{
"cells": [
{
"cell_type": "markdown",
"id": "2f7b0d61",
"metadata": {},
"source": [
"### What is Triage?\n",
"\n",
"Triage refers to the sorting of injured or sick people according to their need for emergency medical\n",
"attention. It is a method of determining priority for who gets care first."
]
},
{
"cell_type": "markdown",
"id": "c9edc660",
"metadata": {},
"source": [
"Triage is the prioritization of patient care (or victims during a disaster) based on illness/injury, symptoms, severity, prognosis, and resource availability. The purpose of triage is to identify patients needing immediate resuscitation; to assign patients to a predesignated patient care area, thereby prioritizing their care; and to initiate diagnostic/therapeutic measures as appropriate.\n",
"\n",
"#### Triage Categories:\n",
"\n",
"- Red: Needs immediate attention for a critical life-threatening injury or illness; transport first for medical help.\n",
"- Yellow: Serious injuries needing immediate attention. In some systems, yellow tags are transported first because they have a better chance of recovery than red-tagged patients.\n",
"- Green: Less serious or minor injuries, non-life-threatening, delayed transport; will eventually need help but can wait for others.\n",
"- Black: Deceased or mortally wounded; black may not mean the person has already died. It may mean that he or she is beyond help and, therefore, is a lower priority than those who can be helped.\n",
"- White: No injury or illness (not used in all systems)"
]
},
{
"cell_type": "markdown",
"id": "4b0b8de6",
"metadata": {},
"source": [
"<img src=\"https://www.disabled-world.com/pics/1/medical-triage-chart.png\" alt=\"Triage Chart\" style=\"width: 800px;\"/>"
]
},
{
"cell_type": "markdown",
"id": "196c53a0",
"metadata": {},
"source": [
"##### Image source: [www.disabled-world.com](https://www.disabled-world.com/calculators-charts/triage.php)"
]
},
{
"cell_type": "markdown",
"id": "2bc885fc",
"metadata": {},
"source": [
"#### Importing Essential Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "324d8820",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "40ea0e2d",
"metadata": {},
"outputs": [],
"source": [
"df=pd.read_csv('data/patient_priority.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "86e81d55",
"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>Unnamed: 0</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>chest pain type</th>\n",
" <th>blood pressure</th>\n",
" <th>cholesterol</th>\n",
" <th>max heart rate</th>\n",
" <th>exercise angina</th>\n",
" <th>plasma glucose</th>\n",
" <th>skin_thickness</th>\n",
" <th>insulin</th>\n",
" <th>bmi</th>\n",
" <th>diabetes_pedigree</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>Residence_type</th>\n",
" <th>smoking_status</th>\n",
" <th>triage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>40.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>140.0</td>\n",
" <td>294.0</td>\n",
" <td>172.0</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>43.0</td>\n",
" <td>92.0</td>\n",
" <td>19.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Urban</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>160.0</td>\n",
" <td>180.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>75.0</td>\n",
" <td>47.0</td>\n",
" <td>90.0</td>\n",
" <td>18.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Urban</td>\n",
" <td>never smoked</td>\n",
" <td>orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>37.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>130.0</td>\n",
" <td>294.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>98.0</td>\n",
" <td>53.0</td>\n",
" <td>102.0</td>\n",
" <td>23.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Urban</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>138.0</td>\n",
" <td>214.0</td>\n",
" <td>156.0</td>\n",
" <td>1.0</td>\n",
" <td>72.0</td>\n",
" <td>51.0</td>\n",
" <td>118.0</td>\n",
" <td>18.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Urban</td>\n",
" <td>never smoked</td>\n",
" <td>orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>54.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>150.0</td>\n",
" <td>195.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>90.0</td>\n",
" <td>83.0</td>\n",
" <td>21.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Urban</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 age gender chest pain type blood pressure cholesterol \\\n",
"0 0 40.0 1.0 2.0 140.0 294.0 \n",
"1 1 49.0 0.0 3.0 160.0 180.0 \n",
"2 2 37.0 1.0 2.0 130.0 294.0 \n",
"3 3 48.0 0.0 4.0 138.0 214.0 \n",
"4 4 54.0 1.0 3.0 150.0 195.0 \n",
"\n",
" max heart rate exercise angina plasma glucose skin_thickness insulin \\\n",
"0 172.0 0.0 108.0 43.0 92.0 \n",
"1 156.0 0.0 75.0 47.0 90.0 \n",
"2 156.0 0.0 98.0 53.0 102.0 \n",
"3 156.0 1.0 72.0 51.0 118.0 \n",
"4 156.0 0.0 108.0 90.0 83.0 \n",
"\n",
" bmi diabetes_pedigree hypertension heart_disease Residence_type \\\n",
"0 19.0 0.467386 0.0 0.0 Urban \n",
"1 18.0 0.467386 0.0 0.0 Urban \n",
"2 23.0 0.467386 0.0 0.0 Urban \n",
"3 18.0 0.467386 0.0 0.0 Urban \n",
"4 21.0 0.467386 0.0 0.0 Urban \n",
"\n",
" smoking_status triage \n",
"0 never smoked yellow \n",
"1 never smoked orange \n",
"2 never smoked yellow \n",
"3 never smoked orange \n",
"4 never smoked yellow "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96e47fdf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6962, 18)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b65eda19",
"metadata": {},
"outputs": [],
"source": [
"df.drop('Unnamed: 0', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fb8614df",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['age', 'gender', 'chest pain type', 'blood pressure', 'cholesterol',\n",
" 'max heart rate', 'exercise angina', 'plasma glucose', 'skin_thickness',\n",
" 'insulin', 'bmi', 'diabetes_pedigree', 'hypertension', 'heart_disease',\n",
" 'Residence_type', 'smoking_status', 'triage'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fbec84b8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6962, 17)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2e590267",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 6962 entries, 0 to 6961\n",
"Data columns (total 17 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 6962 non-null float64\n",
" 1 gender 6961 non-null float64\n",
" 2 chest pain type 6962 non-null float64\n",
" 3 blood pressure 6962 non-null float64\n",
" 4 cholesterol 6962 non-null float64\n",
" 5 max heart rate 6962 non-null float64\n",
" 6 exercise angina 6962 non-null float64\n",
" 7 plasma glucose 6962 non-null float64\n",
" 8 skin_thickness 6962 non-null float64\n",
" 9 insulin 6962 non-null float64\n",
" 10 bmi 6962 non-null float64\n",
" 11 diabetes_pedigree 6962 non-null float64\n",
" 12 hypertension 6962 non-null float64\n",
" 13 heart_disease 6962 non-null float64\n",
" 14 Residence_type 6962 non-null object \n",
" 15 smoking_status 6962 non-null object \n",
" 16 triage 6552 non-null object \n",
"dtypes: float64(14), object(3)\n",
"memory usage: 924.8+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "68836d4b",
