{
"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": [
{
"data": {
"text/html": [
"
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"\n",
"
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" \n",
" \n",
" | \n",
" Pregnancies | \n",
" Glucose | \n",
" BloodPressure | \n",
" SkinThickness | \n",
" Insulin | \n",
" BMI | \n",
" DiabetesPedigreeFunction | \n",
" Age | \n",
" Outcome | \n",
"
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" 0 | \n",
" 6 | \n",
" 148 | \n",
" 72 | \n",
" 35 | \n",
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" 50 | \n",
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" 2 | \n",
" 8 | \n",
" 183 | \n",
" 64 | \n",
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" 0.672 | \n",
" 32 | \n",
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" 3 | \n",
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"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": {
"text/html": [
"\n",
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"
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" | \n",
" age | \n",
" sex | \n",
" cp | \n",
" trestbps | \n",
" chol | \n",
" fbs | \n",
" restecg | \n",
" thalach | \n",
" exang | \n",
" oldpeak | \n",
" slope | \n",
" ca | \n",
" thal | \n",
" target | \n",
"
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" 2 | \n",
" 70 | \n",
" 1 | \n",
" 0 | \n",
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" 174 | \n",
" 0 | \n",
" 1 | \n",
" 125 | \n",
" 1 | \n",
" 2.6 | \n",
" 0 | \n",
" 0 | \n",
" 3 | \n",
" 0 | \n",
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" 3 | \n",
" 61 | \n",
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" 3 | \n",
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"
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],
"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": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Age | \n",
" Gender | \n",
" Height | \n",
" Weight | \n",
" BMI | \n",
" PhysicalActivityLevel | \n",
" ObesityCategory | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 56 | \n",
" Male | \n",
" 173.575262 | \n",
" 71.982051 | \n",
" 23.891783 | \n",
" 4 | \n",
" Normal weight | \n",
"
\n",
" \n",
" 1 | \n",
" 69 | \n",
" Male | \n",
" 164.127306 | \n",
" 89.959256 | \n",
" 33.395209 | \n",
" 2 | \n",
" Obese | \n",
"
\n",
" \n",
" 2 | \n",
" 46 | \n",
" Female | \n",
" 168.072202 | \n",
" 72.930629 | \n",
" 25.817737 | \n",
" 4 | \n",
" Overweight | \n",
"
\n",
" \n",
" 3 | \n",
" 32 | \n",
" Male | \n",
" 168.459633 | \n",
" 84.886912 | \n",
" 29.912247 | \n",
" 3 | \n",
" Overweight | \n",
"
\n",
" \n",
" 4 | \n",
" 60 | \n",
" Male | \n",
" 183.568568 | \n",
" 69.038945 | \n",
" 20.487903 | \n",
" 3 | \n",
" Normal weight | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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,
"metadata": {},
"outputs": [],
"source": []
}
],
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