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SiddheshKukade/Assignment2.ipynb

Created May 27, 2023 03:07
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Assignment2.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8dc82e34",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a4b5bd5b",
"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>id</th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>...</th>\n",
" <th>texture_worst</th>\n",
" <th>perimeter_worst</th>\n",
" <th>area_worst</th>\n",
" <th>smoothness_worst</th>\n",
" <th>compactness_worst</th>\n",
" <th>concavity_worst</th>\n",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" <th>Unnamed: 32</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>M</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
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" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>M</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>M</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>...</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>M</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>...</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>M</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>...</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>926424</td>\n",
" <td>M</td>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>...</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>926682</td>\n",
" <td>M</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>M</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
" <td>M</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>92751</td>\n",
" <td>B</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \n",
"0 842302 M 17.99 10.38 122.80 1001.0 \\\n",
"1 842517 M 20.57 17.77 132.90 1326.0 \n",
"2 84300903 M 19.69 21.25 130.00 1203.0 \n",
"3 84348301 M 11.42 20.38 77.58 386.1 \n",
"4 84358402 M 20.29 14.34 135.10 1297.0 \n",
".. ... ... ... ... ... ... \n",
"564 926424 M 21.56 22.39 142.00 1479.0 \n",
"565 926682 M 20.13 28.25 131.20 1261.0 \n",
"566 926954 M 16.60 28.08 108.30 858.1 \n",
"567 927241 M 20.60 29.33 140.10 1265.0 \n",
"568 92751 B 7.76 24.54 47.92 181.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \n",
"0 0.11840 0.27760 0.30010 0.14710 \\\n",
"1 0.08474 0.07864 0.08690 0.07017 \n",
"2 0.10960 0.15990 0.19740 0.12790 \n",
"3 0.14250 0.28390 0.24140 0.10520 \n",
"4 0.10030 0.13280 0.19800 0.10430 \n",
".. ... ... ... ... \n",
"564 0.11100 0.11590 0.24390 0.13890 \n",
"565 0.09780 0.10340 0.14400 0.09791 \n",
"566 0.08455 0.10230 0.09251 0.05302 \n",
"567 0.11780 0.27700 0.35140 0.15200 \n",
"568 0.05263 0.04362 0.00000 0.00000 \n",
"\n",
" ... texture_worst perimeter_worst area_worst smoothness_worst \n",
"0 ... 17.33 184.60 2019.0 0.16220 \\\n",
"1 ... 23.41 158.80 1956.0 0.12380 \n",
"2 ... 25.53 152.50 1709.0 0.14440 \n",
"3 ... 26.50 98.87 567.7 0.20980 \n",
"4 ... 16.67 152.20 1575.0 0.13740 \n",
".. ... ... ... ... ... \n",
"564 ... 26.40 166.10 2027.0 0.14100 \n",
"565 ... 38.25 155.00 1731.0 0.11660 \n",
"566 ... 34.12 126.70 1124.0 0.11390 \n",
"567 ... 39.42 184.60 1821.0 0.16500 \n",
"568 ... 30.37 59.16 268.6 0.08996 \n",
"\n",
" compactness_worst concavity_worst concave points_worst symmetry_worst \n",
"0 0.66560 0.7119 0.2654 0.4601 \\\n",
"1 0.18660 0.2416 0.1860 0.2750 \n",
"2 0.42450 0.4504 0.2430 0.3613 \n",
"3 0.86630 0.6869 0.2575 0.6638 \n",
"4 0.20500 0.4000 0.1625 0.2364 \n",
".. ... ... ... ... \n",
"564 0.21130 0.4107 0.2216 0.2060 \n",
"565 0.19220 0.3215 0.1628 0.2572 \n",
"566 0.30940 0.3403 0.1418 0.2218 \n",
"567 0.86810 0.9387 0.2650 0.4087 \n",
"568 0.06444 0.0000 0.0000 0.2871 \n",
"\n",
" fractal_dimension_worst Unnamed: 32 \n",
"0 0.11890 NaN \n",
"1 0.08902 NaN \n",
"2 0.08758 NaN \n",
"3 0.17300 NaN \n",
"4 0.07678 NaN \n",
".. ... ... \n",
"564 0.07115 NaN \n",
"565 0.06637 NaN \n",
"566 0.07820 NaN \n",
"567 0.12400 NaN \n",
"568 0.07039 NaN \n",
"\n",
"[569 rows x 33 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('cancer_data_set_batch_3.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "350cb03e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(569, 33)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "589c5f5e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
" 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
" 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
" 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
" 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
" 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
" 'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
" 'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
" 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
" dtype='object')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ca45cc7b",
"metadata": {},
"outputs": [],
"source": [
"# ********************************* 1. Data Cleaning ******************************************"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d8eef388",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0\n",
"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",
"Unnamed: 32 569\n",
"dtype: int64\n"
]
}
],
"source": [
"print(df.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f5a68d7a",
"metadata": {},
"outputs": [],
"source": [
"#Dropping the Unnamed column\n",
"df = df.drop([\"Unnamed: 32\"],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1fa408c7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>...</th>\n",
" <th>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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>M</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>M</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>M</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>M</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>M</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>926424</td>\n",
" <td>M</td>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>926682</td>\n",
" <td>M</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>M</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
" <td>M</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>92751</td>\n",
" <td>B</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 32 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",
"564 926424 M 21.56 22.39 142.00 1479.0 \n",
"565 926682 M 20.13 28.25 131.20 1261.0 \n",
"566 926954 M 16.60 28.08 108.30 858.1 \n",
"567 927241 M 20.60 29.33 140.10 1265.0 \n",
"568 92751 B 7.76 24.54 47.92 181.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \n",
"0 0.11840 0.27760 0.30010 0.14710 \\\n",
"1 0.08474 0.07864 0.08690 0.07017 \n",
"2 0.10960 0.15990 0.19740 0.12790 \n",
"3 0.14250 0.28390 0.24140 0.10520 \n",
"4 0.10030 0.13280 0.19800 0.10430 \n",
".. ... ... ... ... \n",
"564 0.11100 0.11590 0.24390 0.13890 \n",
"565 0.09780 0.10340 0.14400 0.09791 \n",
"566 0.08455 0.10230 0.09251 0.05302 \n",
"567 0.11780 0.27700 0.35140 0.15200 \n",
"568 0.05263 0.04362 0.00000 0.00000 \n",
"\n",
" ... radius_worst texture_worst perimeter_worst area_worst \n",
"0 ... 25.380 17.33 184.60 2019.0 \\\n",
"1 ... 24.990 23.41 158.80 1956.0 \n",
"2 ... 23.570 25.53 152.50 1709.0 \n",
"3 ... 14.910 26.50 98.87 567.7 \n",
"4 ... 22.540 16.67 152.20 1575.0 \n",
".. ... ... ... ... ... \n",
"564 ... 25.450 26.40 166.10 2027.0 \n",
"565 ... 23.690 38.25 155.00 1731.0 \n",
"566 ... 18.980 34.12 126.70 1124.0 \n",
"567 ... 25.740 39.42 184.60 1821.0 \n",
"568 ... 9.456 30.37 59.16 268.6 \n",
"\n",
" smoothness_worst compactness_worst concavity_worst \n",
"0 0.16220 0.66560 0.7119 \\\n",
"1 0.12380 0.18660 0.2416 \n",
"2 0.14440 0.42450 0.4504 \n",
"3 0.20980 0.86630 0.6869 \n",
"4 0.13740 0.20500 0.4000 \n",
".. ... ... ... \n",
"564 0.14100 0.21130 0.4107 \n",
"565 0.11660 0.19220 0.3215 \n",
"566 0.11390 0.30940 0.3403 \n",
"567 0.16500 0.86810 0.9387 \n",
"568 0.08996 0.06444 0.0000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"0 0.2654 0.4601 0.11890 \n",
"1 0.1860 0.2750 0.08902 \n",
"2 0.2430 0.3613 0.08758 \n",
"3 0.2575 0.6638 0.17300 \n",
"4 0.1625 0.2364 0.07678 \n",
".. ... ... ... \n",
"564 0.2216 0.2060 0.07115 \n",
"565 0.1628 0.2572 0.06637 \n",
"566 0.1418 0.2218 0.07820 \n",
"567 0.2650 0.4087 0.12400 \n",
"568 0.0000 0.2871 0.07039 \n",
"\n",
"[569 rows x 32 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "14377433",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" }\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>...</th>\n",
