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| a | b/heart_disease.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "markdown", |
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| 5 | "id": "41bc3d29-1e2f-41ec-b919-37d014f4769b", |
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| 6 | "metadata": {}, |
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| 7 | "source": [ |
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| 8 | "HEART DISEASE\n" |
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| 9 | ] |
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| 10 | }, |
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| 11 | { |
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| 12 | "cell_type": "code", |
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| 13 | "execution_count": 1, |
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| 14 | "id": "596f49af-da22-40fd-b931-972608d711f9", |
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| 15 | "metadata": {}, |
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| 16 | "outputs": [ |
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| 17 | { |
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| 18 | "data": { |
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| 19 | "text/html": [ |
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| 20 | "<div>\n", |
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| 21 | "<style scoped>\n", |
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| 22 | " .dataframe tbody tr th:only-of-type {\n", |
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| 23 | " vertical-align: middle;\n", |
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| 24 | " }\n", |
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| 25 | "\n", |
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| 26 | " .dataframe tbody tr th {\n", |
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| 27 | " vertical-align: top;\n", |
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| 28 | " }\n", |
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| 29 | "\n", |
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| 30 | " .dataframe thead th {\n", |
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| 31 | " text-align: right;\n", |
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| 32 | " }\n", |
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| 33 | "</style>\n", |
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| 34 | "<table border=\"1\" class=\"dataframe\">\n", |
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| 35 | " <thead>\n", |
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| 36 | " <tr style=\"text-align: right;\">\n", |
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| 37 | " <th></th>\n", |
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| 38 | " <th>age</th>\n", |
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| 39 | " <th>sex</th>\n", |
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| 40 | " <th>cp</th>\n", |
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| 41 | " <th>trestbps</th>\n", |
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| 42 | " <th>chol</th>\n", |
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| 43 | " <th>fbs</th>\n", |
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| 44 | " <th>restecg</th>\n", |
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| 45 | " <th>thalach</th>\n", |
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| 46 | " <th>exang</th>\n", |
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| 47 | " <th>oldpeak</th>\n", |
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| 48 | " <th>slope</th>\n", |
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| 49 | " <th>ca</th>\n", |
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| 50 | " <th>thal</th>\n", |
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| 51 | " <th>target</th>\n", |
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| 52 | " </tr>\n", |
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| 53 | " </thead>\n", |
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| 54 | " <tbody>\n", |
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| 55 | " <tr>\n", |
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| 56 | " <th>0</th>\n", |
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| 57 | " <td>63</td>\n", |
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| 58 | " <td>1</td>\n", |
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| 59 | " <td>3</td>\n", |
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| 60 | " <td>145</td>\n", |
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| 61 | " <td>233</td>\n", |
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| 62 | " <td>1</td>\n", |
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| 63 | " <td>0</td>\n", |
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| 64 | " <td>150</td>\n", |
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| 65 | " <td>0</td>\n", |
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| 66 | " <td>2.3</td>\n", |
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| 67 | " <td>0</td>\n", |
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| 68 | " <td>0</td>\n", |
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| 69 | " <td>1</td>\n", |
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| 70 | " <td>1</td>\n", |
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| 71 | " </tr>\n", |
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| 72 | " <tr>\n", |
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| 73 | " <th>1</th>\n", |
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| 74 | " <td>37</td>\n", |
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| 75 | " <td>1</td>\n", |
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| 76 | " <td>2</td>\n", |
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| 77 | " <td>130</td>\n", |
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| 78 | " <td>250</td>\n", |
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| 79 | " <td>0</td>\n", |
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| 80 | " <td>1</td>\n", |
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| 81 | " <td>187</td>\n", |
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| 82 | " <td>0</td>\n", |
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| 83 | " <td>3.5</td>\n", |
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| 84 | " <td>0</td>\n", |
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| 85 | " <td>0</td>\n", |
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| 86 | " <td>2</td>\n", |
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| 87 | " <td>1</td>\n", |
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| 88 | " </tr>\n", |
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| 89 | " <tr>\n", |
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| 90 | " <th>2</th>\n", |
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| 91 | " <td>41</td>\n", |
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| 92 | " <td>0</td>\n", |
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| 93 | " <td>1</td>\n", |
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| 94 | " <td>130</td>\n", |
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| 95 | " <td>204</td>\n", |
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| 96 | " <td>0</td>\n", |
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| 97 | " <td>0</td>\n", |
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| 98 | " <td>172</td>\n", |
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| 99 | " <td>0</td>\n", |
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| 100 | " <td>1.4</td>\n", |
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| 101 | " <td>2</td>\n", |
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| 102 | " <td>0</td>\n", |
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| 103 | " <td>2</td>\n", |
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| 104 | " <td>1</td>\n", |
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| 105 | " </tr>\n", |
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| 106 | " <tr>\n", |
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| 107 | " <th>3</th>\n", |
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| 108 | " <td>56</td>\n", |
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| 109 | " <td>1</td>\n", |
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| 110 | " <td>1</td>\n", |
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| 111 | " <td>120</td>\n", |
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| 112 | " <td>236</td>\n", |
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| 113 | " <td>0</td>\n", |
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| 114 | " <td>1</td>\n", |
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| 115 | " <td>178</td>\n", |
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| 116 | " <td>0</td>\n", |
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| 117 | " <td>0.8</td>\n", |
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| 118 | " <td>2</td>\n", |
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| 119 | " <td>0</td>\n", |
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| 120 | " <td>2</td>\n", |
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| 121 | " <td>1</td>\n", |
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| 122 | " </tr>\n", |
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| 123 | " <tr>\n", |
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| 124 | " <th>4</th>\n", |
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| 125 | " <td>57</td>\n", |
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| 126 | " <td>0</td>\n", |
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| 127 | " <td>0</td>\n", |
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| 128 | " <td>120</td>\n", |
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| 129 | " <td>354</td>\n", |
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| 130 | " <td>0</td>\n", |
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| 131 | " <td>1</td>\n", |
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| 132 | " <td>163</td>\n", |
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| 133 | " <td>1</td>\n", |
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| 134 | " <td>0.6</td>\n", |
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| 135 | " <td>2</td>\n", |
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| 136 | " <td>0</td>\n", |
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| 137 | " <td>2</td>\n", |
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| 138 | " <td>1</td>\n", |
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| 139 | " </tr>\n", |
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| 140 | " <tr>\n", |
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| 141 | " <th>5</th>\n", |
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| 142 | " <td>57</td>\n", |
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| 143 | " <td>1</td>\n", |
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| 144 | " <td>0</td>\n", |
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| 145 | " <td>140</td>\n", |
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| 146 | " <td>192</td>\n", |
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| 147 | " <td>0</td>\n", |
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| 148 | " <td>1</td>\n", |
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| 149 | " <td>148</td>\n", |
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| 150 | " <td>0</td>\n", |
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| 151 | " <td>0.4</td>\n", |
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| 152 | " <td>1</td>\n", |
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| 153 | " <td>0</td>\n", |
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| 154 | " <td>1</td>\n", |
