Brain_Tumour_Classification_and_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-1K/blob/main/Brain_Tumour_Classification_and_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Importing necessary libraries"
      ],
      "metadata": {
        "id": "9ionUoR78jOO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHGkvujylhEH",
        "outputId": "792acf9c-cc2c-4eff-8004-a50ce4a03bd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All necessary packages are installed successfully!\n"
          ]
        }
      ],
      "source": [
        "# Basic necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Machine Learning Libraries\n",
        "from sklearn.metrics import r2_score, mean_squared_error, confusion_matrix, roc_curve, classification_report\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,BaggingClassifier,GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from collections import Counter\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn import preprocessing\n",
        "\n",
        "print(\"All necessary packages are installed successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Importing the datasets"
      ],
      "metadata": {
        "id": "sMLEs4LG8oPY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "l5atadkZl_pJ",
        "outputId": "4e2ff449-f1b0-4e2c-d08c-a36a159e6004"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>9</td>\n",
              "      <td>25</td>\n",
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              "      <td>34</td>\n",
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              "      <td>4</td>\n",
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              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>63</td>\n",
              "      <td>160</td>\n",
              "      <td>30</td>\n",
              "      <td>159</td>\n",
              "      <td>24</td>\n",
              "      <td>69</td>\n",
              "      <td>-2</td>\n",
              "      <td>66</td>\n",
              "      <td>114</td>\n",
              "      <td>35</td>\n",
              "      <td>62</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "      <td>141</td>\n",
              "      <td>260</td>\n",
              "      <td>96</td>\n",
              "      <td>31</td>\n",
              "      <td>73</td>\n",
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              "      <td>18</td>\n",
              "      <td>44</td>\n",
              "      <td>75</td>\n",
              "      <td>5</td>\n",
              "      <td>68</td>\n",
              "      <td>138</td>\n",
              "      <td>53</td>\n",
              "      <td>-4</td>\n",
              "      <td>123</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>-7</td>\n",
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              "      <td>-2</td>\n",
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              "      <td>124</td>\n",
              "      <td>16</td>\n",
              "      <td>-6</td>\n",
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              "      <td>13</td>\n",
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              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>-2</td>\n",
              "      <td>...</td>\n",
              "      <td>66</td>\n",
              "      <td>388</td>\n",
              "      <td>54</td>\n",
              "      <td>100</td>\n",
              "      <td>91</td>\n",
              "      <td>63</td>\n",
              "      <td>16</td>\n",
              "      <td>46</td>\n",
              "      <td>338</td>\n",
              "      <td>63</td>\n",
              "      <td>14</td>\n",
              "      <td>359</td>\n",
              "      <td>31</td>\n",
              "      <td>87</td>\n",
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              "      <td>57</td>\n",
              "      <td>109</td>\n",
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              "      <td>31</td>\n",
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              "      <td>24</td>\n",
              "      <td>110</td>\n",
              "      <td>186</td>\n",
              "      <td>6</td>\n",
              "      <td>60</td>\n",
              "      <td>93</td>\n",
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              "      <td>-3</td>\n",
              "      <td>271</td>\n",
              "      <td>-1</td>\n",
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              "      <td>tumor</td>\n",
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              "    <tr>\n",
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              "      <td>-1</td>\n",
              "      <td>5</td>\n",
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              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>28</td>\n",
              "      <td>13</td>\n",
              "      <td>22</td>\n",
              "      <td>39</td>\n",
              "      <td>27</td>\n",
              "      <td>-2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
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              "      <td>87</td>\n",
              "      <td>108</td>\n",
              "      <td>16</td>\n",
              "      <td>29</td>\n",
              "      <td>98</td>\n",
