From d220d274cafe3174fe28e1a164a6c8eb9ce0fa05 Mon Sep 17 00:00:00 2001
From: KUSHAGRA SRIVASTAVA <kushagra.2024cse1172@kiet.edu>
Date: Thu, 26 May 2022 23:38:06 +0530
Subject: [PATCH] Add files via upload

---
 Breast_cancer.ipynb | 2395 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2395 insertions(+)
 create mode 100644 Breast_cancer.ipynb

diff --git a/Breast_cancer.ipynb b/Breast_cancer.ipynb
new file mode 100644
index 0000000..65fc952
--- /dev/null
+++ b/Breast_cancer.ipynb
@@ -0,0 +1,2395 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Breast cancer.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G03i8TxURuPK"
+      },
+      "source": [
+        "import pandas as pd"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6BGBRFPLtNiq",
+        "outputId": "5c885cfe-9297-4100-d6f4-c90a4995cafa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "foeYIXhxT7oQ"
+      },
+      "source": [
+        "dataframe = pd.read_csv(\"/content/data.csv\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "THvmGLDXUWW1",
+        "outputId": "602577b7-ecde-4ee2-a5ec-c657ca918802"
+      },
+      "source": [
+        "dataframe"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+              "..        ...       ...          ...           ...             ...        ...   \n",
+              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+              "566    926954         M        16.60         28.08          108.30      858.1   \n",
+              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+              "568     92751         B         7.76         24.54           47.92      181.0   \n",
+              "\n",
+              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+              "0            0.11840           0.27760         0.30010              0.14710   \n",
+              "1            0.08474           0.07864         0.08690              0.07017   \n",
+              "2            0.10960           0.15990         0.19740              0.12790   \n",
+              "3            0.14250           0.28390         0.24140              0.10520   \n",
+              "4            0.10030           0.13280         0.19800              0.10430   \n",
+              "..               ...               ...             ...                  ...   \n",
+              "564          0.11100           0.11590         0.24390              0.13890   \n",
+              "565          0.09780           0.10340         0.14400              0.09791   \n",
+              "566          0.08455           0.10230         0.09251              0.05302   \n",
+              "567          0.11780           0.27700         0.35140              0.15200   \n",
+              "568          0.05263           0.04362         0.00000              0.00000   \n",
+              "\n",
+              "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+              "0    ...          17.33           184.60      2019.0           0.16220   \n",
+              "1    ...          23.41           158.80      1956.0           0.12380   \n",
+              "2    ...          25.53           152.50      1709.0           0.14440   \n",
+              "3    ...          26.50            98.87       567.7           0.20980   \n",
+              "4    ...          16.67           152.20      1575.0           0.13740   \n",
+              "..   ...            ...              ...         ...               ...   \n",
+              "564  ...          26.40           166.10      2027.0           0.14100   \n",
+              "565  ...          38.25           155.00      1731.0           0.11660   \n",
+              "566  ...          34.12           126.70      1124.0           0.11390   \n",
+              "567  ...          39.42           184.60      1821.0           0.16500   \n",
+              "568  ...          30.37            59.16       268.6           0.08996   \n",
+              "\n",
+              "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+              "0              0.66560           0.7119                0.2654          0.4601   \n",
+              "1              0.18660           0.2416                0.1860          0.2750   \n",
+              "2              0.42450           0.4504                0.2430          0.3613   \n",
+              "3              0.86630           0.6869                0.2575          0.6638   \n",
+              "4              0.20500           0.4000                0.1625          0.2364   \n",
+              "..                 ...              ...                   ...             ...   \n",
+              "564            0.21130           0.4107                0.2216          0.2060   \n",
+              "565            0.19220           0.3215                0.1628          0.2572   \n",
+              "566            0.30940           0.3403                0.1418          0.2218   \n",
+              "567            0.86810           0.9387                0.2650          0.4087   \n",
+              "568            0.06444           0.0000                0.0000          0.2871   \n",
+              "\n",
+              "     fractal_dimension_worst  Unnamed: 32  \n",
+              "0                    0.11890          NaN  \n",
+              "1                    0.08902          NaN  \n",
+              "2                    0.08758          NaN  \n",
+              "3                    0.17300          NaN  \n",
+              "4                    0.07678          NaN  \n",
+              "..                       ...          ...  \n",
+              "564                  0.07115          NaN  \n",
+              "565                  0.06637          NaN  \n",
+              "566                  0.07820          NaN  \n",
+              "567                  0.12400          NaN  \n",
+              "568                  0.07039          NaN  \n",
+              "\n",
+              "[569 rows x 33 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8f364658-3db9-470e-996d-116ea1062db3\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>diagnosis</th>\n",
+              "      <th>radius_mean</th>\n",
+              "      <th>texture_mean</th>\n",
+              "      <th>perimeter_mean</th>\n",
+              "      <th>area_mean</th>\n",
+              "      <th>smoothness_mean</th>\n",
+              "      <th>compactness_mean</th>\n",
+              "      <th>concavity_mean</th>\n",
+              "      <th>concave points_mean</th>\n",
+              "      <th>...</th>\n",
+              "      <th>texture_worst</th>\n",
+              "      <th>perimeter_worst</th>\n",
+              "      <th>area_worst</th>\n",
+              "      <th>smoothness_worst</th>\n",
+              "      <th>compactness_worst</th>\n",
+              "      <th>concavity_worst</th>\n",
+              "      <th>concave points_worst</th>\n",
+              "      <th>symmetry_worst</th>\n",
+              "      <th>fractal_dimension_worst</th>\n",
+              "      <th>Unnamed: 32</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>842302</td>\n",
+              "      <td>M</td>\n",
+              "      <td>17.99</td>\n",