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| 155 | " <td>1</td>\n", |
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| 156 | " </tr>\n", |
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| 157 | " <tr>\n", |
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| 158 | " <th>6</th>\n", |
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| 159 | " <td>56</td>\n", |
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| 160 | " <td>0</td>\n", |
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| 161 | " <td>1</td>\n", |
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| 162 | " <td>140</td>\n", |
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| 163 | " <td>294</td>\n", |
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| 164 | " <td>0</td>\n", |
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| 165 | " <td>0</td>\n", |
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| 166 | " <td>153</td>\n", |
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| 167 | " <td>0</td>\n", |
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| 168 | " <td>1.3</td>\n", |
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| 169 | " <td>1</td>\n", |
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| 170 | " <td>0</td>\n", |
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| 171 | " <td>2</td>\n", |
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| 172 | " <td>1</td>\n", |
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| 173 | " </tr>\n", |
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| 174 | " <tr>\n", |
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| 175 | " <th>7</th>\n", |
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| 176 | " <td>44</td>\n", |
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| 177 | " <td>1</td>\n", |
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| 178 | " <td>1</td>\n", |
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| 179 | " <td>120</td>\n", |
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| 180 | " <td>263</td>\n", |
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| 181 | " <td>0</td>\n", |
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| 182 | " <td>1</td>\n", |
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| 183 | " <td>173</td>\n", |
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| 184 | " <td>0</td>\n", |
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| 185 | " <td>0.0</td>\n", |
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| 186 | " <td>2</td>\n", |
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| 187 | " <td>0</td>\n", |
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| 188 | " <td>3</td>\n", |
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| 189 | " <td>1</td>\n", |
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| 190 | " </tr>\n", |
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| 191 | " <tr>\n", |
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| 192 | " <th>8</th>\n", |
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| 193 | " <td>52</td>\n", |
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| 194 | " <td>1</td>\n", |
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| 195 | " <td>2</td>\n", |
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| 196 | " <td>172</td>\n", |
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| 197 | " <td>199</td>\n", |
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| 198 | " <td>1</td>\n", |
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| 199 | " <td>1</td>\n", |
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| 200 | " <td>162</td>\n", |
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| 201 | " <td>0</td>\n", |
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| 202 | " <td>0.5</td>\n", |
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| 203 | " <td>2</td>\n", |
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| 204 | " <td>0</td>\n", |
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| 205 | " <td>3</td>\n", |
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| 206 | " <td>1</td>\n", |
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| 207 | " </tr>\n", |
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| 208 | " <tr>\n", |
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| 209 | " <th>9</th>\n", |
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| 210 | " <td>57</td>\n", |
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| 211 | " <td>1</td>\n", |
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| 212 | " <td>2</td>\n", |
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| 213 | " <td>150</td>\n", |
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| 214 | " <td>168</td>\n", |
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| 215 | " <td>0</td>\n", |
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| 216 | " <td>1</td>\n", |
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| 217 | " <td>174</td>\n", |
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| 218 | " <td>0</td>\n", |
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| 219 | " <td>1.6</td>\n", |
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| 220 | " <td>2</td>\n", |
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| 221 | " <td>0</td>\n", |
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| 222 | " <td>2</td>\n", |
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| 223 | " <td>1</td>\n", |
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| 224 | " </tr>\n", |
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| 225 | " <tr>\n", |
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| 226 | " <th>10</th>\n", |
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| 227 | " <td>54</td>\n", |
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| 228 | " <td>1</td>\n", |
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| 229 | " <td>0</td>\n", |
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| 230 | " <td>140</td>\n", |
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| 231 | " <td>239</td>\n", |
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| 232 | " <td>0</td>\n", |
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| 233 | " <td>1</td>\n", |
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| 234 | " <td>160</td>\n", |
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| 235 | " <td>0</td>\n", |
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| 236 | " <td>1.2</td>\n", |
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| 237 | " <td>2</td>\n", |
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| 238 | " <td>0</td>\n", |
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| 239 | " <td>2</td>\n", |
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| 240 | " <td>1</td>\n", |
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| 241 | " </tr>\n", |
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| 242 | " <tr>\n", |
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| 243 | " <th>11</th>\n", |
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| 244 | " <td>48</td>\n", |
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| 245 | " <td>0</td>\n", |
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| 246 | " <td>2</td>\n", |
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| 247 | " <td>130</td>\n", |
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| 248 | " <td>275</td>\n", |
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| 249 | " <td>0</td>\n", |
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| 250 | " <td>1</td>\n", |
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| 251 | " <td>139</td>\n", |
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| 252 | " <td>0</td>\n", |
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| 253 | " <td>0.2</td>\n", |
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| 254 | " <td>2</td>\n", |
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| 255 | " <td>0</td>\n", |
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| 256 | " <td>2</td>\n", |
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| 257 | " <td>1</td>\n", |
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| 258 | " </tr>\n", |
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| 259 | " <tr>\n", |
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| 260 | " <th>12</th>\n", |
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| 261 | " <td>49</td>\n", |
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| 262 | " <td>1</td>\n", |
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| 263 | " <td>1</td>\n", |
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| 264 | " <td>130</td>\n", |
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| 265 | " <td>266</td>\n", |
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| 266 | " <td>0</td>\n", |
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| 267 | " <td>1</td>\n", |
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| 268 | " <td>171</td>\n", |
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| 269 | " <td>0</td>\n", |
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| 270 | " <td>0.6</td>\n", |
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| 271 | " <td>2</td>\n", |
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| 272 | " <td>0</td>\n", |
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| 273 | " <td>2</td>\n", |
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| 274 | " <td>1</td>\n", |
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| 275 | " </tr>\n", |
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| 276 | " <tr>\n", |
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| 277 | " <th>13</th>\n", |
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| 278 | " <td>64</td>\n", |
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| 279 | " <td>1</td>\n", |
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| 280 | " <td>3</td>\n", |
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| 281 | " <td>110</td>\n", |
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| 282 | " <td>211</td>\n", |
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| 283 | " <td>0</td>\n", |
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| 284 | " <td>0</td>\n", |
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| 285 | " <td>144</td>\n", |
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| 286 | " <td>1</td>\n", |
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| 287 | " <td>1.8</td>\n", |
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| 288 | " <td>1</td>\n", |
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| 289 | " <td>0</td>\n", |
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| 290 | " <td>2</td>\n", |
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| 291 | " <td>1</td>\n", |
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| 292 | " </tr>\n", |
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| 293 | " <tr>\n", |
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| 294 | " <th>14</th>\n", |
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| 295 | " <td>58</td>\n", |
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| 296 | " <td>0</td>\n", |
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| 297 | " <td>3</td>\n", |
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| 298 | " <td>150</td>\n", |
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| 299 | " <td>283</td>\n", |
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| 300 | " <td>1</td>\n", |
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| 301 | " <td>0</td>\n", |
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| 302 | " <td>162</td>\n", |
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| 303 | " <td>0</td>\n", |
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| 304 | " <td>1.0</td>\n", |
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| 305 | " <td>2</td>\n", |
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| 306 | " <td>0</td>\n", |
