diff --git a/BreastCancerDetectionKNN.ipynb b/BreastCancerDetectionKNN.ipynb
new file mode 100644
index 0000000..8715b29
--- /dev/null
+++ b/BreastCancerDetectionKNN.ipynb
@@ -0,0 +1,1926 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3WqKX1WmV6T2"
+      },
+      "source": [
+        "**Breast Cancer Detection with Logistic Regression**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "qYA5sD53VKHl"
+      },
+      "outputs": [],
+      "source": [
+        "#importing libraries\n",
+        "import numpy as np\n",
+        "import sklearn.datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "JDudCNzrWLO5",
+        "outputId": "cadc6e48-ce5d-4ad6-cc1c-d2c32c7ba832"
+      },
+      "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|details-start|\\n**References**\\n|details-split|\\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\\n|details-end|\\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": [
+        "#getting the dataset\n",
+        "breast_cancer = sklearn.datasets.load_breast_cancer()\n",
+        "#https://www.kaggle.com/ucim/breast-cancer-wisconsin-data\n",
+        "print(breast_cancer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "4tQrJB_qWOyS"
+      },
+      "outputs": [],
+      "source": [
+        "X = breast_cancer.data\n",
+        "Y = breast_cancer.target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "B2b-WunnWTuX",
+        "outputId": "37fb633f-4ffb-43d2-9ba2-9b4f307b1b96"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
+            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
+            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
+            " ...\n",
+            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
+            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
+            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
+            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            " 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
+            " 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
+            " 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
+            " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
+            " 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
+            " 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
+            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            " 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
+            " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
+            " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
+            " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
+            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
+            " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
+            " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X)\n",
+        "print(Y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "7Q9x-feBWY6e",
+        "outputId": "28fe8500-dc4b-45ab-8099-fc163ab5a759"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(569, 30) (569,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X.shape, Y.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0lm_qTTeW8zz"
+      },
+      "source": [
+        "Import data to the Pandas Data Frame"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "SJgj1bQ2WdsY"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\PC\\AppData\\Local\\Temp\\ipykernel_20352\\4080736814.py:1: DeprecationWarning: \n",
+            "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
+            "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
+            "but was not found to be installed on your system.\n",
+            "If this would cause problems for you,\n",
+            "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
+            "        \n",
+            "  import pandas as pd\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "fxqxsrcOWiFF"
+      },
+      "outputs": [],
+      "source": [
+        "data = pd.DataFrame(breast_cancer.data, columns = breast_cancer.feature_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "c9wQ9ku3Wkgc"
+      },
+      "outputs": [],
+      "source": [
+        "data['class'] =  breast_cancer.target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 258
+        },
+        "id": "MPXt6OsZXMvy",
+        "outputId": "f814d06a-52f2-4607-bcf2-6058f447cad8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "        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>class</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>17.99</td>\n",
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+              "      <td>0.07871</td>\n",
+              "      <td>...</td>\n",
+              "      <td>17.33</td>\n",
+              "      <td>184.60</td>\n",
+              "      <td>2019.0</td>\n",
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+              "      <td>0.6656</td>\n",
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+              "      <td>0.2654</td>\n",
+              "      <td>0.4601</td>\n",
+              "      <td>0.11890</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>20.57</td>\n",
+              "      <td>17.77</td>\n",
+              "      <td>132.90</td>\n",
+              "      <td>1326.0</td>\n",
+              "      <td>0.08474</td>\n",
+              "      <td>0.07864</td>\n",
+              "      <td>0.0869</td>\n",
+              "      <td>0.07017</td>\n",
+              "      <td>0.1812</td>\n",
+              "      <td>0.05667</td>\n",
+              "      <td>...</td>\n",
+              "      <td>23.41</td>\n",
+              "      <td>158.80</td>\n",
+              "      <td>1956.0</td>\n",
+              "      <td>0.1238</td>\n",
+              "      <td>0.1866</td>\n",
+              "      <td>0.2416</td>\n",
+              "      <td>0.1860</td>\n",
+              "      <td>0.2750</td>\n",
+              "      <td>0.08902</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>19.69</td>\n",
+              "      <td>21.25</td>\n",
+              "      <td>130.00</td>\n",
+              "      <td>1203.0</td>\n",
+              "      <td>0.10960</td>\n",
+              "      <td>0.15990</td>\n",
+              "      <td>0.1974</td>\n",
+              "      <td>0.12790</td>\n",
+              "      <td>0.2069</td>\n",
+              "      <td>0.05999</td>\n",
+              "      <td>...</td>\n",
+              "      <td>25.53</td>\n",
+              "      <td>152.50</td>\n",
+              "      <td>1709.0</td>\n",
+              "      <td>0.1444</td>\n",
+              "      <td>0.4245</td>\n",
+              "      <td>0.4504</td>\n",
+              "      <td>0.2430</td>\n",
+              "      <td>0.3613</td>\n",
+              "      <td>0.08758</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.42</td>\n",
+              "      <td>20.38</td>\n",
+              "      <td>77.58</td>\n",
+              "      <td>386.1</td>\n",
+              "      <td>0.14250</td>\n",
+              "      <td>0.28390</td>\n",
+              "      <td>0.2414</td>\n",
+              "      <td>0.10520</td>\n",
+              "      <td>0.2597</td>\n",
+              "      <td>0.09744</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.50</td>\n",
+              "      <td>98.87</td>\n",
+              "      <td>567.7</td>\n",
+              "      <td>0.2098</td>\n",
+              "      <td>0.8663</td>\n",
+              "      <td>0.6869</td>\n",
+              "      <td>0.2575</td>\n",
+              "      <td>0.6638</td>\n",
+              "      <td>0.17300</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>20.29</td>\n",
+              "      <td>14.34</td>\n",
+              "      <td>135.10</td>\n",
+              "      <td>1297.0</td>\n",
+              "      <td>0.10030</td>\n",
+              "      <td>0.13280</td>\n",
+              "      <td>0.1980</td>\n",
+              "      <td>0.10430</td>\n",
+              "      <td>0.1809</td>\n",
+              "      <td>0.05883</td>\n",
+              "      <td>...</td>\n",
+              "      <td>16.67</td>\n",
+              "      <td>152.20</td>\n",
+              "      <td>1575.0</td>\n",
+              "      <td>0.1374</td>\n",
+              "      <td>0.2050</td>\n",
+              "      <td>0.4000</td>\n",
+              "      <td>0.1625</td>\n",
+              "      <td>0.2364</td>\n",
+              "      <td>0.07678</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 31 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+              "3        11.42         20.38           77.58      386.1          0.14250   \n",