              "      <td>19</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>29</td>\n",
              "      <td>...</td>\n",
              "      <td>56</td>\n",
              "      <td>258</td>\n",
              "      <td>46</td>\n",
              "      <td>94</td>\n",
              "      <td>64</td>\n",
              "      <td>51</td>\n",
              "      <td>113</td>\n",
              "      <td>40</td>\n",
              "      <td>122</td>\n",
              "      <td>53</td>\n",
              "      <td>48</td>\n",
              "      <td>193</td>\n",
              "      <td>82</td>\n",
              "      <td>113</td>\n",
              "      <td>203</td>\n",
              "      <td>90</td>\n",
              "      <td>81</td>\n",
              "      <td>43</td>\n",
              "      <td>87</td>\n",
              "      <td>421</td>\n",
              "      <td>61</td>\n",
              "      <td>73</td>\n",
              "      <td>196</td>\n",
              "      <td>77</td>\n",
              "      <td>-6</td>\n",
              "      <td>123</td>\n",
              "      <td>3</td>\n",
              "      <td>89</td>\n",
              "      <td>10</td>\n",
              "      <td>98</td>\n",
              "      <td>186</td>\n",
              "      <td>225</td>\n",
              "      <td>94</td>\n",
              "      <td>62</td>\n",
              "      <td>39</td>\n",
              "      <td>-14</td>\n",
              "      <td>127</td>\n",
              "      <td>7</td>\n",
              "      <td>192</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>871</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>78</td>\n",
              "      <td>-6</td>\n",
              "      <td>85</td>\n",
              "      <td>65</td>\n",
              "      <td>227</td>\n",
              "      <td>19</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>26</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>-88</td>\n",
              "      <td>25</td>\n",
              "      <td>22</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>44</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>64</td>\n",
              "      <td>628</td>\n",
              "      <td>197</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>280</td>\n",
              "      <td>4</td>\n",
              "      <td>84</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>27</td>\n",
              "      <td>...</td>\n",
              "      <td>79</td>\n",
              "      <td>177</td>\n",
              "      <td>82</td>\n",
              "      <td>87</td>\n",
              "      <td>51</td>\n",
              "      <td>45</td>\n",
              "      <td>42</td>\n",
              "      <td>34</td>\n",
              "      <td>59</td>\n",
              "      <td>34</td>\n",
              "      <td>27</td>\n",
              "      <td>57</td>\n",
              "      <td>14</td>\n",
              "      <td>135</td>\n",
              "      <td>72</td>\n",
              "      <td>127</td>\n",
              "      <td>69</td>\n",
              "      <td>20</td>\n",
              "      <td>337</td>\n",
              "      <td>370</td>\n",
              "      <td>20</td>\n",
              "      <td>70</td>\n",
              "      <td>119</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>155</td>\n",
              "      <td>154</td>\n",
              "      <td>36</td>\n",
              "      <td>54</td>\n",
              "      <td>77</td>\n",
              "      <td>6</td>\n",
              "      <td>78</td>\n",
              "      <td>30</td>\n",
              "      <td>87</td>\n",
              "      <td>-4</td>\n",
              "      <td>59</td>\n",
              "      <td>3</td>\n",
              "      <td>367</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>-92</td>\n",
              "      <td>-34</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>-6</td>\n",
              "      <td>27</td>\n",
              "      <td>-8</td>\n",
              "      <td>9</td>\n",
              "      <td>-5</td>\n",
              "      <td>12</td>\n",
              "      <td>-7</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>-6</td>\n",
              "      <td>5</td>\n",
              "      <td>-68</td>\n",
              "      <td>21</td>\n",
              "      <td>50</td>\n",
              "      <td>-2</td>\n",
              "      <td>44</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>61</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>7</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>76</td>\n",
              "      <td>5</td>\n",
              "      <td>51</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>3</td>\n",
              "      <td>-55</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>250</td>\n",
              "      <td>20</td>\n",
              "      <td>91</td>\n",
              "      <td>15</td>\n",
              "      <td>29</td>\n",
              "      <td>67</td>\n",
              "      <td>23</td>\n",
              "      <td>40</td>\n",
              "      <td>61</td>\n",
              "      <td>22</td>\n",
              "      <td>288</td>\n",
              "      <td>111</td>\n",
              "      <td>-7</td>\n",
              "      <td>67</td>\n",
              "      <td>109</td>\n",