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| 307 | " <td>2</td>\n", |
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| 308 | " <td>1</td>\n", |
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| 309 | " </tr>\n", |
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| 310 | " </tbody>\n", |
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| 311 | "</table>\n", |
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| 312 | "</div>" |
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| 313 | ], |
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| 314 | "text/plain": [ |
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| 315 | " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", |
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| 316 | "0 63 1 3 145 233 1 0 150 0 2.3 \n", |
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| 317 | "1 37 1 2 130 250 0 1 187 0 3.5 \n", |
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| 318 | "2 41 0 1 130 204 0 0 172 0 1.4 \n", |
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| 319 | "3 56 1 1 120 236 0 1 178 0 0.8 \n", |
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| 320 | "4 57 0 0 120 354 0 1 163 1 0.6 \n", |
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| 321 | "5 57 1 0 140 192 0 1 148 0 0.4 \n", |
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| 322 | "6 56 0 1 140 294 0 0 153 0 1.3 \n", |
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| 323 | "7 44 1 1 120 263 0 1 173 0 0.0 \n", |
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| 324 | "8 52 1 2 172 199 1 1 162 0 0.5 \n", |
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| 325 | "9 57 1 2 150 168 0 1 174 0 1.6 \n", |
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| 326 | "10 54 1 0 140 239 0 1 160 0 1.2 \n", |
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| 327 | "11 48 0 2 130 275 0 1 139 0 0.2 \n", |
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| 328 | "12 49 1 1 130 266 0 1 171 0 0.6 \n", |
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| 329 | "13 64 1 3 110 211 0 0 144 1 1.8 \n", |
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| 330 | "14 58 0 3 150 283 1 0 162 0 1.0 \n", |
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| 331 | "\n", |
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| 332 | " slope ca thal target \n", |
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| 333 | "0 0 0 1 1 \n", |
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| 334 | "1 0 0 2 1 \n", |
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| 335 | "2 2 0 2 1 \n", |
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| 336 | "3 2 0 2 1 \n", |
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| 337 | "4 2 0 2 1 \n", |
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| 338 | "5 1 0 1 1 \n", |
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| 339 | "6 1 0 2 1 \n", |
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| 340 | "7 2 0 3 1 \n", |
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| 341 | "8 2 0 3 1 \n", |
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| 342 | "9 2 0 2 1 \n", |
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| 343 | "10 2 0 2 1 \n", |
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| 344 | "11 2 0 2 1 \n", |
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| 345 | "12 2 0 2 1 \n", |
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| 346 | "13 1 0 2 1 \n", |
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| 347 | "14 2 0 2 1 " |
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| 348 | ] |
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| 349 | }, |
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| 350 | "execution_count": 1, |
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| 351 | "metadata": {}, |
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| 352 | "output_type": "execute_result" |
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| 353 | } |
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| 354 | ], |
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| 355 | "source": [ |
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| 356 | "\n", |
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| 357 | "import numpy as np\n", |
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| 358 | "import pandas as pd\n", |
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| 359 | "from sklearn.model_selection import train_test_split\n", |
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| 360 | "from sklearn.linear_model import LogisticRegression\n", |
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| 361 | "from sklearn.metrics import accuracy_score\n", |
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| 362 | "\n", |
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| 363 | "\n", |
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| 364 | "df= pd.read_csv(r'C:\\Users\\Pranshu Saini\\Desktop\\disease-prediction-main\\docpat\\datasets\\heart.csv')\n", |
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| 365 | "df.head(15)\n" |
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| 366 | ] |
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| 367 | }, |
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| 368 | { |
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| 369 | "cell_type": "code", |
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| 370 | "execution_count": 2, |
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| 371 | "id": "7bd28756", |
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| 372 | "metadata": {}, |
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| 373 | "outputs": [ |
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| 374 | { |
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| 375 | "data": { |
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| 376 | "text/plain": [ |
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| 377 | "(303, 14)" |
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| 378 | ] |
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| 379 | }, |
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| 380 | "execution_count": 2, |
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| 381 | "metadata": {}, |
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| 382 | "output_type": "execute_result" |
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| 383 | } |
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| 384 | ], |
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| 385 | "source": [ |
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| 386 | "df.shape" |
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| 387 | ] |
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| 388 | }, |
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| 389 | { |
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| 390 | "cell_type": "code", |
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| 391 | "execution_count": 3, |
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| 392 | "id": "3244edd5-3dd2-47f9-85da-b9cc26fed0d7", |
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| 393 | "metadata": {}, |
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| 394 | "outputs": [ |
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| 395 | { |
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| 396 | "data": { |
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| 397 | "text/plain": [ |
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| 398 | "age 0\n", |
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| 399 | "sex 0\n", |
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| 400 | "cp 0\n", |
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| 401 | "trestbps 0\n", |
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| 402 | "chol 0\n", |
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| 403 | "fbs 0\n", |
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| 404 | "restecg 0\n", |
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| 405 | "thalach 0\n", |
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| 406 | "exang 0\n", |
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| 407 | "oldpeak 0\n", |
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| 408 | "slope 0\n", |
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| 409 | "ca 0\n", |
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| 410 | "thal 0\n", |
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| 411 | "target 0\n", |
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| 412 | "dtype: int64" |
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| 413 | ] |
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| 414 | }, |
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| 415 | "execution_count": 3, |
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| 416 | "metadata": {}, |
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| 417 | "output_type": "execute_result" |
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| 418 | } |
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| 419 | ], |
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| 420 | "source": [ |
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| 421 | "df.isna().sum()" |
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| 422 | ] |
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| 423 | }, |
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| 424 | { |
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| 425 | "cell_type": "code", |
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| 426 | "execution_count": 4, |
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| 427 | "id": "348dde1b-1ff9-4d98-91a9-29688f5b0933", |
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| 428 | "metadata": {}, |
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| 429 | "outputs": [ |
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| 430 | { |
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| 431 | "name": "stdout", |
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| 432 | "output_type": "stream", |
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| 433 | "text": [ |
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| 434 | "The reduced dataframe has 14 columns.\n" |
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| 435 | ] |
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| 436 | } |
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| 437 | ], |
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| 438 | "source": [ |
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| 439 | "# removing highly correlated features\n", |
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| 440 | "\n", |
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| 441 | "corr_matrix = df.corr().abs() \n", |
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| 442 | "\n", |
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| 443 | "mask = np.triu(np.ones_like(corr_matrix, dtype = bool))\n", |
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| 444 | "tri_df = corr_matrix.mask(mask)\n", |
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| 445 | "\n", |
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| 446 | "to_drop = [x for x in tri_df.columns if any(tri_df[x] > 0.92)]\n", |
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| 447 | "\n", |
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| 448 | "df = df.drop(to_drop, axis = 1)\n", |
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| 449 | "\n", |
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| 450 | "print(f\"The reduced dataframe has {df.shape[1]} columns.\")" |
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| 451 | ] |
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| 452 | }, |
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| 453 | { |
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| 454 | "cell_type": "code", |
||
| 455 | "execution_count": 5, |
||
| 456 | "id": "16a87f6e", |
||