+              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+              "\n",
+              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+              "0           0.27760          0.3001              0.14710         0.2419   \n",
+              "1           0.07864          0.0869              0.07017         0.1812   \n",
+              "2           0.15990          0.1974              0.12790         0.2069   \n",
+              "3           0.28390          0.2414              0.10520         0.2597   \n",
+              "4           0.13280          0.1980              0.10430         0.1809   \n",
+              "\n",
+              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
+              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
+              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
+              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
+              "3                 0.09744  ...          26.50            98.87       567.7   \n",
+              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
+              "\n",
+              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
+              "0            0.1622             0.6656           0.7119                0.2654   \n",
+              "1            0.1238             0.1866           0.2416                0.1860   \n",
+              "2            0.1444             0.4245           0.4504                0.2430   \n",
+              "3            0.2098             0.8663           0.6869                0.2575   \n",
+              "4            0.1374             0.2050           0.4000                0.1625   \n",
+              "\n",
+              "   worst symmetry  worst fractal dimension  class  \n",
+              "0          0.4601                  0.11890      0  \n",
+              "1          0.2750                  0.08902      0  \n",
+              "2          0.3613                  0.08758      0  \n",
+              "3          0.6638                  0.17300      0  \n",
+              "4          0.2364                  0.07678      0  \n",
+              "\n",
+              "[5 rows x 31 columns]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 351
+        },
+        "id": "QTUBDupdXPZH",
+        "outputId": "54baafb5-0203-44ec-ff8a-48ab9e93f719"
+      },
+      "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>class</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "      <td>569.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>14.127292</td>\n",
+              "      <td>19.289649</td>\n",
+              "      <td>91.969033</td>\n",
+              "      <td>654.889104</td>\n",
+              "      <td>0.096360</td>\n",
+              "      <td>0.104341</td>\n",
+              "      <td>0.088799</td>\n",
+              "      <td>0.048919</td>\n",
+              "      <td>0.181162</td>\n",
+              "      <td>0.062798</td>\n",
+              "      <td>...</td>\n",
+              "      <td>25.677223</td>\n",
+              "      <td>107.261213</td>\n",
+              "      <td>880.583128</td>\n",
+              "      <td>0.132369</td>\n",
+              "      <td>0.254265</td>\n",
+              "      <td>0.272188</td>\n",
+              "      <td>0.114606</td>\n",
+              "      <td>0.290076</td>\n",
+              "      <td>0.083946</td>\n",
+              "      <td>0.627417</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.524049</td>\n",
+              "      <td>4.301036</td>\n",
+              "      <td>24.298981</td>\n",
+              "      <td>351.914129</td>\n",
+              "      <td>0.014064</td>\n",
+              "      <td>0.052813</td>\n",
+              "      <td>0.079720</td>\n",
+              "      <td>0.038803</td>\n",
+              "      <td>0.027414</td>\n",
+              "      <td>0.007060</td>\n",
+              "      <td>...</td>\n",
+              "      <td>6.146258</td>\n",
+              "      <td>33.602542</td>\n",
+              "      <td>569.356993</td>\n",
+              "      <td>0.022832</td>\n",
+              "      <td>0.157336</td>\n",
+              "      <td>0.208624</td>\n",
+              "      <td>0.065732</td>\n",
+              "      <td>0.061867</td>\n",
+              "      <td>0.018061</td>\n",
+              "      <td>0.483918</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>6.981000</td>\n",
+              "      <td>9.710000</td>\n",
+              "      <td>43.790000</td>\n",
+              "      <td>143.500000</td>\n",
+              "      <td>0.052630</td>\n",
+              "      <td>0.019380</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.106000</td>\n",
+              "      <td>0.049960</td>\n",
+              "      <td>...</td>\n",
+              "      <td>12.020000</td>\n",
+              "      <td>50.410000</td>\n",
+              "      <td>185.200000</td>\n",
+              "      <td>0.071170</td>\n",
+              "      <td>0.027290</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.156500</td>\n",
+              "      <td>0.055040</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>11.700000</td>\n",
+              "      <td>16.170000</td>\n",
+              "      <td>75.170000</td>\n",
+              "      <td>420.300000</td>\n",
+              "      <td>0.086370</td>\n",
+              "      <td>0.064920</td>\n",
+              "      <td>0.029560</td>\n",
+              "      <td>0.020310</td>\n",
+              "      <td>0.161900</td>\n",
+              "      <td>0.057700</td>\n",
+              "      <td>...</td>\n",
+              "      <td>21.080000</td>\n",
+              "      <td>84.110000</td>\n",
+              "      <td>515.300000</td>\n",
+              "      <td>0.116600</td>\n",
+              "      <td>0.147200</td>\n",
+              "      <td>0.114500</td>\n",
+              "      <td>0.064930</td>\n",
+              "      <td>0.250400</td>\n",
+              "      <td>0.071460</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>13.370000</td>\n",
+              "      <td>18.840000</td>\n",
+              "      <td>86.240000</td>\n",
+              "      <td>551.100000</td>\n",
+              "      <td>0.095870</td>\n",
+              "      <td>0.092630</td>\n",
+              "      <td>0.061540</td>\n",
+              "      <td>0.033500</td>\n",
+              "      <td>0.179200</td>\n",
+              "      <td>0.061540</td>\n",
+              "      <td>...</td>\n",
+              "      <td>25.410000</td>\n",
+              "      <td>97.660000</td>\n",
+              "      <td>686.500000</td>\n",
+              "      <td>0.131300</td>\n",
+              "      <td>0.211900</td>\n",
+              "      <td>0.226700</td>\n",
+              "      <td>0.099930</td>\n",
+              "      <td>0.282200</td>\n",
+              "      <td>0.080040</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>15.780000</td>\n",
+              "      <td>21.800000</td>\n",
+              "      <td>104.100000</td>\n",
+              "      <td>782.700000</td>\n",
+              "      <td>0.105300</td>\n",
+              "      <td>0.130400</td>\n",
+              "      <td>0.130700</td>\n",
+              "      <td>0.074000</td>\n",
+              "      <td>0.195700</td>\n",
+              "      <td>0.066120</td>\n",
+              "      <td>...</td>\n",
+              "      <td>29.720000</td>\n",
+              "      <td>125.400000</td>\n",
+              "      <td>1084.000000</td>\n",
+              "      <td>0.146000</td>\n",
+              "      <td>0.339100</td>\n",
+              "      <td>0.382900</td>\n",
+              "      <td>0.161400</td>\n",
+              "      <td>0.317900</td>\n",
+              "      <td>0.092080</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>28.110000</td>\n",
+              "      <td>39.280000</td>\n",
+              "      <td>188.500000</td>\n",
+              "      <td>2501.000000</td>\n",
+              "      <td>0.163400</td>\n",
+              "      <td>0.345400</td>\n",
+              "      <td>0.426800</td>\n",
+              "      <td>0.201200</td>\n",
+              "      <td>0.304000</td>\n",
+              "      <td>0.097440</td>\n",
+              "      <td>...</td>\n",
+              "      <td>49.540000</td>\n",
+              "      <td>251.200000</td>\n",
+              "      <td>4254.000000</td>\n",
+              "      <td>0.222600</td>\n",
+              "      <td>1.058000</td>\n",
+              "      <td>1.252000</td>\n",
+              "      <td>0.291000</td>\n",
+              "      <td>0.663800</td>\n",