              "      <td>45</td>\n",
              "      <td>135</td>\n",
              "      <td>124</td>\n",
              "      <td>504</td>\n",
              "      <td>85</td>\n",
              "      <td>96</td>\n",
              "      <td>337</td>\n",
              "      <td>39</td>\n",
              "      <td>-4</td>\n",
              "      <td>122</td>\n",
              "      <td>-1</td>\n",
              "      <td>258</td>\n",
              "      <td>6</td>\n",
              "      <td>142</td>\n",
              "      <td>87</td>\n",
              "      <td>303</td>\n",
              "      <td>204</td>\n",
              "      <td>81</td>\n",
              "      <td>105</td>\n",
              "      <td>9</td>\n",
              "      <td>265</td>\n",
              "      <td>-2</td>\n",
              "      <td>126</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>-13</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>-18</td>\n",
              "      <td>78</td>\n",
              "      <td>4</td>\n",
              "      <td>143</td>\n",
              "      <td>14</td>\n",
              "      <td>-1</td>\n",
              "      <td>17</td>\n",
              "      <td>79</td>\n",
              "      <td>48</td>\n",
              "      <td>-31</td>\n",
              "      <td>15</td>\n",
              "      <td>-4</td>\n",
              "      <td>32</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>16</td>\n",
              "      <td>60</td>\n",
              "      <td>182</td>\n",
              "      <td>23</td>\n",
              "      <td>13</td>\n",
              "      <td>-4</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>198</td>\n",
              "      <td>177</td>\n",
              "      <td>45</td>\n",
              "      <td>21</td>\n",
              "      <td>268</td>\n",
              "      <td>7</td>\n",
              "      <td>133</td>\n",
              "      <td>-8</td>\n",
              "      <td>31</td>\n",
              "      <td>39</td>\n",
              "      <td>...</td>\n",
              "      <td>84</td>\n",
              "      <td>125</td>\n",
              "      <td>59</td>\n",
              "      <td>118</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>32</td>\n",
              "      <td>22</td>\n",
              "      <td>-10</td>\n",
              "      <td>46</td>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "      <td>73</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>35</td>\n",
              "      <td>158</td>\n",
              "      <td>180</td>\n",
              "      <td>24</td>\n",
              "      <td>26</td>\n",
              "      <td>-6</td>\n",
              "      <td>30</td>\n",
              "      <td>-5</td>\n",
              "      <td>54</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>83</td>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>3</td>\n",
              "      <td>131</td>\n",
              "      <td>38</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>145</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>225</td>\n",
              "      <td>118</td>\n",
              "      <td>-5</td>\n",
              "      <td>175</td>\n",
              "      <td>-40</td>\n",
              "      <td>108</td>\n",
              "      <td>54</td>\n",
              "      <td>272</td>\n",
              "      <td>32</td>\n",
              "      <td>3</td>\n",
              "      <td>41</td>\n",
              "      <td>-39</td>\n",
              "      <td>-74</td>\n",
              "      <td>17</td>\n",
              "      <td>-2</td>\n",
              "      <td>-3</td>\n",
              "      <td>-30</td>\n",
              "      <td>36</td>\n",
              "      <td>20</td>\n",
              "      <td>-3</td>\n",
              "      <td>32</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "      <td>211</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>33</td>\n",
              "      <td>262</td>\n",
              "      <td>291</td>\n",
              "      <td>33</td>\n",
              "      <td>26</td>\n",
              "      <td>250</td>\n",
              "      <td>-9</td>\n",
              "      <td>116</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>-4</td>\n",
              "      <td>...</td>\n",
              "      <td>45</td>\n",
              "      <td>96</td>\n",
              "      <td>46</td>\n",
              "      <td>235</td>\n",
              "      <td>33</td>\n",
              "      <td>23</td>\n",
              "      <td>40</td>\n",
              "      <td>98</td>\n",
              "      <td>241</td>\n",
              "      <td>55</td>\n",
              "      <td>27</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>66</td>\n",
              "      <td>-30</td>\n",
              "      <td>67</td>\n",
              "      <td>66</td>\n",
              "      <td>54</td>\n",
              "      <td>69</td>\n",
              "      <td>716</td>\n",
              "      <td>22</td>\n",
              "      <td>60</td>\n",
              "      <td>187</td>\n",
              "      <td>24</td>\n",
              "      <td>-11</td>\n",
              "      <td>67</td>\n",
              "      <td>-4</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>46</td>\n",