| 457 | "metadata": {}, |
||
| 458 | "outputs": [ |
||
| 459 | { |
||
| 460 | "data": { |
||
| 461 | "text/plain": [ |
||
| 462 | "(303, 14)" |
||
| 463 | ] |
||
| 464 | }, |
||
| 465 | "execution_count": 5, |
||
| 466 | "metadata": {}, |
||
| 467 | "output_type": "execute_result" |
||
| 468 | } |
||
| 469 | ], |
||
| 470 | "source": [ |
||
| 471 | "df.shape" |
||
| 472 | ] |
||
| 473 | }, |
||
| 474 | { |
||
| 475 | "cell_type": "code", |
||
| 476 | "execution_count": 6, |
||
| 477 | "id": "ade36649-a20a-4bf2-8368-64f70cd000f2", |
||
| 478 | "metadata": {}, |
||
| 479 | "outputs": [ |
||
| 480 | { |
||
| 481 | "name": "stdout", |
||
| 482 | "output_type": "stream", |
||
| 483 | "text": [ |
||
| 484 | "(303, 13) (242, 13) (61, 13)\n" |
||
| 485 | ] |
||
| 486 | } |
||
| 487 | ], |
||
| 488 | "source": [ |
||
| 489 | "A = df.drop(columns='target', axis=1)\n", |
||
| 490 | "B = df['target']\n", |
||
| 491 | "A_training, A_testing, B_training, B_testing = train_test_split(A, B, test_size=0.2, stratify=B, random_state=2)\n", |
||
| 492 | "\n", |
||
| 493 | "print(A.shape, A_training.shape, A_testing.shape)" |
||
| 494 | ] |
||
| 495 | }, |
||
| 496 | { |
||
| 497 | "cell_type": "markdown", |
||
| 498 | "id": "34f600fd-1faf-4a62-8aba-9a6d0fa38644", |
||
| 499 | "metadata": {}, |
||
| 500 | "source": [ |
||
| 501 | "LogisticRegression" |
||
| 502 | ] |
||
| 503 | }, |
||
| 504 | { |
||
| 505 | "cell_type": "code", |
||
| 506 | "execution_count": 7, |
||
| 507 | "id": "fce2ced2-6375-4077-921e-2f568056fffe", |
||
| 508 | "metadata": {}, |
||
| 509 | "outputs": [ |
||
| 510 | { |
||
| 511 | "name": "stdout", |
||
| 512 | "output_type": "stream", |
||
| 513 | "text": [ |
||
| 514 | "0.8512396694214877\n", |
||
| 515 | "0.819672131147541\n" |
||
| 516 | ] |
||
| 517 | }, |
||
| 518 | { |
||
| 519 | "name": "stderr", |
||
| 520 | "output_type": "stream", |
||
| 521 | "text": [ |
||
| 522 | "c:\\Users\\Pranshu Saini\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", |
||
| 523 | "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", |
||
| 524 | "\n", |
||
| 525 | "Increase the number of iterations (max_iter) or scale the data as shown in:\n", |
||
| 526 | " https://scikit-learn.org/stable/modules/preprocessing.html\n", |
||
| 527 | "Please also refer to the documentation for alternative solver options:\n", |
||
| 528 | " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", |
||
| 529 | " n_iter_i = _check_optimize_result(\n" |
||
| 530 | ] |
||
| 531 | } |
||
| 532 | ], |
||
| 533 | "source": [ |
||
| 534 | "# fitting data to model\n", |
||
| 535 | "\n", |
||
| 536 | "from sklearn.linear_model import LogisticRegression\n", |
||
| 537 | "\n", |
||
| 538 | "log_reg = LogisticRegression()\n", |
||
| 539 | "log_reg.fit(A_training, B_training)\n", |
||
| 540 | "B_pred = log_reg.predict(A_testing)\n", |
||
| 541 | "# accuracy score\n", |
||
| 542 | "\n", |
||
| 543 | "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n", |
||
| 544 | "\n", |
||
| 545 | "print(accuracy_score(B_training, log_reg.predict(A_training)))\n", |
||
| 546 | "\n", |
||
| 547 | "log_reg_acc = accuracy_score(B_testing, log_reg.predict(A_testing))\n", |
||
| 548 | "print(log_reg_acc)" |
||
| 549 | ] |
||
| 550 | }, |
||
| 551 | { |
||
| 552 | "cell_type": "markdown", |
||
| 553 | "id": "f1a6ab2c-fc10-4f0b-8b07-1e01f8ce1243", |
||
| 554 | "metadata": {}, |
||
| 555 | "source": [ |
||
| 556 | "K Neighbors Classifier (KNN)\n" |
||
| 557 | ] |
||
| 558 | }, |
||
| 559 | { |
||
| 560 | "cell_type": "code", |
||
| 561 | "execution_count": 8, |
||
| 562 | "id": "f8aecca7-018d-41ba-bb3c-2a96cf73b007", |
||
| 563 | "metadata": {}, |
||
| 564 | "outputs": [ |
||
| 565 | { |
||
| 566 | "name": "stdout", |
||
| 567 | "output_type": "stream", |
||
| 568 | "text": [ |
||
| 569 | "0.78099173553719\n", |
||
| 570 | "0.6229508196721312\n" |
||
| 571 | ] |
||
| 572 | } |
||
| 573 | ], |
||
| 574 | "source": [ |
||
| 575 | "from sklearn.neighbors import KNeighborsClassifier\n", |
||
| 576 | "\n", |
||
| 577 | "knn = KNeighborsClassifier()\n", |
||
| 578 | "knn.fit(A_training, B_training)\n", |
||
| 579 | "# model predictions \n", |
||
| 580 | "\n", |
||
| 581 | "B_pred = knn.predict(A_testing)\n", |
||
| 582 | "# accuracy score\n", |
||
| 583 | "\n", |
||
| 584 | "print(accuracy_score(B_training, knn.predict(A_training)))\n", |
||
| 585 | "\n", |
||
| 586 | "knn_acc = accuracy_score(B_testing, knn.predict(A_testing))\n", |
||
| 587 | "print(knn_acc)" |
||
| 588 | ] |
||
| 589 | }, |
||
| 590 | { |
||
| 591 | "cell_type": "markdown", |
||
| 592 | "id": "dd05e4cf-6aaf-468e-9eef-6f1b27d99560", |
||
| 593 | "metadata": {}, |
||
| 594 | "source": [ |
||
| 595 | "Support Vector Machine (SVM)" |
||
| 596 | ] |
||
| 597 | }, |
||
| 598 | { |
||
| 599 | "cell_type": "code", |
||
| 600 | "execution_count": 9, |
||
| 601 | "id": "35cc692b-d57f-4738-badf-5fd6c02c2889", |
||
| 602 | "metadata": {}, |
||
| 603 | "outputs": [ |
||
| 604 | { |
||
| 605 | "data": { |
||
| 606 | "text/plain": [ |
||
| 607 | "{'C': 20, 'gamma': 0.0001}" |
||
| 608 | ] |
||
| 609 | }, |
||
| 610 | "execution_count": 9, |
||
| 611 | "metadata": {}, |
||
| 612 | "output_type": "execute_result" |
||
| 613 | } |
||
| 614 | ], |
||
| 615 | "source": [ |
||
| 616 | "from sklearn.svm import SVC\n", |
||
| 617 | "from sklearn.model_selection import GridSearchCV\n", |
||
| 618 | "\n", |
||
| 619 | "svc = SVC(probability=True)\n", |
||
| 620 | "parameters = {\n", |
||
| 621 | " 'gamma' : [0.0001, 0.001, 0.01, 0.1],\n", |
||
| 622 | " 'C' : [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20]\n", |
||
| 623 | "}\n", |
||
| 624 | "\n", |
||
| 625 | "grid_search = GridSearchCV(svc, parameters)\n", |
||
| 626 | "grid_search.fit(A_training, B_training)\n", |
||
| 627 | "# best parameters\n", |
||
| 628 | "\n", |
||
| 629 | "grid_search.best_params_\n", |
||
| 630 | "\n" |
||
| 631 | ] |
||
| 632 | }, |
||
| 633 | { |
||
| 634 | "cell_type": "code", |
||
| 635 | "execution_count": 10, |
||
| 636 | "id": "cb090811-a42c-47ec-b7c7-43889430b93d", |
||
| 637 | "metadata": {}, |
||
| 638 | "outputs": [ |
||
| 639 | { |
||
| 640 | "data": { |
||
| 641 | "text/plain": [ |
||
| 642 | "0.6981292517006803" |
||
| 643 | ] |
||
| 644 | }, |
||
| 645 | "execution_count": 10, |
||
| 646 | "metadata": {}, |
||
| 647 | "output_type": "execute_result" |
||
| 648 | } |
||
| 649 | ], |
||
| 650 | "source": [ |
||
| 651 | "# best score \n", |
||
| 652 | "\n", |
||
| 653 | "grid_search.best_score_\n", |
||
| 654 | "\n" |
||
| 655 | ] |
||
| 656 | }, |
||
| 657 | { |
||
| 658 | "cell_type": "code", |
||
| 659 | "execution_count": 11, |
||
| 660 | "id": "639e3132-7346-40d5-becf-6d230a9004fb", |
||
| 661 | "metadata": {}, |
||
| 662 | "outputs": [ |
||
| 663 | { |
||
| 664 | "name": "stdout", |
||
| 665 | "output_type": "stream", |
||
| 666 | "text": [ |
||
| 667 | "1.0\n", |
||
| 668 | "0.5409836065573771\n", |
||
| 669 | " precision recall f1-score support\n", |
||
| 670 | "\n", |
||
| 671 | " 0 0.50 0.43 0.46 28\n", |
||
| 672 | " 1 0.57 0.64 0.60 33\n", |
||
| 673 | "\n", |
||
| 674 | " accuracy 0.54 61\n", |
||
| 675 | " macro avg 0.53 0.53 0.53 61\n", |
||
| 676 | "weighted avg 0.54 0.54 0.54 61\n", |
||
| 677 | "\n" |
||
| 678 | ] |
||
| 679 | } |
||
| 680 | ], |
||
| 681 | "source": [ |
||
| 682 | "svc = SVC(C = 10, gamma = 0.01, probability=True)\n", |
||
| 683 | "svc.fit(A_training, B_training)\n", |
||
| 684 | "# model predictions \n", |
||
| 685 | "\n", |
||
| 686 | "B_pred = svc.predict(A_testing)\n", |
||
| 687 | "# accuracy score\n", |
||
| 688 | "\n", |
||
| 689 | "print(accuracy_score(B_training, svc.predict(A_training)))\n", |
||
| 690 | "\n", |
||
| 691 | "svc_acc = accuracy_score(B_testing, svc.predict(A_testing))\n", |
||
| 692 | "print(svc_acc)\n", |
||
| 693 | "# classification report\n", |
||
| 694 | "\n", |
||
| 695 | "print(classification_report(B_testing, B_pred))" |
||
| 696 | ] |
||
| 697 | }, |
||
| 698 | { |
||
| 699 | "cell_type": "markdown", |
||
| 700 | "id": "0e3059b1-2e79-46bd-9195-98cbd044b75c", |
||
| 701 | "metadata": {}, |
||
| 702 | "source": [ |
||
| 703 | "DECISION TREE" |
||
| 704 | ] |
||
| 705 | }, |
||
| 706 | { |
||
| 707 | "cell_type": "code", |
||
| 708 | "execution_count": 12, |
||
| 709 | "id": "79b6cc36-eab3-4502-861f-0c4576170ffd", |
||
| 710 | "metadata": {}, |
||
| 711 | "outputs": [ |
||
| 712 | { |
||
| 713 | "name": "stdout", |
||
| 714 | "output_type": "stream", |
||
| 715 | "text": [ |
||
| 716 | "Fitting 5 folds for each of 8640 candidates, totalling 43200 fits\n" |
||
| 717 | ] |
||
| 718 | }, |
||
| 719 | { |
||
| 720 | "data": { |
||
| 721 | "text/html": [ |
||
| 722 | "<style>#sk-container-id-1 {\n", |
||
| 723 | " /* Definition of color scheme common for light and dark mode */\n", |
||
| 724 | " --sklearn-color-text: black;\n", |
||
| 725 | " --sklearn-color-line: gray;\n", |
||
| 726 | " /* Definition of color scheme for unfitted estimators */\n", |
||
| 727 | " --sklearn-color-unfitted-level-0: #fff5e6;\n", |
||
| 728 | " --sklearn-color-unfitted-level-1: #f6e4d2;\n", |
||
| 729 | " --sklearn-color-unfitted-level-2: #ffe0b3;\n", |
||
| 730 | " --sklearn-color-unfitted-level-3: chocolate;\n", |
||
| 731 | " /* Definition of color scheme for fitted estimators */\n", |
||
| 732 | " --sklearn-color-fitted-level-0: #f0f8ff;\n", |
||
| 733 | " --sklearn-color-fitted-level-1: #d4ebff;\n", |
||
| 734 | " --sklearn-color-fitted-level-2: #b3dbfd;\n", |
||
| 735 | " --sklearn-color-fitted-level-3: cornflowerblue;\n", |
||
| 736 | "\n", |
||
| 737 | " /* Specific color for light theme */\n", |
||
| 738 | " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", |
||
| 739 | " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", |
||
| 740 | " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", |
||
| 741 | " --sklearn-color-icon: #696969;\n", |
||
| 742 | "\n", |
||
| 743 | " @media (prefers-color-scheme: dark) {\n", |
||
| 744 | " /* Redefinition of color scheme for dark theme */\n", |
||
| 745 | " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", |
||
| 746 | " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", |
||
| 747 | " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", |
||
| 748 | " --sklearn-color-icon: #878787;\n", |
||
| 749 | " }\n", |
||
| 750 | "}\n", |
||
| 751 | "\n", |
||
| 752 | "#sk-container-id-1 {\n", |
||
| 753 | " color: var(--sklearn-color-text);\n", |
||
| 754 | "}\n", |
||
| 755 | "\n", |
||
| 756 | "#sk-container-id-1 pre {\n", |
||
| 757 | " padding: 0;\n", |
||
| 758 | "}\n", |
||
| 759 | "\n", |
||
| 760 | "#sk-container-id-1 input.sk-hidden--visually {\n", |
||
| 761 | " border: 0;\n", |
||
| 762 | " clip: rect(1px 1px 1px 1px);\n", |
||
| 763 | " clip: rect(1px, 1px, 1px, 1px);\n", |
||
| 764 | " height: 1px;\n", |
||
| 765 | " margin: -1px;\n", |
||
| 766 | " overflow: hidden;\n", |
||
| 767 | " padding: 0;\n", |
||
| 768 | " position: absolute;\n", |
||
| 769 | " width: 1px;\n", |
||
| 770 | "}\n", |
||
| 771 | "\n", |
||
| 772 | "#sk-container-id-1 div.sk-dashed-wrapped {\n", |
||
| 773 | " border: 1px dashed var(--sklearn-color-line);\n", |
||
| 774 | " margin: 0 0.4em 0.5em 0.4em;\n", |
||
| 775 | " box-sizing: border-box;\n", |
||
| 776 | " padding-bottom: 0.4em;\n", |
||
| 777 | " background-color: var(--sklearn-color-background);\n", |
||
| 778 | "}\n", |
||
| 779 | "\n", |
||
| 780 | "#sk-container-id-1 div.sk-container {\n", |
||
| 781 | " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", |
||
| 782 | " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", |
||
| 783 | " so we also need the `!important` here to be able to override the\n", |
||
| 784 | " default hidden behavior on the sphinx rendered scikit-learn.org.\n", |
||
| 785 | " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", |
||
| 786 | " display: inline-block !important;\n", |
||
| 787 | " position: relative;\n", |
||
| 788 | "}\n", |
||
| 789 | "\n", |
||
| 790 | "#sk-container-id-1 div.sk-text-repr-fallback {\n", |
||
| 791 | " display: none;\n", |
||
| 792 | "}\n", |
||
| 793 | "\n", |
||
| 794 | "div.sk-parallel-item,\n", |
||
| 795 | "div.sk-serial,\n", |