"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>gender</th>\n",
" <th>chest pain type</th>\n",
" <th>blood pressure</th>\n",
" <th>cholesterol</th>\n",
" <th>max heart rate</th>\n",
" <th>exercise angina</th>\n",
" <th>plasma glucose</th>\n",
" <th>skin_thickness</th>\n",
" <th>insulin</th>\n",
" <th>bmi</th>\n",
" <th>diabetes_pedigree</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6962.000000</td>\n",
" <td>6961.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" <td>6962.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>57.450014</td>\n",
" <td>0.531964</td>\n",
" <td>0.529015</td>\n",
" <td>109.629991</td>\n",
" <td>184.711290</td>\n",
" <td>163.502442</td>\n",
" <td>0.061764</td>\n",
" <td>98.394283</td>\n",
" <td>56.813416</td>\n",
" <td>111.091640</td>\n",
" <td>27.190908</td>\n",
" <td>0.467386</td>\n",
" <td>0.071531</td>\n",
" <td>0.039500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>11.904948</td>\n",
" <td>0.499013</td>\n",
" <td>1.253791</td>\n",
" <td>21.534852</td>\n",
" <td>32.010359</td>\n",
" <td>15.458693</td>\n",
" <td>0.240743</td>\n",
" <td>28.598084</td>\n",
" <td>22.889316</td>\n",
" <td>17.470033</td>\n",
" <td>7.362886</td>\n",
" <td>0.102663</td>\n",
" <td>0.257729</td>\n",
" <td>0.194796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>60.000000</td>\n",
" <td>150.000000</td>\n",
" <td>138.000000</td>\n",
" <td>0.000000</td>\n",
" <td>55.120000</td>\n",
" <td>21.000000</td>\n",
" <td>81.000000</td>\n",
" <td>10.300000</td>\n",
" <td>0.078000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>48.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>92.000000</td>\n",
" <td>164.000000</td>\n",
" <td>150.000000</td>\n",
" <td>0.000000</td>\n",
" <td>78.707500</td>\n",
" <td>36.000000</td>\n",
" <td>97.000000</td>\n",
" <td>21.800000</td>\n",
" <td>0.467386</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>56.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>111.000000</td>\n",
" <td>179.000000</td>\n",
" <td>163.000000</td>\n",
" <td>0.000000</td>\n",
" <td>93.000000</td>\n",
" <td>55.000000</td>\n",
" <td>111.000000</td>\n",
" <td>26.200000</td>\n",
" <td>0.467386</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>66.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>127.000000</td>\n",
" <td>192.000000</td>\n",
" <td>177.000000</td>\n",
" <td>0.000000</td>\n",
" <td>111.632500</td>\n",
" <td>77.000000</td>\n",
" <td>125.000000</td>\n",
" <td>31.000000</td>\n",
" <td>0.467386</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>82.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>165.000000</td>\n",
" <td>294.000000</td>\n",
" <td>202.000000</td>\n",
" <td>1.000000</td>\n",
" <td>199.000000</td>\n",
" <td>99.000000</td>\n",
" <td>171.000000</td>\n",
" <td>66.800000</td>\n",
" <td>2.420000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age gender chest pain type blood pressure cholesterol \\\n",
"count 6962.000000 6961.000000 6962.000000 6962.000000 6962.000000 \n",
"mean 57.450014 0.531964 0.529015 109.629991 184.711290 \n",
"std 11.904948 0.499013 1.253791 21.534852 32.010359 \n",
"min 28.000000 0.000000 0.000000 60.000000 150.000000 \n",
"25% 48.000000 0.000000 0.000000 92.000000 164.000000 \n",
"50% 56.000000 1.000000 0.000000 111.000000 179.000000 \n",
"75% 66.000000 1.000000 0.000000 127.000000 192.000000 \n",
"max 82.000000 1.000000 4.000000 165.000000 294.000000 \n",
"\n",
" max heart rate exercise angina plasma glucose skin_thickness \\\n",
"count 6962.000000 6962.000000 6962.000000 6962.000000 \n",
"mean 163.502442 0.061764 98.394283 56.813416 \n",
"std 15.458693 0.240743 28.598084 22.889316 \n",
"min 138.000000 0.000000 55.120000 21.000000 \n",
"25% 150.000000 0.000000 78.707500 36.000000 \n",
"50% 163.000000 0.000000 93.000000 55.000000 \n",
"75% 177.000000 0.000000 111.632500 77.000000 \n",
"max 202.000000 1.000000 199.000000 99.000000 \n",
"\n",
" insulin bmi diabetes_pedigree hypertension \\\n",
"count 6962.000000 6962.000000 6962.000000 6962.000000 \n",
"mean 111.091640 27.190908 0.467386 0.071531 \n",
"std 17.470033 7.362886 0.102663 0.257729 \n",
"min 81.000000 10.300000 0.078000 0.000000 \n",
"25% 97.000000 21.800000 0.467386 0.000000 \n",
"50% 111.000000 26.200000 0.467386 0.000000 \n",
"75% 125.000000 31.000000 0.467386 0.000000 \n",
"max 171.000000 66.800000 2.420000 1.000000 \n",
"\n",
" heart_disease \n",
"count 6962.000000 \n",
"mean 0.039500 \n",
"std 0.194796 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a6e567e9",
"metadata": {},
"outputs": [],
"source": [
"# plotsize\n",
"plt.rcParams[\"figure.figsize\"] = (16, 14)"
]
},
{
"cell_type": "markdown",
"id": "2091255b",
"metadata": {},
"source": [
"#### Histograms for all numeric features to get an idea about the distribution of features"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "661a9a52",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x1400 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.hist();"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ba416fcf",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"age 0\n",
"gender 1\n",
"chest pain type 0\n",
"blood pressure 0\n",
"cholesterol 0\n",
"max heart rate 0\n",
"exercise angina 0\n",
"plasma glucose 0\n",
"skin_thickness 0\n",
"insulin 0\n",
"bmi 0\n",
"diabetes_pedigree 0\n",
"hypertension 0\n",
"heart_disease 0\n",
"Residence_type 0\n",
"smoking_status 0\n",
"triage 410\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"id": "46044462",
"metadata": {},
"source": [
"A few rows are not having any Triage categorization so we'll be dropping them along with one row where gender is not present (because it's annoying)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7bdaf309",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6962, 17)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# before\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "df49596f",
"metadata": {},
"outputs": [],
"source": [
"df.dropna(axis=0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c7483ef4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6551, 17)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# after\n",
"df.shape"
]
},
{
"cell_type": "markdown",
"id": "eac17ff0",
"metadata": {},
"source": [
"We've removed 411 rows from the data since 410 of them have no purpose."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "699b9f52",
"metadata": {},
"outputs": [],
"source": [
"df.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ecd04b22",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 6551 entries, 0 to 6550\n",
"Data columns (total 18 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 index 6551 non-null int64 \n",
" 1 age 6551 non-null float64\n",
" 2 gender 6551 non-null float64\n",
" 3 chest pain type 6551 non-null float64\n",
" 4 blood pressure 6551 non-null float64\n",
" 5 cholesterol 6551 non-null float64\n",
" 6 max heart rate 6551 non-null float64\n",
" 7 exercise angina 6551 non-null float64\n",
" 8 plasma glucose 6551 non-null float64\n",
" 9 skin_thickness 6551 non-null float64\n",
" 10 insulin 6551 non-null float64\n",
