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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pregnancies</th>\n",
" <th>Glucose</th>\n",
" <th>BloodPressure</th>\n",
" <th>SkinThickness</th>\n",
" <th>Insulin</th>\n",
" <th>BMI</th>\n",
" <th>DiabetesPedigreeFunction</th>\n",
" <th>Age</th>\n",
" <th>Outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>148</td>\n",
" <td>72</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>33.6</td>\n",
" <td>0.627</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"0 6 148 72 35 0 33.6 \n",
"1 1 85 66 29 0 26.6 \n",
"2 8 183 64 0 0 23.3 \n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"0 0.627 50 1 \n",
"1 0.351 31 0 \n",
"2 0.672 32 1 \n",
"3 0.167 21 0 \n",
"4 2.288 33 1 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_diabetes = pd.read_csv('diabetes.csv')\n",
"df_diabetes.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>age</th>\n",
" <th>sex</th>\n",
" <th>cp</th>\n",
" <th>trestbps</th>\n",
" <th>chol</th>\n",
" <th>fbs</th>\n",
" <th>restecg</th>\n",
" <th>thalach</th>\n",
" <th>exang</th>\n",
" <th>oldpeak</th>\n",
" <th>slope</th>\n",
" <th>ca</th>\n",
" <th>thal</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>52</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>125</td>\n",
" <td>212</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>168</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>140</td>\n",
" <td>203</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>155</td>\n",
" <td>1</td>\n",
" <td>3.1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>145</td>\n",
" <td>174</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>125</td>\n",
" <td>1</td>\n",
" <td>2.6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>61</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>148</td>\n",
" <td>203</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>161</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>62</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>138</td>\n",
" <td>294</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>106</td>\n",
" <td>0</td>\n",
" <td>1.9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n",
"0 52 1 0 125 212 0 1 168 0 1.0 2 \n",
"1 53 1 0 140 203 1 0 155 1 3.1 0 \n",
"2 70 1 0 145 174 0 1 125 1 2.6 0 \n",
"3 61 1 0 148 203 0 1 161 0 0.0 2 \n",
"4 62 0 0 138 294 1 1 106 0 1.9 1 \n",
"\n",
" ca thal target \n",
"0 2 3 0 \n",
"1 0 3 0 \n",
"2 0 3 0 \n",
"3 1 3 0 \n",
"4 3 2 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_heart = pd.read_csv(\"heart_complete.csv\")\n",
"df_heart.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>Age</th>\n",
" <th>Gender</th>\n",
" <th>Height</th>\n",
" <th>Weight</th>\n",
" <th>BMI</th>\n",
" <th>PhysicalActivityLevel</th>\n",
" <th>ObesityCategory</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>Male</td>\n",
" <td>173.575262</td>\n",
" <td>71.982051</td>\n",
" <td>23.891783</td>\n",
" <td>4</td>\n",
" <td>Normal weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>69</td>\n",
" <td>Male</td>\n",
" <td>164.127306</td>\n",
" <td>89.959256</td>\n",
" <td>33.395209</td>\n",
" <td>2</td>\n",
" <td>Obese</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>46</td>\n",
" <td>Female</td>\n",
" <td>168.072202</td>\n",
" <td>72.930629</td>\n",
" <td>25.817737</td>\n",
" <td>4</td>\n",
" <td>Overweight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>32</td>\n",
" <td>Male</td>\n",
" <td>168.459633</td>\n",
" <td>84.886912</td>\n",
" <td>29.912247</td>\n",
" <td>3</td>\n",
" <td>Overweight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>60</td>\n",
" <td>Male</td>\n",
" <td>183.568568</td>\n",
" <td>69.038945</td>\n",
" <td>20.487903</td>\n",
" <td>3</td>\n",
" <td>Normal weight</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Gender Height Weight BMI PhysicalActivityLevel \\\n",
"0 56 Male 173.575262 71.982051 23.891783 4 \n",
"1 69 Male 164.127306 89.959256 33.395209 2 \n",
"2 46 Female 168.072202 72.930629 25.817737 4 \n",
"3 32 Male 168.459633 84.886912 29.912247 3 \n",
"4 60 Male 183.568568 69.038945 20.487903 3 \n",
"\n",
" ObesityCategory \n",
"0 Normal weight \n",
"1 Obese \n",
"2 Overweight \n",
"3 Overweight \n",
"4 Normal weight "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_obesity = pd.read_csv(\"obesity_data.csv\")\n",
"df_obesity.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
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