||
| 796 | "div.sk-item {\n", |
||
| 797 | " /* draw centered vertical line to link estimators */\n", |
||
| 798 | " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", |
||
| 799 | " background-size: 2px 100%;\n", |
||
| 800 | " background-repeat: no-repeat;\n", |
||
| 801 | " background-position: center center;\n", |
||
| 802 | "}\n", |
||
| 803 | "\n", |
||
| 804 | "/* Parallel-specific style estimator block */\n", |
||
| 805 | "\n", |
||
| 806 | "#sk-container-id-1 div.sk-parallel-item::after {\n", |
||
| 807 | " content: \"\";\n", |
||
| 808 | " width: 100%;\n", |
||
| 809 | " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", |
||
| 810 | " flex-grow: 1;\n", |
||
| 811 | "}\n", |
||
| 812 | "\n", |
||
| 813 | "#sk-container-id-1 div.sk-parallel {\n", |
||
| 814 | " display: flex;\n", |
||
| 815 | " align-items: stretch;\n", |
||
| 816 | " justify-content: center;\n", |
||
| 817 | " background-color: var(--sklearn-color-background);\n", |
||
| 818 | " position: relative;\n", |
||
| 819 | "}\n", |
||
| 820 | "\n", |
||
| 821 | "#sk-container-id-1 div.sk-parallel-item {\n", |
||
| 822 | " display: flex;\n", |
||
| 823 | " flex-direction: column;\n", |
||
| 824 | "}\n", |
||
| 825 | "\n", |
||
| 826 | "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", |
||
| 827 | " align-self: flex-end;\n", |
||
| 828 | " width: 50%;\n", |
||
| 829 | "}\n", |
||
| 830 | "\n", |
||
| 831 | "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", |
||
| 832 | " align-self: flex-start;\n", |
||
| 833 | " width: 50%;\n", |
||
| 834 | "}\n", |
||
| 835 | "\n", |
||
| 836 | "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", |
||
| 837 | " width: 0;\n", |
||
| 838 | "}\n", |
||
| 839 | "\n", |
||
| 840 | "/* Serial-specific style estimator block */\n", |
||
| 841 | "\n", |
||
| 842 | "#sk-container-id-1 div.sk-serial {\n", |
||
| 843 | " display: flex;\n", |
||
| 844 | " flex-direction: column;\n", |
||
| 845 | " align-items: center;\n", |
||
| 846 | " background-color: var(--sklearn-color-background);\n", |
||
| 847 | " padding-right: 1em;\n", |
||
| 848 | " padding-left: 1em;\n", |
||
| 849 | "}\n", |
||
| 850 | "\n", |
||
| 851 | "\n", |
||
| 852 | "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", |
||
| 853 | "clickable and can be expanded/collapsed.\n", |
||
| 854 | "- Pipeline and ColumnTransformer use this feature and define the default style\n", |
||
| 855 | "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", |
||
| 856 | "*/\n", |
||
| 857 | "\n", |
||
| 858 | "/* Pipeline and ColumnTransformer style (default) */\n", |
||
| 859 | "\n", |
||
| 860 | "#sk-container-id-1 div.sk-toggleable {\n", |
||
| 861 | " /* Default theme specific background. It is overwritten whether we have a\n", |
||
| 862 | " specific estimator or a Pipeline/ColumnTransformer */\n", |
||
| 863 | " background-color: var(--sklearn-color-background);\n", |
||
| 864 | "}\n", |
||
| 865 | "\n", |
||
| 866 | "/* Toggleable label */\n", |
||
| 867 | "#sk-container-id-1 label.sk-toggleable__label {\n", |
||
| 868 | " cursor: pointer;\n", |
||
| 869 | " display: block;\n", |
||
| 870 | " width: 100%;\n", |
||
| 871 | " margin-bottom: 0;\n", |
||
| 872 | " padding: 0.5em;\n", |
||
| 873 | " box-sizing: border-box;\n", |
||
| 874 | " text-align: center;\n", |
||
| 875 | "}\n", |
||
| 876 | "\n", |
||
| 877 | "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", |
||
| 878 | " /* Arrow on the left of the label */\n", |
||
| 879 | " content: \"▸\";\n", |
||
| 880 | " float: left;\n", |
||
| 881 | " margin-right: 0.25em;\n", |
||
| 882 | " color: var(--sklearn-color-icon);\n", |
||
| 883 | "}\n", |
||
| 884 | "\n", |
||
| 885 | "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", |
||
| 886 | " color: var(--sklearn-color-text);\n", |
||
| 887 | "}\n", |
||
| 888 | "\n", |
||
| 889 | "/* Toggleable content - dropdown */\n", |
||
| 890 | "\n", |
||
| 891 | "#sk-container-id-1 div.sk-toggleable__content {\n", |
||
| 892 | " max-height: 0;\n", |
||
| 893 | " max-width: 0;\n", |
||
| 894 | " overflow: hidden;\n", |
||
| 895 | " text-align: left;\n", |
||
| 896 | " /* unfitted */\n", |
||
| 897 | " background-color: var(--sklearn-color-unfitted-level-0);\n", |
||
| 898 | "}\n", |
||
| 899 | "\n", |
||
| 900 | "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", |
||
| 901 | " /* fitted */\n", |
||
| 902 | " background-color: var(--sklearn-color-fitted-level-0);\n", |
||
| 903 | "}\n", |
||
| 904 | "\n", |
||
| 905 | "#sk-container-id-1 div.sk-toggleable__content pre {\n", |
||
| 906 | " margin: 0.2em;\n", |
||
| 907 | " border-radius: 0.25em;\n", |
||
| 908 | " color: var(--sklearn-color-text);\n", |
||
| 909 | " /* unfitted */\n", |
||
| 910 | " background-color: var(--sklearn-color-unfitted-level-0);\n", |
||
| 911 | "}\n", |
||
| 912 | "\n", |
||
| 913 | "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", |
||
| 914 | " /* unfitted */\n", |
||
| 915 | " background-color: var(--sklearn-color-fitted-level-0);\n", |
||
| 916 | "}\n", |
||
| 917 | "\n", |
||
| 918 | "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", |
||
| 919 | " /* Expand drop-down */\n", |
||
| 920 | " max-height: 200px;\n", |
||
| 921 | " max-width: 100%;\n", |
||
| 922 | " overflow: auto;\n", |
||
| 923 | "}\n", |
||
| 924 | "\n", |
||
| 925 | "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", |
||
| 926 | " content: \"▾\";\n", |
||
| 927 | "}\n", |
||
| 928 | "\n", |
||
| 929 | "/* Pipeline/ColumnTransformer-specific style */\n", |
||
| 930 | "\n", |
||
| 931 | "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 932 | " color: var(--sklearn-color-text);\n", |
||
| 933 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 934 | "}\n", |
||
| 935 | "\n", |
||
| 936 | "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 937 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 938 | "}\n", |
||
| 939 | "\n", |
||
| 940 | "/* Estimator-specific style */\n", |
||
| 941 | "\n", |
||
| 942 | "/* Colorize estimator box */\n", |
||
| 943 | "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 944 | " /* unfitted */\n", |
||
| 945 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 946 | "}\n", |
||
| 947 | "\n", |
||
| 948 | "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 949 | " /* fitted */\n", |
||
| 950 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 951 | "}\n", |
||
| 952 | "\n", |
||
| 953 | "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", |
||
| 954 | "#sk-container-id-1 div.sk-label label {\n", |
||
| 955 | " /* The background is the default theme color */\n", |
||
| 956 | " color: var(--sklearn-color-text-on-default-background);\n", |
||
| 957 | "}\n", |
||
| 958 | "\n", |
||
| 959 | "/* On hover, darken the color of the background */\n", |
||
| 960 | "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", |
||
| 961 | " color: var(--sklearn-color-text);\n", |
||
| 962 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 963 | "}\n", |
||
| 964 | "\n", |
||
| 965 | "/* Label box, darken color on hover, fitted */\n", |
||
| 966 | "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", |
||
| 967 | " color: var(--sklearn-color-text);\n", |
||
| 968 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 969 | "}\n", |
||
| 970 | "\n", |
||
| 971 | "/* Estimator label */\n", |
||
| 972 | "\n", |
||
| 973 | "#sk-container-id-1 div.sk-label label {\n", |
||
| 974 | " font-family: monospace;\n", |
||
| 975 | " font-weight: bold;\n", |
||
| 976 | " display: inline-block;\n", |
||
| 977 | " line-height: 1.2em;\n", |
||
| 978 | "}\n", |
||
| 979 | "\n", |
||
| 980 | "#sk-container-id-1 div.sk-label-container {\n", |
||
| 981 | " text-align: center;\n", |
||
| 982 | "}\n", |
||
| 983 | "\n", |
||
| 984 | "/* Estimator-specific */\n", |
||
| 985 | "#sk-container-id-1 div.sk-estimator {\n", |
||
| 986 | " font-family: monospace;\n", |
||
| 987 | " border: 1px dotted var(--sklearn-color-border-box);\n", |
||
| 988 | " border-radius: 0.25em;\n", |
||
| 989 | " box-sizing: border-box;\n", |
||
| 990 | " margin-bottom: 0.5em;\n", |
||
| 991 | " /* unfitted */\n", |
||
| 992 | " background-color: var(--sklearn-color-unfitted-level-0);\n", |
||
| 993 | "}\n", |
||
| 994 | "\n", |
||
| 995 | "#sk-container-id-1 div.sk-estimator.fitted {\n", |
||
| 996 | " /* fitted */\n", |
||
| 997 | " background-color: var(--sklearn-color-fitted-level-0);\n", |
||
| 998 | "}\n", |
||
| 999 | "\n", |
||
| 1000 | "/* on hover */\n", |
||
| 1001 | "#sk-container-id-1 div.sk-estimator:hover {\n", |
||
| 1002 | " /* unfitted */\n", |
||
| 1003 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 1004 | "}\n", |
||
| 1005 | "\n", |
||
| 1006 | "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", |
||
| 1007 | " /* fitted */\n", |
||
| 1008 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 1009 | "}\n", |
||
| 1010 | "\n", |
||
| 1011 | "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", |
||
| 1012 | "\n", |
||
| 1013 | "/* Common style for \"i\" and \"?\" */\n", |
||
| 1014 | "\n", |
||
| 1015 | ".sk-estimator-doc-link,\n", |
||
| 1016 | "a:link.sk-estimator-doc-link,\n", |
||
| 1017 | "a:visited.sk-estimator-doc-link {\n", |
||
| 1018 | " float: right;\n", |
||
| 1019 | " font-size: smaller;\n", |
||
| 1020 | " line-height: 1em;\n", |
||
| 1021 | " font-family: monospace;\n", |
||
| 1022 | " background-color: var(--sklearn-color-background);\n", |
||
| 1023 | " border-radius: 1em;\n", |
||
| 1024 | " height: 1em;\n", |
||
| 1025 | " width: 1em;\n", |
||
| 1026 | " text-decoration: none !important;\n", |
||
| 1027 | " margin-left: 1ex;\n", |
||
| 1028 | " /* unfitted */\n", |
||
| 1029 | " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", |
||
| 1030 | " color: var(--sklearn-color-unfitted-level-1);\n", |
||
| 1031 | "}\n", |
||
| 1032 | "\n", |
||
| 1033 | ".sk-estimator-doc-link.fitted,\n", |
||
| 1034 | "a:link.sk-estimator-doc-link.fitted,\n", |
||
| 1035 | "a:visited.sk-estimator-doc-link.fitted {\n", |
||
| 1036 | " /* fitted */\n", |
||
| 1037 | " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", |
||
| 1038 | " color: var(--sklearn-color-fitted-level-1);\n", |
||
| 1039 | "}\n", |
||
| 1040 | "\n", |
||
| 1041 | "/* On hover */\n", |
||
| 1042 | "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", |
||
| 1043 | ".sk-estimator-doc-link:hover,\n", |
||
| 1044 | "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", |
||
| 1045 | ".sk-estimator-doc-link:hover {\n", |
||
| 1046 | " /* unfitted */\n", |
||
| 1047 | " background-color: var(--sklearn-color-unfitted-level-3);\n", |
||
| 1048 | " color: var(--sklearn-color-background);\n", |
||
| 1049 | " text-decoration: none;\n", |
||
| 1050 | "}\n", |
||
| 1051 | "\n", |
||
| 1052 | "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", |
||
| 1053 | ".sk-estimator-doc-link.fitted:hover,\n", |
||
| 1054 | "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", |
||
| 1055 | ".sk-estimator-doc-link.fitted:hover {\n", |
||
| 1056 | " /* fitted */\n", |
||
| 1057 | " background-color: var(--sklearn-color-fitted-level-3);\n", |
||
| 1058 | " color: var(--sklearn-color-background);\n", |
||
| 1059 | " text-decoration: none;\n", |
||
| 1060 | "}\n", |
||
| 1061 | "\n", |
||
| 1062 | "/* Span, style for the box shown on hovering the info icon */\n", |
||
| 1063 | ".sk-estimator-doc-link span {\n", |
||
| 1064 | " display: none;\n", |
||
| 1065 | " z-index: 9999;\n", |
||
| 1066 | " position: relative;\n", |
||
| 1067 | " font-weight: normal;\n", |
||
| 1068 | " right: .2ex;\n", |
||
| 1069 | " padding: .5ex;\n", |
||
| 1070 | " margin: .5ex;\n", |
||
| 1071 | " width: min-content;\n", |
||
| 1072 | " min-width: 20ex;\n", |
||
| 1073 | " max-width: 50ex;\n", |
||
| 1074 | " color: var(--sklearn-color-text);\n", |
||
| 1075 | " box-shadow: 2pt 2pt 4pt #999;\n", |
||
| 1076 | " /* unfitted */\n", |
||
| 1077 | " background: var(--sklearn-color-unfitted-level-0);\n", |
||
| 1078 | " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", |
||
| 1079 | "}\n", |
||
| 1080 | "\n", |
||
| 1081 | ".sk-estimator-doc-link.fitted span {\n", |
||
| 1082 | " /* fitted */\n", |
||