" 11 bmi 6551 non-null float64\n",
" 12 diabetes_pedigree 6551 non-null float64\n",
" 13 hypertension 6551 non-null float64\n",
" 14 heart_disease 6551 non-null float64\n",
" 15 Residence_type 6551 non-null object \n",
" 16 smoking_status 6551 non-null object \n",
" 17 triage 6551 non-null object \n",
"dtypes: float64(14), int64(1), object(3)\n",
"memory usage: 921.4+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "893100b7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"index 0\n",
"age 0\n",
"gender 0\n",
"chest pain type 0\n",
"blood pressure 0\n",
"cholesterol 0\n",
"max heart rate 0\n",
"exercise angina 0\n",
"plasma glucose 0\n",
"skin_thickness 0\n",
"insulin 0\n",
"bmi 0\n",
"diabetes_pedigree 0\n",
"hypertension 0\n",
"heart_disease 0\n",
"Residence_type 0\n",
"smoking_status 0\n",
"triage 0\n",
"dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"id": "acb5e958",
"metadata": {},
"source": [
"#### Since there is no null values, we can proceed with feature engineering"
]
},
{
"cell_type": "markdown",
"id": "700fc52e",
"metadata": {},
"source": [
"Residence_type, smoking_status and triage have values as string. While strings are okay for some models, we'll be encoding them anyway."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "90f89cd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Urban', 'Rural'], dtype=object)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Residence_type'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "cc7e19a9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['never smoked', 'smokes', 'formerly smoked', 'Unknown'],\n",
" dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['smoking_status'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a1512696",
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams[\"figure.figsize\"] = (8, 4)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c3254f41",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['smoking_status'].value_counts().plot(kind='bar', color='seagreen');"
]
},
{
"cell_type": "markdown",
"id": "0ef14660",
"metadata": {},
"source": [
"A significant amount of data is missing here for 'Smoking Status' which is a key feature when it comes to health issues.\n",
"It is an important feature but as well as not so much related with Triage and we can't impute this using other features."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "087ac5b3",
"metadata": {},
"outputs": [],
"source": [
"df['Residence_type'] = df['Residence_type'].map({'Urban':1 ,'Rural': 0})"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b6b6a39e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['yellow', 'orange', 'red', 'green'], dtype=object)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['triage'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "30606dc2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['triage'].value_counts().plot(kind='bar');"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "0b434341",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['index', 'age', 'gender', 'chest pain type', 'blood pressure',\n",
" 'cholesterol', 'max heart rate', 'exercise angina', 'plasma glucose',\n",
" 'skin_thickness', 'insulin', 'bmi', 'diabetes_pedigree', 'hypertension',\n",
" 'heart_disease', 'Residence_type', 'smoking_status', 'triage'],\n",
" dtype='object')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "91251f40",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6551, 18)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "b66b1589",
"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>index</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>chest pain type</th>\n",
" <th>blood pressure</th>\n",
" <th>cholesterol</th>\n",
" <th>max heart rate</th>\n",
" <th>exercise angina</th>\n",
" <th>plasma glucose</th>\n",
" <th>skin_thickness</th>\n",
" <th>insulin</th>\n",
" <th>bmi</th>\n",
" <th>diabetes_pedigree</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>Residence_type</th>\n",
" <th>smoking_status</th>\n",
" <th>triage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>40.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>140.0</td>\n",
" <td>294.0</td>\n",
" <td>172.0</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>43.0</td>\n",
" <td>92.0</td>\n",
" <td>19.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>160.0</td>\n",
" <td>180.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>75.0</td>\n",
" <td>47.0</td>\n",
" <td>90.0</td>\n",
" <td>18.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>37.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>130.0</td>\n",
" <td>294.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>98.0</td>\n",
" <td>53.0</td>\n",
" <td>102.0</td>\n",
" <td>23.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>138.0</td>\n",
" <td>214.0</td>\n",
" <td>156.0</td>\n",
" <td>1.0</td>\n",
" <td>72.0</td>\n",
" <td>51.0</td>\n",
" <td>118.0</td>\n",
" <td>18.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>54.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>150.0</td>\n",
" <td>195.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>90.0</td>\n",
" <td>83.0</td>\n",
" <td>21.0</td>\n",
" <td>0.467386</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index age gender chest pain type blood pressure cholesterol \\\n",
"0 0 40.0 1.0 2.0 140.0 294.0 \n",
"1 1 49.0 0.0 3.0 160.0 180.0 \n",
"2 2 37.0 1.0 2.0 130.0 294.0 \n",
"3 3 48.0 0.0 4.0 138.0 214.0 \n",
"4 4 54.0 1.0 3.0 150.0 195.0 \n",
"\n",
" max heart rate exercise angina plasma glucose skin_thickness insulin \\\n",
"0 172.0 0.0 108.0 43.0 92.0 \n",
"1 156.0 0.0 75.0 47.0 90.0 \n",
"2 156.0 0.0 98.0 53.0 102.0 \n",
"3 156.0 1.0 72.0 51.0 118.0 \n",
"4 156.0 0.0 108.0 90.0 83.0 \n",
"\n",
" bmi diabetes_pedigree hypertension heart_disease Residence_type \\\n",
"0 19.0 0.467386 0.0 0.0 1 \n",
"1 18.0 0.467386 0.0 0.0 1 \n",
"2 23.0 0.467386 0.0 0.0 1 \n",
"3 18.0 0.467386 0.0 0.0 1 \n",
"4 21.0 0.467386 0.0 0.0 1 \n",
"\n",
" smoking_status triage \n",
"0 never smoked yellow \n",
"1 never smoked orange \n",
"2 never smoked yellow \n",
"3 never smoked orange \n",
"4 never smoked yellow "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "6c79a63c",
"metadata": {},
"source": [
"### Work on numerical features"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "08bc6d80",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([40., 49., 37., 48., 54., 39., 45., 58., 42., 38., 43., 60., 36.,\n",
" 44., 53., 52., 51., 56., 41., 32., 65., 35., 59., 50., 47., 31.,\n",
" 46., 57., 55., 63., 66., 34., 33., 61., 29., 62., 28., 74., 68.,\n",
" 72., 64., 69., 67., 73., 70., 77., 75., 76., 71., 81., 80., 79.,\n",
" 78., 82.])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['age'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "b483f55d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 0.])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['gender'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "df0c7318",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2., 3., 4., 1., 0.])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['chest pain type'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "450f5549",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([140., 160., 130., 138., 150., 120., 110., 136., 115., 100., 124.,\n",