+              "      <td>10.38</td>\n",
+              "      <td>122.80</td>\n",
+              "      <td>1001.0</td>\n",
+              "      <td>0.11840</td>\n",
+              "      <td>0.27760</td>\n",
+              "      <td>0.30010</td>\n",
+              "      <td>0.14710</td>\n",
+              "      <td>...</td>\n",
+              "      <td>17.33</td>\n",
+              "      <td>184.60</td>\n",
+              "      <td>2019.0</td>\n",
+              "      <td>0.16220</td>\n",
+              "      <td>0.66560</td>\n",
+              "      <td>0.7119</td>\n",
+              "      <td>0.2654</td>\n",
+              "      <td>0.4601</td>\n",
+              "      <td>0.11890</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>842517</td>\n",
+              "      <td>M</td>\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>0.08690</td>\n",
+              "      <td>0.07017</td>\n",
+              "      <td>...</td>\n",
+              "      <td>23.41</td>\n",
+              "      <td>158.80</td>\n",
+              "      <td>1956.0</td>\n",
+              "      <td>0.12380</td>\n",
+              "      <td>0.18660</td>\n",
+              "      <td>0.2416</td>\n",
+              "      <td>0.1860</td>\n",
+              "      <td>0.2750</td>\n",
+              "      <td>0.08902</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>84300903</td>\n",
+              "      <td>M</td>\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>0.19740</td>\n",
+              "      <td>0.12790</td>\n",
+              "      <td>...</td>\n",
+              "      <td>25.53</td>\n",
+              "      <td>152.50</td>\n",
+              "      <td>1709.0</td>\n",
+              "      <td>0.14440</td>\n",
+              "      <td>0.42450</td>\n",
+              "      <td>0.4504</td>\n",
+              "      <td>0.2430</td>\n",
+              "      <td>0.3613</td>\n",
+              "      <td>0.08758</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>84348301</td>\n",
+              "      <td>M</td>\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>0.24140</td>\n",
+              "      <td>0.10520</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.50</td>\n",
+              "      <td>98.87</td>\n",
+              "      <td>567.7</td>\n",
+              "      <td>0.20980</td>\n",
+              "      <td>0.86630</td>\n",
+              "      <td>0.6869</td>\n",
+              "      <td>0.2575</td>\n",
+              "      <td>0.6638</td>\n",
+              "      <td>0.17300</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>84358402</td>\n",
+              "      <td>M</td>\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>0.19800</td>\n",
+              "      <td>0.10430</td>\n",
+              "      <td>...</td>\n",
+              "      <td>16.67</td>\n",
+              "      <td>152.20</td>\n",
+              "      <td>1575.0</td>\n",
+              "      <td>0.13740</td>\n",
+              "      <td>0.20500</td>\n",
+              "      <td>0.4000</td>\n",
+              "      <td>0.1625</td>\n",
+              "      <td>0.2364</td>\n",
+              "      <td>0.07678</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>564</th>\n",
+              "      <td>926424</td>\n",
+              "      <td>M</td>\n",
+              "      <td>21.56</td>\n",
+              "      <td>22.39</td>\n",
+              "      <td>142.00</td>\n",
+              "      <td>1479.0</td>\n",
+              "      <td>0.11100</td>\n",
+              "      <td>0.11590</td>\n",
+              "      <td>0.24390</td>\n",
+              "      <td>0.13890</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.40</td>\n",
+              "      <td>166.10</td>\n",
+              "      <td>2027.0</td>\n",
+              "      <td>0.14100</td>\n",
+              "      <td>0.21130</td>\n",
+              "      <td>0.4107</td>\n",
+              "      <td>0.2216</td>\n",
+              "      <td>0.2060</td>\n",
+              "      <td>0.07115</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>565</th>\n",
+              "      <td>926682</td>\n",
+              "      <td>M</td>\n",
+              "      <td>20.13</td>\n",
+              "      <td>28.25</td>\n",
+              "      <td>131.20</td>\n",
+              "      <td>1261.0</td>\n",
+              "      <td>0.09780</td>\n",
+              "      <td>0.10340</td>\n",
+              "      <td>0.14400</td>\n",
+              "      <td>0.09791</td>\n",
+              "      <td>...</td>\n",
+              "      <td>38.25</td>\n",
+              "      <td>155.00</td>\n",
+              "      <td>1731.0</td>\n",
+              "      <td>0.11660</td>\n",
+              "      <td>0.19220</td>\n",
+              "      <td>0.3215</td>\n",
+              "      <td>0.1628</td>\n",
+              "      <td>0.2572</td>\n",
+              "      <td>0.06637</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>566</th>\n",
+              "      <td>926954</td>\n",
+              "      <td>M</td>\n",
+              "      <td>16.60</td>\n",
+              "      <td>28.08</td>\n",
+              "      <td>108.30</td>\n",
+              "      <td>858.1</td>\n",
+              "      <td>0.08455</td>\n",
+              "      <td>0.10230</td>\n",
+              "      <td>0.09251</td>\n",
+              "      <td>0.05302</td>\n",
+              "      <td>...</td>\n",
+              "      <td>34.12</td>\n",
+              "      <td>126.70</td>\n",
+              "      <td>1124.0</td>\n",
+              "      <td>0.11390</td>\n",
+              "      <td>0.30940</td>\n",
+              "      <td>0.3403</td>\n",
+              "      <td>0.1418</td>\n",
+              "      <td>0.2218</td>\n",
+              "      <td>0.07820</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>567</th>\n",
+              "      <td>927241</td>\n",
+              "      <td>M</td>\n",
+              "      <td>20.60</td>\n",
+              "      <td>29.33</td>\n",
+              "      <td>140.10</td>\n",
+              "      <td>1265.0</td>\n",
+              "      <td>0.11780</td>\n",
+              "      <td>0.27700</td>\n",
+              "      <td>0.35140</td>\n",
+              "      <td>0.15200</td>\n",
+              "      <td>...</td>\n",
+              "      <td>39.42</td>\n",
+              "      <td>184.60</td>\n",
+              "      <td>1821.0</td>\n",
+              "      <td>0.16500</td>\n",
+              "      <td>0.86810</td>\n",
+              "      <td>0.9387</td>\n",
+              "      <td>0.2650</td>\n",
+              "      <td>0.4087</td>\n",
+              "      <td>0.12400</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>568</th>\n",
+              "      <td>92751</td>\n",
+              "      <td>B</td>\n",
+              "      <td>7.76</td>\n",
+              "      <td>24.54</td>\n",
+              "      <td>47.92</td>\n",
+              "      <td>181.0</td>\n",
+              "      <td>0.05263</td>\n",
+              "      <td>0.04362</td>\n",
+              "      <td>0.00000</td>\n",
+              "      <td>0.00000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>30.37</td>\n",