" <th>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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>M</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>M</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>M</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>M</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>M</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>926424</td>\n",
" <td>M</td>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>926682</td>\n",
" <td>M</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>M</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
" <td>M</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>92751</td>\n",
" <td>B</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 32 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",
"564 926424 M 21.56 22.39 142.00 1479.0 \n",
"565 926682 M 20.13 28.25 131.20 1261.0 \n",
"566 926954 M 16.60 28.08 108.30 858.1 \n",
"567 927241 M 20.60 29.33 140.10 1265.0 \n",
"568 92751 B 7.76 24.54 47.92 181.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \n",
"0 0.11840 0.27760 0.30010 0.14710 \\\n",
"1 0.08474 0.07864 0.08690 0.07017 \n",
"2 0.10960 0.15990 0.19740 0.12790 \n",
"3 0.14250 0.28390 0.24140 0.10520 \n",
"4 0.10030 0.13280 0.19800 0.10430 \n",
".. ... ... ... ... \n",
"564 0.11100 0.11590 0.24390 0.13890 \n",
"565 0.09780 0.10340 0.14400 0.09791 \n",
"566 0.08455 0.10230 0.09251 0.05302 \n",
"567 0.11780 0.27700 0.35140 0.15200 \n",
"568 0.05263 0.04362 0.00000 0.00000 \n",
"\n",
" ... radius_worst texture_worst perimeter_worst area_worst \n",
"0 ... 25.380 17.33 184.60 2019.0 \\\n",
"1 ... 24.990 23.41 158.80 1956.0 \n",
"2 ... 23.570 25.53 152.50 1709.0 \n",
"3 ... 14.910 26.50 98.87 567.7 \n",
"4 ... 22.540 16.67 152.20 1575.0 \n",
".. ... ... ... ... ... \n",
"564 ... 25.450 26.40 166.10 2027.0 \n",
"565 ... 23.690 38.25 155.00 1731.0 \n",
"566 ... 18.980 34.12 126.70 1124.0 \n",
"567 ... 25.740 39.42 184.60 1821.0 \n",
"568 ... 9.456 30.37 59.16 268.6 \n",
"\n",
" smoothness_worst compactness_worst concavity_worst \n",
"0 0.16220 0.66560 0.7119 \\\n",
"1 0.12380 0.18660 0.2416 \n",
"2 0.14440 0.42450 0.4504 \n",
"3 0.20980 0.86630 0.6869 \n",
"4 0.13740 0.20500 0.4000 \n",
".. ... ... ... \n",
"564 0.14100 0.21130 0.4107 \n",
"565 0.11660 0.19220 0.3215 \n",
"566 0.11390 0.30940 0.3403 \n",
"567 0.16500 0.86810 0.9387 \n",
"568 0.08996 0.06444 0.0000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"0 0.2654 0.4601 0.11890 \n",
"1 0.1860 0.2750 0.08902 \n",
"2 0.2430 0.3613 0.08758 \n",
"3 0.2575 0.6638 0.17300 \n",
"4 0.1625 0.2364 0.07678 \n",
".. ... ... ... \n",
"564 0.2216 0.2060 0.07115 \n",
"565 0.1628 0.2572 0.06637 \n",
"566 0.1418 0.2218 0.07820 \n",
"567 0.2650 0.4087 0.12400 \n",
"568 0.0000 0.2871 0.07039 \n",
"\n",
"[569 rows x 32 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Removoving the null values \n",
"df = df.dropna(axis=1)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "a826fdbe",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>...</th>\n",
" <th>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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>1</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>1</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>1</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>1</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>926424</td>\n",
" <td>1</td>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>926682</td>\n",
" <td>1</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>1</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
" <td>1</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>92751</td>\n",
" <td>0</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean \n",
"0 842302 1 17.99 10.38 122.80 \\\n",
"1 842517 1 20.57 17.77 132.90 \n",
"2 84300903 1 19.69 21.25 130.00 \n",
"3 84348301 1 11.42 20.38 77.58 \n",
"4 84358402 1 20.29 14.34 135.10 \n",
".. ... ... ... ... ... \n",
"564 926424 1 21.56 22.39 142.00 \n",
"565 926682 1 20.13 28.25 131.20 \n",
"566 926954 1 16.60 28.08 108.30 \n",
"567 927241 1 20.60 29.33 140.10 \n",
"568 92751 0 7.76 24.54 47.92 \n",
"\n",
" area_mean smoothness_mean compactness_mean concavity_mean \n",
"0 1001.0 0.11840 0.27760 0.30010 \\\n",
"1 1326.0 0.08474 0.07864 0.08690 \n",
"2 1203.0 0.10960 0.15990 0.19740 \n",
"3 386.1 0.14250 0.28390 0.24140 \n",
"4 1297.0 0.10030 0.13280 0.19800 \n",
".. ... ... ... ... \n",
"564 1479.0 0.11100 0.11590 0.24390 \n",
"565 1261.0 0.09780 0.10340 0.14400 \n",
"566 858.1 0.08455 0.10230 0.09251 \n",
"567 1265.0 0.11780 0.27700 0.35140 \n",
"568 181.0 0.05263 0.04362 0.00000 \n",
"\n",
" concave points_mean ... radius_worst texture_worst perimeter_worst \n",
"0 0.14710 ... 25.380 17.33 184.60 \\\n",
"1 0.07017 ... 24.990 23.41 158.80 \n",
"2 0.12790 ... 23.570 25.53 152.50 \n",
"3 0.10520 ... 14.910 26.50 98.87 \n",
"4 0.10430 ... 22.540 16.67 152.20 \n",
".. ... ... ... ... ... \n",
"564 0.13890 ... 25.450 26.40 166.10 \n",
"565 0.09791 ... 23.690 38.25 155.00 \n",
"566 0.05302 ... 18.980 34.12 126.70 \n",
"567 0.15200 ... 25.740 39.42 184.60 \n",
"568 0.00000 ... 9.456 30.37 59.16 \n",
"\n",
" area_worst smoothness_worst compactness_worst concavity_worst \n",
"0 2019.0 0.16220 0.66560 0.7119 \\\n",
"1 1956.0 0.12380 0.18660 0.2416 \n",
"2 1709.0 0.14440 0.42450 0.4504 \n",
"3 567.7 0.20980 0.86630 0.6869 \n",
"4 1575.0 0.13740 0.20500 0.4000 \n",
".. ... ... ... ... \n",
"564 2027.0 0.14100 0.21130 0.4107 \n",
"565 1731.0 0.11660 0.19220 0.3215 \n",
"566 1124.0 0.11390 0.30940 0.3403 \n",
"567 1821.0 0.16500 0.86810 0.9387 \n",
"568 268.6 0.08996 0.06444 0.0000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"0 0.2654 0.4601 0.11890 \n",
"1 0.1860 0.2750 0.08902 \n",
"2 0.2430 0.3613 0.08758 \n",
"3 0.2575 0.6638 0.17300 \n",
"4 0.1625 0.2364 0.07678 \n",
".. ... ... ... \n",
"564 0.2216 0.2060 0.07115 \n",
"565 0.1628 0.2572 0.06637 \n",
"566 0.1418 0.2218 0.07820 \n",
"567 0.2650 0.4087 0.12400 \n",
"568 0.0000 0.2871 0.07039 \n",
"\n",
"[569 rows x 32 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Encoding categorical data \n",
"#Making Male as 1 and Female as 0\n",
"from sklearn import preprocessing\n",
"le = preprocessing.LabelEncoder()\n",
"\n",
"# Encode labels in column 'diagnosis'.\n",
"df['diagnosis']= le.fit_transform(df['diagnosis'])\n",
" \n",
"df['diagnosis'].unique()\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "ab60d42a",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <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",
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" <th>radius_worst</th>\n",
" <th>texture_worst</th>\n",
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" <th>area_worst</th>\n",
" <th>smoothness_worst</th>\n",
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" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
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" <th>count</th>\n",
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" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.037183e+07</td>\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",
" <td>...</td>\n",
" <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>1.250206e+08</td>\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>...</td>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>8.670000e+03</td>\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>...</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>8.692180e+05</td>\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>...</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>9.060240e+05</td>\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>...</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>8.813129e+06</td>\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>...</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",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9.113205e+08</td>\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>...</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 × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean \n",
"count 5.690000e+02 569.000000 569.000000 569.000000 569.000000 \\\n",
"mean 3.037183e+07 0.372583 14.127292 19.289649 91.969033 \n",
"std 1.250206e+08 0.483918 3.524049 4.301036 24.298981 \n",
"min 8.670000e+03 0.000000 6.981000 9.710000 43.790000 \n",
