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breast_cancer.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "24fda7b6-4e16-44c9-abef-9c790cf286d0",
"metadata": {},
"source": [
"Breast cancer"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "6853550c-51ef-4fe6-8e66-b51eb64eb4c4",
"metadata": {},
"outputs": [],
"source": [
"# Importing libraries\n",
"\n",
"import pandas as pd\n",
"\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"sns.set()\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "fbe6e81a-874c-4df0-a5b4-5382d1c4b644",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>...</th>\n",
" <th>texture_worst</th>\n",
" <th>perimeter_worst</th>\n",
" <th>area_worst</th>\n",
" <th>smoothness_worst</th>\n",
" <th>compactness_worst</th>\n",
" <th>concavity_worst</th>\n",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" <th>Unnamed: 32</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>M</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.3001</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.1622</td>\n",
" <td>0.6656</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>M</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.0869</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.1238</td>\n",
" <td>0.1866</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>M</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.1974</td>\n",
" <td>0.12790</td>\n",
" <td>...</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.1444</td>\n",
" <td>0.4245</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>M</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.2414</td>\n",
" <td>0.10520</td>\n",
" <td>...</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.2098</td>\n",
" <td>0.8663</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>M</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.1980</td>\n",
" <td>0.10430</td>\n",
" <td>...</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.1374</td>\n",
" <td>0.2050</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"0 842302 M 17.99 10.38 122.80 1001.0 \n",
"1 842517 M 20.57 17.77 132.90 1326.0 \n",
"2 84300903 M 19.69 21.25 130.00 1203.0 \n",
"3 84348301 M 11.42 20.38 77.58 386.1 \n",
"4 84358402 M 20.29 14.34 135.10 1297.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"0 0.11840 0.27760 0.3001 0.14710 \n",
"1 0.08474 0.07864 0.0869 0.07017 \n",
"2 0.10960 0.15990 0.1974 0.12790 \n",
"3 0.14250 0.28390 0.2414 0.10520 \n",
"4 0.10030 0.13280 0.1980 0.10430 \n",
"\n",
" ... texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"0 ... 17.33 184.60 2019.0 0.1622 \n",
"1 ... 23.41 158.80 1956.0 0.1238 \n",
"2 ... 25.53 152.50 1709.0 0.1444 \n",
"3 ... 26.50 98.87 567.7 0.2098 \n",
"4 ... 16.67 152.20 1575.0 0.1374 \n",
"\n",
" compactness_worst concavity_worst concave points_worst symmetry_worst \\\n",
"0 0.6656 0.7119 0.2654 0.4601 \n",
"1 0.1866 0.2416 0.1860 0.2750 \n",
"2 0.4245 0.4504 0.2430 0.3613 \n",
"3 0.8663 0.6869 0.2575 0.6638 \n",
"4 0.2050 0.4000 0.1625 0.2364 \n",
"\n",
" fractal_dimension_worst Unnamed: 32 \n",
"0 0.11890 NaN \n",
"1 0.08902 NaN \n",
"2 0.08758 NaN \n",
"3 0.17300 NaN \n",
"4 0.07678 NaN \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Diagnosis (malignant (cancerous) or benign (non-cancerous))\n",
"df = pd.read_csv(r'C:\\Users\\Pranshu Saini\\Desktop\\disease-prediction-main\\docpat\\datasets\\breast_cancer.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "37a2d7c4-ce0a-4532-bb12-60c908f91992",
"metadata": {},
"outputs": [],
"source": [
"df.drop(['id', 'Unnamed: 32'], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "85a23ebe-5735-4535-8ac4-aa1dcde8c0e5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['M', 'B'], dtype=object)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.diagnosis.unique()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "31f9fac3-323d-4556-94f5-b2a2f1390a3d",
"metadata": {},
"outputs": [],
"source": [
"df['diagnosis'] = df['diagnosis'].apply(lambda val: 1 if val == 'M' else 0)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "551ba2a1-6f0d-4644-aca9-226671a1d2dc",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>symmetry_mean</th>\n",
" <th>...</th>\n",
" <th>radius_worst</th>\n",
" <th>texture_worst</th>\n",
" <th>perimeter_worst</th>\n",
" <th>area_worst</th>\n",
" <th>smoothness_worst</th>\n",
" <th>compactness_worst</th>\n",
" <th>concavity_worst</th>\n",
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" <th>fractal_dimension_worst</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
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" <td>569.000000</td>\n",
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" <tr>\n",
" <th>mean</th>\n",
" <td>0.372583</td>\n",
" <td>14.127292</td>\n",
" <td>19.289649</td>\n",
" <td>91.969033</td>\n",
" <td>654.889104</td>\n",