" 113., 125., 145., 112., 132., 118., 142., 135., 108., 155., 128.,\n",
" 106., 92., 122., 98., 105., 133., 95., 80., 137., 165., 126.,\n",
" 152., 116., 144., 154., 134., 104., 139., 131., 141., 146., 158.,\n",
" 123., 102., 96., 143., 156., 114., 127., 101., 94., 148., 117.,\n",
" 129., 164., 66., 74., 72., 84., 70., 88., 90., 76., 82.,\n",
" 75., 78., 60., 68., 64., 62., 85., 86., 65., 61., 89.,\n",
" 93., 111., 107., 119., 81., 87., 121., 103., 99., 97., 109.,\n",
" 91., 83.])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['blood pressure'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "8a480bbf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([294., 180., 214., 195., 237., 208., 207., 211., 164., 204., 234.,\n",
" 196., 201., 248., 184., 215., 209., 260., 188., 167., 224., 172.,\n",
" 186., 254., 250., 177., 227., 230., 259., 175., 223., 216., 233.,\n",
" 205., 245., 194., 213., 253., 202., 225., 246., 182., 218., 163.,\n",
" 206., 238., 229., 210., 179., 255., 156., 240., 161., 228., 241.,\n",
" 166., 247., 243., 198., 249., 168., 159., 190., 185., 212., 231.,\n",
" 222., 235., 187., 251., 192., 193., 219., 257., 226., 217., 256.,\n",
" 173., 200., 171., 160., 221., 220., 242., 169., 181., 236., 203.,\n",
" 153., 252., 258., 197., 261., 232., 170., 152., 244., 165., 239.,\n",
" 199., 178., 262., 174., 183., 157., 176., 191., 189., 154., 155.,\n",
" 151., 162., 158., 150.])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['cholesterol'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "b099dbf5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([172., 156., 170., 142., 145., 140., 150., 166., 165., 160., 164.,\n",
" 178., 154., 155., 148., 168., 184., 153., 174., 175., 144., 180.,\n",
" 152., 190., 146., 158., 176., 188., 162., 185., 167., 143., 149.,\n",
" 182., 141., 179., 157., 163., 161., 159., 151., 181., 186., 177.,\n",
" 173., 169., 171., 147., 192., 195., 194., 187., 202., 191., 189.,\n",
" 139., 138., 183.])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['max heart rate'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1a320dbe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 1.])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['exercise angina'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "42e377d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([43., 47., 53., 51., 90., 49., 39., 82., 67., 37., 81., 92., 86.,\n",
" 54., 79., 64., 31., 84., 85., 78., 73., 66., 96., 74., 56., 89.,\n",
" 58., 22., 21., 34., 69., 32., 40., 72., 46., 50., 23., 27., 28.,\n",
" 35., 41., 44., 60., 57., 45., 71., 52., 62., 36., 26., 63., 68.,\n",
" 91., 98., 88., 95., 77., 65., 83., 94., 87., 93., 33., 25., 70.,\n",
" 61., 42., 55., 24., 48., 29., 97., 38., 80., 75., 30., 59., 76.,\n",
" 99.])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['skin_thickness'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "e845ee05",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 92., 90., 102., 118., 83., 106., 97., 103., 120., 104., 132.,\n",
" 94., 82., 136., 105., 117., 137., 129., 84., 113., 109., 89.,\n",
" 111., 126., 87., 85., 110., 135., 122., 98., 139., 108., 96.,\n",
" 119., 127., 112., 124., 81., 123., 134., 100., 93., 114., 88.,\n",
" 116., 133., 138., 107., 121., 86., 125., 128., 130., 131., 115.,\n",
" 101., 95., 99., 91., 168., 146., 140., 142., 152., 145., 155.,\n",
" 156., 160., 150., 148., 171., 167., 158., 165., 170., 166., 144.,\n",
" 159.])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['insulin'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "2dffa60e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([19. , 18. , 23. , 21. , 22. , 20. , 28.1, 25.6, 25.8, 30. , 29.6,\n",
" 29. , 23.2, 22.2, 24.8, 19.9, 27.6, 24. , 22.7, 27.4, 29.7, 28. ,\n",
" 19.4, 24.2, 24.4, 25. , 25.4, 19.6, 28.9, 28.6, 22.4, 29.3, 24.6,\n",
" 26.5, 19.1, 23.8, 24.7, 20.4, 28.7, 26.1, 22.5, 26.6, 29.5, 28.2,\n",
" 26.8, 27.9, 21.1, 27.3, 21.9, 25.2, 29.9, 28.4, 27.7, 22.6, 22.9,\n",
" 23.9, 28.8, 23.6, 29.2, 27.1, 25.9, 25.1, 27.5, 27.8, 24.9, 25.3,\n",
" 27. , 26. , 20.8, 25.5, 26.2, 19.3, 23.5, 23.1, 21.8, 27.2, 29.8,\n",
" 24.3, 22.3, 22.1, 26.4, 24.5, 21.2, 26.7, 26.3, 21.7, 28.5, 26.9,\n",
" 19.5, 20.1, 23.4, 28.3, 24.1, 23.3, 32.5, 34.4, 22.8, 36.8, 30.9,\n",
" 37.5, 37.8, 48.9, 44.2, 30.5, 33.7, 32. , 20.2, 33.6, 38.6, 39.2,\n",
" 31.4, 36.5, 33.2, 32.8, 40.4, 30.2, 47.5, 20.3, 31.1, 45.9, 44.1,\n",
" 29.1, 32.3, 41.1, 29.4, 34.6, 30.3, 41.5, 56.6, 31.3, 31. , 31.7,\n",
" 35.8, 38.7, 34.9, 32.9, 31.9, 34.1, 36.9, 37.3, 45.7, 34.2, 37.1,\n",
" 45. , 30.8, 37.4, 34.5, 46. , 42.5, 35.5, 45.5, 31.5, 33. , 30.7,\n",
" 21.5, 40. , 42.2, 35.4, 16.9, 39.3, 32.6, 35.9, 42.4, 40.5, 36.7,\n",
" 17.6, 50.1, 17.7, 54.6, 35. , 39.4, 41.8, 60.9, 23.7, 31.2, 16. ,\n",
" 31.6, 18.3, 36. , 35.3, 40.1, 43.1, 21.4, 34.3, 25.7, 38.4, 54.7,\n",
" 18.6, 48.2, 39.5, 64.8, 35.1, 43.6, 47.3, 16.6, 21.6, 15.5, 20.5,\n",
" 35.6, 16.7, 41.9, 16.4, 17.1, 37.9, 44.6, 39.6, 40.3, 41.6, 39. ,\n",
" 18.9, 36.1, 36.3, 46.5, 16.8, 46.6, 35.2, 20.9, 13.8, 31.8, 15.3,\n",
" 38.2, 45.2, 49.8, 60.2, 44.3, 51. , 39.7, 34.7, 21.3, 41.2, 34.8,\n",
" 19.2, 35.7, 40.8, 32.4, 34. , 32.1, 51.5, 30.6, 40.9, 16.5, 16.2,\n",
" 40.6, 18.4, 42.3, 32.2, 50.2, 17.5, 18.7, 19.7, 42.1, 47.8, 30.1,\n",
" 36.4, 12. , 36.2, 55.7, 43. , 41.7, 33.8, 43.9, 57.5, 37. , 38.5,\n",
" 16.3, 44. , 17.2, 32.7, 54.2, 40.2, 33.3, 17.4, 41.3, 52.3, 17.8,\n",
" 46.1, 36.6, 33.1, 18.1, 43.8, 50.3, 38.9, 43.7, 39.9, 19.8, 12.3,\n",
" 38.3, 41. , 42.6, 43.4, 33.5, 43.2, 30.4, 38. , 33.4, 44.9, 44.7,\n",
" 37.6, 39.8, 53.4, 55.2, 42. , 37.2, 42.8, 18.8, 42.9, 14.3, 37.7,\n",
" 20.7, 14.6, 48.4, 15.9, 50.6, 46.2, 49.5, 43.3, 33.9, 18.5, 44.5,\n",
" 45.4, 55. , 54.8, 17.9, 15.6, 15.1, 52.8, 15.2, 66.8, 55.1, 18.2,\n",
" 48.5, 55.9, 57.3, 10.3, 14.1, 15.7, 56. , 44.8, 17. , 16.1, 51.8,\n",
" 20.6, 38.1, 57.7, 44.4, 38.8, 17.3, 49.3, 39.1, 54. , 56.1, 53.9,\n",
" 13.7, 11.5, 41.4, 14.2, 49.4, 15.4, 45.1, 49.2, 48.7, 53.8, 42.7,\n",
" 48.8, 52.7, 53.5, 50.5, 15.8, 45.3, 51.9, 63.3, 40.7, 61.2, 48. ,\n",
" 46.8, 48.3, 58.1, 50.4, 11.3, 12.8, 13.5, 14.5, 15. , 14.4, 59.7,\n",
" 47.4, 52.5, 52.9, 61.6, 49.9, 54.3, 47.9, 13. , 50.9, 57.2, 64.4,\n",
" 50.8, 57.9, 45.8, 47.6, 14. , 46.4, 46.9, 14.8, 47.1, 48.1, 51.7,\n",
" 46.3, 54.1, 14.9])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['bmi'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "594171f7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 1.])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['hypertension'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "64f05088",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 1.])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['heart_disease'].unique()"
]
},
{
"cell_type": "markdown",
"id": "8295504c",
"metadata": {},
"source": [
"We can convert these categories above from float to int."