"25% 8.692180e+05 0.000000 11.700000 16.170000 75.170000 \n",
"50% 9.060240e+05 0.000000 13.370000 18.840000 86.240000 \n",
"75% 8.813129e+06 1.000000 15.780000 21.800000 104.100000 \n",
"max 9.113205e+08 1.000000 28.110000 39.280000 188.500000 \n",
"\n",
" area_mean smoothness_mean compactness_mean concavity_mean \n",
"count 569.000000 569.000000 569.000000 569.000000 \\\n",
"mean 654.889104 0.096360 0.104341 0.088799 \n",
"std 351.914129 0.014064 0.052813 0.079720 \n",
"min 143.500000 0.052630 0.019380 0.000000 \n",
"25% 420.300000 0.086370 0.064920 0.029560 \n",
"50% 551.100000 0.095870 0.092630 0.061540 \n",
"75% 782.700000 0.105300 0.130400 0.130700 \n",
"max 2501.000000 0.163400 0.345400 0.426800 \n",
"\n",
" concave points_mean ... radius_worst texture_worst perimeter_worst \n",
"count 569.000000 ... 569.000000 569.000000 569.000000 \\\n",
"mean 0.048919 ... 16.269190 25.677223 107.261213 \n",
"std 0.038803 ... 4.833242 6.146258 33.602542 \n",
"min 0.000000 ... 7.930000 12.020000 50.410000 \n",
"25% 0.020310 ... 13.010000 21.080000 84.110000 \n",
"50% 0.033500 ... 14.970000 25.410000 97.660000 \n",
"75% 0.074000 ... 18.790000 29.720000 125.400000 \n",
"max 0.201200 ... 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 32 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "e23bcfd5",
"metadata": {},
"outputs": [],
"source": [
"# ***************************** 2. Data Integration *******************************"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "240bebaf",
"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>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>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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>1</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>1</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>1</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>1</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>926424</td>\n",
" <td>1</td>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>926682</td>\n",
" <td>1</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>1</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
" <td>1</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>92751</td>\n",
" <td>0</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean \n",
"0 842302 1 17.99 10.38 122.80 \\\n",
"1 842517 1 20.57 17.77 132.90 \n",
"2 84300903 1 19.69 21.25 130.00 \n",
"3 84348301 1 11.42 20.38 77.58 \n",
"4 84358402 1 20.29 14.34 135.10 \n",
".. ... ... ... ... ... \n",
"564 926424 1 21.56 22.39 142.00 \n",
"565 926682 1 20.13 28.25 131.20 \n",
"566 926954 1 16.60 28.08 108.30 \n",
"567 927241 1 20.60 29.33 140.10 \n",
"568 92751 0 7.76 24.54 47.92 \n",
"\n",
" area_mean smoothness_mean compactness_mean concavity_mean \n",
"0 1001.0 0.11840 0.27760 0.30010 \\\n",
"1 1326.0 0.08474 0.07864 0.08690 \n",
"2 1203.0 0.10960 0.15990 0.19740 \n",
"3 386.1 0.14250 0.28390 0.24140 \n",
"4 1297.0 0.10030 0.13280 0.19800 \n",
".. ... ... ... ... \n",
"564 1479.0 0.11100 0.11590 0.24390 \n",
"565 1261.0 0.09780 0.10340 0.14400 \n",
"566 858.1 0.08455 0.10230 0.09251 \n",
"567 1265.0 0.11780 0.27700 0.35140 \n",
"568 181.0 0.05263 0.04362 0.00000 \n",
"\n",
" concave points_mean ... radius_worst texture_worst perimeter_worst \n",
"0 0.14710 ... 25.380 17.33 184.60 \\\n",
"1 0.07017 ... 24.990 23.41 158.80 \n",
"2 0.12790 ... 23.570 25.53 152.50 \n",
"3 0.10520 ... 14.910 26.50 98.87 \n",
"4 0.10430 ... 22.540 16.67 152.20 \n",
".. ... ... ... ... ... \n",
"564 0.13890 ... 25.450 26.40 166.10 \n",
"565 0.09791 ... 23.690 38.25 155.00 \n",
"566 0.05302 ... 18.980 34.12 126.70 \n",
"567 0.15200 ... 25.740 39.42 184.60 \n",
"568 0.00000 ... 9.456 30.37 59.16 \n",
"\n",
" area_worst smoothness_worst compactness_worst concavity_worst \n",
"0 2019.0 0.16220 0.66560 0.7119 \\\n",
"1 1956.0 0.12380 0.18660 0.2416 \n",
"2 1709.0 0.14440 0.42450 0.4504 \n",
"3 567.7 0.20980 0.86630 0.6869 \n",
"4 1575.0 0.13740 0.20500 0.4000 \n",
".. ... ... ... ... \n",
"564 2027.0 0.14100 0.21130 0.4107 \n",
"565 1731.0 0.11660 0.19220 0.3215 \n",
"566 1124.0 0.11390 0.30940 0.3403 \n",
"567 1821.0 0.16500 0.86810 0.9387 \n",
"568 268.6 0.08996 0.06444 0.0000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"0 0.2654 0.4601 0.11890 \n",
"1 0.1860 0.2750 0.08902 \n",
"2 0.2430 0.3613 0.08758 \n",
"3 0.2575 0.6638 0.17300 \n",
"4 0.1625 0.2364 0.07678 \n",
".. ... ... ... \n",
"564 0.2216 0.2060 0.07115 \n",
"565 0.1628 0.2572 0.06637 \n",
"566 0.1418 0.2218 0.07820 \n",
"567 0.2650 0.4087 0.12400 \n",
"568 0.0000 0.2871 0.07039 \n",
"\n",
"[569 rows x 32 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "c6ff5d71",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>radius_mean</th>\n",
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" <th>perimeter_mean</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
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" <td>17.99</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
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" <th>2</th>\n",
" <td>84300903</td>\n",
" <td>1</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
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" <td>84348301</td>\n",
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" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
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" <td>1</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
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" <tr>\n",
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" <td>21.56</td>\n",
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" <th>565</th>\n",
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" <td>20.13</td>\n",
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" <td>131.20</td>\n",
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" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>1</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
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" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
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" <td>20.60</td>\n",
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" <td>7.76</td>\n",
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"<p>569 rows × 5 columns</p>\n",
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],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean\n",
"0 842302 1 17.99 10.38 122.80\n",
"1 842517 1 20.57 17.77 132.90\n",
"2 84300903 1 19.69 21.25 130.00\n",
"3 84348301 1 11.42 20.38 77.58\n",
"4 84358402 1 20.29 14.34 135.10\n",
".. ... ... ... ... ...\n",
"564 926424 1 21.56 22.39 142.00\n",
"565 926682 1 20.13 28.25 131.20\n",
"566 926954 1 16.60 28.08 108.30\n",
"567 927241 1 20.60 29.33 140.10\n",
"568 92751 0 7.76 24.54 47.92\n",
"\n",
"[569 rows x 5 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1 = df.iloc[:,0:5]\n",
"data1"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "2b600a9a",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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",
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" <th>fractal_dimension_mean</th>\n",
" <th>radius_se</th>\n",
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" <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",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>1.0950</td>\n",
" <td>0.9053</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>0.5435</td>\n",
" <td>0.7339</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
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" <th>2</th>\n",
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" <td>1203.0</td>\n",
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" <td>0.15990</td>\n",
" <td>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
" <td>0.7456</td>\n",
" <td>0.7869</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>0.4956</td>\n",
" <td>1.1560</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
" <td>0.7572</td>\n",
" <td>0.7813</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
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" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>0.1726</td>\n",
" <td>0.05623</td>\n",
" <td>1.1760</td>\n",
" <td>1.2560</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
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" <th>565</th>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>0.1752</td>\n",