" <td>0.096360</td>\n",
" <td>0.104341</td>\n",
" <td>0.088799</td>\n",
" <td>0.048919</td>\n",
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" <td>16.269190</td>\n",
" <td>25.677223</td>\n",
" <td>107.261213</td>\n",
" <td>880.583128</td>\n",
" <td>0.132369</td>\n",
" <td>0.254265</td>\n",
" <td>0.272188</td>\n",
" <td>0.114606</td>\n",
" <td>0.290076</td>\n",
" <td>0.083946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.483918</td>\n",
" <td>3.524049</td>\n",
" <td>4.301036</td>\n",
" <td>24.298981</td>\n",
" <td>351.914129</td>\n",
" <td>0.014064</td>\n",
" <td>0.052813</td>\n",
" <td>0.079720</td>\n",
" <td>0.038803</td>\n",
" <td>0.027414</td>\n",
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" <td>4.833242</td>\n",
" <td>6.146258</td>\n",
" <td>33.602542</td>\n",
" <td>569.356993</td>\n",
" <td>0.022832</td>\n",
" <td>0.157336</td>\n",
" <td>0.208624</td>\n",
" <td>0.065732</td>\n",
" <td>0.061867</td>\n",
" <td>0.018061</td>\n",
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" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>6.981000</td>\n",
" <td>9.710000</td>\n",
" <td>43.790000</td>\n",
" <td>143.500000</td>\n",
" <td>0.052630</td>\n",
" <td>0.019380</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.106000</td>\n",
" <td>...</td>\n",
" <td>7.930000</td>\n",
" <td>12.020000</td>\n",
" <td>50.410000</td>\n",
" <td>185.200000</td>\n",
" <td>0.071170</td>\n",
" <td>0.027290</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.156500</td>\n",
" <td>0.055040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>11.700000</td>\n",
" <td>16.170000</td>\n",
" <td>75.170000</td>\n",
" <td>420.300000</td>\n",
" <td>0.086370</td>\n",
" <td>0.064920</td>\n",
" <td>0.029560</td>\n",
" <td>0.020310</td>\n",
" <td>0.161900</td>\n",
" <td>...</td>\n",
" <td>13.010000</td>\n",
" <td>21.080000</td>\n",
" <td>84.110000</td>\n",
" <td>515.300000</td>\n",
" <td>0.116600</td>\n",
" <td>0.147200</td>\n",
" <td>0.114500</td>\n",
" <td>0.064930</td>\n",
" <td>0.250400</td>\n",
" <td>0.071460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>13.370000</td>\n",
" <td>18.840000</td>\n",
" <td>86.240000</td>\n",
" <td>551.100000</td>\n",
" <td>0.095870</td>\n",
" <td>0.092630</td>\n",
" <td>0.061540</td>\n",
" <td>0.033500</td>\n",
" <td>0.179200</td>\n",
" <td>...</td>\n",
" <td>14.970000</td>\n",
" <td>25.410000</td>\n",
" <td>97.660000</td>\n",
" <td>686.500000</td>\n",
" <td>0.131300</td>\n",
" <td>0.211900</td>\n",
" <td>0.226700</td>\n",
" <td>0.099930</td>\n",
" <td>0.282200</td>\n",
" <td>0.080040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>15.780000</td>\n",
" <td>21.800000</td>\n",
" <td>104.100000</td>\n",
" <td>782.700000</td>\n",
" <td>0.105300</td>\n",
" <td>0.130400</td>\n",
" <td>0.130700</td>\n",
" <td>0.074000</td>\n",
" <td>0.195700</td>\n",
" <td>...</td>\n",
" <td>18.790000</td>\n",
" <td>29.720000</td>\n",
" <td>125.400000</td>\n",
" <td>1084.000000</td>\n",
" <td>0.146000</td>\n",
" <td>0.339100</td>\n",
" <td>0.382900</td>\n",
" <td>0.161400</td>\n",
" <td>0.317900</td>\n",
" <td>0.092080</td>\n",
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" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>28.110000</td>\n",
" <td>39.280000</td>\n",
" <td>188.500000</td>\n",
" <td>2501.000000</td>\n",
" <td>0.163400</td>\n",
" <td>0.345400</td>\n",
" <td>0.426800</td>\n",
" <td>0.201200</td>\n",
" <td>0.304000</td>\n",
" <td>...</td>\n",
" <td>36.040000</td>\n",
" <td>49.540000</td>\n",
" <td>251.200000</td>\n",
" <td>4254.000000</td>\n",
" <td>0.222600</td>\n",
" <td>1.058000</td>\n",
" <td>1.252000</td>\n",
" <td>0.291000</td>\n",
" <td>0.663800</td>\n",
" <td>0.207500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.372583 14.127292 19.289649 91.969033 654.889104 \n",
"std 0.483918 3.524049 4.301036 24.298981 351.914129 \n",
"min 0.000000 6.981000 9.710000 43.790000 143.500000 \n",
"25% 0.000000 11.700000 16.170000 75.170000 420.300000 \n",
"50% 0.000000 13.370000 18.840000 86.240000 551.100000 \n",
"75% 1.000000 15.780000 21.800000 104.100000 782.700000 \n",
"max 1.000000 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" symmetry_mean ... radius_worst texture_worst perimeter_worst \\\n",
"count 569.000000 ... 569.000000 569.000000 569.000000 \n",
"mean 0.181162 ... 16.269190 25.677223 107.261213 \n",
"std 0.027414 ... 4.833242 6.146258 33.602542 \n",
"min 0.106000 ... 7.930000 12.020000 50.410000 \n",
"25% 0.161900 ... 13.010000 21.080000 84.110000 \n",
"50% 0.179200 ... 14.970000 25.410000 97.660000 \n",
"75% 0.195700 ... 18.790000 29.720000 125.400000 \n",
"max 0.304000 ... 36.040000 49.540000 251.200000 \n",
"\n",
" area_worst smoothness_worst compactness_worst concavity_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 880.583128 0.132369 0.254265 0.272188 \n",
"std 569.356993 0.022832 0.157336 0.208624 \n",
