diff --git "a/Project_19_Breast_Cancer_Classification_using_Machine_Learning.ipynb" "b/Project_19_Breast_Cancer_Classification_using_Machine_Learning.ipynb"
new file mode 100644--- /dev/null
+++ "b/Project_19_Breast_Cancer_Classification_using_Machine_Learning.ipynb"
@@ -0,0 +1,2164 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X3wT8l6lfj--"
+      },
+      "source": [
+        "Importing the Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "XqsQmOXGXXTe"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import sklearn.datasets\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pwJ9zLukg3Q_"
+      },
+      "source": [
+        "Data Collection & Processing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "j6bMZMKUgz7L"
+      },
+      "outputs": [],
+      "source": [
+        "# loading the data from sklearn\n",
+        "breast_cancer_dataset = sklearn.datasets.load_breast_cancer()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xdY6i73KgkDG",
+        "outputId": "13bb7b58-e500-4360-e93f-0c83bbee8601"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "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===================================== ====== ======\\nradius (mean):                        6.981  28.11\\ntexture (mean):                       9.71   39.28\\nperimeter (mean):                     43.79  188.5\\narea (mean):                          143.5  2501.0\\nsmoothness (mean):                    0.053  0.163\\ncompactness (mean):                   0.019  0.345\\nconcavity (mean):                     0.0    0.427\\nconcave points (mean):                0.0    0.201\\nsymmetry (mean):                      0.106  0.304\\nfractal dimension (mean):             0.05   0.097\\nradius (standard error):              0.112  2.873\\ntexture (standard error):             0.36   4.885\\nperimeter (standard error):           0.757  21.98\\narea (standard error):                6.802  542.2\\nsmoothness (standard error):          0.002  0.031\\ncompactness (standard error):         0.002  0.135\\nconcavity (standard error):           0.0    0.396\\nconcave points (standard error):      0.0    0.053\\nsymmetry (standard error):            0.008  0.079\\nfractal dimension (standard error):   0.001  0.03\\nradius (worst):                       7.93   36.04\\ntexture (worst):                      12.02  49.54\\nperimeter (worst):                    50.41  251.2\\narea (worst):                         185.2  4254.0\\nsmoothness (worst):                   0.071  0.223\\ncompactness (worst):                  0.027  1.058\\nconcavity (worst):                    0.0    1.252\\nconcave points (worst):               0.0    0.291\\nsymmetry (worst):                     0.156  0.664\\nfractal dimension (worst):            0.055  0.208\\n===================================== ====== ======\\n\\n:Missing Attribute Values: None\\n\\n:Class Distribution: 212 - Malignant, 357 - Benign\\n\\n:Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n:Donor: Nick Street\\n\\n:Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. dropdown:: References\\n\\n  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\\n    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\\n    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n    San Jose, CA, 1993.\\n  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\\n    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\\n    July-August 1995.\\n  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\\n    163-171.\\n', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
+            "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
+            "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
+            "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
+            "       'smoothness error', 'compactness error', 'concavity error',\n",
+            "       'concave points error', 'symmetry error',\n",
+            "       'fractal dimension error', 'worst radius', 'worst texture',\n",
+            "       'worst perimeter', 'worst area', 'worst smoothness',\n",
+            "       'worst compactness', 'worst concavity', 'worst concave points',\n",
+            "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(breast_cancer_dataset)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "yFamhVTThMdK"
+      },
+      "outputs": [],
+      "source": [
+        "# loading the data to a data frame\n",
+        "data_frame = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 261
+        },
+        "id": "xLiv1maYiGVH",
+        "outputId": "53238fef-68f0-46d7-8d15-9c684b3878af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>"
+            ],
+            "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]"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# print the first 5 rows of the dataframe\n",
+        "data_frame.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "UEvD_aTDiNLF"
+      },
+      "outputs": [],
+      "source": [
+        "# adding the 'target' column to the data frame\n",
+        "data_frame['label'] = breast_cancer_dataset.target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 261
+        },
+        "id": "f_kmjEA5io2v",
+        "outputId": "3112fc43-947a-4c63-ab64-97b345acbb49"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>"
+            ],
+            "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]"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# print last 5 rows of the dataframe\n",
+        "data_frame.tail()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sw3wjdK6iwK4",
+        "outputId": "2794ef2d-a645-474b-81c3-194509ab3c7e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(569, 31)"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# number of rows and columns in the dataset\n",
+        "data_frame.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AWOjMuyBi77M",
+        "outputId": "947c79ac-2f30-4ba7-87d8-58a500f939fd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "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"
+          ]
+        }
+      ],
+      "source": [
+        "# getting some information about the data\n",
+        "data_frame.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RuoVIUTYjLpk",
+        "outputId": "c5648482-6fe7-4877-d82a-5cf6c5813a34"
+      },
+      "outputs": [
+        {