+              "      <td>0.207500</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 31 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       mean radius  mean texture  mean perimeter    mean area  \\\n",
+              "count   569.000000    569.000000      569.000000   569.000000   \n",
+              "mean     14.127292     19.289649       91.969033   654.889104   \n",
+              "std       3.524049      4.301036       24.298981   351.914129   \n",
+              "min       6.981000      9.710000       43.790000   143.500000   \n",
+              "25%      11.700000     16.170000       75.170000   420.300000   \n",
+              "50%      13.370000     18.840000       86.240000   551.100000   \n",
+              "75%      15.780000     21.800000      104.100000   782.700000   \n",
+              "max      28.110000     39.280000      188.500000  2501.000000   \n",
+              "\n",
+              "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
+              "count       569.000000        569.000000      569.000000           569.000000   \n",
+              "mean          0.096360          0.104341        0.088799             0.048919   \n",
+              "std           0.014064          0.052813        0.079720             0.038803   \n",
+              "min           0.052630          0.019380        0.000000             0.000000   \n",
+              "25%           0.086370          0.064920        0.029560             0.020310   \n",
+              "50%           0.095870          0.092630        0.061540             0.033500   \n",
+              "75%           0.105300          0.130400        0.130700             0.074000   \n",
+              "max           0.163400          0.345400        0.426800             0.201200   \n",
+              "\n",
+              "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
+              "count     569.000000              569.000000  ...     569.000000   \n",
+              "mean        0.181162                0.062798  ...      25.677223   \n",
+              "std         0.027414                0.007060  ...       6.146258   \n",
+              "min         0.106000                0.049960  ...      12.020000   \n",
+              "25%         0.161900                0.057700  ...      21.080000   \n",
+              "50%         0.179200                0.061540  ...      25.410000   \n",
+              "75%         0.195700                0.066120  ...      29.720000   \n",
+              "max         0.304000                0.097440  ...      49.540000   \n",
+              "\n",
+              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
+              "count       569.000000   569.000000        569.000000         569.000000   \n",
+              "mean        107.261213   880.583128          0.132369           0.254265   \n",
+              "std          33.602542   569.356993          0.022832           0.157336   \n",
+              "min          50.410000   185.200000          0.071170           0.027290   \n",
+              "25%          84.110000   515.300000          0.116600           0.147200   \n",
+              "50%          97.660000   686.500000          0.131300           0.211900   \n",
+              "75%         125.400000  1084.000000          0.146000           0.339100   \n",
+              "max         251.200000  4254.000000          0.222600           1.058000   \n",
+              "\n",
+              "       worst concavity  worst concave points  worst symmetry  \\\n",
+              "count       569.000000            569.000000      569.000000   \n",
+              "mean          0.272188              0.114606        0.290076   \n",
+              "std           0.208624              0.065732        0.061867   \n",
+              "min           0.000000              0.000000        0.156500   \n",
+              "25%           0.114500              0.064930        0.250400   \n",
+              "50%           0.226700              0.099930        0.282200   \n",
+              "75%           0.382900              0.161400        0.317900   \n",
+              "max           1.252000              0.291000        0.663800   \n",
+              "\n",
+              "       worst fractal dimension       class  \n",
+              "count               569.000000  569.000000  \n",
+              "mean                  0.083946    0.627417  \n",
+              "std                   0.018061    0.483918  \n",
+              "min                   0.055040    0.000000  \n",
+              "25%                   0.071460    0.000000  \n",
+              "50%                   0.080040    1.000000  \n",
+              "75%                   0.092080    1.000000  \n",
+              "max                   0.207500    1.000000  \n",
+              "\n",
+              "[8 rows x 31 columns]"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "ard7qMIwXVgO",
+        "outputId": "3a78a525-e7f3-48a8-a268-d91357c847b8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "class\n",
+            "1    357\n",
+            "0    212\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(data['class'].value_counts())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "d14AxiQDXdIU",
+        "outputId": "999e81b8-c231-4cbd-a51f-e0b56e8b7050"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['malignant' 'benign']\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(breast_cancer.target_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 196
+        },
+        "id": "9dQOD_nVXhVM",
+        "outputId": "260044dd-2012-44a0-9c44-f1609783994b"
+      },
+      "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>class</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <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",
+              "class                                                                           \n",
+              "0        17.462830     21.604906      115.365377  978.376415         0.102898   \n",
+              "1        12.146524     17.914762       78.075406  462.790196         0.092478   \n",
+              "\n",
+              "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+              "class                                                                         \n",
+              "0              0.145188        0.160775             0.087990       0.192909   \n",
+              "1              0.080085        0.046058             0.025717       0.174186   \n",
+              "\n",
+              "       mean fractal dimension  ...  worst radius  worst texture  \\\n",
+              "class                          ...                                \n",
+              "0                    0.062680  ...     21.134811      29.318208   \n",
+              "1                    0.062867  ...     13.379801      23.515070   \n",
+              "\n",
+              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
+              "class                                                                      \n",
+              "0           141.370330  1422.286321          0.144845           0.374824   \n",
+              "1            87.005938   558.899440          0.124959           0.182673   \n",
+              "\n",
+              "       worst concavity  worst concave points  worst symmetry  \\\n",
+              "class                                                          \n",
+              "0             0.450606              0.182237        0.323468   \n",
+              "1             0.166238              0.074444        0.270246   \n",
+              "\n",
+              "       worst fractal dimension  \n",
+              "class                           \n",
+              "0                     0.091530  \n",
+              "1                     0.079442  \n",
+              "\n",
+              "[2 rows x 30 columns]"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data.groupby('class').mean()\n",
+        "#0 = malignant\n",
+        "#1 = benign"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3ZLnktrYXuZ4"
+      },
+      "source": [
+        "Train and Test Data Split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "RDCD-3t2XmNG"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "JgoRzmx4Xs0j",
+        "outputId": "1b4a6567-90ed-4500-d7ff-c38db180b6cd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(569,) (426,) (143,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(Y.shape, Y_train.shape, Y_test.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "TfXjgKecX2aR"
+      },
+      "outputs": [],
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1)\n",
+        "#test_size --> to specify the percentage of test data needed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "Vayg1nTMX5GA",