"min 185.200000 0.071170 0.027290 0.000000 \n",
"25% 515.300000 0.116600 0.147200 0.114500 \n",
"50% 686.500000 0.131300 0.211900 0.226700 \n",
"75% 1084.000000 0.146000 0.339100 0.382900 \n",
"max 4254.000000 0.222600 1.058000 1.252000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.114606 0.290076 0.083946 \n",
"std 0.065732 0.061867 0.018061 \n",
"min 0.000000 0.156500 0.055040 \n",
"25% 0.064930 0.250400 0.071460 \n",
"50% 0.099930 0.282200 0.080040 \n",
"75% 0.161400 0.317900 0.092080 \n",
"max 0.291000 0.663800 0.207500 \n",
"\n",
"[8 rows x 31 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "97ded7b0-cb65-43db-a79e-80bb90e43d89",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"diagnosis 0\n",
"radius_mean 0\n",
"texture_mean 0\n",
"perimeter_mean 0\n",
"area_mean 0\n",
"smoothness_mean 0\n",
"compactness_mean 0\n",
"concavity_mean 0\n",
"concave points_mean 0\n",
"symmetry_mean 0\n",
"fractal_dimension_mean 0\n",
"radius_se 0\n",
"texture_se 0\n",
"perimeter_se 0\n",
"area_se 0\n",
"smoothness_se 0\n",
"compactness_se 0\n",
"concavity_se 0\n",
"concave points_se 0\n",
"symmetry_se 0\n",
"fractal_dimension_se 0\n",
"radius_worst 0\n",
"texture_worst 0\n",
"perimeter_worst 0\n",
"area_worst 0\n",
"smoothness_worst 0\n",
"compactness_worst 0\n",
"concavity_worst 0\n",
"concave points_worst 0\n",
"symmetry_worst 0\n",
"fractal_dimension_worst 0\n",
"dtype: int64"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# checking for null values\n",
"\n",
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "f73513a5-0374-4ed0-a1d4-43ac2c9c41b1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The reduced dataframe has 23 columns.\n",
" diagnosis texture_mean smoothness_mean compactness_mean \\\n",
"0 1 10.38 0.11840 0.27760 \n",
"1 1 17.77 0.08474 0.07864 \n",
"2 1 21.25 0.10960 0.15990 \n",
"3 1 20.38 0.14250 0.28390 \n",
"4 1 14.34 0.10030 0.13280 \n",
".. ... ... ... ... \n",
"564 1 22.39 0.11100 0.11590 \n",
"565 1 28.25 0.09780 0.10340 \n",
"566 1 28.08 0.08455 0.10230 \n",
"567 1 29.33 0.11780 0.27700 \n",
"568 0 24.54 0.05263 0.04362 \n",
"\n",
" concave points_mean symmetry_mean fractal_dimension_mean texture_se \\\n",
"0 0.14710 0.2419 0.07871 0.9053 \n",
"1 0.07017 0.1812 0.05667 0.7339 \n",
"2 0.12790 0.2069 0.05999 0.7869 \n",
"3 0.10520 0.2597 0.09744 1.1560 \n",
"4 0.10430 0.1809 0.05883 0.7813 \n",
".. ... ... ... ... \n",
"564 0.13890 0.1726 0.05623 1.2560 \n",
"565 0.09791 0.1752 0.05533 2.4630 \n",
"566 0.05302 0.1590 0.05648 1.0750 \n",
"567 0.15200 0.2397 0.07016 1.5950 \n",
"568 0.00000 0.1587 0.05884 1.4280 \n",
"\n",
" area_se smoothness_se ... symmetry_se fractal_dimension_se \\\n",
"0 153.40 0.006399 ... 0.03003 0.006193 \n",
"1 74.08 0.005225 ... 0.01389 0.003532 \n",
"2 94.03 0.006150 ... 0.02250 0.004571 \n",
"3 27.23 0.009110 ... 0.05963 0.009208 \n",
"4 94.44 0.011490 ... 0.01756 0.005115 \n",
".. ... ... ... ... ... \n",
"564 158.70 0.010300 ... 0.01114 0.004239 \n",
"565 99.04 0.005769 ... 0.01898 0.002498 \n",
"566 48.55 0.005903 ... 0.01318 0.003892 \n",
"567 86.22 0.006522 ... 0.02324 0.006185 \n",
"568 19.15 0.007189 ... 0.02676 0.002783 \n",
"\n",
" texture_worst area_worst smoothness_worst compactness_worst \\\n",
"0 17.33 2019.0 0.16220 0.66560 \n",
"1 23.41 1956.0 0.12380 0.18660 \n",
"2 25.53 1709.0 0.14440 0.42450 \n",
"3 26.50 567.7 0.20980 0.86630 \n",
"4 16.67 1575.0 0.13740 0.20500 \n",
".. ... ... ... ... \n",
"564 26.40 2027.0 0.14100 0.21130 \n",
"565 38.25 1731.0 0.11660 0.19220 \n",
"566 34.12 1124.0 0.11390 0.30940 \n",
"567 39.42 1821.0 0.16500 0.86810 \n",
"568 30.37 268.6 0.08996 0.06444 \n",
"\n",
" concavity_worst concave points_worst symmetry_worst \\\n",
"0 0.7119 0.2654 0.4601 \n",
"1 0.2416 0.1860 0.2750 \n",
"2 0.4504 0.2430 0.3613 \n",
"3 0.6869 0.2575 0.6638 \n",
"4 0.4000 0.1625 0.2364 \n",
".. ... ... ... \n",
"564 0.4107 0.2216 0.2060 \n",
"565 0.3215 0.1628 0.2572 \n",
"566 0.3403 0.1418 0.2218 \n",
"567 0.9387 0.2650 0.4087 \n",
"568 0.0000 0.0000 0.2871 \n",
"\n",
" fractal_dimension_worst \n",
"0 0.11890 \n",
"1 0.08902 \n",
"2 0.08758 \n",
"3 0.17300 \n",
"4 0.07678 \n",
".. ... \n",
"564 0.07115 \n",
"565 0.06637 \n",
"566 0.07820 \n",
"567 0.12400 \n",
"568 0.07039 \n",
"\n",
"[569 rows x 23 columns]\n"
]
}
],
"source": [
"# removing highly correlated features\n",
"\n",
"corr_matrix = df.corr().abs() \n",
"\n",
"mask = np.triu(np.ones_like(corr_matrix, dtype = bool))\n",
"tri_df = corr_matrix.mask(mask)\n",
"\n",
"to_drop = [x for x in tri_df.columns if any(tri_df[x] > 0.92)]\n",
"\n",
"df = df.drop(to_drop, axis = 1)\n",
"\n",
"print(f\"The reduced dataframe has {df.shape[1]} columns.\")\n",
"print(df)\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "62fb56f2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.0, 10.38, 0.1184, 0.2776, 0.1471, 0.2419, 0.07871, 0.9053, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003, 0.006193, 17.33, 2019.0, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189]\n",