              "      <td>64</td>\n",
              "      <td>-3</td>\n",
              "      <td>142</td>\n",
              "      <td>55</td>\n",
              "      <td>202</td>\n",
              "      <td>-23</td>\n",
              "      <td>110</td>\n",
              "      <td>7</td>\n",
              "      <td>-14</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>-346</td>\n",
              "      <td>-35</td>\n",
              "      <td>37</td>\n",
              "      <td>42</td>\n",
              "      <td>39</td>\n",
              "      <td>18</td>\n",
              "      <td>14</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>-7</td>\n",
              "      <td>-5</td>\n",
              "      <td>104</td>\n",
              "      <td>-73</td>\n",
              "      <td>-11</td>\n",
              "      <td>-9</td>\n",
              "      <td>-27</td>\n",
              "      <td>-35</td>\n",
              "      <td>30</td>\n",
              "      <td>33</td>\n",
              "      <td>5</td>\n",
              "      <td>-36</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>74</td>\n",
              "      <td>-5</td>\n",
              "      <td>-8</td>\n",
              "      <td>10</td>\n",
              "      <td>-3</td>\n",
              "      <td>12</td>\n",
              "      <td>-42</td>\n",
              "      <td>100</td>\n",
              "      <td>16</td>\n",
              "      <td>-30</td>\n",
              "      <td>84</td>\n",
              "      <td>-15</td>\n",
              "      <td>67</td>\n",
              "      <td>7</td>\n",
              "      <td>-4</td>\n",
              "      <td>-32</td>\n",
              "      <td>...</td>\n",
              "      <td>84</td>\n",
              "      <td>69</td>\n",
              "      <td>50</td>\n",
              "      <td>201</td>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "      <td>33</td>\n",
              "      <td>47</td>\n",
              "      <td>102</td>\n",
              "      <td>47</td>\n",
              "      <td>50</td>\n",
              "      <td>56</td>\n",
              "      <td>5</td>\n",
              "      <td>75</td>\n",
              "      <td>61</td>\n",
              "      <td>40</td>\n",
              "      <td>11</td>\n",
              "      <td>16</td>\n",
              "      <td>961</td>\n",
              "      <td>243</td>\n",
              "      <td>12</td>\n",
              "      <td>63</td>\n",
              "      <td>52</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>67</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>248</td>\n",
              "      <td>15</td>\n",
              "      <td>84</td>\n",
              "      <td>-5</td>\n",
              "      <td>13</td>\n",
              "      <td>102</td>\n",
              "      <td>27</td>\n",
              "      <td>17</td>\n",
              "      <td>30</td>\n",
              "      <td>-5</td>\n",
              "      <td>-53</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>-378</td>\n",
              "      <td>31</td>\n",
              "      <td>-29</td>\n",
              "      <td>105</td>\n",
              "      <td>-54</td>\n",
              "      <td>19</td>\n",
              "      <td>-4</td>\n",
              "      <td>11</td>\n",
              "      <td>16</td>\n",
              "      <td>-9</td>\n",
              "      <td>57</td>\n",
              "      <td>-49</td>\n",
              "      <td>10</td>\n",
              "      <td>-41</td>\n",
              "      <td>-10</td>\n",
              "      <td>25</td>\n",
              "      <td>-14</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>65</td>\n",
              "      <td>99</td>\n",
              "      <td>51</td>\n",
              "      <td>-11</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>-647</td>\n",
              "      <td>-21</td>\n",
              "      <td>116</td>\n",
              "      <td>19</td>\n",
              "      <td>28</td>\n",
              "      <td>173</td>\n",
              "      <td>15</td>\n",
              "      <td>83</td>\n",
              "      <td>-10</td>\n",
              "      <td>36</td>\n",
              "      <td>-9</td>\n",
              "      <td>...</td>\n",
              "      <td>41</td>\n",
              "      <td>92</td>\n",
              "      <td>40</td>\n",
              "      <td>92</td>\n",
              "      <td>46</td>\n",
              "      <td>17</td>\n",
              "      <td>34</td>\n",
              "      <td>5</td>\n",
              "      <td>1383</td>\n",
              "      <td>85</td>\n",
              "      <td>17</td>\n",
              "      <td>55</td>\n",
              "      <td>-1</td>\n",
              "      <td>21</td>\n",
              "      <td>30</td>\n",
              "      <td>58</td>\n",
              "      <td>23</td>\n",
              "      <td>43</td>\n",
              "      <td>56</td>\n",
              "      <td>226</td>\n",
              "      <td>6</td>\n",
              "      <td>53</td>\n",
              "      <td>57</td>\n",
              "      <td>61</td>\n",
              "      <td>958</td>\n",
              "      <td>74</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>100</td>\n",
              "      <td>14</td>\n",
