive system"
      ],
      "metadata": {
        "id": "igymPWLgqXSz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189)\n",
        "\n",
        "# change the input_data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the numpy array as we are predicting for one data point\n",
        "input_data_reshape = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "# standardizing the input data\n",
        "input_data_std = scaler.transform(input_data_reshape)\n",
        "\n",
        "prediction = model.predict(input_data_std)\n",
        "print(prediction)\n",
        "\n",
        "prediction_label = [np.argmax(prediction)]\n",
        "print(prediction_label)\n",
        "\n",
        "if(prediction_label[0] == 0):\n",
        "  print('Tumor is Malignant')\n",
        "\n",
        "else:\n",
        "  print('Tumor is Benign')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNHgXxACqJUZ",
        "outputId": "8dcad901-16ff-4d98-c89e-8319804eff14"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 35ms/step\n",
            "[[0.9999813 0.4630865]]\n",
            "[0]\n",
            "Tumor is Malignant\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dRt2-DKVsUe3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}