"23\n"
]
}
],
"source": [
"# Filter rows where diagnosis == 1\n",
"filtered_df = df[df['diagnosis'] == 1]\n",
"\n",
"# Select the first row and convert it to a list\n",
"if not filtered_df.empty: # Ensure there is at least one matching row\n",
" first_row_values = filtered_df.iloc[0].tolist()\n",
" print(first_row_values)\n",
"else:\n",
" print(\"No rows with diagnosis == 1\")\n",
"print(len(first_row_values))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "05eb4899-946c-44c2-bc4f-e2046b236c50",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['radius_mean',\n",
" 'perimeter_mean',\n",
" 'area_mean',\n",
" 'concavity_mean',\n",
" 'radius_se',\n",
" 'perimeter_se',\n",
" 'radius_worst',\n",
" 'perimeter_worst']"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_drop"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "ccb85aaf-b9e3-47a1-9b79-83f9ac2a2ea6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 23 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 diagnosis 569 non-null int64 \n",
" 1 texture_mean 569 non-null float64\n",
" 2 smoothness_mean 569 non-null float64\n",
" 3 compactness_mean 569 non-null float64\n",
" 4 concave points_mean 569 non-null float64\n",
" 5 symmetry_mean 569 non-null float64\n",
" 6 fractal_dimension_mean 569 non-null float64\n",
" 7 texture_se 569 non-null float64\n",
" 8 area_se 569 non-null float64\n",
" 9 smoothness_se 569 non-null float64\n",
" 10 compactness_se 569 non-null float64\n",
" 11 concavity_se 569 non-null float64\n",
" 12 concave points_se 569 non-null float64\n",
" 13 symmetry_se 569 non-null float64\n",
" 14 fractal_dimension_se 569 non-null float64\n",
" 15 texture_worst 569 non-null float64\n",
" 16 area_worst 569 non-null float64\n",
" 17 smoothness_worst 569 non-null float64\n",
" 18 compactness_worst 569 non-null float64\n",
" 19 concavity_worst 569 non-null float64\n",
" 20 concave points_worst 569 non-null float64\n",
" 21 symmetry_worst 569 non-null float64\n",
" 22 fractal_dimension_worst 569 non-null float64\n",
"dtypes: float64(22), int64(1)\n",
"memory usage: 102.4 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "00204005-3df0-420d-a39e-8335e2acc8d3",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.describe()\n",
"plt.hist(df['diagnosis'])\n",
"plt.title('Diagnosis (M=1 , B=0)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d387c1e9-bce6-4b0d-9a60-64abc4428ffc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"id": "91687a11-60d1-4f73-9643-7db868ba4d26",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(569, 22)\n"
]
}
],
"source": [
"# creating features and label \n",
"\n",
"X = df.drop('diagnosis', axis = 1)\n",
"y = df['diagnosis']\n",
"print(X.shape)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d7835666-a4a1-4813-a05a-2f78a7226fb9",
"metadata": {},
"outputs": [],
"source": [
"# splitting data into training and test set\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "597ed2b7-1e2c-4c4a-9dc0-b6fd1584fd9d",
"metadata": {},
"outputs": [],
"source": [
"# scaling data\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler()\n",
"\n",
"X_train = scaler.fit_transform(X_train)\n",
"X_test = scaler.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2a3673cf-d003-45be-9709-aa624c8ff82d",
"metadata": {},
"outputs": [],
"source": [
"#Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "56268b65-d0c7-459a-b271-621c24323747",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
],
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fitting data to model\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"log_reg = LogisticRegression()\n",
"log_reg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "0c5dee15-65a4-42db-a13e-830bda27448a",
"metadata": {},
"outputs": [],
"source": [
"# model predictions\n",
"\n",
"y_pred = log_reg.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "9d60f6bb-7df3-45a3-b7b9-941a3a426bc1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9899497487437185\n",
"0.9590643274853801\n",
"[[106 2]\n",
" [ 5 58]]\n",
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.98 0.97 108\n",
" 1 0.97 0.92 0.94 63\n",
"\n",
" accuracy 0.96 171\n",
" macro avg 0.96 0.95 0.96 171\n",
"weighted avg 0.96 0.96 0.96 171\n",
"\n"
]
}
],
"source": [
"# accuracy score\n",
"\n",
"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
"\n",
"print(accuracy_score(y_train, log_reg.predict(X_train)))\n",
"\n",
"log_reg_acc = accuracy_score(y_test, log_reg.predict(X_test))\n",
"print(log_reg_acc)\n",
"\n",
"# confusion matrix\n",
"\n",
"print(confusion_matrix(y_test, y_pred))\n",
"\n",
"# classification report\n",
"\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "4562d0a2-f764-4616-bb97-51bfed00c49f",