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "99e3b221",
"metadata": {},
"outputs": [],
"source": [
"df.drop('index', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "f812320c",
"metadata": {},
"outputs": [],
"source": [
"df = df.astype({\"age\":'int', \"gender\":'int', \"chest pain type\":'int', \\\n",
" \"blood pressure\": 'int', \"cholesterol\":'int', \"max heart rate\":'int', \\\n",
" \"exercise angina\":'int', \"skin_thickness\":'int', \"insulin\":'int',\\\n",
" \"hypertension\":'int', \"heart_disease\":'int', \"Residence_type\":'int'})"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "de73d1cc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 6551 entries, 0 to 6550\n",
"Data columns (total 17 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 6551 non-null int32 \n",
" 1 gender 6551 non-null int32 \n",
" 2 chest pain type 6551 non-null int32 \n",
" 3 blood pressure 6551 non-null int32 \n",
" 4 cholesterol 6551 non-null int32 \n",
" 5 max heart rate 6551 non-null int32 \n",
" 6 exercise angina 6551 non-null int32 \n",
" 7 plasma glucose 6551 non-null float64\n",
" 8 skin_thickness 6551 non-null int32 \n",
" 9 insulin 6551 non-null int32 \n",
" 10 bmi 6551 non-null float64\n",
" 11 diabetes_pedigree 6551 non-null float64\n",
" 12 hypertension 6551 non-null int32 \n",
" 13 heart_disease 6551 non-null int32 \n",
" 14 Residence_type 6551 non-null int32 \n",
" 15 smoking_status 6551 non-null object \n",
" 16 triage 6551 non-null object \n",
"dtypes: float64(3), int32(12), object(2)\n",
"memory usage: 563.1+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"id": "2fad3e73",
"metadata": {},
"source": [
"#### Checking Correlations"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "0e03d491",
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams[\"figure.figsize\"] = (10, 7)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "e409daa1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\rick\\AppData\\Local\\Temp\\ipykernel_14656\\1193077595.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
" corr = df.corr(method='pearson')\n"
]
}
],
"source": [
"corr = df.corr(method='pearson')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "10e28772",
"metadata": {},
"outputs": [],
"source": [
"triu = np.triu(corr)\n",
"np.fill_diagonal(triu, False)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "c416cd47",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(corr, annot=True, fmt='.1f',annot_kws={\"size\":12}, linewidth=.6, mask=triu);"
]
},
{
"cell_type": "markdown",
"id": "9a07175b",
"metadata": {},
"source": [
"Here we can see the following.\n",
"\n",
"- There is a moderate correlation between chest pain type and cholesterol. Provided the feature 'Chest pain type' is ordinal and recorded on a 0-5 scale where 0 = No pain and 5 = Severe pain.\n",
"\n",
"- There is a moderate correlation between 'Exercise angina' and 'Chest pain type' which is obvious.\n",
"\n",
"- There is some negative correlation between 'chest pain type', 'cholesterol', 'excericise angina' and 'bmi'. Well when BMI is low, it is often considered that the person is in underweight range, so there will be less chance of developing any heart disease hence the cholestrol is also negatively correlated. "
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "6d845db6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(data=df, x='chest pain type', y='cholesterol', kind='box');"
]
},
{
"cell_type": "markdown",
"id": "7535f08a",
"metadata": {},
"source": [
"Cholesterol and exericise angina has a 0.4 pearson correlation between them."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "207b7dc4",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1200x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='cholesterol', col='exercise angina', kde=True, height=3, aspect=2);"
]
},
{
"cell_type": "markdown",
"id": "9be3b065",
"metadata": {},
"source": [
"#### Checking for outliers"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "8c23f301",
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams[\"figure.figsize\"] = (6, 2)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "e0ae7e4d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='blood pressure', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "16e8823e",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='blood pressure');"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "73c5bb7b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='cholesterol', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "80c5e6ec",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='cholesterol');"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "a00cd546",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='max heart rate', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "f133263c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='max heart rate');"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "6345d508",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='plasma glucose', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "22167667",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='plasma glucose');"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "595637af",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='skin_thickness', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "b0f463f5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='skin_thickness');"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "a794e747",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='insulin', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "2a52d2ee",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='insulin');"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "a9c6acde",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='bmi', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "8b8b63e4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='bmi');"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "2e8be838",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 450x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(data=df, x='diabetes_pedigree', kde=True, height=3, aspect=1.5);"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "9c7c2727",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 600x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(data=df, x='diabetes_pedigree');"
]
},
{
"cell_type": "markdown",
"id": "4dfa8cda",
"metadata": {},
"source": [
"As we can see, we've got outliers for the below columns.\n",
"- cholesterol\n",
"- plasma glucose\n",
"- insulin\n",
"- bmi"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "1f197d6f",
"metadata": {},
"outputs": [],
"source": [
"# handling outliers\n",
"def remove_outliers(df,columns,n_std):\n",
" for col in columns:\n",
" print('Currently removing: {}'.format(col))\n",
" mean = df[col].mean()\n",
" sd = df[col].std()\n",
" df = df[(df[col] <= mean+(n_std*sd))]\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "7af58248",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6551, 17)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "00caea2c",
"metadata": {},
"outputs": [],
"source": [
"columns = ['cholesterol', 'plasma glucose', 'insulin', 'bmi']\n",
"n_std = 3 # Three standard deviations away from the mean to differentiate outlier from non-outlier."