+          "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"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# checking for missing values\n",
+        "data_frame.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        },
+        "id": "oLMuXI33jlkq",
+        "outputId": "8e1d9a5f-578d-432d-93e7-1f4682d0bed9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>"
+            ],
+            "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]"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# statistical measures about the data\n",
+        "data_frame.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tC8Yii4Yjzer",
+        "outputId": "39273efc-e950-4cfe-fbc5-046f93ecc719"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "label\n",
+              "1    357\n",
+              "0    212\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# checking the distribution of Target Varibale\n",
+        "data_frame['label'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbbkzWeFkjqc"
+      },
+      "source": [
+        "1 --> Benign\n",
+        "\n",
+        "0 --> Malignant"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 198
+        },
+        "id": "YGWHjrVSkN5c",
+        "outputId": "b7863331-c13d-4f2a-a88a-37905eeb14e6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>"
+            ],
+            "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]"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data_frame.groupby('label').mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tUPYps4DlVFR"
+      },
+      "source": [
+        "Separating the features and target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "Z5pD8rP5kzKD"
+      },
+      "outputs": [],
+      "source": [
+        "X = data_frame.drop(columns='label', axis=1)\n",
+        "Y = data_frame['label']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z-GWJHpAlpWJ",
+        "outputId": "31cc5c11-1b83-418e-8a49-23647e6fdd9d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+            "0          17.99         10.38          122.80     1001.0          0.11840   \n",
+            "1          20.57         17.77          132.90     1326.0          0.08474   \n",
+            "2          19.69         21.25          130.00     1203.0          0.10960   \n",
+            "3          11.42         20.38           77.58      386.1          0.14250   \n",
+            "4          20.29         14.34          135.10     1297.0          0.10030   \n",
+            "..           ...           ...             ...        ...              ...   \n",
+            "564        21.56         22.39          142.00     1479.0          0.11100   \n",
+            "565        20.13         28.25          131.20     1261.0          0.09780   \n",
+            "566        16.60         28.08          108.30      858.1          0.08455   \n",
+            "567        20.60         29.33          140.10     1265.0          0.11780   \n",
+            "568         7.76         24.54           47.92      181.0          0.05263   \n",
+            "\n",
+            "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+            "0             0.27760         0.30010              0.14710         0.2419   \n",
+            "1             0.07864         0.08690              0.07017         0.1812   \n",
+            "2             0.15990         0.19740              0.12790         0.2069   \n",
+            "3             0.28390         0.24140              0.10520         0.2597   \n",
+            "4             0.13280         0.19800              0.10430         0.1809   \n",
+            "..                ...             ...                  ...            ...   \n",
+            "564           0.11590         0.24390              0.13890         0.1726   \n",
+            "565           0.10340         0.14400              0.09791         0.1752   \n",
+            "566           0.10230         0.09251              0.05302         0.1590   \n",
+            "567           0.27700         0.35140              0.15200         0.2397   \n",
+            "568           0.04362         0.00000              0.00000         0.1587   \n",
+            "\n",
+            "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
+            "0                   0.07871  ...        25.380          17.33   \n",
+            "1                   0.05667  ...        24.990          23.41   \n",
+            "2                   0.05999  ...        23.570          25.53   \n",
+            "3                   0.09744  ...        14.910          26.50   \n",
+            "4                   0.05883  ...        22.540          16.67   \n",
+            "..                      ...  ...           ...            ...   \n",
+            "564                 0.05623  ...        25.450          26.40   \n",
+            "565                 0.05533  ...        23.690          38.25   \n",
+            "566                 0.05648  ...        18.980          34.12   \n",
+            "567                 0.07016  ...        25.740          39.42   \n",
+            "568                 0.05884  ...         9.456          30.37   \n",
+            "\n",
+            "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
+            "0             184.60      2019.0           0.16220            0.66560   \n",
+            "1             158.80      1956.0           0.12380            0.18660   \n",
+            "2             152.50      1709.0           0.14440            0.42450   \n",
+            "3              98.87       567.7           0.20980            0.86630   \n",
+            "4             152.20      1575.0           0.13740            0.20500   \n",
+            "..               ...         ...               ...                ...   \n",
+            "564           166.10      2027.0           0.14100            0.21130   \n",
+            "565           155.00      1731.0           0.11660            0.19220   \n",
+            "566           126.70      1124.0           0.11390            0.30940   \n",
+            "567           184.60      1821.0           0.16500            0.86810   \n",
+            "568            59.16       268.6           0.08996            0.06444   \n",
+            "\n",
+            "     worst concavity  worst concave points  worst symmetry  \\\n",
+            "0             0.7119                0.2654          0.4601   \n",