+              "      <td>59.16</td>\n",
+              "      <td>268.6</td>\n",
+              "      <td>0.08996</td>\n",
+              "      <td>0.06444</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.2871</td>\n",
+              "      <td>0.07039</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>569 rows × 33 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f364658-3db9-470e-996d-116ea1062db3')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-8f364658-3db9-470e-996d-116ea1062db3 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-8f364658-3db9-470e-996d-116ea1062db3');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CwwVWCjQUZY5"
+      },
+      "source": [
+        "y=dataframe.iloc[:,1].values"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ieklOnmvU51x",
+        "outputId": "33223f33-031e-4ca8-9bfe-15bb0c626e7b"
+      },
+      "source": [
+        "y"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
+              "       'M', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'M', 'M',\n",
+              "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'M',\n",
+              "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B',\n",
+              "       'B', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
+              "       'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'M', 'B', 'M',\n",
+              "       'M', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'M', 'M', 'B', 'B', 'B',\n",
+              "       'M', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B',\n",
+              "       'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'M', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'M',\n",
+              "       'B', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B',\n",
+              "       'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'M', 'M',\n",
+              "       'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M',\n",
+              "       'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M',\n",
+              "       'B', 'M', 'M', 'M', 'M', 'B', 'M', 'M', 'M', 'B', 'M', 'B', 'M',\n",
+              "       'B', 'B', 'M', 'B', 'M', 'M', 'M', 'M', 'B', 'B', 'M', 'M', 'B',\n",
+              "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'M',\n",
+              "       'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'M', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
+              "       'M', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M',\n",
+              "       'B', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
+              "       'B', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'B',\n",
+              "       'B', 'B', 'B', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
+              "       'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'M', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'B', 'M', 'M', 'B', 'M', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'B',\n",
+              "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M',\n",
+              "       'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
+              "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'B', 'M', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M',\n",
+              "       'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B',\n",
+              "       'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'M', 'B', 'B',\n",
+              "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'M', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
+              "       'B', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'B'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BNuU_gjHU-Bt",
+        "outputId": "f4d70885-7a36-431f-f0e7-45e6bebf98fa"
+      },
+      "source": [
+        "y.shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(569,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qte2sVrCVB8r"
+      },
+      "source": [
+        "x=dataframe.iloc[:,2:].values"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ebU7UaAWVMCY",
+        "outputId": "9e18aa72-abc2-43cb-9003-100d61cb9997"
+      },
+      "source": [
+        "x.shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(569, 31)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ROqOCf_brvBI",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b6b0aebb-5bed-4e6e-8cae-30ca2d8f32f3"
+      },
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "imputer=SimpleImputer(missing_values=np.nan,strategy='mean')\n",
+        "imputer.fit(x)\n",
+        "x=imputer.transform(x)\n",
+        "print(x)\n",
+        "#for fitting some values in place of empty(null) values"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
+            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
+            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
+            " ...\n",
+            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
+            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
+            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8Y6u4MLbbZ8y",
+        "outputId": "c1441ed2-64bd-4fa7-8248-ed1cc14b1068"
+      },
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "le = LabelEncoder()\n",
+        "#label encoder encodes the two types of cancer into 0(maybe not cancerous) and 1(the other case)\n",
+        "y=le.fit_transform(y)\n",
+        "print(y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+            " 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1\n",
+            " 0 1 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0\n",
+            " 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0\n",
+            " 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1\n",
+            " 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0\n",
+            " 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1\n",
+            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            " 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0\n",
+            " 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1\n",
+            " 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
+            " 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0\n",
+            " 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0\n",
+            " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1\n",
+            " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "laUz8Qr-cbJT"
+      },
+      "source": [
+        "#now we have to divide data into 2 parts : one for training and one for testing\n",
+        "from sklearn.model_selection import train_test_split #****\n",
+        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=1)#**\n",
+        "#test_size = 0.2 means 20% data will be used for testing"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "25ZqM-I-eNgN"
+      },
+      "source": [
+        "#test me transform lagega hamesha fit transform nhi lagega\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "sc = StandardScaler()\n",
+        "x_train = sc.fit_transform(x_train)\n",