+        "outputId": "3b91257c-711d-4a9c-d8a8-d352b3d6416b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(569,) (512,) (57,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(Y.shape, Y_train.shape, Y_test.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "s3TkXPZ8X72R",
+        "outputId": "86dcf406-c372-41e5-aab0-e79c7c84e9b7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.6274165202108963 0.62109375 0.6842105263157895\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(Y.mean(), Y_train.mean(), Y_test.mean())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "_jFZwc9OX-Av"
+      },
+      "outputs": [],
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y)\n",
+        "#stratify --> for correct distribution of data as of the original data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "GZY9NLkNYBez",
+        "outputId": "be216507-a170-4797-942d-025b8629dcd2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.6274165202108963 0.626953125 0.631578947368421\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(Y.mean(), Y_train.mean(), Y_test.mean())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "6hnHZaOWYNb1"
+      },
+      "outputs": [],
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y, random_state=1)\n",
+        "#random_state --> specific split of data, each value of random_state splits the data differently"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "P9t5iXyIYP95",
+        "outputId": "e3d22056-9b9b-44f2-a5cb-434341a6c8b2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "61.31637960106119 67.04963097269005 61.890712339519624\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X_train.mean(), X_test.mean(), X.mean())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "v-fNBVlrYR4I",
+        "outputId": "44bea766-7649-4732-94a7-f24acb0337a8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[1.490e+01 2.253e+01 1.021e+02 ... 2.475e-01 2.866e-01 1.155e-01]\n",
+            " [1.205e+01 1.463e+01 7.804e+01 ... 6.548e-02 2.747e-01 8.301e-02]\n",
+            " [1.311e+01 1.556e+01 8.721e+01 ... 1.986e-01 3.147e-01 1.405e-01]\n",
+            " ...\n",
+            " [1.258e+01 1.840e+01 7.983e+01 ... 8.772e-03 2.505e-01 6.431e-02]\n",
+            " [1.349e+01 2.230e+01 8.691e+01 ... 1.282e-01 2.871e-01 6.917e-02]\n",
+            " [1.919e+01 1.594e+01 1.263e+02 ... 1.777e-01 2.443e-01 6.251e-02]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(X_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dlt2G3FqYd4_"
+      },
+      "source": [
+        "**Logistic Regression**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "A63VGGb9YVRp"
+      },
+      "outputs": [],
+      "source": [
+        "#import KNeighborsClassifier from sklearn\n",
+        "from sklearn.neighbors import KNeighborsClassifier"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "dNLK1uKmYZEM"
+      },
+      "outputs": [],
+      "source": [
+        "classifier = KNeighborsClassifier(n_neighbors=3)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "XAMvZvsrYjhw",
+        "outputId": "c849137f-c146-4b53-d813-6239d4b25a68"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "KNeighborsClassifier(n_neighbors=3)"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#training the model on training data\n",
+        "classifier.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ev9Eq9QMYxhA"
+      },
+      "source": [
+        "**Evaluation of the Model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "x4Cw-CwoYlzE"
+      },
+      "outputs": [],
+      "source": [
+        "#import accuracy_score\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "J7iCwz2QYrN_"
+      },
+      "outputs": [],
+      "source": [
+        "prediction_on_training_data = classifier.predict(X_train)\n",
+        "accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "SYIS_NDgZaxk",
+        "outputId": "3e0ff651-9f72-4791-8b47-839caa45a6cd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy on training data : 0.955078125\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('Accuracy on training data :', accuracy_on_training_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "OzBEFHyyZc0X"
+      },
+      "outputs": [],
+      "source": [
+        "#prediction on test_data\n",
+        "prediction_on_test_data = classifier.predict(X_test)\n",
+        "accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "DO7tebMAZhgC",
+        "outputId": "a47d433c-4503-4f93-f4b9-83e1231d112c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data : 0.9298245614035088\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('Accuracy on test data :', accuracy_on_test_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hgTziDZmZpX8"
+      },
+      "source": [
+        "Detecting whether the Patient has Breast Cancer in Benign or Malignant Stage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "NgZj6gRwZmOe",
+        "outputId": "59299578-eff6-42cb-8fb1-19ff6125a9cf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(17.99, 10.38, 122.8, 1001, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871, 1.095, 0.9053, 8.589, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003, 0.006193, 25.38, 17.33, 184.6, 2019, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189)\n"
+          ]
+        }
+      ],
+      "source": [
+        "input_data = (17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189)\n",
+        "#change the input_data to numpy_array to make prediction\n",
+        "input_data_as_numpy_array = np.array(input_data)\n",
+        "print(input_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "_0QS56eSZ6xR"
+      },
+      "outputs": [],
+      "source": [
+        "#reshape the array we are predicting the output for one instance\n",
+        "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "F-HjwKa_Z-pT",
+        "outputId": "b7f617b4-db60-4a21-9b5f-9e0a46919e1a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "#prediction\n",
+        "prediction = classifier.predict(input_data_reshaped)\n",
+        "print(prediction)   #returns a list with elements 0 [if Malignant] and 1 [if Benign]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "65lx_nTzaDHn",
+        "outputId": "503b3b16-d731-4111-ec93-4bbd3f2eef93"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The Breast Cancer is at Malignant Stage.\n"
+          ]
+        }
+      ],
+      "source": [
+        "if (prediction[0]==0):\n",
+        "    print(\"The Breast Cancer is at Malignant Stage.\")\n",
+        "else:\n",
+        "    print(\"The Breast Cancer is at Benign Stage.\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "BreastCancerDetection.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.10.3 64-bit",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.3"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "ecf692a57d25ce2e9c36ee4bfd0c74b739bffc9771b8483c93bb91a271af2e18"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/BreastCancerDetection.ipynb b/BreastCancerDetectionLogisticRegression.ipynb
similarity index 54%
rename from BreastCancerDetection.ipynb
rename to BreastCancerDetectionLogisticRegression.ipynb
index f9e0878..c5dec74 100644
--- a/BreastCancerDetection.ipynb
+++ b/BreastCancerDetectionLogisticRegression.ipynb
@@ -11,7 +11,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 1,
       "metadata": {
         "id": "qYA5sD53VKHl"
       },
@@ -24,7 +24,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -75,7 +75,7 @@
             "       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]), '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        largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
+            "       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|details-start|\\n**References**\\n|details-split|\\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\\n|details-end|\\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",
@@ -84,7 +84,7 @@
             "       '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': '/usr/local/lib/python3.7/dist-packages/sklearn/datasets/data/breast_cancer.csv'}\n"
+            "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
           ]
         }
       ],
@@ -97,7 +97,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 3,
       "metadata": {
         "id": "4tQrJB_qWOyS"
       },
@@ -109,7 +109,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -156,7 +156,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -189,18 +189,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 6,
       "metadata": {
         "id": "SJgj1bQ2WdsY"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\PC\\AppData\\Local\\Temp\\ipykernel_19748\\4080736814.py:1: DeprecationWarning: \n",
+            "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