"metadata": {},
"outputs": [],
"source": [
"#K Neighbors Classifier (KNN)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "e5f1df4d-ca96-43a1-8075-f4154f7e8423",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-2 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-2 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-2 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-2 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-2 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-2 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-2 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-2 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-2 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-2 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-2 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-2 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-2 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-2 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-2 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
],
"text/plain": [
"KNeighborsClassifier()"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"knn = KNeighborsClassifier()\n",
"knn.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "6b97f6ac-8aaf-4d21-bb52-7daf80946b1b",
"metadata": {},
"outputs": [],
"source": [
"# model predictions \n",
"\n",
"y_pred = knn.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b5c9a2e3-f6c1-4b0d-b3fb-3062a1f94601",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9623115577889447\n",
"0.935672514619883\n"
]
}
],
"source": [
"# accuracy score\n",
"\n",
"print(accuracy_score(y_train, knn.predict(X_train)))\n",
"\n",
"knn_acc = accuracy_score(y_test, knn.predict(X_test))\n",
"print(knn_acc)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "e883bd32-64a0-4b11-acfb-caaf79e2e4d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[105 3]\n",
" [ 8 55]]\n"
]
}
],
"source": [
"# confusion matrix\n",
"\n",
"print(confusion_matrix(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f7efba3c-78c6-416e-a0a6-37b3e1274792",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.97 0.95 108\n",
" 1 0.95 0.87 0.91 63\n",
"\n",
" accuracy 0.94 171\n",
" macro avg 0.94 0.92 0.93 171\n",
"weighted avg 0.94 0.94 0.94 171\n",
"\n"
]
}
],
"source": [
"# classification report\n",
"\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "257247cb-3666-4f34-9fcb-4f577a0f76d0",
"metadata": {},
"outputs": [],
"source": [
"#Support Vector Machine (SVM)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "9b313b13-c437-4a06-8dbd-687744187b8e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-3 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-3 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-3 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-3 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-3 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-3 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-3 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-3 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-3 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-3 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-3 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-3 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-3 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-3 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-3 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-3 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
" param_grid={'C': [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20],\n",
" 'gamma': [0.0001, 0.001, 0.01, 0.1]})</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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(estimator=SVC(probability=True),\n",
" param_grid={'C': [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20],\n",
" 'gamma': [0.0001, 0.001, 0.01, 0.1]})</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(probability=True)</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(probability=True)</pre></div> </div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"GridSearchCV(estimator=SVC(probability=True),\n",
" param_grid={'C': [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20],\n",
" 'gamma': [0.0001, 0.001, 0.01, 0.1]})"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"svc = SVC(probability=True)\n",
"parameters = {\n",
" 'gamma' : [0.0001, 0.001, 0.01, 0.1],\n",
" 'C' : [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20]\n",
"}\n",
"\n",
"grid_search = GridSearchCV(svc, parameters)\n",
"grid_search.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "8e4df41f-e105-4673-9fdf-f18db0ebbbe7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9774683544303798"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# best parameters\n",