+        "x_test = sc.transform(x_test)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F0MxORDbffqY",
+        "outputId": "937e4d50-38f6-4fac-b6fb-7efa65393ddc"
+      },
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "classifier = SVC(kernel='linear',random_state=0)\n",
+        "classifier.fit(x_train,y_train)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(kernel='linear', random_state=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kw-QAwE4gx2A"
+      },
+      "source": [
+        "y_pred = classifier.predict(x_test)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sO6ztX1Ag8co",
+        "outputId": "0230dee0-93c3-45ac-a885-c47848263392"
+      },
+      "source": [
+        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
+        "cm = confusion_matrix(y_test,y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy for Linear Regression: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[71  1]\n",
+            " [ 3 39]]\n",
+            "\n",
+            "Accuracy for Linear Regression: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9649122807017544"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "23WYACaOi4BA",
+        "outputId": "379578a5-866b-4849-ad89-a49dcaf76b41"
+      },
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "classifier2 = LogisticRegression(random_state = 0)\n",
+        "classifier2.fit(x_train, y_train)\n",
+        "y_pred = classifier2.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy for Logistic Regression: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[71  1]\n",
+            " [ 2 40]]\n",
+            "\n",
+            "Accuracy for Logistic Regression: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9736842105263158"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "classifier3 = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
+        "classifier3.fit(x_train, y_train)\n",
+        "y_pred = classifier3.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy for Random Forest: \")\n",
+        "accuracy_score(y_test,y_pred)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "o1QOzVD9wyf2",
+        "outputId": "4302d5a7-373f-457c-f8a3-ab30a7c35d05"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[71  1]\n",
+            " [ 5 37]]\n",
+            "\n",
+            "Accuracy for Random Forest: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9473684210526315"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "classifier4 = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
+        "classifier4.fit(x_train, y_train)\n",
+        "y_pred = classifier4.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy for Decision Tree: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "en-CZhPGxam5",
+        "outputId": "c4384355-485c-4f23-813c-0da9c6558996"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[72  0]\n",
+            " [ 6 36]]\n",
+            "\n",
+            "Accuracy for Decision Tree: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9473684210526315"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "classifier5 = GaussianNB()\n",
+        "classifier5.fit(x_train, y_train)\n",
+        "y_pred = classifier5.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy for Naïve Bayes Algorithm: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fUiNRY8txyXm",
+        "outputId": "b65b2218-0c6f-4ce8-b292-924a32432b59"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[70  2]\n",
+            " [ 4 38]]\n",
+            "\n",
+            "Accuracy for Naïve Bayes Algorithm: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9473684210526315"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "classifier6 = SVC(kernel = 'rbf', random_state = 0)\n",
+        "classifier6.fit(x_train, y_train)\n",
+        "y_pred = classifier6.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy Using Kernel SVM Algorithm: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yWc_DAMs2l8f",
+        "outputId": "d98004c7-04d8-4c77-8b2b-e3d87e1ba85b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[71  1]\n",
+            " [ 2 40]]\n",
+            "\n",
+            "Accuracy Using Kernel SVM Algorithm: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9736842105263158"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "classifier7 = SVC(kernel = 'linear', random_state = 0)\n",
+        "classifier7.fit(x_train, y_train)\n",
+        "y_pred = classifier7.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy Using Support Vector Machine Algorithm: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pEjg9nww4H7e",
+        "outputId": "456d388a-b49d-476b-ac96-2ee6477bfd83"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[71  1]\n",
+            " [ 3 39]]\n",
+            "\n",
+            "Accuracy Using Support Vector Machine Algorithm: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9649122807017544"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "classifier8 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
+        "classifier8.fit(x_train, y_train)\n",
+        "y_pred = classifier8.predict(x_test)\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "print(\"\\nAccuracy for KNN: \")\n",
+        "accuracy_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WAzcXMxs4S3Z",
+        "outputId": "d61023d5-4f79-4422-cf0d-985839d678cd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[72  0]\n",
+            " [ 5 37]]\n",
+            "\n",
+            "Accuracy for KNN: \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.956140350877193"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modelxyz = SVC(kernel=\"rbf\",C=30,gamma='auto')\n",
+        "modelxyz.fit(x_train,y_train)\n",
+        "modelxyz.score(x_test,y_test)"
+      ],
+      "metadata": {
+        "id": "A2mtxgjL4qXG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aea9ce9f-b74e-428d-d358-d01ca224c9ef"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9649122807017544"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "clf = GridSearchCV(SVC(gamma='auto'),{\n",
+        "    'C': [1,10,20,30,40,50,60,70,80],\n",
+        "    'kernel': ['rbf','linear']\n",
+        "}, cv=5, return_train_score=False)\n",
+        "\n",
+        "clf.fit(x,y)\n",
+        "clf.cv_results_"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rQZw3TEhaqJx",
+        "outputId": "dfc58c4a-649e-4faf-b4fe-24a0772bc1a9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'mean_fit_time': array([0.02990212, 1.5451304 , 0.0216116 , 2.67975316, 0.01789918,\n",
+              "        4.38982725, 0.01766524, 4.13524318, 0.01783051, 5.60923376,\n",
+              "        0.01791935, 5.72623005, 0.01807461, 7.97815323, 0.01971617,\n",
+              "        7.93726101, 0.01765423, 7.63122673]),\n",
+              " 'mean_score_time': array([0.01029086, 0.00098829, 0.00586171, 0.00078259, 0.0051774 ,\n",
+              "        0.00076194, 0.0051949 , 0.0007658 , 0.00510898, 0.00078254,\n",
+              "        0.00536723, 0.00080252, 0.00508952, 0.00076923, 0.00572472,\n",
+              "        0.0007926 , 0.00502954, 0.00088406]),\n",
+              " 'mean_test_score': array([0.6274181 , 0.94553641, 0.6274181 , 0.95081509, 0.6274181 ,\n",