+            "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
+            "but was not found to be installed on your system.\n",
+            "If this would cause problems for you,\n",
+            "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
+            "        \n",
+            "  import pandas as pd\n"
+          ]
+        }
+      ],
       "source": [
         "import pandas as pd"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 7,
       "metadata": {
         "id": "fxqxsrcOWiFF"
       },
@@ -211,7 +226,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 8,
       "metadata": {
         "id": "c9wQ9ku3Wkgc"
       },
@@ -222,7 +237,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -263,17 +278,7 @@
               "      <th>mean concave points</th>\n",
               "      <th>mean symmetry</th>\n",
               "      <th>mean fractal dimension</th>\n",
-              "      <th>radius error</th>\n",
-              "      <th>texture error</th>\n",
-              "      <th>perimeter error</th>\n",
-              "      <th>area error</th>\n",
-              "      <th>smoothness error</th>\n",
-              "      <th>compactness error</th>\n",
-              "      <th>concavity error</th>\n",
-              "      <th>concave points error</th>\n",
-              "      <th>symmetry error</th>\n",
-              "      <th>fractal dimension error</th>\n",
-              "      <th>worst radius</th>\n",
+              "      <th>...</th>\n",
               "      <th>worst texture</th>\n",
               "      <th>worst perimeter</th>\n",
               "      <th>worst area</th>\n",
@@ -299,17 +304,7 @@
               "      <td>0.14710</td>\n",
               "      <td>0.2419</td>\n",
               "      <td>0.07871</td>\n",
-              "      <td>1.0950</td>\n",
-              "      <td>0.9053</td>\n",
-              "      <td>8.589</td>\n",
-              "      <td>153.40</td>\n",
-              "      <td>0.006399</td>\n",
-              "      <td>0.04904</td>\n",
-              "      <td>0.05373</td>\n",
-              "      <td>0.01587</td>\n",
-              "      <td>0.03003</td>\n",
-              "      <td>0.006193</td>\n",
-              "      <td>25.38</td>\n",
+              "      <td>...</td>\n",
               "      <td>17.33</td>\n",
               "      <td>184.60</td>\n",
               "      <td>2019.0</td>\n",
@@ -333,17 +328,7 @@
               "      <td>0.07017</td>\n",
               "      <td>0.1812</td>\n",
               "      <td>0.05667</td>\n",
-              "      <td>0.5435</td>\n",
-              "      <td>0.7339</td>\n",
-              "      <td>3.398</td>\n",
-              "      <td>74.08</td>\n",
-              "      <td>0.005225</td>\n",
-              "      <td>0.01308</td>\n",
-              "      <td>0.01860</td>\n",
-              "      <td>0.01340</td>\n",
-              "      <td>0.01389</td>\n",
-              "      <td>0.003532</td>\n",
-              "      <td>24.99</td>\n",
+              "      <td>...</td>\n",
               "      <td>23.41</td>\n",
               "      <td>158.80</td>\n",
               "      <td>1956.0</td>\n",
@@ -367,17 +352,7 @@
               "      <td>0.12790</td>\n",
               "      <td>0.2069</td>\n",
               "      <td>0.05999</td>\n",
-              "      <td>0.7456</td>\n",
-              "      <td>0.7869</td>\n",
-              "      <td>4.585</td>\n",
-              "      <td>94.03</td>\n",
-              "      <td>0.006150</td>\n",
-              "      <td>0.04006</td>\n",
-              "      <td>0.03832</td>\n",
-              "      <td>0.02058</td>\n",
-              "      <td>0.02250</td>\n",
-              "      <td>0.004571</td>\n",
-              "      <td>23.57</td>\n",
+              "      <td>...</td>\n",
               "      <td>25.53</td>\n",
               "      <td>152.50</td>\n",
               "      <td>1709.0</td>\n",
@@ -401,17 +376,7 @@
               "      <td>0.10520</td>\n",
               "      <td>0.2597</td>\n",
               "      <td>0.09744</td>\n",
-              "      <td>0.4956</td>\n",
-              "      <td>1.1560</td>\n",
-              "      <td>3.445</td>\n",
-              "      <td>27.23</td>\n",
-              "      <td>0.009110</td>\n",
-              "      <td>0.07458</td>\n",
-              "      <td>0.05661</td>\n",
-              "      <td>0.01867</td>\n",
-              "      <td>0.05963</td>\n",
-              "      <td>0.009208</td>\n",
-              "      <td>14.91</td>\n",
+              "      <td>...</td>\n",
               "      <td>26.50</td>\n",
               "      <td>98.87</td>\n",
               "      <td>567.7</td>\n",
@@ -435,17 +400,7 @@
               "      <td>0.10430</td>\n",
               "      <td>0.1809</td>\n",
               "      <td>0.05883</td>\n",
-              "      <td>0.7572</td>\n",
-              "      <td>0.7813</td>\n",
-              "      <td>5.438</td>\n",
-              "      <td>94.44</td>\n",
-              "      <td>0.011490</td>\n",
-              "      <td>0.02461</td>\n",
-              "      <td>0.05688</td>\n",
-              "      <td>0.01885</td>\n",
-              "      <td>0.01756</td>\n",
-              "      <td>0.005115</td>\n",
-              "      <td>22.54</td>\n",
+              "      <td>...</td>\n",
               "      <td>16.67</td>\n",
               "      <td>152.20</td>\n",
               "      <td>1575.0</td>\n",
@@ -459,20 +414,49 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
+              "<p>5 rows × 31 columns</p>\n",
               "</div>"
             ],
             "text/plain": [
-              "   mean radius  mean texture  ...  worst fractal dimension  class\n",
-              "0        17.99         10.38  ...                  0.11890      0\n",
-              "1        20.57         17.77  ...                  0.08902      0\n",
-              "2        19.69         21.25  ...                  0.08758      0\n",
-              "3        11.42         20.38  ...                  0.17300      0\n",
-              "4        20.29         14.34  ...                  0.07678      0\n",
+              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+              "3        11.42         20.38           77.58      386.1          0.14250   \n",
+              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+              "\n",
+              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+              "0           0.27760          0.3001              0.14710         0.2419   \n",
+              "1           0.07864          0.0869              0.07017         0.1812   \n",
+              "2           0.15990          0.1974              0.12790         0.2069   \n",
+              "3           0.28390          0.2414              0.10520         0.2597   \n",
+              "4           0.13280          0.1980              0.10430         0.1809   \n",
+              "\n",
+              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
+              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
+              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
+              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
+              "3                 0.09744  ...          26.50            98.87       567.7   \n",
+              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
+              "\n",
+              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
+              "0            0.1622             0.6656           0.7119                0.2654   \n",
+              "1            0.1238             0.1866           0.2416                0.1860   \n",
+              "2            0.1444             0.4245           0.4504                0.2430   \n",
+              "3            0.2098             0.8663           0.6869                0.2575   \n",
+              "4            0.1374             0.2050           0.4000                0.1625   \n",
+              "\n",
+              "   worst symmetry  worst fractal dimension  class  \n",
+              "0          0.4601                  0.11890      0  \n",
+              "1          0.2750                  0.08902      0  \n",
+              "2          0.3613                  0.08758      0  \n",
+              "3          0.6638                  0.17300      0  \n",
+              "4          0.2364                  0.07678      0  \n",
               "\n",
               "[5 rows x 31 columns]"
             ]
           },
-          "execution_count": 11,
+          "execution_count": 9,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -483,7 +467,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -524,17 +508,7 @@