              "      <td>87</td>\n",
              "      <td>-4</td>\n",
              "      <td>110</td>\n",
              "      <td>25</td>\n",
              "      <td>18</td>\n",
              "      <td>15</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>-51</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>475</td>\n",
              "      <td>-79</td>\n",
              "      <td>20</td>\n",
              "      <td>41</td>\n",
              "      <td>10</td>\n",
              "      <td>65</td>\n",
              "      <td>-13</td>\n",
              "      <td>225</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>34</td>\n",
              "      <td>57</td>\n",
              "      <td>25</td>\n",
              "      <td>-8</td>\n",
              "      <td>-5</td>\n",
              "      <td>-23</td>\n",
              "      <td>80</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>17</td>\n",
              "      <td>37</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>345</td>\n",
              "      <td>240</td>\n",
              "      <td>21</td>\n",
              "      <td>-62</td>\n",
              "      <td>135</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>-1</td>\n",
              "      <td>-66</td>\n",
              "      <td>22</td>\n",
              "      <td>...</td>\n",
              "      <td>30</td>\n",
              "      <td>182</td>\n",
              "      <td>116</td>\n",
              "      <td>78</td>\n",
              "      <td>56</td>\n",
              "      <td>33</td>\n",
              "      <td>-2</td>\n",
              "      <td>30</td>\n",
              "      <td>174</td>\n",
              "      <td>-71</td>\n",
              "      <td>20</td>\n",
              "      <td>80</td>\n",
              "      <td>3</td>\n",
              "      <td>113</td>\n",
              "      <td>31</td>\n",
              "      <td>53</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>484</td>\n",
              "      <td>230</td>\n",
              "      <td>7</td>\n",
              "      <td>36</td>\n",
              "      <td>97</td>\n",
              "      <td>55</td>\n",
              "      <td>10</td>\n",
              "      <td>63</td>\n",
              "      <td>15</td>\n",
              "      <td>70</td>\n",
              "      <td>118</td>\n",
              "      <td>18</td>\n",
              "      <td>115</td>\n",
              "      <td>-4</td>\n",
              "      <td>95</td>\n",
              "      <td>47</td>\n",
              "      <td>41</td>\n",
              "      <td>-4</td>\n",
              "      <td>76</td>\n",
              "      <td>11</td>\n",
              "      <td>22</td>\n",
              "      <td>tumor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 7466 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d18b703d-1471-4789-a292-b1d800d6dfe2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </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",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d18b703d-1471-4789-a292-b1d800d6dfe2 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d18b703d-1471-4789-a292-b1d800d6dfe2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ],
            "text/plain": [
              "   Unnamed: 0  X53416  M83670  X90908  ...  X54489  T55008  M10065.2      y\n",
              "0           0      70     -81      25  ...     123       2        19  tumor\n",
              "1           1     108     -30      -7  ...     271      -1       -51  tumor\n",
              "2           2      75      -1       5  ...     127       7       192  tumor\n",
              "3           3     871       4      14  ...      59       3       367  tumor\n",
              "4           4     -92     -34      14  ...     265      -2       126  tumor\n",
              "5           5      21     -13       5  ...       3       9       145  tumor\n",
              "6           6     225     118      -5  ...     110       7       -14  tumor\n",
              "7           7    -346     -35      37  ...      30      -5       -53  tumor\n",
              "8           8    -378      31     -29  ...      97       0       -51  tumor\n",
              "9           9     475     -79      20  ...      76      11        22  tumor\n",
              "\n",
              "[10 rows x 7466 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df=pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/brain_tumor.csv',error_bad_lines=False)\n",
        "\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Removing Null\n"
      ],
      "metadata": {
        "id": "4PDiG9ic8rkb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoH0Dg1HmK4N",
        "outputId": "f55773d8-4cf9-4c43-a9f5-cda7c7a72aaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "X53416        0\n",
              "M83670        0\n",
              "X90908        0\n",
              "M97496        0\n",
              "             ..\n",
              "M13699.1      0\n",
              "X54489        0\n",
              "T55008        0\n",
              "M10065.2      0\n",
              "y             0\n",
              "Length: 7466, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyapmQKzmOBF",
        "outputId": "2b49d573-8af8-4c34-e72f-1252fd542640"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 36 entries, 0 to 35\n",