+              "        0.95081509, 0.6274181 , 0.952585  , 0.6274181 , 0.95784816,\n",
+              "        0.6274181 , 0.95256948, 0.6274181 , 0.95432386, 0.6274181 ,\n",
+              "        0.95432386, 0.6274181 , 0.95255395]),\n",
+              " 'param_C': masked_array(data=[1, 1, 10, 10, 20, 20, 30, 30, 40, 40, 50, 50, 60, 60,\n",
+              "                    70, 70, 80, 80],\n",
+              "              mask=[False, False, False, False, False, False, False, False,\n",
+              "                    False, False, False, False, False, False, False, False,\n",
+              "                    False, False],\n",
+              "        fill_value='?',\n",
+              "             dtype=object),\n",
+              " 'param_kernel': masked_array(data=['rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear',\n",
+              "                    'rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear',\n",
+              "                    'rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear'],\n",
+              "              mask=[False, False, False, False, False, False, False, False,\n",
+              "                    False, False, False, False, False, False, False, False,\n",
+              "                    False, False],\n",
+              "        fill_value='?',\n",
+              "             dtype=object),\n",
+              " 'params': [{'C': 1, 'kernel': 'rbf'},\n",
+              "  {'C': 1, 'kernel': 'linear'},\n",
+              "  {'C': 10, 'kernel': 'rbf'},\n",
+              "  {'C': 10, 'kernel': 'linear'},\n",
+              "  {'C': 20, 'kernel': 'rbf'},\n",
+              "  {'C': 20, 'kernel': 'linear'},\n",
+              "  {'C': 30, 'kernel': 'rbf'},\n",
+              "  {'C': 30, 'kernel': 'linear'},\n",
+              "  {'C': 40, 'kernel': 'rbf'},\n",
+              "  {'C': 40, 'kernel': 'linear'},\n",
+              "  {'C': 50, 'kernel': 'rbf'},\n",
+              "  {'C': 50, 'kernel': 'linear'},\n",
+              "  {'C': 60, 'kernel': 'rbf'},\n",
+              "  {'C': 60, 'kernel': 'linear'},\n",
+              "  {'C': 70, 'kernel': 'rbf'},\n",
+              "  {'C': 70, 'kernel': 'linear'},\n",
+              "  {'C': 80, 'kernel': 'rbf'},\n",
+              "  {'C': 80, 'kernel': 'linear'}],\n",
+              " 'rank_test_score': array([10,  9, 10,  7, 10,  7, 10,  4, 10,  1, 10,  5, 10,  2, 10,  2, 10,\n",
+              "         6], dtype=int32),\n",
+              " 'split0_test_score': array([0.62280702, 0.94736842, 0.62280702, 0.93859649, 0.62280702,\n",
+              "        0.93859649, 0.62280702, 0.92982456, 0.62280702, 0.93859649,\n",
+              "        0.62280702, 0.92982456, 0.62280702, 0.92982456, 0.62280702,\n",
+              "        0.92982456, 0.62280702, 0.92982456]),\n",
+              " 'split1_test_score': array([0.62280702, 0.92982456, 0.62280702, 0.93859649, 0.62280702,\n",
+              "        0.93859649, 0.62280702, 0.93859649, 0.62280702, 0.95614035,\n",
+              "        0.62280702, 0.94736842, 0.62280702, 0.94736842, 0.62280702,\n",
+              "        0.94736842, 0.62280702, 0.94736842]),\n",
+              " 'split2_test_score': array([0.63157895, 0.97368421, 0.63157895, 0.97368421, 0.63157895,\n",
+              "        0.97368421, 0.63157895, 0.97368421, 0.63157895, 0.97368421,\n",
+              "        0.63157895, 0.97368421, 0.63157895, 0.97368421, 0.63157895,\n",
+              "        0.97368421, 0.63157895, 0.97368421]),\n",
+              " 'split3_test_score': array([0.63157895, 0.92105263, 0.63157895, 0.93859649, 0.63157895,\n",
+              "        0.93859649, 0.63157895, 0.94736842, 0.63157895, 0.94736842,\n",
+              "        0.63157895, 0.94736842, 0.63157895, 0.95614035, 0.63157895,\n",
+              "        0.95614035, 0.63157895, 0.95614035]),\n",
+              " 'split4_test_score': array([0.62831858, 0.95575221, 0.62831858, 0.96460177, 0.62831858,\n",
+              "        0.96460177, 0.62831858, 0.97345133, 0.62831858, 0.97345133,\n",
+              "        0.62831858, 0.96460177, 0.62831858, 0.96460177, 0.62831858,\n",
+              "        0.96460177, 0.62831858, 0.95575221]),\n",
+              " 'std_fit_time': array([1.43591318e-03, 3.63438763e-01, 3.71925596e-03, 3.38731073e-01,\n",
+              "        2.48843473e-04, 9.59890076e-01, 2.15279731e-04, 6.15422987e-01,\n",
+              "        4.13181987e-04, 1.65625532e+00, 4.22119765e-04, 1.29921533e+00,\n",
+              "        7.47276195e-04, 4.14420744e+00, 9.65537489e-04, 2.64174946e+00,\n",
+              "        2.96906377e-04, 4.89833929e+00]),\n",
+              " 'std_score_time': array([2.75344883e-03, 1.10914763e-04, 1.18816508e-03, 2.14473912e-05,\n",
+              "        1.28012137e-04, 2.27038591e-05, 1.44955986e-04, 1.75246576e-05,\n",
+              "        8.62766442e-05, 1.56946805e-05, 4.56816030e-04, 9.76288420e-05,\n",
+              "        9.81799487e-05, 2.87314074e-05, 3.56127912e-04, 6.51490448e-05,\n",
+              "        3.04853746e-05, 1.99582014e-04]),\n",
+              " 'std_test_score': array([0.00394868, 0.01868869, 0.00394868, 0.01523779, 0.00394868,\n",
+              "        0.01523779, 0.00394868, 0.01800838, 0.00394868, 0.0139829 ,\n",
+              "        0.00394868, 0.01524494, 0.00394868, 0.01504893, 0.00394868,\n",
+              "        0.01504893, 0.00394868, 0.01423442])}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.DataFrame(clf.cv_results_)\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "sS22-FJ-bfM6",
+        "outputId": "bcb8946f-92b4-4349-e8ef-55925273f8ef"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
+              "0        0.029902      0.001436         0.010291        0.002753       1   \n",
+              "1        1.545130      0.363439         0.000988        0.000111       1   \n",
+              "2        0.021612      0.003719         0.005862        0.001188      10   \n",
+              "3        2.679753      0.338731         0.000783        0.000021      10   \n",
+              "4        0.017899      0.000249         0.005177        0.000128      20   \n",
+              "5        4.389827      0.959890         0.000762        0.000023      20   \n",
+              "6        0.017665      0.000215         0.005195        0.000145      30   \n",
+              "7        4.135243      0.615423         0.000766        0.000018      30   \n",
+              "8        0.017831      0.000413         0.005109        0.000086      40   \n",
+              "9        5.609234      1.656255         0.000783        0.000016      40   \n",
+              "10       0.017919      0.000422         0.005367        0.000457      50   \n",
+              "11       5.726230      1.299215         0.000803        0.000098      50   \n",
+              "12       0.018075      0.000747         0.005090        0.000098      60   \n",
+              "13       7.978153      4.144207         0.000769        0.000029      60   \n",
+              "14       0.019716      0.000966         0.005725        0.000356      70   \n",