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "4dffd144",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Currently removing: cholesterol\n",
"Currently removing: plasma glucose\n",
"Currently removing: insulin\n",
"Currently removing: bmi\n"
]
}
],
"source": [
"filtered_df = remove_outliers(df,columns,n_std)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "e5e02be5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5959, 17)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "642a5779",
"metadata": {},
"outputs": [],
"source": [
"filtered_df.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "f9916e16",
"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>index</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>chest pain type</th>\n",
" <th>blood pressure</th>\n",
" <th>cholesterol</th>\n",
" <th>max heart rate</th>\n",
" <th>exercise angina</th>\n",
" <th>plasma glucose</th>\n",
" <th>skin_thickness</th>\n",
" <th>insulin</th>\n",
" <th>bmi</th>\n",
" <th>diabetes_pedigree</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>Residence_type</th>\n",
" <th>smoking_status</th>\n",
" <th>triage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>49</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>160</td>\n",
" <td>180</td>\n",
" <td>156</td>\n",
" <td>0</td>\n",
" <td>75.00</td>\n",
" <td>47</td>\n",
" <td>90</td>\n",
" <td>18.0</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>138</td>\n",
" <td>214</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>72.00</td>\n",
" <td>51</td>\n",
" <td>118</td>\n",
" <td>18.0</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>54</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>150</td>\n",
" <td>195</td>\n",
" <td>156</td>\n",
" <td>0</td>\n",
" <td>108.00</td>\n",
" <td>90</td>\n",
" <td>83</td>\n",
" <td>21.0</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>130</td>\n",
" <td>237</td>\n",
" <td>170</td>\n",
" <td>0</td>\n",
" <td>116.00</td>\n",
" <td>39</td>\n",
" <td>97</td>\n",
" <td>23.0</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7</td>\n",
" <td>54</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>110</td>\n",
" <td>208</td>\n",
" <td>142</td>\n",
" <td>0</td>\n",
" <td>124.00</td>\n",
" <td>82</td>\n",
" <td>103</td>\n",
" <td>19.0</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</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",
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" <td>...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5954</th>\n",
" <td>6546</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111</td>\n",
" <td>153</td>\n",
" <td>166</td>\n",
" <td>0</td>\n",
" <td>83.75</td>\n",
" <td>31</td>\n",
" <td>108</td>\n",
" <td>18.6</td>\n",
" <td>0.467386</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5955</th>\n",
" <td>6547</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>123</td>\n",
" <td>157</td>\n",
" <td>160</td>\n",
" <td>0</td>\n",
" <td>125.20</td>\n",
" <td>23</td>\n",
" <td>89</td>\n",
" <td>40.0</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5956</th>\n",
" <td>6548</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>127</td>\n",
" <td>185</td>\n",
" <td>141</td>\n",
" <td>0</td>\n",
" <td>82.99</td>\n",
" <td>41</td>\n",
" <td>95</td>\n",
" <td>30.6</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>never smoked</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5957</th>\n",
" <td>6549</td>\n",
" <td>51</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>123</td>\n",
" <td>161</td>\n",
" <td>162</td>\n",
" <td>0</td>\n",
" <td>166.29</td>\n",
" <td>34</td>\n",
" <td>93</td>\n",
" <td>25.6</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>formerly smoked</td>\n",
" <td>green</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5958</th>\n",
" <td>6550</td>\n",
" <td>44</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>125</td>\n",
" <td>176</td>\n",
" <td>172</td>\n",
" <td>0</td>\n",
" <td>85.28</td>\n",
" <td>57</td>\n",
" <td>113</td>\n",
" <td>26.2</td>\n",
" <td>0.467386</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Unknown</td>\n",
" <td>yellow</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5959 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" index age gender chest pain type blood pressure cholesterol \\\n",
"0 1 49 0 3 160 180 \n",
"1 3 48 0 4 138 214 \n",
"2 4 54 1 3 150 195 \n",
"3 6 45 0 2 130 237 \n",
"4 7 54 1 2 110 208 \n",
"... ... ... ... ... ... ... \n",
"5954 6546 80 0 0 111 153 \n",
"5955 6547 81 0 0 123 157 \n",
"5956 6548 81 0 0 127 185 \n",
"5957 6549 51 1 0 123 161 \n",
"5958 6550 44 0 0 125 176 \n",
"\n",
" max heart rate exercise angina plasma glucose skin_thickness \\\n",
"0 156 0 75.00 47 \n",
"1 156 1 72.00 51 \n",
"2 156 0 108.00 90 \n",
"3 170 0 116.00 39 \n",
"4 142 0 124.00 82 \n",
"... ... ... ... ... \n",
"5954 166 0 83.75 31 \n",
"5955 160 0 125.20 23 \n",
"5956 141 0 82.99 41 \n",
"5957 162 0 166.29 34 \n",
"5958 172 0 85.28 57 \n",
"\n",
" insulin bmi diabetes_pedigree hypertension heart_disease \\\n",
"0 90 18.0 0.467386 0 0 \n",
"1 118 18.0 0.467386 0 0 \n",
"2 83 21.0 0.467386 0 0 \n",
"3 97 23.0 0.467386 0 0 \n",
"4 103 19.0 0.467386 0 0 \n",
"... ... ... ... ... ... \n",
"5954 108 18.6 0.467386 1 0 \n",
"5955 89 40.0 0.467386 0 0 \n",
"5956 95 30.6 0.467386 0 0 \n",
"5957 93 25.6 0.467386 0 0 \n",
"5958 113 26.2 0.467386 0 0 \n",
"\n",
" Residence_type smoking_status triage \n",
"0 1 never smoked orange \n",
"1 1 never smoked orange \n",
"2 1 never smoked yellow \n",
"3 1 never smoked yellow \n",
"4 1 never smoked yellow \n",
"... ... ... ... \n",
"5954 1 never smoked yellow \n",
"5955 1 never smoked yellow \n",
"5956 0 never smoked yellow \n",
"5957 0 formerly smoked green \n",
"5958 1 Unknown yellow \n",
"\n",
"[5959 rows x 18 columns]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_df"
]
},
{
"cell_type": "markdown",
"id": "b94068f3",
"metadata": {},
"source": [
"#### One hot encoding for smoking status"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "e44e6e56",
"metadata": {},
"outputs": [],
"source": [
"filtered_df['smoking_status'] = filtered_df['smoking_status'].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "eb523db5",
"metadata": {},
"outputs": [],
"source": [
"ohe = pd.get_dummies(filtered_df.smoking_status, prefix='smoking_status')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "fb975745",
"metadata": {},
"outputs": [],
"source": [
"filtered_df = filtered_df.join(ohe)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "f003a493",
"metadata": {},
"outputs": [],
"source": [
"drop_cols = ['smoking_status', 'index', 'smoking_status_Unknown']"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "14e00f45",
"metadata": {},
"outputs": [],
"source": [
"filtered_df.drop(drop_cols, axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "1c1a3219",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['age', 'gender', 'chest pain type', 'blood pressure', 'cholesterol',\n",
" 'max heart rate', 'exercise angina', 'plasma glucose', 'skin_thickness',\n",
" 'insulin', 'bmi', 'diabetes_pedigree', 'hypertension', 'heart_disease',\n",
" 'Residence_type', 'triage', 'smoking_status_formerly smoked',\n",
" 'smoking_status_never smoked', 'smoking_status_smokes'],\n",
" dtype='object')"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "ab004164",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5959 entries, 0 to 5958\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 5959 non-null int32 \n",
" 1 gender 5959 non-null int32 \n",
" 2 chest pain type 5959 non-null int32 \n",
" 3 blood pressure 5959 non-null int32 \n",
" 4 cholesterol 5959 non-null int32 \n",
" 5 max heart rate 5959 non-null int32 \n",
" 6 exercise angina 5959 non-null int32 \n",
" 7 plasma glucose 5959 non-null float64\n",
" 8 skin_thickness 5959 non-null int32 \n",
" 9 insulin 5959 non-null int32 \n",
" 10 bmi 5959 non-null float64\n",
" 11 diabetes_pedigree 5959 non-null float64\n",
" 12 hypertension 5959 non-null int32 \n",
" 13 heart_disease 5959 non-null int32 \n",
" 14 Residence_type 5959 non-null int32 \n",
" 15 triage 5959 non-null object \n",
" 16 smoking_status_formerly smoked 5959 non-null uint8 \n",
" 17 smoking_status_never smoked 5959 non-null uint8 \n",