| 1083 | " background: var(--sklearn-color-fitted-level-0);\n", |
||
| 1084 | " border: var(--sklearn-color-fitted-level-3);\n", |
||
| 1085 | "}\n", |
||
| 1086 | "\n", |
||
| 1087 | ".sk-estimator-doc-link:hover span {\n", |
||
| 1088 | " display: block;\n", |
||
| 1089 | "}\n", |
||
| 1090 | "\n", |
||
| 1091 | "/* \"?\"-specific style due to the `<a>` HTML tag */\n", |
||
| 1092 | "\n", |
||
| 1093 | "#sk-container-id-1 a.estimator_doc_link {\n", |
||
| 1094 | " float: right;\n", |
||
| 1095 | " font-size: 1rem;\n", |
||
| 1096 | " line-height: 1em;\n", |
||
| 1097 | " font-family: monospace;\n", |
||
| 1098 | " background-color: var(--sklearn-color-background);\n", |
||
| 1099 | " border-radius: 1rem;\n", |
||
| 1100 | " height: 1rem;\n", |
||
| 1101 | " width: 1rem;\n", |
||
| 1102 | " text-decoration: none;\n", |
||
| 1103 | " /* unfitted */\n", |
||
| 1104 | " color: var(--sklearn-color-unfitted-level-1);\n", |
||
| 1105 | " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", |
||
| 1106 | "}\n", |
||
| 1107 | "\n", |
||
| 1108 | "#sk-container-id-1 a.estimator_doc_link.fitted {\n", |
||
| 1109 | " /* fitted */\n", |
||
| 1110 | " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", |
||
| 1111 | " color: var(--sklearn-color-fitted-level-1);\n", |
||
| 1112 | "}\n", |
||
| 1113 | "\n", |
||
| 1114 | "/* On hover */\n", |
||
| 1115 | "#sk-container-id-1 a.estimator_doc_link:hover {\n", |
||
| 1116 | " /* unfitted */\n", |
||
| 1117 | " background-color: var(--sklearn-color-unfitted-level-3);\n", |
||
| 1118 | " color: var(--sklearn-color-background);\n", |
||
| 1119 | " text-decoration: none;\n", |
||
| 1120 | "}\n", |
||
| 1121 | "\n", |
||
| 1122 | "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", |
||
| 1123 | " /* fitted */\n", |
||
| 1124 | " background-color: var(--sklearn-color-fitted-level-3);\n", |
||
| 1125 | "}\n", |
||
| 1126 | "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n", |
||
| 1127 | " param_grid={'criterion': ['gini', 'entropy'],\n", |
||
| 1128 | " 'max_depth': range(2, 32),\n", |
||
| 1129 | " 'min_samples_leaf': range(1, 10),\n", |
||
| 1130 | " 'min_samples_split': range(2, 10),\n", |
||
| 1131 | " 'splitter': ['best', 'random']},\n", |
||
| 1132 | " verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n", |
||
| 1133 | " param_grid={'criterion': ['gini', 'entropy'],\n", |
||
| 1134 | " 'max_depth': range(2, 32),\n", |
||
| 1135 | " 'min_samples_leaf': range(1, 10),\n", |
||
| 1136 | " 'min_samples_split': range(2, 10),\n", |
||
| 1137 | " 'splitter': ['best', 'random']},\n", |
||
| 1138 | " verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>" |
||
| 1139 | ], |
||
| 1140 | "text/plain": [ |
||
| 1141 | "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n", |
||
| 1142 | " param_grid={'criterion': ['gini', 'entropy'],\n", |
||
| 1143 | " 'max_depth': range(2, 32),\n", |
||
| 1144 | " 'min_samples_leaf': range(1, 10),\n", |
||
| 1145 | " 'min_samples_split': range(2, 10),\n", |
||
| 1146 | " 'splitter': ['best', 'random']},\n", |
||
| 1147 | " verbose=1)" |
||
| 1148 | ] |
||
| 1149 | }, |
||
| 1150 | "execution_count": 12, |
||
| 1151 | "metadata": {}, |
||
| 1152 | "output_type": "execute_result" |
||
| 1153 | } |
||
| 1154 | ], |
||
| 1155 | "source": [ |
||
| 1156 | "from sklearn.tree import DecisionTreeClassifier\n", |
||
| 1157 | "\n", |
||
| 1158 | "dtc = DecisionTreeClassifier()\n", |
||
| 1159 | "\n", |
||
| 1160 | "parameters = {\n", |
||
| 1161 | " 'criterion' : ['gini', 'entropy'],\n", |
||
| 1162 | " 'max_depth' : range(2, 32, 1),\n", |
||
| 1163 | " 'min_samples_leaf' : range(1, 10, 1),\n", |
||
| 1164 | " 'min_samples_split' : range(2, 10, 1),\n", |
||
| 1165 | " 'splitter' : ['best', 'random']\n", |
||
| 1166 | "}\n", |
||
| 1167 | "\n", |
||
| 1168 | "grid_search_dt = GridSearchCV(dtc, parameters, cv = 5, n_jobs = -1, verbose = 1)\n", |
||
| 1169 | "grid_search_dt.fit(A_training, B_training)" |
||
| 1170 | ] |
||
| 1171 | }, |
||
| 1172 | { |
||
| 1173 | "cell_type": "code", |
||
| 1174 | "execution_count": 13, |
||
| 1175 | "id": "362b8867-1ec8-4eba-84b0-c98486db2d2f", |
||
| 1176 | "metadata": {}, |
||
| 1177 | "outputs": [ |
||
| 1178 | { |
||
| 1179 | "data": { |
||
| 1180 | "text/html": [ |
||
| 1181 | "<style>#sk-container-id-2 {\n", |
||
| 1182 | " /* Definition of color scheme common for light and dark mode */\n", |
||
| 1183 | " --sklearn-color-text: black;\n", |
||
| 1184 | " --sklearn-color-line: gray;\n", |
||
| 1185 | " /* Definition of color scheme for unfitted estimators */\n", |
||
| 1186 | " --sklearn-color-unfitted-level-0: #fff5e6;\n", |
||
| 1187 | " --sklearn-color-unfitted-level-1: #f6e4d2;\n", |
||
| 1188 | " --sklearn-color-unfitted-level-2: #ffe0b3;\n", |
||
| 1189 | " --sklearn-color-unfitted-level-3: chocolate;\n", |
||
| 1190 | " /* Definition of color scheme for fitted estimators */\n", |
||
| 1191 | " --sklearn-color-fitted-level-0: #f0f8ff;\n", |
||
| 1192 | " --sklearn-color-fitted-level-1: #d4ebff;\n", |
||
| 1193 | " --sklearn-color-fitted-level-2: #b3dbfd;\n", |
||
| 1194 | " --sklearn-color-fitted-level-3: cornflowerblue;\n", |
||
| 1195 | "\n", |
||
| 1196 | " /* Specific color for light theme */\n", |
||
| 1197 | " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", |
||
| 1198 | " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", |
||
| 1199 | " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", |
||
| 1200 | " --sklearn-color-icon: #696969;\n", |
||
| 1201 | "\n", |
||
| 1202 | " @media (prefers-color-scheme: dark) {\n", |
||
| 1203 | " /* Redefinition of color scheme for dark theme */\n", |
||
| 1204 | " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", |
||
| 1205 | " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", |
||
| 1206 | " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", |
||
| 1207 | " --sklearn-color-icon: #878787;\n", |
||
| 1208 | " }\n", |
||
| 1209 | "}\n", |
||
| 1210 | "\n", |
||
| 1211 | "#sk-container-id-2 {\n", |
||
| 1212 | " color: var(--sklearn-color-text);\n", |
||
| 1213 | "}\n", |
||
| 1214 | "\n", |
||
| 1215 | "#sk-container-id-2 pre {\n", |
||
| 1216 | " padding: 0;\n", |
||
| 1217 | "}\n", |
||
| 1218 | "\n", |
||
| 1219 | "#sk-container-id-2 input.sk-hidden--visually {\n", |
||
| 1220 | " border: 0;\n", |
||
| 1221 | " clip: rect(1px 1px 1px 1px);\n", |
||
| 1222 | " clip: rect(1px, 1px, 1px, 1px);\n", |
||
| 1223 | " height: 1px;\n", |
||
| 1224 | " margin: -1px;\n", |
||
| 1225 | " overflow: hidden;\n", |
||
| 1226 | " padding: 0;\n", |
||
| 1227 | " position: absolute;\n", |
||
| 1228 | " width: 1px;\n", |
||
| 1229 | "}\n", |
||
| 1230 | "\n", |
||
| 1231 | "#sk-container-id-2 div.sk-dashed-wrapped {\n", |
||
| 1232 | " border: 1px dashed var(--sklearn-color-line);\n", |
||
| 1233 | " margin: 0 0.4em 0.5em 0.4em;\n", |
||
| 1234 | " box-sizing: border-box;\n", |
||
| 1235 | " padding-bottom: 0.4em;\n", |
||
| 1236 | " background-color: var(--sklearn-color-background);\n", |
||
| 1237 | "}\n", |
||
| 1238 | "\n", |
||
| 1239 | "#sk-container-id-2 div.sk-container {\n", |
||
| 1240 | " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", |
||
| 1241 | " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", |
||
| 1242 | " so we also need the `!important` here to be able to override the\n", |
||
| 1243 | " default hidden behavior on the sphinx rendered scikit-learn.org.\n", |
||
| 1244 | " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", |
||
| 1245 | " display: inline-block !important;\n", |
||
| 1246 | " position: relative;\n", |
||
| 1247 | "}\n", |
||
| 1248 | "\n", |
||
| 1249 | "#sk-container-id-2 div.sk-text-repr-fallback {\n", |
||
| 1250 | " display: none;\n", |
||
| 1251 | "}\n", |
||
| 1252 | "\n", |
||
| 1253 | "div.sk-parallel-item,\n", |
||
| 1254 | "div.sk-serial,\n", |
||
| 1255 | "div.sk-item {\n", |
||
| 1256 | " /* draw centered vertical line to link estimators */\n", |
||
| 1257 | " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", |
||
| 1258 | " background-size: 2px 100%;\n", |
||
| 1259 | " background-repeat: no-repeat;\n", |
||
| 1260 | " background-position: center center;\n", |
||
| 1261 | "}\n", |
||
| 1262 | "\n", |
||
| 1263 | "/* Parallel-specific style estimator block */\n", |
||
| 1264 | "\n", |
||
| 1265 | "#sk-container-id-2 div.sk-parallel-item::after {\n", |
||
| 1266 | " content: \"\";\n", |
||
| 1267 | " width: 100%;\n", |
||
| 1268 | " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", |
||
| 1269 | " flex-grow: 1;\n", |
||
| 1270 | "}\n", |
||
| 1271 | "\n", |
||
| 1272 | "#sk-container-id-2 div.sk-parallel {\n", |
||
| 1273 | " display: flex;\n", |
||
| 1274 | " align-items: stretch;\n", |
||
| 1275 | " justify-content: center;\n", |
||
| 1276 | " background-color: var(--sklearn-color-background);\n", |
||
| 1277 | " position: relative;\n", |
||
| 1278 | "}\n", |
||
| 1279 | "\n", |
||
| 1280 | "#sk-container-id-2 div.sk-parallel-item {\n", |
||
| 1281 | " display: flex;\n", |
||
| 1282 | " flex-direction: column;\n", |
||
| 1283 | "}\n", |
||
| 1284 | "\n", |
||
| 1285 | "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n", |
||
| 1286 | " align-self: flex-end;\n", |
||
| 1287 | " width: 50%;\n", |
||
| 1288 | "}\n", |
||
| 1289 | "\n", |
||
| 1290 | "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n", |
||
| 1291 | " align-self: flex-start;\n", |
||
| 1292 | " width: 50%;\n", |
||
| 1293 | "}\n", |
||
| 1294 | "\n", |
||
| 1295 | "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n", |
||
| 1296 | " width: 0;\n", |
||
| 1297 | "}\n", |
||
| 1298 | "\n", |
||
| 1299 | "/* Serial-specific style estimator block */\n", |
||
| 1300 | "\n", |
||
| 1301 | "#sk-container-id-2 div.sk-serial {\n", |
||
| 1302 | " display: flex;\n", |
||
| 1303 | " flex-direction: column;\n", |
||
| 1304 | " align-items: center;\n", |
||
| 1305 | " background-color: var(--sklearn-color-background);\n", |
||
| 1306 | " padding-right: 1em;\n", |
||
| 1307 | " padding-left: 1em;\n", |
||
| 1308 | "}\n", |
||
| 1309 | "\n", |
||
| 1310 | "\n", |
||
| 1311 | "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", |
||
| 1312 | "clickable and can be expanded/collapsed.\n", |
||
| 1313 | "- Pipeline and ColumnTransformer use this feature and define the default style\n", |
||
| 1314 | "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", |
||
| 1315 | "*/\n", |
||
| 1316 | "\n", |
||
| 1317 | "/* Pipeline and ColumnTransformer style (default) */\n", |
||
| 1318 | "\n", |
||
| 1319 | "#sk-container-id-2 div.sk-toggleable {\n", |
||
| 1320 | " /* Default theme specific background. It is overwritten whether we have a\n", |
||
| 1321 | " specific estimator or a Pipeline/ColumnTransformer */\n", |
||
| 1322 | " background-color: var(--sklearn-color-background);\n", |
||
| 1323 | "}\n", |
||
| 1324 | "\n", |
||
| 1325 | "/* Toggleable label */\n", |
||
| 1326 | "#sk-container-id-2 label.sk-toggleable__label {\n", |
||
| 1327 | " cursor: pointer;\n", |
||
| 1328 | " display: block;\n", |
||
| 1329 | " width: 100%;\n", |
||
| 1330 | " margin-bottom: 0;\n", |
||
| 1331 | " padding: 0.5em;\n", |
||
| 1332 | " box-sizing: border-box;\n", |
||
| 1333 | " text-align: center;\n", |
||
| 1334 | "}\n", |
||
| 1335 | "\n", |
||
| 1336 | "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n", |
||
| 1337 | " /* Arrow on the left of the label */\n", |
||
| 1338 | " content: \"▸\";\n", |
||
| 1339 | " float: left;\n", |
||
| 1340 | " margin-right: 0.25em;\n", |
||
| 1341 | " color: var(--sklearn-color-icon);\n", |
||
| 1342 | "}\n", |
||
| 1343 | "\n", |
||
| 1344 | "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n", |
||
| 1345 | " color: var(--sklearn-color-text);\n", |
||
| 1346 | "}\n", |
||
| 1347 | "\n", |
||
| 1348 | "/* Toggleable content - dropdown */\n", |
||
| 1349 | "\n", |
||
| 1350 | "#sk-container-id-2 div.sk-toggleable__content {\n", |
||
| 1351 | " max-height: 0;\n", |
||
| 1352 | " max-width: 0;\n", |
||
| 1353 | " overflow: hidden;\n", |
||
| 1354 | " text-align: left;\n", |
||
| 1355 | " /* unfitted */\n", |
||
| 1356 | " background-color: var(--sklearn-color-unfitted-level-0);\n", |
||
| 1357 | "}\n", |
||