+              "15       7.937261      2.641749         0.000793        0.000065      70   \n",
+              "16       0.017654      0.000297         0.005030        0.000030      80   \n",
+              "17       7.631227      4.898339         0.000884        0.000200      80   \n",
+              "\n",
+              "   param_kernel                         params  split0_test_score  \\\n",
+              "0           rbf      {'C': 1, 'kernel': 'rbf'}           0.622807   \n",
+              "1        linear   {'C': 1, 'kernel': 'linear'}           0.947368   \n",
+              "2           rbf     {'C': 10, 'kernel': 'rbf'}           0.622807   \n",
+              "3        linear  {'C': 10, 'kernel': 'linear'}           0.938596   \n",
+              "4           rbf     {'C': 20, 'kernel': 'rbf'}           0.622807   \n",
+              "5        linear  {'C': 20, 'kernel': 'linear'}           0.938596   \n",
+              "6           rbf     {'C': 30, 'kernel': 'rbf'}           0.622807   \n",
+              "7        linear  {'C': 30, 'kernel': 'linear'}           0.929825   \n",
+              "8           rbf     {'C': 40, 'kernel': 'rbf'}           0.622807   \n",
+              "9        linear  {'C': 40, 'kernel': 'linear'}           0.938596   \n",
+              "10          rbf     {'C': 50, 'kernel': 'rbf'}           0.622807   \n",
+              "11       linear  {'C': 50, 'kernel': 'linear'}           0.929825   \n",
+              "12          rbf     {'C': 60, 'kernel': 'rbf'}           0.622807   \n",
+              "13       linear  {'C': 60, 'kernel': 'linear'}           0.929825   \n",
+              "14          rbf     {'C': 70, 'kernel': 'rbf'}           0.622807   \n",
+              "15       linear  {'C': 70, 'kernel': 'linear'}           0.929825   \n",
+              "16          rbf     {'C': 80, 'kernel': 'rbf'}           0.622807   \n",
+              "17       linear  {'C': 80, 'kernel': 'linear'}           0.929825   \n",
+              "\n",
+              "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+              "0            0.622807           0.631579           0.631579   \n",
+              "1            0.929825           0.973684           0.921053   \n",
+              "2            0.622807           0.631579           0.631579   \n",
+              "3            0.938596           0.973684           0.938596   \n",
+              "4            0.622807           0.631579           0.631579   \n",
+              "5            0.938596           0.973684           0.938596   \n",
+              "6            0.622807           0.631579           0.631579   \n",
+              "7            0.938596           0.973684           0.947368   \n",
+              "8            0.622807           0.631579           0.631579   \n",
+              "9            0.956140           0.973684           0.947368   \n",
+              "10           0.622807           0.631579           0.631579   \n",
+              "11           0.947368           0.973684           0.947368   \n",
+              "12           0.622807           0.631579           0.631579   \n",
+              "13           0.947368           0.973684           0.956140   \n",
+              "14           0.622807           0.631579           0.631579   \n",
+              "15           0.947368           0.973684           0.956140   \n",
+              "16           0.622807           0.631579           0.631579   \n",
+              "17           0.947368           0.973684           0.956140   \n",
+              "\n",
+              "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+              "0            0.628319         0.627418        0.003949               10  \n",
+              "1            0.955752         0.945536        0.018689                9  \n",
+              "2            0.628319         0.627418        0.003949               10  \n",
+              "3            0.964602         0.950815        0.015238                7  \n",
+              "4            0.628319         0.627418        0.003949               10  \n",
+              "5            0.964602         0.950815        0.015238                7  \n",
+              "6            0.628319         0.627418        0.003949               10  \n",
+              "7            0.973451         0.952585        0.018008                4  \n",
+              "8            0.628319         0.627418        0.003949               10  \n",
+              "9            0.973451         0.957848        0.013983                1  \n",
+              "10           0.628319         0.627418        0.003949               10  \n",
+              "11           0.964602         0.952569        0.015245                5  \n",
+              "12           0.628319         0.627418        0.003949               10  \n",
+              "13           0.964602         0.954324        0.015049                2  \n",
+              "14           0.628319         0.627418        0.003949               10  \n",
+              "15           0.964602         0.954324        0.015049                2  \n",
+              "16           0.628319         0.627418        0.003949               10  \n",
+              "17           0.955752         0.952554        0.014234                6  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-274de024-a912-4181-8729-6ddd37cac5fe\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mean_fit_time</th>\n",
+              "      <th>std_fit_time</th>\n",
+              "      <th>mean_score_time</th>\n",
+              "      <th>std_score_time</th>\n",
+              "      <th>param_C</th>\n",
+              "      <th>param_kernel</th>\n",
+              "      <th>params</th>\n",
+              "      <th>split0_test_score</th>\n",
+              "      <th>split1_test_score</th>\n",
+              "      <th>split2_test_score</th>\n",
+              "      <th>split3_test_score</th>\n",
+              "      <th>split4_test_score</th>\n",
+              "      <th>mean_test_score</th>\n",
+              "      <th>std_test_score</th>\n",
+              "      <th>rank_test_score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.029902</td>\n",
+              "      <td>0.001436</td>\n",
+              "      <td>0.010291</td>\n",
+              "      <td>0.002753</td>\n",
+              "      <td>1</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.545130</td>\n",
+              "      <td>0.363439</td>\n",
+              "      <td>0.000988</td>\n",
+              "      <td>0.000111</td>\n",
+              "      <td>1</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.929825</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.921053</td>\n",
+              "      <td>0.955752</td>\n",
+              "      <td>0.945536</td>\n",
+              "      <td>0.018689</td>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.021612</td>\n",
+              "      <td>0.003719</td>\n",
+              "      <td>0.005862</td>\n",
+              "      <td>0.001188</td>\n",
+              "      <td>10</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.679753</td>\n",
+              "      <td>0.338731</td>\n",