               "      <th>mean concave points</th>\n",
               "      <th>mean symmetry</th>\n",
               "      <th>mean fractal dimension</th>\n",
-              "      <th>radius error</th>\n",
-              "      <th>texture error</th>\n",
-              "      <th>perimeter error</th>\n",
-              "      <th>area error</th>\n",
-              "      <th>smoothness error</th>\n",
-              "      <th>compactness error</th>\n",
-              "      <th>concavity error</th>\n",
-              "      <th>concave points error</th>\n",
-              "      <th>symmetry error</th>\n",
-              "      <th>fractal dimension error</th>\n",
-              "      <th>worst radius</th>\n",
+              "      <th>...</th>\n",
               "      <th>worst texture</th>\n",
               "      <th>worst perimeter</th>\n",
               "      <th>worst area</th>\n",
@@ -560,17 +534,7 @@
               "      <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>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",
@@ -594,17 +558,7 @@
               "      <td>0.048919</td>\n",
               "      <td>0.181162</td>\n",
               "      <td>0.062798</td>\n",
-              "      <td>0.405172</td>\n",
-              "      <td>1.216853</td>\n",
-              "      <td>2.866059</td>\n",
-              "      <td>40.337079</td>\n",
-              "      <td>0.007041</td>\n",
-              "      <td>0.025478</td>\n",
-              "      <td>0.031894</td>\n",
-              "      <td>0.011796</td>\n",
-              "      <td>0.020542</td>\n",
-              "      <td>0.003795</td>\n",
-              "      <td>16.269190</td>\n",
+              "      <td>...</td>\n",
               "      <td>25.677223</td>\n",
               "      <td>107.261213</td>\n",
               "      <td>880.583128</td>\n",
@@ -628,17 +582,7 @@
               "      <td>0.038803</td>\n",
               "      <td>0.027414</td>\n",
               "      <td>0.007060</td>\n",
-              "      <td>0.277313</td>\n",
-              "      <td>0.551648</td>\n",
-              "      <td>2.021855</td>\n",
-              "      <td>45.491006</td>\n",
-              "      <td>0.003003</td>\n",
-              "      <td>0.017908</td>\n",
-              "      <td>0.030186</td>\n",
-              "      <td>0.006170</td>\n",
-              "      <td>0.008266</td>\n",
-              "      <td>0.002646</td>\n",
-              "      <td>4.833242</td>\n",
+              "      <td>...</td>\n",
               "      <td>6.146258</td>\n",
               "      <td>33.602542</td>\n",
               "      <td>569.356993</td>\n",
@@ -662,17 +606,7 @@
               "      <td>0.000000</td>\n",
               "      <td>0.106000</td>\n",
               "      <td>0.049960</td>\n",
-              "      <td>0.111500</td>\n",
-              "      <td>0.360200</td>\n",
-              "      <td>0.757000</td>\n",
-              "      <td>6.802000</td>\n",
-              "      <td>0.001713</td>\n",
-              "      <td>0.002252</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.007882</td>\n",
-              "      <td>0.000895</td>\n",
-              "      <td>7.930000</td>\n",
+              "      <td>...</td>\n",
               "      <td>12.020000</td>\n",
               "      <td>50.410000</td>\n",
               "      <td>185.200000</td>\n",
@@ -696,17 +630,7 @@
               "      <td>0.020310</td>\n",
               "      <td>0.161900</td>\n",
               "      <td>0.057700</td>\n",
-              "      <td>0.232400</td>\n",
-              "      <td>0.833900</td>\n",
-              "      <td>1.606000</td>\n",
-              "      <td>17.850000</td>\n",
-              "      <td>0.005169</td>\n",
-              "      <td>0.013080</td>\n",
-              "      <td>0.015090</td>\n",
-              "      <td>0.007638</td>\n",
-              "      <td>0.015160</td>\n",
-              "      <td>0.002248</td>\n",
-              "      <td>13.010000</td>\n",
+              "      <td>...</td>\n",
               "      <td>21.080000</td>\n",
               "      <td>84.110000</td>\n",
               "      <td>515.300000</td>\n",
@@ -730,17 +654,7 @@
               "      <td>0.033500</td>\n",
               "      <td>0.179200</td>\n",
               "      <td>0.061540</td>\n",
-              "      <td>0.324200</td>\n",
-              "      <td>1.108000</td>\n",
-              "      <td>2.287000</td>\n",
-              "      <td>24.530000</td>\n",
-              "      <td>0.006380</td>\n",
-              "      <td>0.020450</td>\n",
-              "      <td>0.025890</td>\n",
-              "      <td>0.010930</td>\n",
-              "      <td>0.018730</td>\n",
-              "      <td>0.003187</td>\n",
-              "      <td>14.970000</td>\n",
+              "      <td>...</td>\n",
               "      <td>25.410000</td>\n",
               "      <td>97.660000</td>\n",
               "      <td>686.500000</td>\n",
@@ -764,17 +678,7 @@
               "      <td>0.074000</td>\n",
               "      <td>0.195700</td>\n",
               "      <td>0.066120</td>\n",
-              "      <td>0.478900</td>\n",
-              "      <td>1.474000</td>\n",
-              "      <td>3.357000</td>\n",
-              "      <td>45.190000</td>\n",
-              "      <td>0.008146</td>\n",
-              "      <td>0.032450</td>\n",
-              "      <td>0.042050</td>\n",
-              "      <td>0.014710</td>\n",
-              "      <td>0.023480</td>\n",
-              "      <td>0.004558</td>\n",
-              "      <td>18.790000</td>\n",
+              "      <td>...</td>\n",
               "      <td>29.720000</td>\n",
               "      <td>125.400000</td>\n",
               "      <td>1084.000000</td>\n",
@@ -798,17 +702,7 @@
               "      <td>0.201200</td>\n",
               "      <td>0.304000</td>\n",
               "      <td>0.097440</td>\n",
-              "      <td>2.873000</td>\n",
-              "      <td>4.885000</td>\n",
-              "      <td>21.980000</td>\n",
-              "      <td>542.200000</td>\n",
-              "      <td>0.031130</td>\n",
-              "      <td>0.135400</td>\n",
-              "      <td>0.396000</td>\n",
-              "      <td>0.052790</td>\n",
-              "      <td>0.078950</td>\n",
-              "      <td>0.029840</td>\n",
-              "      <td>36.040000</td>\n",
+              "      <td>...</td>\n",
               "      <td>49.540000</td>\n",
               "      <td>251.200000</td>\n",
               "      <td>4254.000000</td>\n",
@@ -822,23 +716,74 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
+              "<p>8 rows × 31 columns</p>\n",
               "</div>"
             ],
             "text/plain": [
-              "       mean radius  mean texture  ...  worst fractal dimension       class\n",
-              "count   569.000000    569.000000  ...               569.000000  569.000000\n",
-              "mean     14.127292     19.289649  ...                 0.083946    0.627417\n",
-              "std       3.524049      4.301036  ...                 0.018061    0.483918\n",
-              "min       6.981000      9.710000  ...                 0.055040    0.000000\n",
-              "25%      11.700000     16.170000  ...                 0.071460    0.000000\n",
-              "50%      13.370000     18.840000  ...                 0.080040    1.000000\n",
-              "75%      15.780000     21.800000  ...                 0.092080    1.000000\n",
-              "max      28.110000     39.280000  ...                 0.207500    1.000000\n",
+              "       mean radius  mean texture  mean perimeter    mean area  \\\n",
+              "count   569.000000    569.000000      569.000000   569.000000   \n",
+              "mean     14.127292     19.289649       91.969033   654.889104   \n",
+              "std       3.524049      4.301036       24.298981   351.914129   \n",
+              "min       6.981000      9.710000       43.790000   143.500000   \n",
+              "25%      11.700000     16.170000       75.170000   420.300000   \n",
+              "50%      13.370000     18.840000       86.240000   551.100000   \n",
+              "75%      15.780000     21.800000      104.100000   782.700000   \n",
+              "max      28.110000     39.280000      188.500000  2501.000000   \n",
+              "\n",
+              "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
+              "count       569.000000        569.000000      569.000000           569.000000   \n",
+              "mean          0.096360          0.104341        0.088799             0.048919   \n",