| 1358 | "\n", |
||
| 1359 | "#sk-container-id-2 div.sk-toggleable__content.fitted {\n", |
||
| 1360 | " /* fitted */\n", |
||
| 1361 | " background-color: var(--sklearn-color-fitted-level-0);\n", |
||
| 1362 | "}\n", |
||
| 1363 | "\n", |
||
| 1364 | "#sk-container-id-2 div.sk-toggleable__content pre {\n", |
||
| 1365 | " margin: 0.2em;\n", |
||
| 1366 | " border-radius: 0.25em;\n", |
||
| 1367 | " color: var(--sklearn-color-text);\n", |
||
| 1368 | " /* unfitted */\n", |
||
| 1369 | " background-color: var(--sklearn-color-unfitted-level-0);\n", |
||
| 1370 | "}\n", |
||
| 1371 | "\n", |
||
| 1372 | "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n", |
||
| 1373 | " /* unfitted */\n", |
||
| 1374 | " background-color: var(--sklearn-color-fitted-level-0);\n", |
||
| 1375 | "}\n", |
||
| 1376 | "\n", |
||
| 1377 | "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", |
||
| 1378 | " /* Expand drop-down */\n", |
||
| 1379 | " max-height: 200px;\n", |
||
| 1380 | " max-width: 100%;\n", |
||
| 1381 | " overflow: auto;\n", |
||
| 1382 | "}\n", |
||
| 1383 | "\n", |
||
| 1384 | "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", |
||
| 1385 | " content: \"▾\";\n", |
||
| 1386 | "}\n", |
||
| 1387 | "\n", |
||
| 1388 | "/* Pipeline/ColumnTransformer-specific style */\n", |
||
| 1389 | "\n", |
||
| 1390 | "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 1391 | " color: var(--sklearn-color-text);\n", |
||
| 1392 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 1393 | "}\n", |
||
| 1394 | "\n", |
||
| 1395 | "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 1396 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 1397 | "}\n", |
||
| 1398 | "\n", |
||
| 1399 | "/* Estimator-specific style */\n", |
||
| 1400 | "\n", |
||
| 1401 | "/* Colorize estimator box */\n", |
||
| 1402 | "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 1403 | " /* unfitted */\n", |
||
| 1404 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 1405 | "}\n", |
||
| 1406 | "\n", |
||
| 1407 | "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", |
||
| 1408 | " /* fitted */\n", |
||
| 1409 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 1410 | "}\n", |
||
| 1411 | "\n", |
||
| 1412 | "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n", |
||
| 1413 | "#sk-container-id-2 div.sk-label label {\n", |
||
| 1414 | " /* The background is the default theme color */\n", |
||
| 1415 | " color: var(--sklearn-color-text-on-default-background);\n", |
||
| 1416 | "}\n", |
||
| 1417 | "\n", |
||
| 1418 | "/* On hover, darken the color of the background */\n", |
||
| 1419 | "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n", |
||
| 1420 | " color: var(--sklearn-color-text);\n", |
||
| 1421 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 1422 | "}\n", |
||
| 1423 | "\n", |
||
| 1424 | "/* Label box, darken color on hover, fitted */\n", |
||
| 1425 | "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", |
||
| 1426 | " color: var(--sklearn-color-text);\n", |
||
| 1427 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 1428 | "}\n", |
||
| 1429 | "\n", |
||
| 1430 | "/* Estimator label */\n", |
||
| 1431 | "\n", |
||
| 1432 | "#sk-container-id-2 div.sk-label label {\n", |
||
| 1433 | " font-family: monospace;\n", |
||
| 1434 | " font-weight: bold;\n", |
||
| 1435 | " display: inline-block;\n", |
||
| 1436 | " line-height: 1.2em;\n", |
||
| 1437 | "}\n", |
||
| 1438 | "\n", |
||
| 1439 | "#sk-container-id-2 div.sk-label-container {\n", |
||
| 1440 | " text-align: center;\n", |
||
| 1441 | "}\n", |
||
| 1442 | "\n", |
||
| 1443 | "/* Estimator-specific */\n", |
||
| 1444 | "#sk-container-id-2 div.sk-estimator {\n", |
||
| 1445 | " font-family: monospace;\n", |
||
| 1446 | " border: 1px dotted var(--sklearn-color-border-box);\n", |
||
| 1447 | " border-radius: 0.25em;\n", |
||
| 1448 | " box-sizing: border-box;\n", |
||
| 1449 | " margin-bottom: 0.5em;\n", |
||
| 1450 | " /* unfitted */\n", |
||
| 1451 | " background-color: var(--sklearn-color-unfitted-level-0);\n", |
||
| 1452 | "}\n", |
||
| 1453 | "\n", |
||
| 1454 | "#sk-container-id-2 div.sk-estimator.fitted {\n", |
||
| 1455 | " /* fitted */\n", |
||
| 1456 | " background-color: var(--sklearn-color-fitted-level-0);\n", |
||
| 1457 | "}\n", |
||
| 1458 | "\n", |
||
| 1459 | "/* on hover */\n", |
||
| 1460 | "#sk-container-id-2 div.sk-estimator:hover {\n", |
||
| 1461 | " /* unfitted */\n", |
||
| 1462 | " background-color: var(--sklearn-color-unfitted-level-2);\n", |
||
| 1463 | "}\n", |
||
| 1464 | "\n", |
||
| 1465 | "#sk-container-id-2 div.sk-estimator.fitted:hover {\n", |
||
| 1466 | " /* fitted */\n", |
||
| 1467 | " background-color: var(--sklearn-color-fitted-level-2);\n", |
||
| 1468 | "}\n", |
||
| 1469 | "\n", |
||
| 1470 | "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", |
||
| 1471 | "\n", |
||
| 1472 | "/* Common style for \"i\" and \"?\" */\n", |
||
| 1473 | "\n", |
||
| 1474 | ".sk-estimator-doc-link,\n", |
||
| 1475 | "a:link.sk-estimator-doc-link,\n", |
||
| 1476 | "a:visited.sk-estimator-doc-link {\n", |
||
| 1477 | " float: right;\n", |
||
| 1478 | " font-size: smaller;\n", |
||
| 1479 | " line-height: 1em;\n", |
||
| 1480 | " font-family: monospace;\n", |
||
| 1481 | " background-color: var(--sklearn-color-background);\n", |
||
| 1482 | " border-radius: 1em;\n", |
||
| 1483 | " height: 1em;\n", |
||
| 1484 | " width: 1em;\n", |
||
| 1485 | " text-decoration: none !important;\n", |
||
| 1486 | " margin-left: 1ex;\n", |
||
| 1487 | " /* unfitted */\n", |
||
| 1488 | " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", |
||
| 1489 | " color: var(--sklearn-color-unfitted-level-1);\n", |
||
| 1490 | "}\n", |
||
| 1491 | "\n", |
||
| 1492 | ".sk-estimator-doc-link.fitted,\n", |
||
| 1493 | "a:link.sk-estimator-doc-link.fitted,\n", |
||
| 1494 | "a:visited.sk-estimator-doc-link.fitted {\n", |
||
| 1495 | " /* fitted */\n", |
||
| 1496 | " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", |
||
| 1497 | " color: var(--sklearn-color-fitted-level-1);\n", |
||
| 1498 | "}\n", |
||
| 1499 | "\n", |
||
| 1500 | "/* On hover */\n", |
||
| 1501 | "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", |
||
| 1502 | ".sk-estimator-doc-link:hover,\n", |
||
| 1503 | "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", |
||
| 1504 | ".sk-estimator-doc-link:hover {\n", |
||
| 1505 | " /* unfitted */\n", |
||
| 1506 | " background-color: var(--sklearn-color-unfitted-level-3);\n", |
||
| 1507 | " color: var(--sklearn-color-background);\n", |
||
| 1508 | " text-decoration: none;\n", |
||
| 1509 | "}\n", |
||
| 1510 | "\n", |
||
| 1511 | "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", |
||
| 1512 | ".sk-estimator-doc-link.fitted:hover,\n", |
||
| 1513 | "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", |
||
| 1514 | ".sk-estimator-doc-link.fitted:hover {\n", |
||
| 1515 | " /* fitted */\n", |
||
| 1516 | " background-color: var(--sklearn-color-fitted-level-3);\n", |
||
| 1517 | " color: var(--sklearn-color-background);\n", |
||
| 1518 | " text-decoration: none;\n", |
||
| 1519 | "}\n", |
||
| 1520 | "\n", |
||
| 1521 | "/* Span, style for the box shown on hovering the info icon */\n", |
||
| 1522 | ".sk-estimator-doc-link span {\n", |
||
| 1523 | " display: none;\n", |
||
| 1524 | " z-index: 9999;\n", |
||
| 1525 | " position: relative;\n", |
||
| 1526 | " font-weight: normal;\n", |
||
| 1527 | " right: .2ex;\n", |
||
| 1528 | " padding: .5ex;\n", |
||
| 1529 | " margin: .5ex;\n", |
||
| 1530 | " width: min-content;\n", |
||
| 1531 | " min-width: 20ex;\n", |
||
| 1532 | " max-width: 50ex;\n", |
||
| 1533 | " color: var(--sklearn-color-text);\n", |
||
| 1534 | " box-shadow: 2pt 2pt 4pt #999;\n", |
||
| 1535 | " /* unfitted */\n", |
||
| 1536 | " background: var(--sklearn-color-unfitted-level-0);\n", |
||
| 1537 | " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", |
||
| 1538 | "}\n", |
||
| 1539 | "\n", |
||
| 1540 | ".sk-estimator-doc-link.fitted span {\n", |
||
| 1541 | " /* fitted */\n", |
||
| 1542 | " background: var(--sklearn-color-fitted-level-0);\n", |
||
| 1543 | " border: var(--sklearn-color-fitted-level-3);\n", |
||
| 1544 | "}\n", |
||
| 1545 | "\n", |
||
| 1546 | ".sk-estimator-doc-link:hover span {\n", |
||
| 1547 | " display: block;\n", |
||
| 1548 | "}\n", |
||
| 1549 | "\n", |
||
| 1550 | "/* \"?\"-specific style due to the `<a>` HTML tag */\n", |
||
| 1551 | "\n", |
||
| 1552 | "#sk-container-id-2 a.estimator_doc_link {\n", |
||
| 1553 | " float: right;\n", |
||
| 1554 | " font-size: 1rem;\n", |
||
| 1555 | " line-height: 1em;\n", |
||
| 1556 | " font-family: monospace;\n", |
||
| 1557 | " background-color: var(--sklearn-color-background);\n", |
||
| 1558 | " border-radius: 1rem;\n", |
||
| 1559 | " height: 1rem;\n", |
||
| 1560 | " width: 1rem;\n", |
||
| 1561 | " text-decoration: none;\n", |
||
| 1562 | " /* unfitted */\n", |
||
| 1563 | " color: var(--sklearn-color-unfitted-level-1);\n", |
||
| 1564 | " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", |
||
| 1565 | "}\n", |
||
| 1566 | "\n", |
||
| 1567 | "#sk-container-id-2 a.estimator_doc_link.fitted {\n", |
||
| 1568 | " /* fitted */\n", |
||
| 1569 | " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", |
||
| 1570 | " color: var(--sklearn-color-fitted-level-1);\n", |
||
| 1571 | "}\n", |
||
| 1572 | "\n", |
||
| 1573 | "/* On hover */\n", |
||
| 1574 | "#sk-container-id-2 a.estimator_doc_link:hover {\n", |
||
| 1575 | " /* unfitted */\n", |
||
| 1576 | " background-color: var(--sklearn-color-unfitted-level-3);\n", |
||
| 1577 | " color: var(--sklearn-color-background);\n", |
||
| 1578 | " text-decoration: none;\n", |
||
| 1579 | "}\n", |
||
| 1580 | "\n", |
||
| 1581 | "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n", |
||
| 1582 | " /* fitted */\n", |
||
| 1583 | " background-color: var(--sklearn-color-fitted-level-3);\n", |
||
| 1584 | "}\n", |
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| 1585 | "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n", |
||
| 1586 | " min_samples_split=6, splitter='random')</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n", |
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| 1587 | " min_samples_split=6, splitter='random')</pre></div> </div></div></div></div>" |
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| 1588 | ], |
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| 1589 | "text/plain": [ |
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| 1590 | "DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n", |
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| 1591 | " min_samples_split=6, splitter='random')" |
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| 1592 | ] |
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| 1593 | }, |
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| 1594 | "execution_count": 13, |
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| 1595 | "metadata": {}, |
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| 1596 | "output_type": "execute_result" |
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| 1597 | } |
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| 1598 | ], |
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| 1599 | "source": [ |
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| 1600 | "\n", |
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| 1601 | "dtc = DecisionTreeClassifier(criterion= 'entropy', max_depth= 19, min_samples_leaf= 4, min_samples_split= 6, splitter= 'random')\n", |
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| 1602 | "dtc.fit(A_training, B_training)" |
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| 1603 | ] |
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| 1604 | }, |
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| 1605 | { |
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| 1606 | "cell_type": "code", |
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| 1607 | "execution_count": 14, |
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| 1608 | "id": "2ac9e062-42ce-4baa-81f6-b004efa79279", |