" 18 smoking_status_smokes 5959 non-null uint8 \n",
"dtypes: float64(3), int32(12), object(1), uint8(3)\n",
"memory usage: 483.1+ KB\n"
]
}
],
"source": [
"filtered_df.info()"
]
},
{
"cell_type": "markdown",
"id": "e42e6cf2",
"metadata": {},
"source": [
"#### End of Feature engineering"
]
},
{
"cell_type": "markdown",
"id": "af6b8949",
"metadata": {},
"source": [
"### Preparation for Classification:\n",
"Below are the parameters that needs to be taken into account:\n",
"- Multiclass classification problem\n",
"- Imbalanced dataset"
]
},
{
"cell_type": "markdown",
"id": "7a516880",
"metadata": {},
"source": [
"Preparing X and y"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "0090fbe8",
"metadata": {},
"outputs": [],
"source": [
"target_names = filtered_df['triage'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "9ed5f714",
"metadata": {},
"outputs": [],
"source": [
"X, y = filtered_df.drop(columns = ['triage'], axis=1).copy(), filtered_df['triage']"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "1f583dc7",
"metadata": {},
"outputs": [],
"source": [
"X_names = X.columns\n",
"y_name = y.name"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "5fce671e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['orange', 'yellow', 'red', 'green'], dtype=object)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_names"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "1bdd712f",
"metadata": {},
"outputs": [],
"source": [
"feature_list = list(X.columns)"
]
},
{
"cell_type": "markdown",
"id": "c7e34851",
"metadata": {},
"source": [
"### Splitting data in Train, Validation and Test"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "0726f8a7",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "431239b8",
"metadata": {},
"outputs": [],
"source": [
"# We want to split the data in train:valid:test dataset\n",
"\n",
"train_size=0.33"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "35ddca43",
"metadata": {},
"outputs": [],
"source": [
"# In the first step we will split the data in training and remaining dataset\n",
"\n",
"X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=train_size, random_state=17)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "a74f2edc",
"metadata": {},
"outputs": [],
"source": [
"test_size = 0.5\n",
"X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size=test_size, random_state=17)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "03609133",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1966, 18)\n",
"(1966,)\n",
"(1996, 18)\n",
"(1996,)\n",
"(1997, 18)\n",
"(1997,)\n"
]
},
{
"data": {
"text/plain": [
"(None, None)"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(X_train.shape), print(y_train.shape)\n",
"print(X_valid.shape), print(y_valid.shape)\n",
"print(X_test.shape), print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"id": "37843bc3",
"metadata": {},
"source": [
"### Cross Validation and Model Selection"
]
},
{
"cell_type": "markdown",
"id": "3a8aeceb",
"metadata": {},
"source": [
"Using 3 best fit classifier models according to the case here to check the initial accuracy on training data.\n",
"- DecisionTree\n",
"- RandomForest\n",
"- LGBM"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "4e8c653b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fold:1, Train set: 1572,Test set:394\n",
"Fold:2, Train set: 1573,Test set:393\n",
"Fold:3, Train set: 1573,Test set:393\n",
"Fold:4, Train set: 1573,Test set:393\n",
"Fold:5, Train set: 1573,Test set:393\n"
]
}
],
"source": [
"#Splitting the data into a train and validation set\n",
"#Model Score using KFold\n",
"#The folds are made by preserving the percentages of samples for each class\n",
"\n",
"from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
"\n",
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=17)\n",
"\n",
"count = 1\n",
"#we use split method that will generate indices to split data into train and test sets\n",
"\n",
"for train_index, test_index in skf.split(X_train, y_train):\n",
" print(f'Fold:{count}, Train set: {len(train_index)},Test set:{len(test_index)}')\n",
" count+=1"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "e7ba1c8a",
"metadata": {},
"outputs": [],
"source": [
"import lightgbm as lgb\n",
"from lightgbm import LGBMClassifier\n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "markdown",
"id": "8306753a",
"metadata": {},
"source": [
"#### Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "3fc5f3fc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scores for each fold are: [0.99746193 0.99236641 0.99745547 0.98982188 0.99236641]\n",
"Average score: 0.99\n"
]
}
],
"source": [
"score = cross_val_score(RandomForestClassifier(random_state=17), \n",
" X_train, y_train, cv=skf, scoring=\"accuracy\")\n",
"print(f'Scores for each fold are: {score}')\n",
"print(f'Average score: {\"{:.2f}\".format(score.mean())}')"
]
},
{
"cell_type": "markdown",
"id": "4285f620",
"metadata": {},
"source": [
"#### LGBM Classifier"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "99c8e3a4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scores for each fold are: [0.99492386 1. 0.99745547 0.99491094 1. ]\n",
"Average score: 1.00\n"
]
}
],
"source": [
"score = cross_val_score(LGBMClassifier(random_state=17), \n",
" X_train, y_train, cv=skf, scoring=\"accuracy\")\n",
"print(f'Scores for each fold are: {score}')\n",
"print(f'Average score: {\"{:.2f}\".format(score.mean())}')"
]
},
{
"cell_type": "markdown",
"id": "f6f901f9",
"metadata": {},
"source": [
"We can proceed with Light GBM or Random Forest here. We could've also used Decision Tree but if we're going with a tree based model, it's safer to use a model that uses Ensemble learning."
]
},
{
"cell_type": "markdown",
"id": "5caa384e",
"metadata": {},
"source": [
"### Random Forest approach"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "ce09bb03",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"RandomForestClassifier()"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Instantiate model with decision trees\n",
"rf = RandomForestClassifier()\n",
"\n",
"# Train the model using the training data\n",
"rf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "72f7536a",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "bd0396c8",
"metadata": {},
"outputs": [],
"source": [
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=17)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "21ac7c69",
"metadata": {},
"outputs": [],
"source": [
"rf_params = {'n_estimators': np.array([400, 450, 500, 550, 600, 650, 700, 750]),\\\n",
" 'max_depth': np.arange(4, 12),\\\n",
" 'random_state' : [17]}"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "df293db4",
"metadata": {},
"outputs": [],
"source": [
"grid_rf = GridSearchCV(estimator=rf, param_grid=rf_params, cv=skf, n_jobs=-1, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "34545a7b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 64 candidates, totalling 320 fits\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=17, shuffle=True),\n",
" estimator=RandomForestClassifier(), n_jobs=-1,\n",
" param_grid={'max_depth': array([ 4, 5, 6, 7, 8, 9, 10, 11]),\n",
" 'n_estimators': array([400, 450, 500, 550, 600, 650, 700, 750]),\n",
" 'random_state': [17]},\n",
" verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=17, shuffle=True),\n",
" estimator=RandomForestClassifier(), n_jobs=-1,\n",
" param_grid={'max_depth': array([ 4, 5, 6, 7, 8, 9, 10, 11]),\n",
" 'n_estimators': array([400, 450, 500, 550, 600, 650, 700, 750]),\n",
" 'random_state': [17]},\n",
" verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=17, shuffle=True),\n",
" estimator=RandomForestClassifier(), n_jobs=-1,\n",
" param_grid={'max_depth': array([ 4, 5, 6, 7, 8, 9, 10, 11]),\n",
" 'n_estimators': array([400, 450, 500, 550, 600, 650, 700, 750]),\n",
" 'random_state': [17]},\n",
" verbose=1)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_rf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"id": "5b625b46",
"metadata": {},
"outputs": [],
"source": [
"#Predictions on validation\n",
"predictions_rf = grid_rf.predict(X_valid)"
]
},
{
"cell_type": "markdown",
"id": "1608d969",
"metadata": {},
"source": [
"Training Score"
]
},
{
"cell_type": "code",
"execution_count": 102,