+            "1             0.2416                0.1860          0.2750   \n",
+            "2             0.4504                0.2430          0.3613   \n",
+            "3             0.6869                0.2575          0.6638   \n",
+            "4             0.4000                0.1625          0.2364   \n",
+            "..               ...                   ...             ...   \n",
+            "564           0.4107                0.2216          0.2060   \n",
+            "565           0.3215                0.1628          0.2572   \n",
+            "566           0.3403                0.1418          0.2218   \n",
+            "567           0.9387                0.2650          0.4087   \n",
+            "568           0.0000                0.0000          0.2871   \n",
+            "\n",
+            "     worst fractal dimension  \n",
+            "0                    0.11890  \n",
+            "1                    0.08902  \n",
+            "2                    0.08758  \n",
+            "3                    0.17300  \n",
+            "4                    0.07678  \n",
+            "..                       ...  \n",
+            "564                  0.07115  \n",
+            "565                  0.06637  \n",
+            "566                  0.07820  \n",
+            "567                  0.12400  \n",
+            "568                  0.07039  \n",
+            "\n",
+            "[569 rows x 30 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r5rs8pColqsn",
+        "outputId": "842ccf4a-94a2-4409-df9a-79a25ad46f15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0      0\n",
+            "1      0\n",
+            "2      0\n",
+            "3      0\n",
+            "4      0\n",
+            "      ..\n",
+            "564    0\n",
+            "565    0\n",
+            "566    0\n",
+            "567    0\n",
+            "568    1\n",
+            "Name: label, Length: 569, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(Y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B5yk9d-Nl4VV"
+      },
+      "source": [
+        "Splitting the data into training data & Testing data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "vZtU30bPluG_"
+      },
+      "outputs": [],
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zWaeuX3amqYH",
+        "outputId": "30695494-2fed-44bd-d533-34bbae6a7712"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(569, 30) (455, 30) (114, 30)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X.shape, X_train.shape, X_test.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ttUvP8-im6z3"
+      },
+      "source": [
+        "Model Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OPPOng9Em8eb"
+      },
+      "source": [
+        "Logistic Regression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "xhBPDKZmm0dk"
+      },
+      "outputs": [],
+      "source": [
+        "model = LogisticRegression()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NkQlXVftnKOm",
+        "outputId": "2cdd7798-4283-4965-9a04-dc47600f5aa5"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+            "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
+            "\n",
+            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+            "Please also refer to the documentation for alternative solver options:\n",
+            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+            "  n_iter_i = _check_optimize_result(\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "LogisticRegression()"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# training the Logistic Regression model using Training data\n",
+        "\n",
+        "model.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_Wvez7R5nj5P"
+      },
+      "source": [
+        "Model Evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Um1zCUo1nmI1"
+      },
+      "source": [
+        "Accuracy Score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "Xy_Ob3Zcnh32"
+      },
+      "outputs": [],
+      "source": [
+        "# accuracy on training data\n",
+        "X_train_prediction = model.predict(X_train)\n",
+        "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fsx-3RlfoMzN",
+        "outputId": "c30e9778-f98b-42f7-b0f5-84b32778a574"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy on training data =  0.9318681318681319\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('Accuracy on training data = ', training_data_accuracy)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "1QYDU6YjoTFl"
+      },
+      "outputs": [],
+      "source": [
+        "# accuracy on test data\n",
+        "X_test_prediction = model.predict(X_test)\n",
+        "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7HbERDa8orMw",
+        "outputId": "59e3c5aa-4965-4eab-b349-dcaa60e2c29c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data =  0.9298245614035088\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('Accuracy on test data = ', test_data_accuracy)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDV18UAJpB2B"
+      },
+      "source": [
+        "Building a Predictive System"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Rf_x3hPpouQh",
+        "outputId": "406e9d47-b912-40d3-fda5-1f6d67c21270"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[1]\n",
+            "The Breast Cancer is Benign\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "input_data = (13.54,14.36,87.46,566.3,0.09779,0.08129,0.06664,0.04781,0.1885,0.05766,0.2699,0.7886,2.058,23.56,0.008462,0.0146,0.02387,0.01315,0.0198,0.0023,15.11,19.26,99.7,711.2,0.144,0.1773,0.239,0.1288,0.2977,0.07259)\n",
+        "\n",
+        "# change the input data to a numpy array\n",
+        "input_data_as_numpy_array = np.asarray(input_data)\n",
+        "\n",
+        "# reshape the numpy array as we are predicting for one datapoint\n",
+        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
+        "\n",
+        "prediction = model.predict(input_data_reshaped)\n",
+        "print(prediction)\n",
+        "\n",
+        "if (prediction[0] == 0):\n",
+        "  print('The Breast cancer is Malignant')\n",
+        "\n",
+        "else:\n",
+        "  print('The Breast Cancer is Benign')\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tZVUPp5hqJn1"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}