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| 1609 | "metadata": {}, |
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| 1610 | "outputs": [ |
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| 1611 | { |
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| 1612 | "name": "stdout", |
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| 1613 | "output_type": "stream", |
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| 1614 | "text": [ |
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| 1615 | "0.9132231404958677\n", |
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| 1616 | "0.6885245901639344\n" |
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| 1617 | ] |
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| 1618 | } |
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| 1619 | ], |
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| 1620 | "source": [ |
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| 1621 | "B_pred = dtc.predict(A_testing)\n", |
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| 1622 | "# accuracy score\n", |
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| 1623 | "\n", |
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| 1624 | "print(accuracy_score(B_training, dtc.predict(A_training)))\n", |
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| 1625 | "\n", |
||
| 1626 | "dtc_acc = accuracy_score(B_testing, dtc.predict(A_testing))\n", |
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| 1627 | "print(dtc_acc)" |
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| 1628 | ] |
||
| 1629 | }, |
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| 1630 | { |
||
| 1631 | "cell_type": "code", |
||
| 1632 | "execution_count": 15, |
||
| 1633 | "id": "23941f55-8363-4bdd-a552-1bbb83f1c206", |
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| 1634 | "metadata": {}, |
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| 1635 | "outputs": [ |
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| 1636 | { |
||
| 1637 | "name": "stdout", |
||
| 1638 | "output_type": "stream", |
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| 1639 | "text": [ |
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| 1640 | " precision recall f1-score support\n", |
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| 1641 | "\n", |
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| 1642 | " 0 0.67 0.64 0.65 28\n", |
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| 1643 | " 1 0.71 0.73 0.72 33\n", |
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| 1644 | "\n", |
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| 1645 | " accuracy 0.69 61\n", |
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| 1646 | " macro avg 0.69 0.69 0.69 61\n", |
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| 1647 | "weighted avg 0.69 0.69 0.69 61\n", |
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| 1648 | "\n" |
||
| 1649 | ] |
||
| 1650 | } |
||
| 1651 | ], |
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| 1652 | "source": [ |
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| 1653 | "# classification report\n", |
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| 1654 | "\n", |
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| 1655 | "print(classification_report(B_testing, B_pred))" |
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| 1656 | ] |
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| 1657 | }, |
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| 1658 | { |
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| 1659 | "cell_type": "code", |
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| 1660 | "execution_count": 16, |
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| 1661 | "id": "029bfdba-3d0e-4d3a-9ce1-06fe8de61f82", |
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| 1662 | "metadata": {}, |
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| 1663 | "outputs": [ |
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| 1664 | { |
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| 1665 | "data": { |
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| 1666 | "text/html": [ |
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| 1667 | "<div>\n", |
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| 1668 | "<style scoped>\n", |
||
| 1669 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1670 | " vertical-align: middle;\n", |
||
| 1671 | " }\n", |
||
| 1672 | "\n", |
||
| 1673 | " .dataframe tbody tr th {\n", |
||
| 1674 | " vertical-align: top;\n", |
||
| 1675 | " }\n", |
||
| 1676 | "\n", |
||
| 1677 | " .dataframe thead th {\n", |
||
| 1678 | " text-align: right;\n", |
||
| 1679 | " }\n", |
||
| 1680 | "</style>\n", |
||
| 1681 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1682 | " <thead>\n", |
||
| 1683 | " <tr style=\"text-align: right;\">\n", |
||
| 1684 | " <th></th>\n", |
||
| 1685 | " <th>Model</th>\n", |
||
| 1686 | " <th>Score</th>\n", |
||
| 1687 | " </tr>\n", |
||
| 1688 | " </thead>\n", |
||
| 1689 | " <tbody>\n", |
||
| 1690 | " <tr>\n", |
||
| 1691 | " <th>0</th>\n", |
||
| 1692 | " <td>Logistic Regression</td>\n", |
||
| 1693 | " <td>81.97</td>\n", |
||
| 1694 | " </tr>\n", |
||
| 1695 | " <tr>\n", |
||
| 1696 | " <th>3</th>\n", |
||
| 1697 | " <td>Decision Tree Classifier</td>\n", |
||
| 1698 | " <td>68.85</td>\n", |
||
| 1699 | " </tr>\n", |
||
| 1700 | " <tr>\n", |
||
| 1701 | " <th>1</th>\n", |
||
| 1702 | " <td>KNN</td>\n", |
||
| 1703 | " <td>62.30</td>\n", |
||
| 1704 | " </tr>\n", |
||
| 1705 | " <tr>\n", |
||
| 1706 | " <th>2</th>\n", |
||
| 1707 | " <td>SVM</td>\n", |
||
| 1708 | " <td>54.10</td>\n", |
||
| 1709 | " </tr>\n", |
||
| 1710 | " </tbody>\n", |
||
| 1711 | "</table>\n", |
||
| 1712 | "</div>" |
||
| 1713 | ], |
||
| 1714 | "text/plain": [ |
||
| 1715 | " Model Score\n", |
||
| 1716 | "0 Logistic Regression 81.97\n", |
||
| 1717 | "3 Decision Tree Classifier 68.85\n", |
||
| 1718 | "1 KNN 62.30\n", |
||
| 1719 | "2 SVM 54.10" |
||
| 1720 | ] |
||
| 1721 | }, |
||
| 1722 | "execution_count": 16, |
||
| 1723 | "metadata": {}, |
||
| 1724 | "output_type": "execute_result" |
||
| 1725 | } |
||
| 1726 | ], |
||
| 1727 | "source": [ |
||
| 1728 | "models = pd.DataFrame({\n", |
||
| 1729 | " 'Model': ['Logistic Regression', 'KNN', 'SVM', 'Decision Tree Classifier'],\n", |
||
| 1730 | " 'Score': [100*round(log_reg_acc,4), 100*round(knn_acc,4), 100*round(svc_acc,4), 100*round(dtc_acc,4)]\n", |
||
| 1731 | "})\n", |
||
| 1732 | "models.sort_values(by = 'Score', ascending = False)" |
||
| 1733 | ] |
||
| 1734 | }, |
||
| 1735 | { |
||
| 1736 | "cell_type": "code", |
||
| 1737 | "execution_count": 17, |
||
| 1738 | "id": "12de1159", |
||
| 1739 | "metadata": {}, |
||
| 1740 | "outputs": [ |
||
| 1741 | { |
||
| 1742 | "name": "stdout", |
||
| 1743 | "output_type": "stream", |
||
| 1744 | "text": [ |
||
| 1745 | " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", |
||
| 1746 | "165 67 1 0 160 286 0 0 108 1 1.5 \n", |
||
| 1747 | "166 67 1 0 120 229 0 0 129 1 2.6 \n", |
||
| 1748 | "167 62 0 0 140 268 0 0 160 0 3.6 \n", |
||
| 1749 | "168 63 1 0 130 254 0 0 147 0 1.4 \n", |
||
| 1750 | "169 53 1 0 140 203 1 0 155 1 3.1 \n", |
||
| 1751 | ".. ... ... .. ... ... ... ... ... ... ... \n", |
||
| 1752 | "298 57 0 0 140 241 0 1 123 1 0.2 \n", |
||
| 1753 | "299 45 1 3 110 264 0 1 132 0 1.2 \n", |
||
| 1754 | "300 68 1 0 144 193 1 1 141 0 3.4 \n", |
||
| 1755 | "301 57 1 0 130 131 0 1 115 1 1.2 \n", |
||
| 1756 | "302 57 0 1 130 236 0 0 174 0 0.0 \n", |
||
| 1757 | "\n", |
||
| 1758 | " slope ca thal target \n", |
||
| 1759 | "165 1 3 2 0 \n", |
||
| 1760 | "166 1 2 3 0 \n", |
||
| 1761 | "167 0 2 2 0 \n", |
||
| 1762 | "168 1 1 3 0 \n", |
||
| 1763 | "169 0 0 3 0 \n", |
||
| 1764 | ".. ... .. ... ... \n", |
||
| 1765 | "298 1 0 3 0 \n", |
||
| 1766 | "299 1 0 3 0 \n", |
||
| 1767 | "300 1 2 3 0 \n", |
||
| 1768 | "301 1 1 3 0 \n", |
||
| 1769 | "302 1 1 2 0 \n", |
||
| 1770 | "\n", |
||
| 1771 | "[138 rows x 14 columns]\n" |
||
| 1772 | ] |
||
| 1773 | } |
||
| 1774 | ], |
||
| 1775 | "source": [ |
||
| 1776 | "filtered_df = df[df['target'] == 0]\n", |
||
| 1777 | "C = pd.DataFrame(filtered_df) \n", |
||
| 1778 | "print(C)\n" |
||
| 1779 | ] |
||
| 1780 | }, |
||
| 1781 | { |
||
| 1782 | "cell_type": "code", |
||
| 1783 | "execution_count": null, |
||
| 1784 | "id": "9ea84040-eb10-42f2-97c7-f91b52129aef", |
||
| 1785 | "metadata": {}, |
||
| 1786 | "outputs": [ |
||
| 1787 | { |
||
| 1788 | "name": "stdout", |
||
| 1789 | "output_type": "stream", |
||
| 1790 | "text": [ |
||
| 1791 | "[1]\n" |
||
| 1792 | ] |
||
| 1793 | }, |
||
| 1794 | { |
||
| 1795 | "name": "stderr", |
||
| 1796 | "output_type": "stream", |
||
| 1797 | "text": [ |
||
| 1798 | "c:\\Users\\Pranshu Saini\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n", |
||
| 1799 | " warnings.warn(\n" |
||
| 1800 | ] |
||
| 1801 | } |
||
| 1802 | ], |
||
| 1803 | "source": [ |
||
| 1804 | "import numpy as np\n", |
||
| 1805 | "\n", |
||
| 1806 | "a = [63, 1, 3, 145, 233, 1, 0, 150, 0, 2.3, 0, 0, 1]\n", |
||
| 1807 | "b=[67 , 1, 0, 160, 286, 0, 0, 108, 1, 1.5, 1, 3, 2 ]\n", |
||
| 1808 | "a_reshaped = np.array().reshape(1, -1)\n", |
||
| 1809 | "\n", |
||
| 1810 | "B = log_reg.predict(a_reshaped)\n", |
||
| 1811 | "print(B)\n" |
||
| 1812 | ] |
||
| 1813 | }, |
||
| 1814 | { |
||
| 1815 | "cell_type": "code", |
||
| 1816 | "execution_count": null, |
||
| 1817 | "id": "e66f5d96-79a7-4ac9-a01b-e753181af587", |
||
| 1818 | "metadata": {}, |
||
| 1819 | "outputs": [], |
||
| 1820 | "source": [] |
||
| 1821 | }, |
||
| 1822 | { |
||
| 1823 | "cell_type": "code", |
||
| 1824 | "execution_count": 20, |
||
| 1825 | "id": "7552b5a0-d407-4bf9-98e0-56b8bd7c3245", |
||
| 1826 | "metadata": {}, |
||
| 1827 | "outputs": [], |
||
| 1828 | "source": [ |
||
| 1829 | "import pickle\n", |
||
| 1830 | "filename = r'C:\\Users\\Pranshu Saini\\Desktop\\disease-prediction-main\\docpat\\model\\heart_disease_model.pkl'\n", |
||
| 1831 | "pickle.dump(log_reg, open(filename, 'wb'))" |
||
| 1832 | ] |
||
| 1833 | }, |
||
| 1834 | { |
||
| 1835 | "cell_type": "code", |
||
| 1836 | "execution_count": null, |
||
| 1837 | "id": "4f01bd6a-95cc-4383-a17c-0e0ae3ff2e69", |
||
| 1838 | "metadata": {}, |
||
| 1839 | "outputs": [], |
||
| 1840 | "source": [ |
||
| 1841 | "'''import pickle\n", |
||
| 1842 | "def load_model(path):\n", |
||
| 1843 | " with open(path, 'rb') as file:\n", |
||
| 1844 | " model = pickle.load(file)\n", |
||
| 1845 | "heart_model = load_model(r'C:\\Users\\DELL\\Desktop\\app\\heart_disease_model.pkl')\n", |
||
| 1846 | "def predict(inputs):\n", |
||
| 1847 | " return heart_model.predict(inputs)'''" |
||
| 1848 | ] |
||
| 1849 | }, |
||
| 1850 | { |
||
| 1851 | "cell_type": "code", |
||
| 1852 | "execution_count": null, |
||
| 1853 | "id": "3765e3cf-d221-4413-a91e-9b7d4d49bd3c", |
||
| 1854 | "metadata": {}, |
||
| 1855 | "outputs": [], |
||
| 1856 | "source": [] |
||
| 1857 | }, |
||
| 1858 | { |
||
| 1859 | "cell_type": "code", |
||
| 1860 | "execution_count": null, |
||
| 1861 | "id": "a8fc26d0", |
||
| 1862 | "metadata": {}, |
||
| 1863 | "outputs": [], |
||
| 1864 | "source": [] |
||
| 1865 | }, |
||
| 1866 | { |
||
| 1867 | "cell_type": "code", |
||
| 1868 | "execution_count": null, |
||
| 1869 | "id": "66257935", |
||
| 1870 | "metadata": {}, |
||
| 1871 | "outputs": [], |
||
| 1872 | "source": [] |
||
| 1873 | } |
||
| 1874 | ], |
||
| 1875 | "metadata": { |
||
| 1876 | "kernelspec": { |
||
| 1877 | "display_name": "Python 3", |
||
| 1878 | "language": "python", |
||
| 1879 | "name": "python3" |
||
| 1880 | }, |
||
| 1881 | "language_info": { |
||
| 1882 | "codemirror_mode": { |
||
| 1883 | "name": "ipython", |
||
| 1884 | "version": 3 |
||
| 1885 | }, |
||
| 1886 | "file_extension": ".py", |
||
| 1887 | "mimetype": "text/x-python", |
||
| 1888 | "name": "python", |
||
| 1889 | "nbconvert_exporter": "python", |
||
| 1890 | "pygments_lexer": "ipython3", |
||
| 1891 | "version": "3.12.3" |
||
| 1892 | } |
||
| 1893 | }, |
||
| 1894 | "nbformat": 4, |
||
| 1895 | "nbformat_minor": 5 |
||
| 1896 | } |