"\n",
"grid_search.best_params_\n",
"\n",
"# best score \n",
"\n",
"grid_search.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "6076c39d-66fa-47f2-bffb-6575d3375b4c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-4 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-4 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-4 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-4 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-4 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-4 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-4 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-4 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-4 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-4 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-4 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-4 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-4 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-4 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-4 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-4 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, gamma=0.01, probability=True)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10, gamma=0.01, probability=True)</pre></div> </div></div></div></div>"
],
"text/plain": [
"SVC(C=10, gamma=0.01, probability=True)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svc = SVC(C = 10, gamma = 0.01, probability=True)\n",
"svc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "1f87cfee-c742-4c21-beaf-ab385161289a",
"metadata": {},
"outputs": [],
"source": [
"# model predictions \n",
"\n",
"y_pred = svc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "0d2dbb00-9681-440b-9c0b-db06448077d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9874371859296482\n",
"0.9766081871345029\n",
"[[107 1]\n",
" [ 3 60]]\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 0.99 0.98 108\n",
" 1 0.98 0.95 0.97 63\n",
"\n",
" accuracy 0.98 171\n",
" macro avg 0.98 0.97 0.97 171\n",
"weighted avg 0.98 0.98 0.98 171\n",
"\n"
]
}
],
"source": [
"# accuracy score\n",
"\n",
"print(accuracy_score(y_train, svc.predict(X_train)))\n",
"\n",
"svc_acc = accuracy_score(y_test, svc.predict(X_test))\n",
"print(svc_acc)\n",
"\n",
"# confusion matrix\n",
"\n",
"print(confusion_matrix(y_test, y_pred))\n",
"\n",
"# classification report\n",
"\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "81886176-aad3-4c2d-a285-4fd3f3d1ebca",
"metadata": {},
"outputs": [],
"source": [
"#Decision Tree Classifier"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "c1678772-3656-4443-9f16-fc749ecc3b54",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 8640 candidates, totalling 43200 fits\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-5 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-5 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-5 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-5 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-5 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-5 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-5 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-5 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-5 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-5 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-5 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-5 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-5 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-5 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-5 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-5 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
" param_grid={'criterion': ['gini', 'entropy'],\n",
" 'max_depth': range(2, 32),\n",
" 'min_samples_leaf': range(1, 10),\n",
" 'min_samples_split': range(2, 10),\n",
" 'splitter': ['best', 'random']},\n",
" 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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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",
" param_grid={'criterion': ['gini', 'entropy'],\n",
" 'max_depth': range(2, 32),\n",
" 'min_samples_leaf': range(1, 10),\n",
" 'min_samples_split': range(2, 10),\n",
" 'splitter': ['best', 'random']},\n",
" 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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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>"
],
"text/plain": [
"GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
" param_grid={'criterion': ['gini', 'entropy'],\n",
" 'max_depth': range(2, 32),\n",
" 'min_samples_leaf': range(1, 10),\n",
" 'min_samples_split': range(2, 10),\n",
" 'splitter': ['best', 'random']},\n",
" verbose=1)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"dtc = DecisionTreeClassifier()\n",
"\n",
"parameters = {\n",
" 'criterion' : ['gini', 'entropy'],\n",