            "Columns: 7466 entries, Unnamed: 0 to y\n",
            "dtypes: int64(7465), object(1)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.dropna(inplace=True)\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Data exploration"
      ],
      "metadata": {
        "id": "txCzFJyu8v-j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bjttph-AmQYF",
        "outputId": "ffeb547c-ddfe-4f85-deec-1b53e338ea65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'X53416', 'M83670', 'X90908', 'M97496', 'X90908.1',\n",
              "       'U37019', 'R48602', 'T96548', 'X64559',\n",
              "       ...\n",
              "       'H87456', 'R64130', 'H11125.1', 'U22055', 'L22524', 'M13699.1',\n",
              "       'X54489', 'T55008', 'M10065.2', 'y'],\n",
              "      dtype='object', length=7466)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCtwyE3vmTBk",
        "outputId": "6aed6998-c1c9-4709-e61b-714a54e1cfdc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['tumor', 'Normal'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['y'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "40sIaOhbmVnh"
      },
      "outputs": [],
      "source": [
        "df.drop(columns=['Unnamed: 0'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ftggPYimXr7",
        "outputId": "c9226753-4b58-45de-af09-9f99ac4c7faa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "label_encoder = preprocessing.LabelEncoder()\n",
        "df['y']= label_encoder.fit_transform(df['y'])\n",
        "df['y'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UxdCGVyJmf8N"
      },
      "outputs": [],
      "source": [
        "for i in df.columns:\n",
        "    for each in df[i].values:\n",
        "        \n",
        "        if each > 1 or each < -1:\n",
        "            df[i] = (df[i] - np.min(df[i]))/(np.max(df[i]) - np.min(df[i]))\n",
        "        else:\n",
        "            pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "pUgfBl48mh7t"
      },
      "outputs": [],
      "source": [
        "y = df[\"y\"]\n",
        "X = df.drop([\"y\"],axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Machine Learning Modelling"
      ],
      "metadata": {
        "id": "QkVA_hZ_80f2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### i. Linear Regression"
      ],
      "metadata": {
        "id": "7SBf2x2I84BJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "6d-51uVOnRMS"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVtjnvYDnT-4",
        "outputId": "57d3667e-6ff7-4c66-dc3b-b94f7e177d7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25, 7464)\n",
            "(25,)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ii. Logistic Regression"
      ],
      "metadata": {
        "id": "d_YS9LkX8-eN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ue8_mb_SnWC5",
        "outputId": "3fd2f1d5-6c55-4ee2-f3e3-8e4ce895f3e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=900, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "log_reg=LogisticRegression(random_state=0,max_iter=900)\n",
        "log_reg.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wk0-FE1qnYTQ",
        "outputId": "0206bdc4-1151-4bb1-fe9e-85422f4a3b8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "y_pred=log_reg.predict(X_test)\n",
        "log_reg.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05OFiUConZ7o",
        "outputId": "0b36bab8-82fa-4a87-94b2-479c9c4b4985"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "35 fits failed out of a total of 70.\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",
            "35 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",
            "  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 0.72  nan 0.96  nan 0.96  nan 0.96  nan 0.96  nan 0.96  nan 0.96]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.01, 'penalty': 'l2'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "params = {'C': np.logspace(-3, 3, 7), 'penalty': ['l1', 'l2']}\n",
        "lr_model = LogisticRegression(random_state = 0)\n",
        "lr_cv = GridSearchCV(lr_model,params,cv = 5).fit(X_train,y_train)\n",
        "lr_cv.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UU5a10nSnckS",
        "outputId": "9cf9a453-0841-4f42-847d-ed2603bd692b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.01, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "log_reg2 = LogisticRegression(C = .01,random_state = 0,penalty= 'l2')\n",
        "log_reg2.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mq-KKYkjnijL",
        "outputId": "f1e32d0b-5dde-4c1c-d745-8d548780c277"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "log_reg2.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHfUF7J0nkbM",
        "outputId": "7f79b856-bded-4b09-b5e2-9c9c25ddc787"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "log_reg2.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### iii. K-Nearest Neighbours Classifier"
      ],
      "metadata": {
        "id": "LQ5WwJpW9EHQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eerV2A-Wnl-B",
        "outputId": "3bcec751-3cc5-4a24-e868-76ebdda1ec4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8181818181818182"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 2).fit(X_train,y_train)\n",
        "knn.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### iv. Decision Tree"
      ],