" <td>0.05533</td>\n",
" <td>0.7655</td>\n",
" <td>2.4630</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>0.1590</td>\n",
" <td>0.05648</td>\n",
" <td>0.4564</td>\n",
" <td>1.0750</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
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" <th>567</th>\n",
" <td>140.10</td>\n",
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" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>0.2397</td>\n",
" <td>0.07016</td>\n",
" <td>0.7260</td>\n",
" <td>1.5950</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.1587</td>\n",
" <td>0.05884</td>\n",
" <td>0.3857</td>\n",
" <td>1.4280</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
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],
"text/plain": [
" perimeter_mean area_mean smoothness_mean compactness_mean \n",
"0 122.80 1001.0 0.11840 0.27760 \\\n",
"1 132.90 1326.0 0.08474 0.07864 \n",
"2 130.00 1203.0 0.10960 0.15990 \n",
"3 77.58 386.1 0.14250 0.28390 \n",
"4 135.10 1297.0 0.10030 0.13280 \n",
".. ... ... ... ... \n",
"564 142.00 1479.0 0.11100 0.11590 \n",
"565 131.20 1261.0 0.09780 0.10340 \n",
"566 108.30 858.1 0.08455 0.10230 \n",
"567 140.10 1265.0 0.11780 0.27700 \n",
"568 47.92 181.0 0.05263 0.04362 \n",
"\n",
" concavity_mean concave points_mean symmetry_mean \n",
"0 0.30010 0.14710 0.2419 \\\n",
"1 0.08690 0.07017 0.1812 \n",
"2 0.19740 0.12790 0.2069 \n",
"3 0.24140 0.10520 0.2597 \n",
"4 0.19800 0.10430 0.1809 \n",
".. ... ... ... \n",
"564 0.24390 0.13890 0.1726 \n",
"565 0.14400 0.09791 0.1752 \n",
"566 0.09251 0.05302 0.1590 \n",
"567 0.35140 0.15200 0.2397 \n",
"568 0.00000 0.00000 0.1587 \n",
"\n",
" fractal_dimension_mean radius_se texture_se ... radius_worst \n",
"0 0.07871 1.0950 0.9053 ... 25.380 \\\n",
"1 0.05667 0.5435 0.7339 ... 24.990 \n",
"2 0.05999 0.7456 0.7869 ... 23.570 \n",
"3 0.09744 0.4956 1.1560 ... 14.910 \n",
"4 0.05883 0.7572 0.7813 ... 22.540 \n",
".. ... ... ... ... ... \n",
"564 0.05623 1.1760 1.2560 ... 25.450 \n",
"565 0.05533 0.7655 2.4630 ... 23.690 \n",
"566 0.05648 0.4564 1.0750 ... 18.980 \n",
"567 0.07016 0.7260 1.5950 ... 25.740 \n",
"568 0.05884 0.3857 1.4280 ... 9.456 \n",
"\n",
" texture_worst perimeter_worst area_worst smoothness_worst \n",
"0 17.33 184.60 2019.0 0.16220 \\\n",
"1 23.41 158.80 1956.0 0.12380 \n",
"2 25.53 152.50 1709.0 0.14440 \n",
"3 26.50 98.87 567.7 0.20980 \n",
"4 16.67 152.20 1575.0 0.13740 \n",
".. ... ... ... ... \n",
"564 26.40 166.10 2027.0 0.14100 \n",
"565 38.25 155.00 1731.0 0.11660 \n",
"566 34.12 126.70 1124.0 0.11390 \n",
"567 39.42 184.60 1821.0 0.16500 \n",
"568 30.37 59.16 268.6 0.08996 \n",
"\n",
" compactness_worst concavity_worst concave points_worst symmetry_worst \n",
"0 0.66560 0.7119 0.2654 0.4601 \\\n",
"1 0.18660 0.2416 0.1860 0.2750 \n",
"2 0.42450 0.4504 0.2430 0.3613 \n",
"3 0.86630 0.6869 0.2575 0.6638 \n",
"4 0.20500 0.4000 0.1625 0.2364 \n",
".. ... ... ... ... \n",
"564 0.21130 0.4107 0.2216 0.2060 \n",
"565 0.19220 0.3215 0.1628 0.2572 \n",
"566 0.30940 0.3403 0.1418 0.2218 \n",
"567 0.86810 0.9387 0.2650 0.4087 \n",
"568 0.06444 0.0000 0.0000 0.2871 \n",
"\n",
" fractal_dimension_worst \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 28 columns]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2 = df.iloc[:,4:]\n",
"data2"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "29a8953f",
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" <th>...</th>\n",
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" </tr>\n",
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" <td>1</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
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" <th>1</th>\n",
" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
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" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
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" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1288.0</td>\n",
" <td>0.10000</td>\n",
" <td>0.10880</td>\n",
" <td>0.15190</td>\n",
" <td>0.09333</td>\n",
" <td>...</td>\n",
" <td>24.330</td>\n",
" <td>39.16</td>\n",
" <td>162.30</td>\n",
" <td>1844.0</td>\n",
" <td>0.15220</td>\n",
" <td>0.29450</td>\n",
" <td>0.3788</td>\n",
" <td>0.1697</td>\n",
" <td>0.3151</td>\n",
" <td>0.07999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>887549</td>\n",
" <td>1</td>\n",
" <td>20.31</td>\n",
" <td>27.06</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>887549</td>\n",
" <td>1</td>\n",
" <td>20.31</td>\n",
" <td>27.06</td>\n",
" <td>132.90</td>\n",
" <td>1288.0</td>\n",
" <td>0.10000</td>\n",
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" <td>0.15190</td>\n",
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" <td>...</td>\n",
" <td>24.330</td>\n",
" <td>39.16</td>\n",
" <td>162.30</td>\n",
" <td>1844.0</td>\n",
" <td>0.15220</td>\n",
" <td>0.29450</td>\n",
" <td>0.3788</td>\n",
" <td>0.1697</td>\n",
" <td>0.3151</td>\n",
" <td>0.07999</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <th>664</th>\n",
" <td>926424</td>\n",
" <td>1</td>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.24390</td>\n",
" <td>0.13890</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665</th>\n",
" <td>926682</td>\n",
" <td>1</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>666</th>\n",
" <td>926954</td>\n",
" <td>1</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>667</th>\n",
" <td>927241</td>\n",
" <td>1</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
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" <tr>\n",
" <th>668</th>\n",
" <td>92751</td>\n",
" <td>0</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
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"</table>\n",
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"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \n",
"0 842302 1 17.99 10.38 122.80 1001.0 \\\n",
"1 842517 1 20.57 17.77 132.90 1326.0 \n",
"2 842517 1 20.57 17.77 132.90 1288.0 \n",
"3 887549 1 20.31 27.06 132.90 1326.0 \n",
"4 887549 1 20.31 27.06 132.90 1288.0 \n",
".. ... ... ... ... ... ... \n",
"664 926424 1 21.56 22.39 142.00 1479.0 \n",
"665 926682 1 20.13 28.25 131.20 1261.0 \n",
"666 926954 1 16.60 28.08 108.30 858.1 \n",
"667 927241 1 20.60 29.33 140.10 1265.0 \n",
"668 92751 0 7.76 24.54 47.92 181.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \n",
"0 0.11840 0.27760 0.30010 0.14710 \\\n",
"1 0.08474 0.07864 0.08690 0.07017 \n",
"2 0.10000 0.10880 0.15190 0.09333 \n",
"3 0.08474 0.07864 0.08690 0.07017 \n",
"4 0.10000 0.10880 0.15190 0.09333 \n",
".. ... ... ... ... \n",
"664 0.11100 0.11590 0.24390 0.13890 \n",
"665 0.09780 0.10340 0.14400 0.09791 \n",
"666 0.08455 0.10230 0.09251 0.05302 \n",
"667 0.11780 0.27700 0.35140 0.15200 \n",
"668 0.05263 0.04362 0.00000 0.00000 \n",
"\n",
" ... radius_worst texture_worst perimeter_worst area_worst \n",
"0 ... 25.380 17.33 184.60 2019.0 \\\n",
"1 ... 24.990 23.41 158.80 1956.0 \n",
"2 ... 24.330 39.16 162.30 1844.0 \n",
"3 ... 24.990 23.41 158.80 1956.0 \n",
"4 ... 24.330 39.16 162.30 1844.0 \n",
".. ... ... ... ... ... \n",
"664 ... 25.450 26.40 166.10 2027.0 \n",
"665 ... 23.690 38.25 155.00 1731.0 \n",
"666 ... 18.980 34.12 126.70 1124.0 \n",
"667 ... 25.740 39.42 184.60 1821.0 \n",
"668 ... 9.456 30.37 59.16 268.6 \n",
"\n",
" smoothness_worst compactness_worst concavity_worst \n",
"0 0.16220 0.66560 0.7119 \\\n",
"1 0.12380 0.18660 0.2416 \n",
"2 0.15220 0.29450 0.3788 \n",
"3 0.12380 0.18660 0.2416 \n",
"4 0.15220 0.29450 0.3788 \n",
".. ... ... ... \n",
"664 0.14100 0.21130 0.4107 \n",
"665 0.11660 0.19220 0.3215 \n",
"666 0.11390 0.30940 0.3403 \n",
"667 0.16500 0.86810 0.9387 \n",
"668 0.08996 0.06444 0.0000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"0 0.2654 0.4601 0.11890 \n",
"1 0.1860 0.2750 0.08902 \n",
"2 0.1697 0.3151 0.07999 \n",
"3 0.1860 0.2750 0.08902 \n",
"4 0.1697 0.3151 0.07999 \n",
".. ... ... ... \n",
"664 0.2216 0.2060 0.07115 \n",
"665 0.1628 0.2572 0.06637 \n",
"666 0.1418 0.2218 0.07820 \n",
"667 0.2650 0.4087 0.12400 \n",
"668 0.0000 0.2871 0.07039 \n",
"\n",
"[669 rows x 32 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Data mearging \n",
"# It overrides the perimeter_mean \n",
"\n",
"data = pd.merge(data1,data2)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "f06d6cd0",
"metadata": {},
"outputs": [],
"source": [