+              "      <td>0.000783</td>\n",
+              "      <td>0.000021</td>\n",
+              "      <td>10</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.964602</td>\n",
+              "      <td>0.950815</td>\n",
+              "      <td>0.015238</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.017899</td>\n",
+              "      <td>0.000249</td>\n",
+              "      <td>0.005177</td>\n",
+              "      <td>0.000128</td>\n",
+              "      <td>20</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 20, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4.389827</td>\n",
+              "      <td>0.959890</td>\n",
+              "      <td>0.000762</td>\n",
+              "      <td>0.000023</td>\n",
+              "      <td>20</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 20, 'kernel': 'linear'}</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.964602</td>\n",
+              "      <td>0.950815</td>\n",
+              "      <td>0.015238</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0.017665</td>\n",
+              "      <td>0.000215</td>\n",
+              "      <td>0.005195</td>\n",
+              "      <td>0.000145</td>\n",
+              "      <td>30</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 30, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>4.135243</td>\n",
+              "      <td>0.615423</td>\n",
+              "      <td>0.000766</td>\n",
+              "      <td>0.000018</td>\n",
+              "      <td>30</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 30, 'kernel': 'linear'}</td>\n",
+              "      <td>0.929825</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.973451</td>\n",
+              "      <td>0.952585</td>\n",
+              "      <td>0.018008</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>0.017831</td>\n",
+              "      <td>0.000413</td>\n",
+              "      <td>0.005109</td>\n",
+              "      <td>0.000086</td>\n",
+              "      <td>40</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 40, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>5.609234</td>\n",
+              "      <td>1.656255</td>\n",
+              "      <td>0.000783</td>\n",
+              "      <td>0.000016</td>\n",
+              "      <td>40</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 40, 'kernel': 'linear'}</td>\n",
+              "      <td>0.938596</td>\n",
+              "      <td>0.956140</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.973451</td>\n",
+              "      <td>0.957848</td>\n",
+              "      <td>0.013983</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>0.017919</td>\n",
+              "      <td>0.000422</td>\n",
+              "      <td>0.005367</td>\n",
+              "      <td>0.000457</td>\n",
+              "      <td>50</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 50, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>5.726230</td>\n",
+              "      <td>1.299215</td>\n",
+              "      <td>0.000803</td>\n",
+              "      <td>0.000098</td>\n",
+              "      <td>50</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 50, 'kernel': 'linear'}</td>\n",
+              "      <td>0.929825</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.964602</td>\n",
+              "      <td>0.952569</td>\n",
+              "      <td>0.015245</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>0.018075</td>\n",
+              "      <td>0.000747</td>\n",
+              "      <td>0.005090</td>\n",
+              "      <td>0.000098</td>\n",
+              "      <td>60</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 60, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>7.978153</td>\n",
+              "      <td>4.144207</td>\n",
+              "      <td>0.000769</td>\n",
+              "      <td>0.000029</td>\n",
+              "      <td>60</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 60, 'kernel': 'linear'}</td>\n",
+              "      <td>0.929825</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.956140</td>\n",
+              "      <td>0.964602</td>\n",
+              "      <td>0.954324</td>\n",
+              "      <td>0.015049</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>0.019716</td>\n",
+              "      <td>0.000966</td>\n",
+              "      <td>0.005725</td>\n",
+              "      <td>0.000356</td>\n",
+              "      <td>70</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 70, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>7.937261</td>\n",
+              "      <td>2.641749</td>\n",
+              "      <td>0.000793</td>\n",
+              "      <td>0.000065</td>\n",
+              "      <td>70</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 70, 'kernel': 'linear'}</td>\n",
+              "      <td>0.929825</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.956140</td>\n",
+              "      <td>0.964602</td>\n",
+              "      <td>0.954324</td>\n",
+              "      <td>0.015049</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>0.017654</td>\n",
+              "      <td>0.000297</td>\n",
+              "      <td>0.005030</td>\n",
+              "      <td>0.000030</td>\n",
+              "      <td>80</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>{'C': 80, 'kernel': 'rbf'}</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.622807</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.631579</td>\n",
+              "      <td>0.628319</td>\n",
+              "      <td>0.627418</td>\n",
+              "      <td>0.003949</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>7.631227</td>\n",
+              "      <td>4.898339</td>\n",
+              "      <td>0.000884</td>\n",
+              "      <td>0.000200</td>\n",
+              "      <td>80</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>{'C': 80, 'kernel': 'linear'}</td>\n",
+              "      <td>0.929825</td>\n",
+              "      <td>0.947368</td>\n",
+              "      <td>0.973684</td>\n",
+              "      <td>0.956140</td>\n",
+              "      <td>0.955752</td>\n",
+              "      <td>0.952554</td>\n",
+              "      <td>0.014234</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-274de024-a912-4181-8729-6ddd37cac5fe')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-274de024-a912-4181-8729-6ddd37cac5fe button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-274de024-a912-4181-8729-6ddd37cac5fe');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[['param_C','param_kernel','mean_test_score']]\n",
+        "#df[['param_kernel','mean_test_score']]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 614
+        },
+        "id": "tTYcEIGPcvgQ",
+        "outputId": "332559ac-2c29-4c42-fd73-3d36d3b6f9b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   param_C param_kernel  mean_test_score\n",
+              "0        1          rbf         0.627418\n",
+              "1        1       linear         0.945536\n",
+              "2       10          rbf         0.627418\n",
+              "3       10       linear         0.950815\n",