+              "std           0.014064          0.052813        0.079720             0.038803   \n",
+              "min           0.052630          0.019380        0.000000             0.000000   \n",
+              "25%           0.086370          0.064920        0.029560             0.020310   \n",
+              "50%           0.095870          0.092630        0.061540             0.033500   \n",
+              "75%           0.105300          0.130400        0.130700             0.074000   \n",
+              "max           0.163400          0.345400        0.426800             0.201200   \n",
+              "\n",
+              "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
+              "count     569.000000              569.000000  ...     569.000000   \n",
+              "mean        0.181162                0.062798  ...      25.677223   \n",
+              "std         0.027414                0.007060  ...       6.146258   \n",
+              "min         0.106000                0.049960  ...      12.020000   \n",
+              "25%         0.161900                0.057700  ...      21.080000   \n",
+              "50%         0.179200                0.061540  ...      25.410000   \n",
+              "75%         0.195700                0.066120  ...      29.720000   \n",
+              "max         0.304000                0.097440  ...      49.540000   \n",
+              "\n",
+              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
+              "count       569.000000   569.000000        569.000000         569.000000   \n",
+              "mean        107.261213   880.583128          0.132369           0.254265   \n",
+              "std          33.602542   569.356993          0.022832           0.157336   \n",
+              "min          50.410000   185.200000          0.071170           0.027290   \n",
+              "25%          84.110000   515.300000          0.116600           0.147200   \n",
+              "50%          97.660000   686.500000          0.131300           0.211900   \n",
+              "75%         125.400000  1084.000000          0.146000           0.339100   \n",
+              "max         251.200000  4254.000000          0.222600           1.058000   \n",
+              "\n",
+              "       worst concavity  worst concave points  worst symmetry  \\\n",
+              "count       569.000000            569.000000      569.000000   \n",
+              "mean          0.272188              0.114606        0.290076   \n",
+              "std           0.208624              0.065732        0.061867   \n",
+              "min           0.000000              0.000000        0.156500   \n",
+              "25%           0.114500              0.064930        0.250400   \n",
+              "50%           0.226700              0.099930        0.282200   \n",
+              "75%           0.382900              0.161400        0.317900   \n",
+              "max           1.252000              0.291000        0.663800   \n",
+              "\n",
+              "       worst fractal dimension       class  \n",
+              "count               569.000000  569.000000  \n",
+              "mean                  0.083946    0.627417  \n",
+              "std                   0.018061    0.483918  \n",
+              "min                   0.055040    0.000000  \n",
+              "25%                   0.071460    0.000000  \n",
+              "50%                   0.080040    1.000000  \n",
+              "75%                   0.092080    1.000000  \n",
+              "max                   0.207500    1.000000  \n",
               "\n",
               "[8 rows x 31 columns]"
             ]
           },
-          "execution_count": 12,
+          "execution_count": 10,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -849,7 +794,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -863,9 +808,10 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
+            "class\n",
             "1    357\n",
             "0    212\n",
-            "Name: class, dtype: int64\n"
+            "Name: count, dtype: int64\n"
           ]
         }
       ],
@@ -875,7 +821,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -899,7 +845,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -940,16 +886,7 @@
               "      <th>mean concave points</th>\n",
               "      <th>mean symmetry</th>\n",
               "      <th>mean fractal dimension</th>\n",
-              "      <th>radius error</th>\n",
-              "      <th>texture error</th>\n",
-              "      <th>perimeter error</th>\n",
-              "      <th>area error</th>\n",
-              "      <th>smoothness error</th>\n",
-              "      <th>compactness error</th>\n",
-              "      <th>concavity error</th>\n",
-              "      <th>concave points error</th>\n",
-              "      <th>symmetry error</th>\n",
-              "      <th>fractal dimension error</th>\n",
+              "      <th>...</th>\n",
               "      <th>worst radius</th>\n",
               "      <th>worst texture</th>\n",
               "      <th>worst perimeter</th>\n",
@@ -984,15 +921,6 @@
               "      <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",
@@ -1008,16 +936,7 @@
               "      <td>0.087990</td>\n",
               "      <td>0.192909</td>\n",
               "      <td>0.062680</td>\n",
-              "      <td>0.609083</td>\n",
-              "      <td>1.210915</td>\n",
-              "      <td>4.323929</td>\n",
-              "      <td>72.672406</td>\n",
-              "      <td>0.006780</td>\n",
-              "      <td>0.032281</td>\n",
-              "      <td>0.041824</td>\n",
-              "      <td>0.015060</td>\n",
-              "      <td>0.020472</td>\n",
-              "      <td>0.004062</td>\n",
+              "      <td>...</td>\n",
               "      <td>21.134811</td>\n",
               "      <td>29.318208</td>\n",
               "      <td>141.370330</td>\n",
@@ -1041,16 +960,7 @@
               "      <td>0.025717</td>\n",
               "      <td>0.174186</td>\n",
               "      <td>0.062867</td>\n",
-              "      <td>0.284082</td>\n",
-              "      <td>1.220380</td>\n",
-              "      <td>2.000321</td>\n",
-              "      <td>21.135148</td>\n",
-              "      <td>0.007196</td>\n",
-              "      <td>0.021438</td>\n",
-              "      <td>0.025997</td>\n",
-              "      <td>0.009858</td>\n",
-              "      <td>0.020584</td>\n",
-              "      <td>0.003636</td>\n",
+              "      <td>...</td>\n",
               "      <td>13.379801</td>\n",
               "      <td>23.515070</td>\n",
               "      <td>87.005938</td>\n",
@@ -1064,18 +974,44 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
+              "<p>2 rows × 30 columns</p>\n",
               "</div>"
             ],
             "text/plain": [
-              "       mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
-              "class                             ...                                         \n",
-              "0        17.462830     21.604906  ...        0.323468                 0.091530\n",
-              "1        12.146524     17.914762  ...        0.270246                 0.079442\n",
+              "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
+              "class                                                                           \n",
+              "0        17.462830     21.604906      115.365377  978.376415         0.102898   \n",
+              "1        12.146524     17.914762       78.075406  462.790196         0.092478   \n",
+              "\n",
+              "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+              "class                                                                         \n",
+              "0              0.145188        0.160775             0.087990       0.192909   \n",
+              "1              0.080085        0.046058             0.025717       0.174186   \n",
+              "\n",
+              "       mean fractal dimension  ...  worst radius  worst texture  \\\n",
+              "class                          ...                                \n",
+              "0                    0.062680  ...     21.134811      29.318208   \n",
+              "1                    0.062867  ...     13.379801      23.515070   \n",
+              "\n",
+              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
+              "class                                                                      \n",
+              "0           141.370330  1422.286321          0.144845           0.374824   \n",