"# ************************************ 3. Data Transformation *****************************"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "27d92a0d",
"metadata": {},
"outputs": [],
"source": [
"new_row = {'id':670, 'diagnosis':1, 'radius_mean':20, 'texture_mean':50, 'perimeter_mean':50,\n",
" 'area_mean':50, 'smoothness_mean':90, 'compactness_mean':90, 'concavity_mean':90,\n",
" 'concave points_mean':90, 'symmetry_mean':90, 'fractal_dimension_mean':90,\n",
" 'radius_se':90, 'texture_se':90, 'perimeter_se':90, 'area_se':90, 'smoothness_se':90,\n",
" 'compactness_se':90, 'concavity_se':90, 'concave points_se':90, 'symmetry_se':90,\n",
" 'fractal_dimension_se':90, 'radius_worst':90, 'texture_worst':90,\n",
" 'perimeter_worst':90, 'area_worst':90, 'smoothness_worst':90,\n",
" 'compactness_worst':90, 'concavity_worst':90, 'concave points_worst':90,\n",
" 'symmetry_worst':90, 'fractal_dimension_worst':90}\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "fbf41fd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 670,\n",
" 'diagnosis': 1,\n",
" 'radius_mean': 20,\n",
" 'texture_mean': 50,\n",
" 'perimeter_mean': 50,\n",
" 'area_mean': 50,\n",
" 'smoothness_mean': 90,\n",
" 'compactness_mean': 90,\n",
" 'concavity_mean': 90,\n",
" 'concave points_mean': 90,\n",
" 'symmetry_mean': 90,\n",
" 'fractal_dimension_mean': 90,\n",
" 'radius_se': 90,\n",
" 'texture_se': 90,\n",
" 'perimeter_se': 90,\n",
" 'area_se': 90,\n",
" 'smoothness_se': 90,\n",
" 'compactness_se': 90,\n",
" 'concavity_se': 90,\n",
" 'concave points_se': 90,\n",
" 'symmetry_se': 90,\n",
" 'fractal_dimension_se': 90,\n",
" 'radius_worst': 90,\n",
" 'texture_worst': 90,\n",
" 'perimeter_worst': 90,\n",
" 'area_worst': 90,\n",
" 'smoothness_worst': 90,\n",
" 'compactness_worst': 90,\n",
" 'concavity_worst': 90,\n",
" 'concave points_worst': 90,\n",
" 'symmetry_worst': 90,\n",
" 'fractal_dimension_worst': 90}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_row"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "3fdb3140",
"metadata": {},
"outputs": [
{
"data": {
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
" <td>1</td>\n",
" <td>17.99</td>\n",
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" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>842517</td>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1288.0</td>\n",
" <td>0.10000</td>\n",
" <td>0.10880</td>\n",
" <td>0.15190</td>\n",
" <td>0.09333</td>\n",
" <td>...</td>\n",
" <td>24.330</td>\n",
" <td>39.16</td>\n",
" <td>162.30</td>\n",
" <td>1844.0</td>\n",
" <td>0.15220</td>\n",
" <td>0.29450</td>\n",
" <td>0.3788</td>\n",
" <td>0.1697</td>\n",
" <td>0.3151</td>\n",
" <td>0.07999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>887549</td>\n",
" <td>1</td>\n",
" <td>20.31</td>\n",
" <td>27.06</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>887549</td>\n",
" <td>1</td>\n",
" <td>20.31</td>\n",
" <td>27.06</td>\n",
" <td>132.90</td>\n",
" <td>1288.0</td>\n",
" <td>0.10000</td>\n",
" <td>0.10880</td>\n",
" <td>0.15190</td>\n",
" <td>0.09333</td>\n",
" <td>...</td>\n",
" <td>24.330</td>\n",
" <td>39.16</td>\n",
" <td>162.30</td>\n",
" <td>1844.0</td>\n",
" <td>0.15220</td>\n",
" <td>0.29450</td>\n",
" <td>0.3788</td>\n",
" <td>0.1697</td>\n",
" <td>0.3151</td>\n",
" <td>0.07999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665</th>\n",
" <td>926682</td>\n",
" <td>1</td>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>666</th>\n",
" <td>926954</td>\n",
" <td>1</td>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>667</th>\n",
" <td>927241</td>\n",
" <td>1</td>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>668</th>\n",
" <td>92751</td>\n",
" <td>0</td>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>669</th>\n",
" <td>670</td>\n",
" <td>1</td>\n",
" <td>20.00</td>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>50.0</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>...</td>\n",
" <td>90.000</td>\n",
" <td>90.00</td>\n",
" <td>90.00</td>\n",
" <td>90.0</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.0000</td>\n",
" <td>90.0000</td>\n",
" <td>90.0000</td>\n",
" <td>90.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>670 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \n",
"0 842302 1 17.99 10.38 122.80 1001.0 \\\n",
"1 842517 1 20.57 17.77 132.90 1326.0 \n",
"2 842517 1 20.57 17.77 132.90 1288.0 \n",
"3 887549 1 20.31 27.06 132.90 1326.0 \n",
"4 887549 1 20.31 27.06 132.90 1288.0 \n",
".. ... ... ... ... ... ... \n",
"665 926682 1 20.13 28.25 131.20 1261.0 \n",
"666 926954 1 16.60 28.08 108.30 858.1 \n",
"667 927241 1 20.60 29.33 140.10 1265.0 \n",
"668 92751 0 7.76 24.54 47.92 181.0 \n",
"669 670 1 20.00 50.00 50.00 50.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \n",
"0 0.11840 0.27760 0.30010 0.14710 \\\n",
"1 0.08474 0.07864 0.08690 0.07017 \n",
"2 0.10000 0.10880 0.15190 0.09333 \n",
"3 0.08474 0.07864 0.08690 0.07017 \n",
"4 0.10000 0.10880 0.15190 0.09333 \n",
".. ... ... ... ... \n",
"665 0.09780 0.10340 0.14400 0.09791 \n",
"666 0.08455 0.10230 0.09251 0.05302 \n",
"667 0.11780 0.27700 0.35140 0.15200 \n",
"668 0.05263 0.04362 0.00000 0.00000 \n",
"669 90.00000 90.00000 90.00000 90.00000 \n",
"\n",
" ... radius_worst texture_worst perimeter_worst area_worst \n",
"0 ... 25.380 17.33 184.60 2019.0 \\\n",
"1 ... 24.990 23.41 158.80 1956.0 \n",
"2 ... 24.330 39.16 162.30 1844.0 \n",
"3 ... 24.990 23.41 158.80 1956.0 \n",
"4 ... 24.330 39.16 162.30 1844.0 \n",
".. ... ... ... ... ... \n",
"665 ... 23.690 38.25 155.00 1731.0 \n",
"666 ... 18.980 34.12 126.70 1124.0 \n",
"667 ... 25.740 39.42 184.60 1821.0 \n",
"668 ... 9.456 30.37 59.16 268.6 \n",
"669 ... 90.000 90.00 90.00 90.0 \n",
"\n",
" smoothness_worst compactness_worst concavity_worst \n",
"0 0.16220 0.66560 0.7119 \\\n",
"1 0.12380 0.18660 0.2416 \n",
"2 0.15220 0.29450 0.3788 \n",
"3 0.12380 0.18660 0.2416 \n",
"4 0.15220 0.29450 0.3788 \n",
".. ... ... ... \n",
"665 0.11660 0.19220 0.3215 \n",
"666 0.11390 0.30940 0.3403 \n",
"667 0.16500 0.86810 0.9387 \n",
"668 0.08996 0.06444 0.0000 \n",
"669 90.00000 90.00000 90.0000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \n",
"0 0.2654 0.4601 0.11890 \n",
"1 0.1860 0.2750 0.08902 \n",
"2 0.1697 0.3151 0.07999 \n",
"3 0.1860 0.2750 0.08902 \n",
"4 0.1697 0.3151 0.07999 \n",
".. ... ... ... \n",
"665 0.1628 0.2572 0.06637 \n",
"666 0.1418 0.2218 0.07820 \n",
"667 0.2650 0.4087 0.12400 \n",
"668 0.0000 0.2871 0.07039 \n",
"669 90.0000 90.0000 90.00000 \n",
"\n",
"[670 rows x 32 columns]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.concat([data, pd.DataFrame([new_row])], ignore_index=True)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "399ab7c8",
"metadata": {},
"outputs": [],
"source": [
"# ************************* 4. Error Detecting ************************************************"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "67fca29e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f9810559d60>]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data['radius_mean'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3600dd3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 58,
"id": "776d4a31",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f98104b5250>]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data['texture_mean'])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "a6ba30ed",
"metadata": {},
"outputs": [],
"source": [
"# ****************************** 5. Model Building ********************************"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "8078fd4b",
"metadata": {},
"outputs": [],
"source": [
"from sklearn import linear_model"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "c33594f3",
"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>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>fractal_dimension_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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.30010</td>\n",
" <td>0.14710</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1288.0</td>\n",
" <td>0.10000</td>\n",
" <td>0.10880</td>\n",
" <td>0.15190</td>\n",
" <td>0.09333</td>\n",
" <td>0.1814</td>\n",
" <td>0.05572</td>\n",
" <td>...</td>\n",
" <td>24.330</td>\n",
" <td>39.16</td>\n",
" <td>162.30</td>\n",
" <td>1844.0</td>\n",
" <td>0.15220</td>\n",
" <td>0.29450</td>\n",
" <td>0.3788</td>\n",
" <td>0.1697</td>\n",
" <td>0.3151</td>\n",
" <td>0.07999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20.31</td>\n",