      "metadata": {
        "id": "bNpJ_5PN9JvA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxYyRBKunnzb",
        "outputId": "e6169031-5c3f-48d7-ea60-11e4a3edba5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "tree = DecisionTreeClassifier(random_state = 0).fit(X_train,y_train)\n",
        "tree.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfCx9126npin",
        "outputId": "40031aa7-7039-4aeb-f41f-3f169192beef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 1, 'min_samples_split': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "params = {\"max_depth\": range(1,10),\n",
        "            \"min_samples_split\" : list(range(2,50))}\n",
        "tree_model = DecisionTreeClassifier(random_state = 0)\n",
        "tree_cv = GridSearchCV(tree_model, params, cv = 10, n_jobs = -1).fit(X_train,y_train)\n",
        "tree_cv.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "RkAFWVc4nrLb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "952d2c0b-6d82-44ad-8958-30f8f764239f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tree_tuned = DecisionTreeClassifier(max_depth = 1, min_samples_leaf = 7, min_samples_split = 2,random_state = 42).fit(X_train,y_train)\n",
        "tree_tuned.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### v. Random Forest"
      ],
      "metadata": {
        "id": "iaubqrg69NHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(random_state = 0).fit(X_train,y_train)\n",
        "rf.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ptMwEJw8EP-",
        "outputId": "dbc51cf5-a74b-4e7c-f535-cc9f44a8e47b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### vi. Support Vector Machine"
      ],
      "metadata": {
        "id": "7gYMqgJ39Q6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm = SVC(random_state = 42).fit(X_train,y_train)\n",
        "svm.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dr4B60BA8Ewx",
        "outputId": "4062e72c-25e7-4b18-8cb8-bd8d80caba1c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### vii. Gradient Boosting"
      ],
      "metadata": {
        "id": "CBcmmLlo9UCA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gbm = GradientBoostingClassifier(random_state = 42).fit(X_train,y_train)\n",
        "gbm.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zm7I1Vhv8HCd",
        "outputId": "5922f9d1-c238-4559-bf71-d106ab1a6f97"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbm_tuned = GradientBoostingClassifier(max_depth = 2, learning_rate = 0.01, min_samples_split = 2, n_estimators = 100, random_state = 42).fit(X_train,y_train)\n",
        "gbm_tuned.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-Wgm4-p8Ixq",
        "outputId": "dd6ffe6b-437c-4f3a-b9fe-8e98ab62ec7b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### viii. AdaBoost"
      ],
      "metadata": {
        "id": "GSIuSbxX9Ysa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ada = AdaBoostClassifier(random_state = 42).fit(X_train,y_train)\n",
        "ada.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ks40NvOH8KH-",
        "outputId": "6af108e0-e645-4b98-8050-63456adaec65"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_tuned = AdaBoostClassifier(learning_rate = 0.01,n_estimators = 1000,random_state = 42).fit(X_train,y_train)\n",
        "ada_tuned.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PMfFinX8LkW",
        "outputId": "515dd939-c0ed-4df1-eee2-eb6bc3bbd332"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ix. Bagging"
      ],
      "metadata": {
        "id": "6di4WTPh9gDk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bag = BaggingClassifier(random_state = 42).fit(X_train,y_train)\n",
        "bag.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dStnMNZx8M8G",
        "outputId": "2ef6ed28-0b9d-40eb-f84e-f9c2f4c6f2ad"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bag_tuned = BaggingClassifier(n_estimators = 45,random_state = 42).fit(X_train,y_train)\n",
        "bag_tuned.score(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJu6Qbne8Oj_",
        "outputId": "4d93d6a2-72cd-480a-fefa-aff9df201b07"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Visualization of Results"
      ],
      "metadata": {
        "id": "P98-44sM-Pbj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_list = [log_reg2,knn,rf,gbm_tuned,svm,ada_tuned,bag_tuned]\n",
        "\n",
        "for i in pred_list:\n",
        "    print(\"Score : \",i.score(X_test,y_test))\n",
        "    y_pred = i.predict(X_test)\n",
        "    sns.heatmap(confusion_matrix(y_test,y_pred),annot = True)\n",
        "    plt.xlabel(\"Y_pred\")\n",
        "    plt.ylabel(\"Y_test\")\n",
        "    plt.title(i)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OEtj0UXl8QPu",