"id": "4ad1d619",
"metadata": {},
"outputs": [],
"source": [
"score = grid_rf.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"id": "9a4a2249",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100.0 %\n"
]
}
],
"source": [
"print(round((score*100), 2),'%')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "93f08abc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RandomForestClassifier(max_depth=10, n_estimators=500, random_state=17)\n"
]
}
],
"source": [
"print(grid_rf.best_estimator_)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "bc2f8b44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" orange 1.00 0.98 0.99 92\n",
" yellow 0.90 1.00 0.95 101\n",
" red 1.00 0.73 0.85 45\n",
" green 1.00 1.00 1.00 1758\n",
"\n",
" accuracy 0.99 1996\n",
" macro avg 0.97 0.93 0.95 1996\n",
"weighted avg 0.99 0.99 0.99 1996\n",
"\n"
]
}
],
"source": [
"## Classification report for Random Forest Classifier\n",
"\n",
"from sklearn.metrics import classification_report\n",
"print(classification_report(y_valid, predictions_rf, target_names=target_names))"
]
},
{
"cell_type": "markdown",
"id": "4d1b3a2a",
"metadata": {},
"source": [
"#### Getting Feature importances"
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "1ae297fe",
"metadata": {},
"outputs": [],
"source": [
"#Get numerical feature importances\n",
"importances = list(rf.feature_importances_)\n",
"\n",
"#List of tupples with variable and importance\n",
"\n",
"feature_importances = [(X_train, round(importance, 2)) for X_train,\n",
" importance in zip(feature_list, importances)]\n",
"\n",
"\n",
"#Sort the feature importances by most important first\n",
"feature_importances = sorted(feature_importances, key = lambda x: x[1], \n",
" reverse = False)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "4c8f98df",
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams[\"figure.figsize\"] = (20, 10)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"id": "74f24fdc",
"metadata": {},
"outputs": [
{
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"text/plain": [
"<Figure size 2000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.DataFrame(feature_importances).plot.barh(x=0);"
]
},
{
"cell_type": "markdown",
"id": "af3681f2",
"metadata": {},
"source": [
"### Light GBM approach"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "77f9b1af",
"metadata": {},
"outputs": [],
"source": [
"lgbc = LGBMClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "b9a56d96",
"metadata": {},
"outputs": [],
"source": [
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=17)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"id": "24e4be29",
"metadata": {},
"outputs": [],
"source": [
"lgb_parms = {'learning_rate': [0.005, 0.05, 0.02, 0.01],\\\n",
" 'n_estimators': [32, 40, 48, 56, 64, 72, 80, 88, 96],\\\n",
" 'min_data_in_leaf' : [10],\\\n",
" 'num_leaves': [2, 4, 6, 8],\\\n",
" 'boosting_type' : ['gbdt', 'dart'],\\\n",
" 'objective' : ['multiclass'],\\\n",
" 'random_state' : [17]}"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "4b32ff53",
"metadata": {},
"outputs": [],
"source": [
"grid_lgb = GridSearchCV(lgbc, lgb_parms, verbose=1, cv=skf, n_jobs=-1)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "ddad46ac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 288 candidates, totalling 1440 fits\n",
"[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=17, shuffle=True),\n",
" estimator=LGBMClassifier(), n_jobs=-1,\n",
" param_grid={'boosting_type': ['gbdt', 'dart'],\n",
" 'learning_rate': [0.005, 0.05, 0.02, 0.01],\n",
" 'min_data_in_leaf': [10],\n",
" 'n_estimators': [32, 40, 48, 56, 64, 72, 80, 88, 96],\n",
" 'num_leaves': [2, 4, 6, 8],\n",
" 'objective': ['multiclass'], 'random_state': [17]},\n",
" verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=17, shuffle=True),\n",
" estimator=LGBMClassifier(), n_jobs=-1,\n",
" param_grid={'boosting_type': ['gbdt', 'dart'],\n",
" 'learning_rate': [0.005, 0.05, 0.02, 0.01],\n",
" 'min_data_in_leaf': [10],\n",
" 'n_estimators': [32, 40, 48, 56, 64, 72, 80, 88, 96],\n",
" 'num_leaves': [2, 4, 6, 8],\n",
" 'objective': ['multiclass'], 'random_state': [17]},\n",
" verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=17, shuffle=True),\n",
" estimator=LGBMClassifier(), n_jobs=-1,\n",
" param_grid={'boosting_type': ['gbdt', 'dart'],\n",
" 'learning_rate': [0.005, 0.05, 0.02, 0.01],\n",
" 'min_data_in_leaf': [10],\n",
" 'n_estimators': [32, 40, 48, 56, 64, 72, 80, 88, 96],\n",
" 'num_leaves': [2, 4, 6, 8],\n",
" 'objective': ['multiclass'], 'random_state': [17]},\n",
" verbose=1)"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_lgb.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "28d3e81a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'boosting_type': 'gbdt', 'learning_rate': 0.05, 'min_data_in_leaf': 10, 'n_estimators': 64, 'num_leaves': 6, 'objective': 'multiclass', 'random_state': 17}\n"
]
}
],
"source": [
"print(grid_lgb.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"id": "99f8c9dc",
"metadata": {},
"outputs": [],
"source": [
"predictions_lgb = grid_lgb.predict(X_valid)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "a8488f72",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" orange 1.00 0.98 0.99 92\n",
" yellow 0.99 1.00 1.00 101\n",
" red 1.00 0.98 0.99 45\n",
" green 1.00 1.00 1.00 1758\n",
"\n",
" accuracy 1.00 1996\n",
" macro avg 1.00 0.99 0.99 1996\n",
"weighted avg 1.00 1.00 1.00 1996\n",
"\n"
]
}
],
"source": [
"## Classification report for LGBM classifier\n",
"\n",
"from sklearn.metrics import classification_report\n",
"print(classification_report(y_valid, predictions_lgb, target_names=target_names))"
]
},
{
"cell_type": "markdown",
"id": "a38fd189",
"metadata": {},
"source": [
"### Confusion metrics and overall performanec on Test Data"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "03aeecde",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix, accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "1a377e39",
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams[\"figure.figsize\"] = (6, 4)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "a01f7d81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1997, 18), (1997,))"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape, y_test.shape"
]
},
{
"cell_type": "markdown",
"id": "d191a55f",
"metadata": {},
"source": [
"### For Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "ee00902f",
"metadata": {},
"outputs": [],
"source": [
"# prediction on test split\n",
"\n",
"y_pred_rf = grid_rf.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "8d5d9f41",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9944917376064096\n"
]
}
],
"source": [
"ack = accuracy_score(y_test, y_pred_rf, normalize=True)\n",
"print(ack)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "4fb04bb4",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 600x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cf_mat_rf = confusion_matrix(y_test, y_pred_rf)\n",
"\n",
"ax= plt.subplot()\n",
"sns.heatmap(cf_mat_rf, annot=True, fmt='g', ax=ax, robust=True, cmap = 'Blues_r');\n",
"\n",
"ax.xaxis.set_ticklabels(target_names);\n",
"ax.yaxis.set_ticklabels(target_names);"
]
},
{
"cell_type": "markdown",
"id": "fda23790",
"metadata": {},
"source": [
"### For Light GBM"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "2c960fb0",
"metadata": {},
"outputs": [],
"source": [
"# prediction on test split\n",
"\n",
"y_pred_lgb = grid_lgb.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "42e91815",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9984977466199298\n"
]
}
],
"source": [
"ack = accuracy_score(y_test, y_pred_lgb, normalize=True)\n",
"print(ack)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "536fa173",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 600x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cf_mat_lgb = confusion_matrix(y_test, y_pred_lgb)\n",
"\n",
"ax= plt.subplot()\n",
"sns.heatmap(cf_mat_lgb, annot=True, fmt='g', ax=ax, robust=True, cmap = 'Blues_r');\n",
"\n",
"ax.xaxis.set_ticklabels(target_names);\n",
"ax.yaxis.set_ticklabels(target_names);"
]
}
],
"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": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}