" <td>27.06</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.31</td>\n",
" <td>27.06</td>\n",
" <td>132.90</td>\n",
" <td>1288.0</td>\n",
" <td>0.10000</td>\n",
" <td>0.10880</td>\n",
" <td>0.15190</td>\n",
" <td>0.09333</td>\n",
" <td>0.1814</td>\n",
" <td>0.05572</td>\n",
" <td>...</td>\n",
" <td>24.330</td>\n",
" <td>39.16</td>\n",
" <td>162.30</td>\n",
" <td>1844.0</td>\n",
" <td>0.15220</td>\n",
" <td>0.29450</td>\n",
" <td>0.3788</td>\n",
" <td>0.1697</td>\n",
" <td>0.3151</td>\n",
" <td>0.07999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665</th>\n",
" <td>20.13</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.14400</td>\n",
" <td>0.09791</td>\n",
" <td>0.1752</td>\n",
" <td>0.05533</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>666</th>\n",
" <td>16.60</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.09251</td>\n",
" <td>0.05302</td>\n",
" <td>0.1590</td>\n",
" <td>0.05648</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>667</th>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.35140</td>\n",
" <td>0.15200</td>\n",
" <td>0.2397</td>\n",
" <td>0.07016</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>668</th>\n",
" <td>7.76</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.1587</td>\n",
" <td>0.05884</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>669</th>\n",
" <td>20.00</td>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>50.0</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.0000</td>\n",
" <td>90.00000</td>\n",
" <td>...</td>\n",
" <td>90.000</td>\n",
" <td>90.00</td>\n",
" <td>90.00</td>\n",
" <td>90.0</td>\n",
" <td>90.00000</td>\n",
" <td>90.00000</td>\n",
" <td>90.0000</td>\n",
" <td>90.0000</td>\n",
" <td>90.0000</td>\n",
" <td>90.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>670 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" radius_mean texture_mean perimeter_mean area_mean smoothness_mean \n",
"0 17.99 10.38 122.80 1001.0 0.11840 \\\n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 20.57 17.77 132.90 1288.0 0.10000 \n",
"3 20.31 27.06 132.90 1326.0 0.08474 \n",
"4 20.31 27.06 132.90 1288.0 0.10000 \n",
".. ... ... ... ... ... \n",
"665 20.13 28.25 131.20 1261.0 0.09780 \n",
"666 16.60 28.08 108.30 858.1 0.08455 \n",
"667 20.60 29.33 140.10 1265.0 0.11780 \n",
"668 7.76 24.54 47.92 181.0 0.05263 \n",
"669 20.00 50.00 50.00 50.0 90.00000 \n",
"\n",
" compactness_mean concavity_mean concave points_mean symmetry_mean \n",
"0 0.27760 0.30010 0.14710 0.2419 \\\n",
"1 0.07864 0.08690 0.07017 0.1812 \n",
"2 0.10880 0.15190 0.09333 0.1814 \n",
"3 0.07864 0.08690 0.07017 0.1812 \n",
"4 0.10880 0.15190 0.09333 0.1814 \n",
".. ... ... ... ... \n",
"665 0.10340 0.14400 0.09791 0.1752 \n",
"666 0.10230 0.09251 0.05302 0.1590 \n",
"667 0.27700 0.35140 0.15200 0.2397 \n",
"668 0.04362 0.00000 0.00000 0.1587 \n",
"669 90.00000 90.00000 90.00000 90.0000 \n",
"\n",
" fractal_dimension_mean ... radius_worst texture_worst \n",
"0 0.07871 ... 25.380 17.33 \\\n",
"1 0.05667 ... 24.990 23.41 \n",
"2 0.05572 ... 24.330 39.16 \n",
"3 0.05667 ... 24.990 23.41 \n",
"4 0.05572 ... 24.330 39.16 \n",
".. ... ... ... ... \n",
"665 0.05533 ... 23.690 38.25 \n",
"666 0.05648 ... 18.980 34.12 \n",
"667 0.07016 ... 25.740 39.42 \n",
"668 0.05884 ... 9.456 30.37 \n",
"669 90.00000 ... 90.000 90.00 \n",
"\n",
" perimeter_worst area_worst smoothness_worst compactness_worst \n",
"0 184.60 2019.0 0.16220 0.66560 \\\n",
"1 158.80 1956.0 0.12380 0.18660 \n",
"2 162.30 1844.0 0.15220 0.29450 \n",
"3 158.80 1956.0 0.12380 0.18660 \n",
"4 162.30 1844.0 0.15220 0.29450 \n",
".. ... ... ... ... \n",
"665 155.00 1731.0 0.11660 0.19220 \n",
"666 126.70 1124.0 0.11390 0.30940 \n",
"667 184.60 1821.0 0.16500 0.86810 \n",
"668 59.16 268.6 0.08996 0.06444 \n",
"669 90.00 90.0 90.00000 90.00000 \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 0.3788 0.1697 0.3151 \n",
"3 0.2416 0.1860 0.2750 \n",
"4 0.3788 0.1697 0.3151 \n",
".. ... ... ... \n",
"665 0.3215 0.1628 0.2572 \n",
"666 0.3403 0.1418 0.2218 \n",
"667 0.9387 0.2650 0.4087 \n",
"668 0.0000 0.0000 0.2871 \n",
"669 90.0000 90.0000 90.0000 \n",
"\n",
" fractal_dimension_worst \n",
"0 0.11890 \n",
"1 0.08902 \n",
"2 0.07999 \n",
"3 0.08902 \n",
"4 0.07999 \n",
".. ... \n",
"665 0.06637 \n",
"666 0.07820 \n",
"667 0.12400 \n",
"668 0.07039 \n",
"669 90.00000 \n",
"\n",
"[670 rows x 30 columns]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Defining feature set\n",
"X = data.iloc[:,2:]\n",
"X"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "89287f14",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"\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>diagnosis</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>666</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>667</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>668</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>669</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>670 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" diagnosis\n",
"0 1\n",
"1 1\n",
"2 1\n",
"3 1\n",
"4 1\n",
".. ...\n",
"665 1\n",
"666 1\n",
"667 1\n",
"668 0\n",
"669 1\n",
"\n",
"[670 rows x 1 columns]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Label\n",
"y = data.iloc[:,1:2]\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "0c7551a1",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "ff45c27d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" 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>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>fractal_dimension_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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>13.61</td>\n",
" <td>24.69</td>\n",
" <td>87.76</td>\n",
" <td>572.6</td>\n",
" <td>0.09258</td>\n",
" <td>0.07862</td>\n",
" <td>0.05285</td>\n",
" <td>0.03085</td>\n",
" <td>0.1761</td>\n",
" <td>0.06130</td>\n",
" <td>...</td>\n",
" <td>16.89</td>\n",
" <td>35.64</td>\n",
" <td>113.20</td>\n",
" <td>848.7</td>\n",
" <td>0.1471</td>\n",
" <td>0.2884</td>\n",
" <td>0.37960</td>\n",
" <td>0.13290</td>\n",
" <td>0.3470</td>\n",
" <td>0.07900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>17.05</td>\n",
" <td>19.08</td>\n",
" <td>113.40</td>\n",
" <td>895.0</td>\n",
" <td>0.11410</td>\n",
" <td>0.15720</td>\n",
" <td>0.19100</td>\n",
" <td>0.10900</td>\n",
" <td>0.2131</td>\n",
" <td>0.06325</td>\n",
" <td>...</td>\n",
" <td>19.59</td>\n",
" <td>24.89</td>\n",
" <td>133.50</td>\n",
" <td>1189.0</td>\n",
" <td>0.1703</td>\n",
" <td>0.3934</td>\n",
" <td>0.50180</td>\n",
" <td>0.25430</td>\n",
" <td>0.3109</td>\n",
" <td>0.09061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>19.45</td>\n",
" <td>19.33</td>\n",
" <td>126.50</td>\n",
" <td>1169.0</td>\n",
" <td>0.10350</td>\n",
" <td>0.11880</td>\n",
" <td>0.13790</td>\n",
" <td>0.08591</td>\n",
" <td>0.1776</td>\n",
" <td>0.05647</td>\n",
" <td>...</td>\n",
" <td>25.70</td>\n",
" <td>24.57</td>\n",
" <td>163.10</td>\n",
" <td>1972.0</td>\n",
" <td>0.1497</td>\n",
" <td>0.3161</td>\n",
" <td>0.43170</td>\n",
" <td>0.19990</td>\n",
" <td>0.3379</td>\n",
" <td>0.08950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>13.70</td>\n",
" <td>17.64</td>\n",
" <td>87.76</td>\n",
" <td>575.5</td>\n",
" <td>0.09277</td>\n",
" <td>0.07255</td>\n",
" <td>0.01752</td>\n",
" <td>0.01880</td>\n",
" <td>0.1631</td>\n",
" <td>0.06155</td>\n",
" <td>...</td>\n",
" <td>15.85</td>\n",
" <td>20.20</td>\n",
" <td>101.60</td>\n",
" <td>773.4</td>\n",
" <td>0.1264</td>\n",
" <td>0.1564</td>\n",
" <td>0.12060</td>\n",
" <td>0.08704</td>\n",
" <td>0.2806</td>\n",
" <td>0.07782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>424</th>\n",
" <td>12.67</td>\n",
" <td>17.30</td>\n",
" <td>81.25</td>\n",
" <td>476.3</td>\n",
" <td>0.11580</td>\n",
" <td>0.10850</td>\n",
" <td>0.05928</td>\n",
" <td>0.03279</td>\n",
" <td>0.1943</td>\n",
" <td>0.06612</td>\n",
" <td>...</td>\n",
" <td>13.57</td>\n",
" <td>21.40</td>\n",
" <td>86.67</td>\n",
" <td>552.0</td>\n",
" <td>0.1580</td>\n",
" <td>0.1751</td>\n",
" <td>0.18890</td>\n",
" <td>0.08411</td>\n",
" <td>0.3155</td>\n",
" <td>0.07538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>422</th>\n",
" <td>12.20</td>\n",
" <td>15.21</td>\n",
" <td>78.01</td>\n",
" <td>457.9</td>\n",
" <td>0.08673</td>\n",
" <td>0.06545</td>\n",
" <td>0.01994</td>\n",
" <td>0.01692</td>\n",
" <td>0.1638</td>\n",
" <td>0.06129</td>\n",
" <td>...</td>\n",
" <td>13.75</td>\n",
" <td>21.38</td>\n",