        "outputId": "bfd58a3c-23be-44ea-830c-b28cc88b80ba"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  0.8181818181818182\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score :  1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cr06bmVWEqyzMzCrCrSzMzCrCJWQzs4pwCdnMrCJcQjYzqwi3sjAzq4gWlpAlPQIsBTqBjojYsd70DshmZkWtr0N+e0Q83siEDshmZkUl1iG3lZaymVkVRVfjXQNLA66VdKek6X1N7BKymVlRR+MP9XKQLQbaGRExo9C/a0QslPRK4DpJ90TELb0tzwHZzKwooolJYwYwo874hfn/xyRdAuwM9BqQXWVhZlbU1dV4V4ekMZLGdf8N7A3MqTePS8hmZkWte6i3EXCJJEix9oKIuKbeDA7IZmZFLWr2FhEPAW9oZh4HZDOzIr86bWZWEZ3+xRAzs2pwCdnMrCL8+U0zs2qIrsbbIbeaA7KZWZGrLMzMKsJVFmZmFdHhVhZmZtXgz29aX55duozPHfdtDpz2CQ48ZDqz58wrO0tWsn323p2759zCPXNv5ctfOrLs7AwfEY13LeYS8hDx3dPOZJc378gPTzqe9vZ2XnjxpbKzZCVqa2vjR6efxNT9prFgwWJuv+0qLr/iWubNu7/srA19LiFbPUuXPcedf5vDew7cB4CRI0eyzrixJefKyrTzTtvz4IOP8PDDj9Le3s7MmZfyznx82AB1ReNdiw16CVnSNsBBwMQ8aCFwWUT4nrtBCxctYb11x3P8Sady7wMPse2UrTj26MNZe/SosrNmJZkwcWPmL1i0vH/BwsXsvNP2JeZoGCnx1elBLSFLOgb4NSDgL7kTcKGkY+vMN13SLEmzfnbehYOZxSGho7OTefc9wPvfvT8Xn3MGo0eP4ue/nFl2tsyGpejqarhrtcEuIR8GbBcR7cWBkk4F7ga+29NMxa/wtz/+UHmvzVTExq/cgI023IDXb7cNAHvvvis/O98BeXW2aOESJm06YXn/phM3YdGiJSXmaBgp8U29wa5D7gIm9DB8kzzOGrDBK9Zn41duyMN/XwDA7XfO5tWTNys5V1amO2bNZsstt2Dy5EmMHDmSgw8+iMuvuLbsbA0Prf2R06YMdgn5aOAGSfcD8/OwzYAtgU8PctrDylc/dwTHfOMU2jvamTRhE7711c+VnSUrUWdnJ0cdfTxXXXkBI9raOOfci5g7976yszU8lFhCVgxCW7qVEpDaSD/sV3yod0dENFRz7ioL68noCbuVnQWroI6XF2qgy3juxGkNx5wxJ1444PSKBr2VRUR0AbcPdjpmZi3hD9SbmVWEP79pZlYNg9GcrVEOyGZmRS4hm5lVhAOymVlF+AP1ZmbVEB0OyGZm1eAqCzOzinArCzOzinAJ2cysIlockCWNAGYBCyPigHrTOiCbmRVEZ8urLI4C5gHr9DWhf8LJzKyohT/hJGlTYH/gZ40k7YBsZlYQXdFwV/x1o9xNr1ncacCXafD7766yMDMraqIOufjrRrUkHQA8FhF3Stq9keU5IJuZFbWuCnkX4J2S9gNGAetIOj8iPtjbDK6yMDMraKbKou5yIr4SEZtGxGTgA8CN9YIxuIRsZrayDrdDNjOrhL5Kvv1aZsTNwM19TeeAbGZWVN6b0w7IZmZFg1FCbpQDsplZkUvIZmbVEB3lpe2AbGZWUOIPhjggm5mtxAHZzKwaXEI2M6sIB2Qzs4pwQDYzq4joVGlpOyCbmRVElwOymVkluMrCzKwiIlxCNjOrBJeQzcwqwnXIZmYV0eVWFmZm1VBmCbnh39STdEMjw8zMhrKIxrtW67OELGkUsDawgaT1gO7LxzrAxNZnycysPFWvQ/4kcDQwAbiTFQH5WeDHg5QvM7NSVLrZW0ScDpwu6TMR8Z+rIE9mZqXpLPGhXsN1yMASSeMAJB0v6beSdhikfJmZlSJCDXet1kxAPiEilkraFdgT+Dnw05bnyMysRNGlhrtWayYgd+b/9wdmRMSVwJotz5GZWYkq3cqiYKGk/wL2Ar4naS2aC+hmZpU3JNohAwcDvwP2iYingfWBLw1KrszMStIVarhrtYZLyBHxvKTHgF2B+4GO/L+Z2bDRVfF2yABI+jqwIzAFOBsYCZwP7DI4WTMzW/VaVfLNL9XdAqxFirUXR8TX683TTB3yu4Htgb8CRMSi7mZwZmbDRQubs70EvCMilkkaCdwq6eqIuL23GZoJyC9HREgKAEljBphZM7PKaVXriYgIYFnuHZm7uktvJiDPzK0s1pX0CeBjwM/6k9FmjJ6w22AnYUPQ0guOKDsLNkw1U2UhaTowvTBoRkTMKIwfQfrkxJbAGRHx53rLa+ah3n9I2ov0DYspwNci4rqGc25mNgQ0U2WRg++MOuM7gTdKWhe4RNJrI2JOb9M381DvexFxDHBdD8PMzIaFzkFozhYRT0u6CZgK9BqQm2mHvFcPw/ZtNmNmZlXWqnbIkjbMJWMkjSbF0HvqzdPI95CPAD4FvErSXYVR44A/9jW/mdlQ0sJWFpsA5+Z65DZgZkRcUW+GRqosLgCuBk4Gji0MXxoRT3b3SFovIp5qPs9mZtXRqh+djoi7SE2FG9bI95CfAZ4BpvUx6Q2AP8dpZkNaMATe1GtAeWthZtYiHVX+xZAmDMLH6MzMVq3hUkI2MxvyWlWH3B99NnuTdJWkyQ0sy1UWZjbkBWq4a7VG2iGfDVwr6bj8gYze7NGiPJmZlaaria7VGmll8RtJVwMnALMk/bKYl4g4Nf//ZC+LMDMbMsqssmi0Dvll4DnSdz3HUW6ezcwGTacq/FBP0lTgVOAyYIeIeH7Qc2VmVpKuireyOA54X0TcPdiZMTMrW5ntdxupQ/YHic1stTEU6pDNzFYLXVWuQzYzW51UusrCzGx10lHiK24OyGZmBVVvZWFmttpwlYWZWUV0ucrCzKwa3OzNzKwiOl1CNjOrBpeQzcwqwgHZzKwiSvxJPQdkM7Mil5DNzCrCAdnMrCLcysLMrCJcQjYzq4gyA3IjvzptZrbaiCa6eiRNknSTpLmS7pZ0VF9pu4RsZlbQwm9ZdABfiIi/ShoH3CnpuoiY29sMDshmZgWdLVpORCwGFue/l0qaB0wEeg3IrrIwMyvoIhruJE2XNKvQTe9pmZImA9sDf66XtkvIZmYFzTzUi4gZwIx600gaC/w3cHREPFtvWgdkM7OCVn6gXtJIUjD+VUT8tq/pHZDNzApa1exNkoCfA/Mi4tRG5nEdsplZQZca7/qwC/Ah4B2SZuduv3ozuIRsZlbQ2aJKi4i4FZr7xVQHZDOzAr86bWZWEV0l/u60A7KZWUF54dgB2cxsJa6yMDOrCFdZmJlVRKu+ZdEfDshmZgXhErKZWTW4Dtn6tM/eu3Pqqd9kRFsbvzj7Qk75/hllZ8kqYN/v/5Yxa42kTWKNNnHBkfuXnaUhz3XIVldbWxs/Ov0kpu43jQULFnP7bVdx+RXXMm/e/WVnzSrgrMP2Yr0xo8rOxrBRZrM3f8tiCNh5p+158MFHePjhR2lvb2fmzEt554H7lJ0ts2Gpg2i4azWXkIeACRM3Zv6CRcv7FyxczM47bV9ijqwqJDji7BuQ4D07bcV7d9667CwNeavlQz1JH42Is8tK32w4OPsTU9lo/No8uewFDj/7BrbYcDxv2mKjsrM1pK2uvzr9jd5GFH8WpavruVWZp0patHAJkzadsLx/04mbsGjRkhJzZFWx0fi1AVh/7Gjevu0k5ix4vOQcDX3RxL9WG9QSsqS7ehsF9HoZL/4syhprTiyzjr0S7pg1my233ILJkyexcOESDj74ID704SPLzpaV7IWX2+kKGLPWSF54uZ3bHljMJ9/+urKzNeQN52ZvGwH7AE/VDBfwp0FOe9jo7OzkqKOP56orL2BEWxvnnHsRc+feV3a2rGRPLHuRz//q9wB0dHWx7+u3YJetJ5acq6GvK4ZvHfIVwNiImF07QtLNg5z2sHL1NTdy9TU3lp0Nq5BN1x/HzM8cUHY2hp1WfaC+PwY1IEfEYXXGHTKYaZuZ9cdq2crCzKyKhnMdspnZkOJXp83MKsJVFmZmFeEqCzOziuiM8kKyA7KZWYFLyGZmFeE6ZDOzinArCzOziogSX532B+rNzAo6iYa7vkj6haTHJM1pJG0HZDOzgi6i4a4B5wBTG03bVRZmZgWtrLKIiFskTW50egdkM7OCMh/qucrCzKygmV8MKf66Ue6mDyRtl5DNzAqa+UB98deNWsEB2cysoMwP1LvKwsysoJWtLCRdCNwGTJG0QFKvP9oBLiGbma2kxa0spjUzvQOymVmBX502M6sIf1zIzKwiyvyWhQOymVmBP1BvZlYRrkM2M6sI1yGbmVVEM2/qtZoDsplZgUvIZmYV4Yd6ZmYV4SoLM7OKcJWFmVlFuIRsZlYRLiGbmVVE+KGemVk1uJWFmVlF+NVpM7OK8NfezMwqwq0szMwqwq0szMwqwlUWZmYV4VYWZmYV4TpkM7OKcJWFmVlFuB2ymVlFuIRsZlYRfqhnZlYRfqhnZlYRZVZZtJWWsplZBUUT//oiaaqkeyU9IOnYvqZ3CdnMrKBVJWRJI4AzgL2ABcAdki6LiLm9zeMSsplZQUQ03PVhZ+CBiHgoIl4Gfg0cVG+GypeQO15eqLLzUBWSpkfEjLLzYdXi46K12puIOZKmA9MLg2YU9sVEYH5h3ALgzfWW5xLy0DK970lsNeTjoiQRMSMidix0A7owOiCbmQ2OhcCkQv+meVivHJDNzAbHHcBWkraQtCbwAeCyejNUvg7ZVuJ6QuuJj4sKiogOSZ8GfgeMAH4REXfXm0dlNoI2M7MVXGVhZlYRDshmZhXhgDxENPsKpg1/kn4h6TFJc8rOi7WGA/IQUHgFc19gW2CapG3LzZVVwDnA1LIzYa3jgDw0NP0Kpg1/EXEL8GTZ+bDWcUAeGnp6BXNiSXkxs0HigGxmVhEOyEND069gmtnQ44A8NDT9CqaZDT0OyENARHQA3a9gzgNm9vUKpg1/ki4EbgOmSFog6bCy82QD41enzcwqwiVkM7OKcEA2M6sIB2Qzs4pwQDYzqwgHZDOzinBANjOrCAdkW+WU3Cpp38Kw90m6ZhWkvbukKwY7HbP+8G/q2SoXESHpcOA3km4iHYffYQCfkpQ0IiI6W5VHszI4IFspImKOpMuBY4AxwHkR8WDtdJImA9cAdwI7AHcDH46I5yU9AlwE7AWcIulJ4BvAWsCDwEcjYpmkqcBpwPPArYO8amb95ioLK9M3gENIH94/pc50U4CfRMRrgGeBTxXGPREROwDXA8cDe+b+WcDnJY0CzgIOBN4EbNzytTBrEQdkK01EPEcq4f4yIl6qM+n8iPhj/vt8YNfCuIvy/28h/ZrKHyXNBj4CbA5sAzwcEfdH+k7A+a1cB7NWcpWFla0rd/XUfnCl2P9c/l/AdRExrTihpDcOLHtmq45LyDYUbCbprfnvQ+i5Hvh2YBdJWwJIGiNpa+AeYLKkV+fppvUwr1klOCDbUHAvcKSkecB6wE9rJ4iIfwKHAhdKuov0WcptIuJFYDpwpaS/Ao+tslybNcmf37RKy60sroiI15acFbNB5xKymVlFuIRslSDpFcANPYzaIyKeWNX5MSuDA7KZWUW4ysLMrCIckM3MKsIB2cysIhyQzcwq4n8B/w7jkTlD16QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ShFKd6-g8S9-"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Brain Tumour Classification and Prediction",
      "provenance": [],
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