" 'max_depth' : range(2, 32, 1),\n",
" 'min_samples_leaf' : range(1, 10, 1),\n",
" 'min_samples_split' : range(2, 10, 1),\n",
" 'splitter' : ['best', 'random']\n",
"}\n",
"\n",
"grid_search_dt = GridSearchCV(dtc, parameters, cv = 5, n_jobs = -1, verbose = 1)\n",
"grid_search_dt.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "96fb1e94-efa2-4981-82d4-2e6049c70031",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'criterion': 'entropy',\n",
" 'max_depth': 12,\n",
" 'min_samples_leaf': 4,\n",
" 'min_samples_split': 2,\n",
" 'splitter': 'random'}"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# best parameters\n",
"\n",
"grid_search_dt.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "c9880239-069c-4ee8-b5d7-15c97ffcb467",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9623734177215189"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# best score\n",
"\n",
"grid_search_dt.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "f2a4bd0f-3c12-41ea-a335-4212fb48030b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-6 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-6 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-6 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-6 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-6 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-6 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-6 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-6 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-6 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-6 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-6 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-6 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-6 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-6 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-6 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-6 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n",
" 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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" 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",
" min_samples_split=6, splitter='random')</pre></div> </div></div></div></div>"
],
"text/plain": [
"DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n",
" min_samples_split=6, splitter='random')"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtc = DecisionTreeClassifier(criterion= 'entropy', max_depth= 19, min_samples_leaf= 4, min_samples_split= 6, splitter= 'random')\n",
"dtc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "682c5cf4-bc68-4751-ad34-6b21b24e232e",
"metadata": {},
"outputs": [],
"source": [
"y_pred = dtc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "c7c6d8a4-9c4e-4d9c-b1fc-b2e4cd93f224",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9673366834170855\n",
"0.9298245614035088\n",
"[[100 8]\n",
" [ 4 59]]\n",
" precision recall f1-score support\n",
"\n",
" 0 0.96 0.93 0.94 108\n",
" 1 0.88 0.94 0.91 63\n",
"\n",
" accuracy 0.93 171\n",
" macro avg 0.92 0.93 0.93 171\n",
"weighted avg 0.93 0.93 0.93 171\n",
"\n"
]
}
],
"source": [
"# accuracy score\n",
"\n",
"print(accuracy_score(y_train, dtc.predict(X_train)))\n",
"\n",
"dtc_acc = accuracy_score(y_test, dtc.predict(X_test))\n",
"print(dtc_acc)\n",
"\n",
"# confusion matrix\n",
"\n",
"print(confusion_matrix(y_test, y_pred))\n",
"\n",
"# classification report\n",
"\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "6295c993-7685-4e18-90a7-ccc6864d2502",
"metadata": {},
"outputs": [],
"source": [
"#Model Comparison"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "1c52303f-c9ca-46cc-833b-136d95546b1c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>SVM</td>\n",
" <td>97.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Logistic Regression</td>\n",
" <td>95.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>KNN</td>\n",
" <td>93.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Decision Tree Classifier</td>\n",
" <td>92.98</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Score\n",
"2 SVM 97.66\n",
"0 Logistic Regression 95.91\n",
"1 KNN 93.57\n",
"3 Decision Tree Classifier 92.98"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"models = pd.DataFrame({\n",
" 'Model': ['Logistic Regression', 'KNN', 'SVM', 'Decision Tree Classifier'],\n",
" 'Score': [100*round(log_reg_acc,4), 100*round(knn_acc,4), 100*round(svc_acc,4), 100*round(dtc_acc,4)]\n",
"})\n",
"models.sort_values(by = 'Score', ascending = False)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "54f350fc-845d-448c-b7ef-76ab096d9f94",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1]\n",
"M\n"
]
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"input_data =(10.38, 0.1184, 0.2776, 0.1471, 0.2419, 0.07871, 0.9053, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003, 0.006193, 17.33, 2019.0, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189\n",