" <td>91.11</td>\n",
" <td>583.1</td>\n",
" <td>0.1256</td>\n",
" <td>0.1928</td>\n",
" <td>0.11670</td>\n",
" <td>0.05556</td>\n",
" <td>0.2661</td>\n",
" <td>0.07961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>595</th>\n",
" <td>13.71</td>\n",
" <td>18.68</td>\n",
" <td>88.73</td>\n",
" <td>571.0</td>\n",
" <td>0.09916</td>\n",
" <td>0.10700</td>\n",
" <td>0.05385</td>\n",
" <td>0.03783</td>\n",
" <td>0.1714</td>\n",
" <td>0.06843</td>\n",
" <td>...</td>\n",
" <td>15.11</td>\n",
" <td>25.63</td>\n",
" <td>99.43</td>\n",
" <td>701.9</td>\n",
" <td>0.1425</td>\n",
" <td>0.2566</td>\n",
" <td>0.19350</td>\n",
" <td>0.12840</td>\n",
" <td>0.2849</td>\n",
" <td>0.09031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>14.54</td>\n",
" <td>27.54</td>\n",
" <td>96.73</td>\n",
" <td>658.8</td>\n",
" <td>0.11390</td>\n",
" <td>0.15950</td>\n",
" <td>0.16390</td>\n",
" <td>0.07364</td>\n",
" <td>0.2303</td>\n",
" <td>0.07077</td>\n",
" <td>...</td>\n",
" <td>17.46</td>\n",
" <td>37.13</td>\n",
" <td>124.10</td>\n",
" <td>943.2</td>\n",
" <td>0.1678</td>\n",
" <td>0.6577</td>\n",
" <td>0.70260</td>\n",
" <td>0.17120</td>\n",
" <td>0.4218</td>\n",
" <td>0.13410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>504</th>\n",
" <td>10.26</td>\n",
" <td>12.22</td>\n",
" <td>65.75</td>\n",
" <td>321.6</td>\n",
" <td>0.09996</td>\n",
" <td>0.07542</td>\n",
" <td>0.01923</td>\n",
" <td>0.01968</td>\n",
" <td>0.1800</td>\n",
" <td>0.06569</td>\n",
" <td>...</td>\n",
" <td>11.38</td>\n",
" <td>15.65</td>\n",
" <td>73.23</td>\n",
" <td>394.5</td>\n",
" <td>0.1343</td>\n",
" <td>0.1650</td>\n",
" <td>0.08615</td>\n",
" <td>0.06696</td>\n",
" <td>0.2937</td>\n",
" <td>0.07722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>13.38</td>\n",
" <td>30.72</td>\n",
" <td>86.34</td>\n",
" <td>557.2</td>\n",
" <td>0.09245</td>\n",
" <td>0.07426</td>\n",
" <td>0.02819</td>\n",
" <td>0.03264</td>\n",
" <td>0.1375</td>\n",
" <td>0.06016</td>\n",
" <td>...</td>\n",
" <td>15.05</td>\n",
" <td>41.61</td>\n",
" <td>96.69</td>\n",
" <td>705.6</td>\n",
" <td>0.1172</td>\n",
" <td>0.1421</td>\n",
" <td>0.07003</td>\n",
" <td>0.07763</td>\n",
" <td>0.2196</td>\n",
" <td>0.07675</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>502 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" radius_mean texture_mean perimeter_mean area_mean smoothness_mean \n",
"187 13.61 24.69 87.76 572.6 0.09258 \\\n",
"326 17.05 19.08 113.40 895.0 0.11410 \n",
"353 19.45 19.33 126.50 1169.0 0.10350 \n",
"192 13.70 17.64 87.76 575.5 0.09277 \n",
"424 12.67 17.30 81.25 476.3 0.11580 \n",
".. ... ... ... ... ... \n",
"422 12.20 15.21 78.01 457.9 0.08673 \n",
"595 13.71 18.68 88.73 571.0 0.09916 \n",
"27 14.54 27.54 96.73 658.8 0.11390 \n",
"504 10.26 12.22 65.75 321.6 0.09996 \n",
"566 13.38 30.72 86.34 557.2 0.09245 \n",
"\n",
" compactness_mean concavity_mean concave points_mean symmetry_mean \n",
"187 0.07862 0.05285 0.03085 0.1761 \\\n",
"326 0.15720 0.19100 0.10900 0.2131 \n",
"353 0.11880 0.13790 0.08591 0.1776 \n",
"192 0.07255 0.01752 0.01880 0.1631 \n",
"424 0.10850 0.05928 0.03279 0.1943 \n",
".. ... ... ... ... \n",
"422 0.06545 0.01994 0.01692 0.1638 \n",
"595 0.10700 0.05385 0.03783 0.1714 \n",
"27 0.15950 0.16390 0.07364 0.2303 \n",
"504 0.07542 0.01923 0.01968 0.1800 \n",
"566 0.07426 0.02819 0.03264 0.1375 \n",
"\n",
" fractal_dimension_mean ... radius_worst texture_worst \n",
"187 0.06130 ... 16.89 35.64 \\\n",
"326 0.06325 ... 19.59 24.89 \n",
"353 0.05647 ... 25.70 24.57 \n",
"192 0.06155 ... 15.85 20.20 \n",
"424 0.06612 ... 13.57 21.40 \n",
".. ... ... ... ... \n",
"422 0.06129 ... 13.75 21.38 \n",
"595 0.06843 ... 15.11 25.63 \n",
"27 0.07077 ... 17.46 37.13 \n",
"504 0.06569 ... 11.38 15.65 \n",
"566 0.06016 ... 15.05 41.61 \n",
"\n",
" perimeter_worst area_worst smoothness_worst compactness_worst \n",
"187 113.20 848.7 0.1471 0.2884 \\\n",
"326 133.50 1189.0 0.1703 0.3934 \n",
"353 163.10 1972.0 0.1497 0.3161 \n",
"192 101.60 773.4 0.1264 0.1564 \n",
"424 86.67 552.0 0.1580 0.1751 \n",
".. ... ... ... ... \n",
"422 91.11 583.1 0.1256 0.1928 \n",
"595 99.43 701.9 0.1425 0.2566 \n",
"27 124.10 943.2 0.1678 0.6577 \n",
"504 73.23 394.5 0.1343 0.1650 \n",
"566 96.69 705.6 0.1172 0.1421 \n",
"\n",
" concavity_worst concave points_worst symmetry_worst \n",
"187 0.37960 0.13290 0.3470 \\\n",
"326 0.50180 0.25430 0.3109 \n",
"353 0.43170 0.19990 0.3379 \n",
"192 0.12060 0.08704 0.2806 \n",
"424 0.18890 0.08411 0.3155 \n",
".. ... ... ... \n",
"422 0.11670 0.05556 0.2661 \n",
"595 0.19350 0.12840 0.2849 \n",
"27 0.70260 0.17120 0.4218 \n",
"504 0.08615 0.06696 0.2937 \n",
"566 0.07003 0.07763 0.2196 \n",
"\n",
" fractal_dimension_worst \n",
"187 0.07900 \n",
"326 0.09061 \n",
"353 0.08950 \n",
"192 0.07782 \n",
"424 0.07538 \n",
".. ... \n",
"422 0.07961 \n",
"595 0.09031 \n",
"27 0.13410 \n",
"504 0.07722 \n",
"566 0.07675 \n",
"\n",
"[502 rows x 30 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "978b866f",
"metadata": {},
"outputs": [
{
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" <tr>\n",
" <th>187</th>\n",
" <td>1</td>\n",
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"text/plain": [
" diagnosis\n",
"187 1\n",
"326 1\n",
"353 1\n",
"192 0\n",
"424 0\n",
".. ...\n",
"422 0\n",
"595 0\n",
"27 1\n",
"504 0\n",
"566 0\n",
"\n",
"[502 rows x 1 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "ac419b8a",
"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>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>fractal_dimension_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",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>613</th>\n",
" <td>13.820</td>\n",
" <td>24.49</td>\n",
" <td>92.33</td>\n",
" <td>595.9</td>\n",
" <td>0.11620</td>\n",
" <td>0.16810</td>\n",
" <td>0.13570</td>\n",
" <td>0.06759</td>\n",
" <td>0.2275</td>\n",
" <td>0.07237</td>\n",
" <td>...</td>\n",
" <td>16.010</td>\n",
" <td>32.94</td>\n",
" <td>106.00</td>\n",
" <td>788.0</td>\n",
" <td>0.1794</td>\n",
" <td>0.3966</td>\n",
" <td>0.33810</td>\n",
" <td>0.15210</td>\n",
" <td>0.3651</td>\n",
" <td>0.11830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>423</th>\n",
" <td>12.670</td>\n",
" <td>17.30</td>\n",
" <td>81.25</td>\n",
" <td>489.9</td>\n",
" <td>0.10280</td>\n",
" <td>0.07664</td>\n",
" <td>0.03193</td>\n",
" <td>0.02107</td>\n",
" <td>0.1707</td>\n",
" <td>0.05984</td>\n",
" <td>...</td>\n",
" <td>13.710</td>\n",
" <td>21.10</td>\n",
" <td>88.70</td>\n",
" <td>574.4</td>\n",
" <td>0.1384</td>\n",
" <td>0.1212</td>\n",
" <td>0.10200</td>\n",
" <td>0.05602</td>\n",
" <td>0.2688</td>\n",
" <td>0.06888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>14.680</td>\n",
" <td>20.13</td>\n",
" <td>94.74</td>\n",
" <td>684.5</td>\n",
" <td>0.09867</td>\n",
" <td>0.07200</td>\n",
" <td>0.07395</td>\n",
" <td>0.05259</td>\n",
" <td>0.1586</td>\n",
" <td>0.05922</td>\n",
" <td>...</td>\n",
" <td>19.070</td>\n",
" <td>30.88</td>\n",
" <td>123.40</td>\n",
" <td>1138.0</td>\n",
" <td>0.1464</td>\n",
" <td>0.1871</td>\n",
" <td>0.29140</td>\n",
" <td>0.16090</td>\n",
" <td>0.3029</td>\n",
" <td>0.08216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>489</th>\n",
" <td>11.270</td>\n",
" <td>12.96</td>\n",
" <td>73.16</td>\n",
" <td>386.3</td>\n",
" <td>0.12370</td>\n",
" <td>0.11110</td>\n",
" <td>0.07900</td>\n",
" <td>0.05550</td>\n",
" <td>0.2018</td>\n",
" <td>0.06914</td>\n",
" <td>...</td>\n",
" <td>12.840</td>\n",
" <td>20.53</td>\n",
" <td>84.93</td>\n",
" <td>476.1</td>\n",
" <td>0.1610</td>\n",
" <td>0.2429</td>\n",
" <td>0.22470</td>\n",
" <td>0.13180</td>\n",
" <td>0.3343</td>\n",
" <td>0.09215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>289</th>\n",
" <td>12.470</td>\n",
" <td>18.60</td>\n",
" <td>81.09</td>\n",
" <td>481.9</td>\n",
" <td>0.09965</td>\n",
" <td>0.10580</td>\n",
" <td>0.08005</td>\n",
" <td>0.03821</td>\n",
" <td>0.1925</td>\n",
" <td>0.06373</td>\n",
" <td>...</td>\n",
" <td>14.970</td>\n",
" <td>24.64</td>\n",
" <td>96.05</td>\n",
" <td>677.9</td>\n",
" <td>0.1426</td>\n",
" <td>0.2378</td>\n",
" <td>0.26710</td>\n",
" <td>0.10150</td>\n",
" <td>0.3014</td>\n",
" <td>0.08750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>11.280</td>\n",
" <td>13.39</td>\n",
" <td>73.00</td>\n",
" <td>384.8</td>\n",
" <td>0.11640</td>\n",
" <td>0.11360</td>\n",
" <td>0.04635</td>\n",
" <td>0.04796</td>\n",
" <td>0.1771</td>\n",
" <td>0.06072</td>\n",
" <td>...</td>\n",
" <td>11.920</td>\n",