+              "4       20          rbf         0.627418\n",
+              "5       20       linear         0.950815\n",
+              "6       30          rbf         0.627418\n",
+              "7       30       linear         0.952585\n",
+              "8       40          rbf         0.627418\n",
+              "9       40       linear         0.957848\n",
+              "10      50          rbf         0.627418\n",
+              "11      50       linear         0.952569\n",
+              "12      60          rbf         0.627418\n",
+              "13      60       linear         0.954324\n",
+              "14      70          rbf         0.627418\n",
+              "15      70       linear         0.954324\n",
+              "16      80          rbf         0.627418\n",
+              "17      80       linear         0.952554"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7fad378b-bb88-4f42-97d1-0dad8980cd2b\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>param_C</th>\n",
+              "      <th>param_kernel</th>\n",
+              "      <th>mean_test_score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.945536</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.950815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>20</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>20</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.950815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>30</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>30</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.952585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>40</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>40</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.957848</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>50</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>50</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.952569</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>60</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>60</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.954324</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>70</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>70</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.954324</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>80</td>\n",
+              "      <td>rbf</td>\n",
+              "      <td>0.627418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>80</td>\n",
+              "      <td>linear</td>\n",
+              "      <td>0.952554</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fad378b-bb88-4f42-97d1-0dad8980cd2b')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-7fad378b-bb88-4f42-97d1-0dad8980cd2b button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-7fad378b-bb88-4f42-97d1-0dad8980cd2b');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf.best_score_"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lI8Dy0Lyc8ue",
+        "outputId": "81c3bfdc-c00a-4b06-9243-4881b6ac5db9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9578481602235678"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "###LINEAR REGRESSION HYPERPARAMETER TUNING"
+      ],
+      "metadata": {
+        "id": "Y5yg8EHwdIeB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "param_grid = [    \n",
+        "    {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
+        "    'C' : np.logspace(-4, 4, 20),\n",
+        "    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],\n",
+        "    'max_iter' : [100, 1000,2500, 5000]\n",
+        "    }\n",
+        "]"
+      ],
+      "metadata": {
+        "id": "VXf9FwGemhIa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modellr=LogisticRegression()\n",
+        "clf2 = GridSearchCV(modellr, param_grid = param_grid, cv = 3, verbose=True, n_jobs=-1)\n",
+        "best_clf = clf2.fit(x,y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xjW4CcV2miHP",
+        "outputId": "1d23868e-8ba3-447b-8abf-07be55da73c1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Fitting 3 folds for each of 1600 candidates, totalling 4800 fits\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
+            "2160 fits failed out of a total of 4800.\n",
+            "The score on these train-test partitions for these parameters will be set to nan.\n",
+            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+            "\n",
+            "Below are more details about the failures:\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
+            "    % (solver, penalty)\n",
+            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
+            "    % (solver, penalty)\n",
+            "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
+            "    % (solver, penalty)\n",
+            "ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
+            "    % (solver, penalty)\n",
+            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
+            "    % (solver, penalty)\n",
+            "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 459, in _check_solver\n",
+            "    solver\n",
+            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
+            "    % (solver, penalty)\n",
+            "ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1473, in fit\n",
+            "    % self.l1_ratio\n",
+            "ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)\n",
+            "\n",
+            "--------------------------------------------------------------------------------\n",
+            "240 fits failed with the following error:\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
+            "    estimator.fit(X_train, y_train, **fit_params)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
+            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 464, in _check_solver\n",
+            "    raise ValueError(\"penalty='none' is not supported for the liblinear solver\")\n",
+            "ValueError: penalty='none' is not supported for the liblinear solver\n",
+            "\n",
+            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.3725796  ...        nan 0.92091339 0.92267706]\n",
+            "  category=UserWarning,\n",
+            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
+            "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
+            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(best_clf.best_estimator_)\n",
+        "print (f'Accuracy: {best_clf.score(x,y):.3f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cMWT6T5xmnR9",
+        "outputId": "3300d07e-73ff-42c1-836d-8446ecbfb9a2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "LogisticRegression(C=0.0001, max_iter=5000, penalty='none')\n",
+            "Accuracy: 0.986\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_clf.best_estimator_"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4ONmIWVsnGby",
+        "outputId": "20c6d67f-46c5-4e30-b76c-0b7d48e84308"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression(C=0.0001, max_iter=5000, penalty='none')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "UY0huq3D097C"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file