")\n",
"#100,12,1,0,1,1,1,1,1,1,1,1,1,1,0,0,1,1,0,0,0,1) \n",
"\n",
"'''(14.36,0.09779,0.08129,0.04781,0.1885,0.05766,0.7886,23.56,0.008462,\n",
"0.0146,0.02387,0.01315,0.0198,0.0023,15.11,711.2,0.144,0.1773,0.239,0.1288,0.2977,0.07259\n",
")'''\n",
"\n",
"from sklearn import svm\n",
"# changing the input_data to numpy array\n",
"input_data_as_numpy_array = np.asarray(input_data)\n",
"model = LogisticRegression()\n",
"model.fit(X_test, y_test)\n",
"\n",
"# reshape the array as we are predicting for one instance\n",
"input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
"predictions = model.predict(input_data_reshaped)\n",
"print(predictions)\n",
"if (predictions == 0):\n",
" print('B')\n",
"else:\n",
" print('M')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "06e06ade-cff3-45ae-95cd-c8eb82bd21fd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: LR\n",
"Mean Accuracy: 95.91%\n",
"Mean ROC AUC: 99.37%\n",
"------------------------------\n",
"Model: DT\n",
"Mean Accuracy: 92.98%\n",
"Mean ROC AUC: 95.06%\n",
"------------------------------\n",
"Model: SVM\n",
"Mean Accuracy: 97.66%\n",
"Mean ROC AUC: 99.81%\n",
"------------------------------\n",
"Model: KNN\n",
"Mean Accuracy: 93.57%\n",
"Mean ROC AUC: 97.02%\n",
"------------------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn import metrics\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Define models with labels\n",
"models = [\n",
" {'label': 'LR', 'model': log_reg},\n",
" {'label': 'DT', 'model': dtc},\n",
" {'label': 'SVM', 'model': svc},\n",
" {'label': 'KNN', 'model': knn}\n",
"]\n",
"\n",
"# Performance metrics\n",
"means_roc = []\n",
"means_accuracy = []\n",
"\n",
"# Evaluate each model\n",
"for m in models:\n",
" model = m['model']\n",
" label = m['label']\n",
" \n",
" # Calculate predictions and probabilities\n",
" y_pred = model.predict(X_test)\n",
" y_pred_prob = model.predict_proba(X_test)[:,1] if hasattr(model, 'predict_proba') else None\n",
" \n",
" # Calculate accuracy\n",
" accuracy = metrics.accuracy_score(y_test, y_pred)\n",
" mean_accuracy = 100 * round(accuracy, 4)\n",
" means_accuracy.append(mean_accuracy)\n",
" \n",
" # Calculate ROC AUC\n",
" if y_pred_prob is not None:\n",
" auc = metrics.roc_auc_score(y_test, y_pred_prob)\n",
" mean_roc = 100 * round(auc, 4)\n",
" else:\n",
" mean_roc = np.nan # Append NaN if predict_proba is not available\n",
" \n",
" means_roc.append(mean_roc)\n",
" \n",
" # Display mean accuracy and mean ROC for each model\n",
" print(f\"Model: {label}\")\n",
" print(f\"Mean Accuracy: {mean_accuracy:.2f}%\")\n",
" print(f\"Mean ROC AUC: {mean_roc:.2f}%\")\n",
" print(\"-\" * 30)\n",
"\n",
"# Plotting\n",
"index = np.arange(len(models))\n",
"bar_width = 0.35\n",
"\n",
"# Create plot\n",
"fig, ax = plt.subplots(figsize=(8, 5))\n",
"\n",
"rects1 = plt.bar(index, means_accuracy, bar_width, alpha=0.8, color='mediumpurple', label='Accuracy (%)')\n",
"rects2 = plt.bar(index + bar_width, means_roc, bar_width, alpha=0.8, color='rebeccapurple', label='ROC AUC (%)')\n",
"\n",
"# Labeling\n",
"ax.set_xlabel('Models')\n",
"ax.set_ylabel('Performance (%)')\n",
"ax.set_title('Performance Evaluation - Breast Cancer Prediction')\n",
"ax.set_xticks(index + bar_width / 2)\n",
"ax.set_xticklabels([m['label'] for m in models], rotation=40, ha='center')\n",
"ax.legend()\n",
"\n",
"# Display plot\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "cff6a609-fd28-43b7-bf32-879226455388",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import pickle\n",
"model = log_reg\n",
"filename = r'C:\\Users\\Pranshu Saini\\Desktop\\disease-prediction-main\\docpat\\model\\breast_cancer_svm_model.pkl'\n",
"pickle.dump(model, open(filename,'wb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38d0b155-68d7-4b66-850d-5eefedf09eae",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 159-160: truncated \\UXXXXXXXX escape (1223555901.py, line 8)",
"output_type": "error",
"traceback": [
"\u001b[1;36m Cell \u001b[1;32mIn[57], line 8\u001b[1;36m\u001b[0m\n\u001b[1;33m return breast_cancer_model.predict(inputs)'''\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 159-160: truncated \\UXXXXXXXX escape\n"
]
}
],
"source": [
"'''import pickle\n",
"def load_model(path):\n",
" with open(path, 'rb') as file:\n",
" model = pickle.load(file)\n",
" return model\n",
"breast_cancer_model = load_model(r'C:\\Users\\DELL\\Desktop\\app\\breast_cancer.pkl')\n",
"def predict(inputs):\n",
" return breast_cancer_model.predict(inputs)'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29b7193c-5ee9-4324-be94-85c758937ff3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d00dae6-9852-45c0-bddd-828316c8b1df",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8cb62fc-c2ba-4f52-8d41-a88f89ef09b6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}