" <td>15.77</td>\n",
" <td>76.53</td>\n",
" <td>434.0</td>\n",
" <td>0.1367</td>\n",
" <td>0.1822</td>\n",
" <td>0.08669</td>\n",
" <td>0.08611</td>\n",
" <td>0.2102</td>\n",
" <td>0.06784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>332</th>\n",
" <td>11.220</td>\n",
" <td>33.81</td>\n",
" <td>70.79</td>\n",
" <td>365.6</td>\n",
" <td>0.09687</td>\n",
" <td>0.09752</td>\n",
" <td>0.05263</td>\n",
" <td>0.02788</td>\n",
" <td>0.1619</td>\n",
" <td>0.06408</td>\n",
" <td>...</td>\n",
" <td>11.620</td>\n",
" <td>26.51</td>\n",
" <td>76.43</td>\n",
" <td>407.5</td>\n",
" <td>0.1428</td>\n",
" <td>0.2510</td>\n",
" <td>0.21230</td>\n",
" <td>0.09861</td>\n",
" <td>0.2289</td>\n",
" <td>0.08278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19.170</td>\n",
" <td>24.80</td>\n",
" <td>132.40</td>\n",
" <td>1123.0</td>\n",
" <td>0.09740</td>\n",
" <td>0.24580</td>\n",
" <td>0.20650</td>\n",
" <td>0.11180</td>\n",
" <td>0.2397</td>\n",
" <td>0.07800</td>\n",
" <td>...</td>\n",
" <td>20.960</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
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" <tr>\n",
" <th>127</th>\n",
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" <td>14.110</td>\n",
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"text/plain": [
" radius_mean texture_mean perimeter_mean area_mean smoothness_mean \n",
"613 13.820 24.49 92.33 595.9 0.11620 \\\n",
"423 12.670 17.30 81.25 489.9 0.10280 \n",
"28 14.680 20.13 94.74 684.5 0.09867 \n",
"489 11.270 12.96 73.16 386.3 0.12370 \n",
"289 12.470 18.60 81.09 481.9 0.09965 \n",
".. ... ... ... ... ... \n",
"206 11.280 13.39 73.00 384.8 0.11640 \n",
"332 11.220 33.81 70.79 365.6 0.09687 \n",
"18 19.170 24.80 132.40 1123.0 0.09740 \n",
"177 8.726 15.83 55.84 230.9 0.11500 \n",
"127 12.310 16.52 79.19 470.9 0.09172 \n",
"\n",
" compactness_mean concavity_mean concave points_mean symmetry_mean \n",
"613 0.16810 0.13570 0.06759 0.2275 \\\n",
"423 0.07664 0.03193 0.02107 0.1707 \n",
"28 0.07200 0.07395 0.05259 0.1586 \n",
"489 0.11110 0.07900 0.05550 0.2018 \n",
"289 0.10580 0.08005 0.03821 0.1925 \n",
".. ... ... ... ... \n",
"206 0.11360 0.04635 0.04796 0.1771 \n",
"332 0.09752 0.05263 0.02788 0.1619 \n",
"18 0.24580 0.20650 0.11180 0.2397 \n",
"177 0.08201 0.04132 0.01924 0.1649 \n",
"127 0.06829 0.03372 0.02272 0.1720 \n",
"\n",
" fractal_dimension_mean ... radius_worst texture_worst \n",
"613 0.07237 ... 16.010 32.94 \\\n",
"423 0.05984 ... 13.710 21.10 \n",
"28 0.05922 ... 19.070 30.88 \n",
"489 0.06914 ... 12.840 20.53 \n",
"289 0.06373 ... 14.970 24.64 \n",
".. ... ... ... ... \n",
"206 0.06072 ... 11.920 15.77 \n",
"332 0.06408 ... 11.620 26.51 \n",
"18 0.07800 ... 20.960 29.94 \n",
"177 0.07633 ... 9.628 19.62 \n",
"127 0.05914 ... 14.110 23.21 \n",
"\n",
" perimeter_worst area_worst smoothness_worst compactness_worst \n",
"613 106.00 788.0 0.1794 0.3966 \\\n",
"423 88.70 574.4 0.1384 0.1212 \n",
"28 123.40 1138.0 0.1464 0.1871 \n",
"489 84.93 476.1 0.1610 0.2429 \n",
"289 96.05 677.9 0.1426 0.2378 \n",
".. ... ... ... ... \n",
"206 76.53 434.0 0.1367 0.1822 \n",
"332 76.43 407.5 0.1428 0.2510 \n",
"18 151.70 1332.0 0.1037 0.3903 \n",
"177 64.48 284.4 0.1724 0.2364 \n",
"127 89.71 611.1 0.1176 0.1843 \n",
"\n",
" concavity_worst concave points_worst symmetry_worst \n",
"613 0.33810 0.15210 0.3651 \\\n",
"423 0.10200 0.05602 0.2688 \n",
"28 0.29140 0.16090 0.3029 \n",
"489 0.22470 0.13180 0.3343 \n",
"289 0.26710 0.10150 0.3014 \n",
".. ... ... ... \n",
"206 0.08669 0.08611 0.2102 \n",
"332 0.21230 0.09861 0.2289 \n",
"18 0.36390 0.17670 0.3176 \n",
"177 0.24560 0.10500 0.2926 \n",
"127 0.17030 0.08660 0.2618 \n",
"\n",
" fractal_dimension_worst \n",
"613 0.11830 \n",
"423 0.06888 \n",
"28 0.08216 \n",
"489 0.09215 \n",
"289 0.08750 \n",
".. ... \n",
"206 0.06784 \n",
"332 0.08278 \n",
"18 0.10230 \n",
"177 0.10170 \n",
"127 0.07609 \n",
"\n",
"[168 rows x 30 columns]"
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},
"execution_count": 66,
"metadata": {},
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],
"source": [
"X_test"
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"id": "564ac922",
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"DecisionTreeClassifier(criterion='entropy')"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Using tree classifier for model Building \n",
"from sklearn import tree\n",
"tree_clas = tree.DecisionTreeClassifier(criterion='entropy')\n",
"tree_clas.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "537be80a",
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"execution_count": 69,
"metadata": {},
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],
"source": [
"y_test_predict = tree_clas.predict(X_test)\n",
"y_test_predict"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "89c29a02",
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},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Prediction on training dataset\n",
"y_train_predict = tree_clas.predict(X_train)\n",
"y_train_predict"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "75b8c8f0",
"metadata": {},
"outputs": [
{
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},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(y_train)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "3a2f8b4b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9166666666666666\n"
]
}
],
"source": [
"#Calculating the accuracy of prediction on the training set\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"test_accuracy = accuracy_score(y_test,y_test_predict)\n",
"print(test_accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "fe0c404a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"train_accuracy = accuracy_score(y_train,y_train_predict)\n",
"print(train_accuracy)\n",
"#this model is overfitted due to accuracy is 1 "
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "b2717d8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[90, 2],\n",
" [12, 64]])"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"confusion_matrix(y_test,y_test_predict)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "2b8e519e",
"metadata": {},
"outputs": [
{
"data": {
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" Text(0.20833333333333334, 0.6875, 'x[3] <= 694.1\\nentropy = 0.112\\nsamples = 268\\nvalue = [264, 4]'),\n",
" Text(0.125, 0.5625, 'x[18] <= 0.011\\nentropy = 0.036\\nsamples = 263\\nvalue = [262, 1]'),\n",
" Text(0.08333333333333333, 0.4375, 'x[1] <= 21.165\\nentropy = 0.918\\nsamples = 3\\nvalue = [2, 1]'),\n",
" Text(0.041666666666666664, 0.3125, 'entropy = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
" Text(0.125, 0.3125, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
" Text(0.16666666666666666, 0.4375, 'entropy = 0.0\\nsamples = 260\\nvalue = [260, 0]'),\n",
" Text(0.2916666666666667, 0.5625, 'x[10] <= 0.222\\nentropy = 0.971\\nsamples = 5\\nvalue = [2, 3]'),\n",
" Text(0.25, 0.4375, 'entropy = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
" Text(0.3333333333333333, 0.4375, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
" Text(0.6041666666666666, 0.6875, 'x[1] <= 19.71\\nentropy = 0.968\\nsamples = 76\\nvalue = [46, 30]'),\n",
" Text(0.4583333333333333, 0.5625, 'x[21] <= 25.43\\nentropy = 0.469\\nsamples = 40\\nvalue = [36, 4]'),\n",
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" Text(0.5, 0.4375, 'x[28] <= 0.355\\nentropy = 0.764\\nsamples = 18\\nvalue = [14, 4]'),\n",
" Text(0.4583333333333333, 0.3125, 'x[27] <= 0.12\\nentropy = 0.544\\nsamples = 16\\nvalue = [14, 2]'),\n",
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" Text(0.5, 0.1875, 'entropy = 0.0\\nsamples = 13\\nvalue = [13, 0]'),\n",
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" Text(0.75, 0.5625, 'x[7] <= 0.045\\nentropy = 0.852\\nsamples = 36\\nvalue = [10, 26]'),\n",
" Text(0.6666666666666666, 0.4375, 'x[18] <= 0.013\\nentropy = 0.684\\nsamples = 11\\nvalue = [9, 2]'),\n",
" Text(0.625, 0.3125, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
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"metadata": {},
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tree.plot_tree(tree_clas)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3095f82",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2927d682",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6343aa1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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