+              "1            87.005938   558.899440          0.124959           0.182673   \n",
+              "\n",
+              "       worst concavity  worst concave points  worst symmetry  \\\n",
+              "class                                                          \n",
+              "0             0.450606              0.182237        0.323468   \n",
+              "1             0.166238              0.074444        0.270246   \n",
+              "\n",
+              "       worst fractal dimension  \n",
+              "class                           \n",
+              "0                     0.091530  \n",
+              "1                     0.079442  \n",
               "\n",
               "[2 rows x 30 columns]"
             ]
           },
-          "execution_count": 15,
+          "execution_count": 13,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1097,7 +1033,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 14,
       "metadata": {
         "id": "RDCD-3t2XmNG"
       },
@@ -1109,7 +1045,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1133,7 +1069,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 16,
       "metadata": {
         "id": "TfXjgKecX2aR"
       },
@@ -1145,7 +1081,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 17,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1169,7 +1105,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 18,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1183,7 +1119,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "0.6274165202108963 0.630859375 0.5964912280701754\n"
+            "0.6274165202108963 0.623046875 0.6666666666666666\n"
           ]
         }
       ],
@@ -1193,7 +1129,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 19,
       "metadata": {
         "id": "_jFZwc9OX-Av"
       },
@@ -1205,7 +1141,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 20,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1229,7 +1165,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 21,
       "metadata": {
         "id": "6hnHZaOWYNb1"
       },
@@ -1241,7 +1177,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 22,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1265,7 +1201,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 23,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1304,7 +1240,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 24,
       "metadata": {
         "id": "A63VGGb9YVRp"
       },
@@ -1316,7 +1252,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 25,
       "metadata": {
         "id": "dNLK1uKmYZEM"
       },
@@ -1327,7 +1263,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 26,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1341,27 +1277,430 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+            "d:\\Users\\PC\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: 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",
-            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\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: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+            ],
             "text/plain": [
-              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-              "                   warm_start=False)"
+              "LogisticRegression()"
             ]
           },
-          "execution_count": 28,
+          "execution_count": 26,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1382,7 +1721,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": 27,
       "metadata": {
         "id": "x4Cw-CwoYlzE"
       },
@@ -1394,7 +1733,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": 28,
       "metadata": {
         "id": "J7iCwz2QYrN_"
       },
@@ -1406,7 +1745,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": 29,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1430,7 +1769,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 33,
+      "execution_count": 30,
       "metadata": {
         "id": "OzBEFHyyZc0X"
       },
@@ -1443,7 +1782,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": 31,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1476,7 +1815,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 35,
+      "execution_count": 32,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1503,7 +1842,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
+      "execution_count": 33,
       "metadata": {
         "id": "_0QS56eSZ6xR"
       },
@@ -1515,7 +1854,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 37,
+      "execution_count": 34,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1541,7 +1880,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": 35,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1579,7 +1918,15 @@
       "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": "3.10.3"
     },
     "vscode": {
diff --git a/breastcancerdetection.py b/breastcancerdetection.py
deleted file mode 100644
index a92950c..0000000
--- a/breastcancerdetection.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# Importing libraries
-from sklearn import datasets
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score
-import numpy as np
-
-# Fetching the dataset
-breast_cancer = datasets.load_breast_cancer()
-# print(breast_cancer)  # printing the dataset to check whether is has been loaded or not.
-
-X = breast_cancer.data
-Y = breast_cancer.target
-
-# Printing the Data and Target of the Dataset
-# print(X)
-# print(Y)
-
-# printing the shape of X and Y or the instances of X and Y
-# print(X.shape, Y.shape)
-
-"""Import data to the Pandas Data Frame"""
-data = pd.DataFrame(breast_cancer.data, columns = breast_cancer.feature_names)
-
-data['class'] =  breast_cancer.target
-
-# print(data.head())    # print some data samples
-
-# print(data.describe())    # prints statistical data
-
-# print(data['class'].value_counts())   # prints the number of cases for malignant and benign
-
-# print(breast_cancer.target_names)     # Displays the target names, i.e., Malignant and Benign
-
-# data.groupby('class').mean()  # printing the mean feature values for each of the targets
-#0 = malignant
-#1 = benign
-
-"""Train and Test Data Split"""
-
-# X_train, X_test, Y_train, Y_test = train_test_split(X,Y)
-
-# print(Y.shape, Y_train.shape, Y_test.shape)
-# After running this data we will see that we have 426 training data and 143 testing data(we don't need so many)
-
-# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1)
-#test_size --> to specify the percentage of test data needed
-
-# print(Y.shape, Y_train.shape, Y_test.shape)
-# Now there are 512 training data and 57 testing data.
-
-# print(Y.mean(), Y_train.mean(), Y_test.mean())
-# This output lets us know the distribution of the data of the mean and we found out that the distribution of the test data is a bit lo.
-
-X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y)
-#stratify --> for correct distribution of data as of the original data
-
-# print(Y.mean(), Y_train.mean(), Y_test.mean())
-#Now there is equal distribution between the training data of Malignant and Benign
-
-X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y, random_state=1)
-#random_state --> specific split of data, each value of random_state splits the data differently
-
-# print(X_train.mean(), X_test.mean(), X.mean())
-
-# print(X_train)
-
-"""**Logistic Regression**"""
-classifier = LogisticRegression()   #loading the logistic regression model to the variable "classifier"
-
-# training the model on training data
-classifier.fit(X_train, Y_train)
-
-"""**Evaluation of the Model**"""
-prediction_on_training_data = classifier.predict(X_train)
-accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)
-
-print('Accuracy on training data :', accuracy_on_training_data)
-
-#prediction on test_data
-prediction_on_test_data = classifier.predict(X_test)
-accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)
-
-print('Accuracy on test data :', accuracy_on_test_data)
-
-"""Detecting whether the Patient has Breast Cancer in Benign or Malignant Stage"""
-
-input_data = (17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,0.095,1.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189)
-#change the input_data to numpy_array to make prediction
-input_data_as_numpy_array = np.array(input_data)
-print(input_data)
-
-#reshape the array we are predicting the output for one instance
-input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
-
-#prediction
-prediction = classifier.predict(input_data_reshaped)
-print(prediction)   #returns a list with elements 0 [if Malignant] and 1 [if Benign]
-
-if (prediction[0]==0):
-    print("The Breast Cancer is at Malignant Stage.")
-else:
-    print("The Breast Cancer is at Benign Stage.")
\ No newline at end of file