{
"cells": [
{
"cell_type": "markdown",
"id": "74012e00",
"metadata": {},
"source": [
"# Install and import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cd7ae131",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Install libraries\n",
"!pip install -q missingno\n",
"\n",
"# import libraries\n",
"import missingno as msno\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "acc6fb0f",
"metadata": {},
"source": [
"# Importing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "99953ed1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(308854, 19)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Poor</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>150.0</td>\n",
" <td>32.66</td>\n",
" <td>14.54</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>16.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>165.0</td>\n",
" <td>77.11</td>\n",
" <td>28.29</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>60-64</td>\n",
" <td>163.0</td>\n",
" <td>88.45</td>\n",
" <td>33.47</td>\n",
" <td>No</td>\n",
" <td>4.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Poor</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>75-79</td>\n",
" <td>180.0</td>\n",
" <td>93.44</td>\n",
" <td>28.73</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>30.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>80+</td>\n",
" <td>191.0</td>\n",
" <td>88.45</td>\n",
" <td>24.37</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 Poor Within the past 2 years No No No \n",
"1 Very Good Within the past year No Yes No \n",
"2 Very Good Within the past year Yes No No \n",
"3 Poor Within the past year Yes Yes No \n",
"4 Good Within the past year No No No \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 No No No Yes Female 70-74 \n",
"1 No No Yes No Female 70-74 \n",
"2 No No Yes No Female 60-64 \n",
"3 No No Yes No Male 75-79 \n",
"4 No No No No Male 80+ \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History Alcohol_Consumption \\\n",
"0 150.0 32.66 14.54 Yes 0.0 \n",
"1 165.0 77.11 28.29 No 0.0 \n",
"2 163.0 88.45 33.47 No 4.0 \n",
"3 180.0 93.44 28.73 No 0.0 \n",
"4 191.0 88.45 24.37 Yes 0.0 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 30.0 16.0 12.0 \n",
"1 30.0 0.0 4.0 \n",
"2 12.0 3.0 16.0 \n",
"3 30.0 30.0 8.0 \n",
"4 8.0 4.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Read the dataset\n",
"dataset_path = \"C:\\My\\Top-up Degree\\Data Science\\Data Science - Assignment\\Data set\\Kaggle\\Cardiovascular Diseases Risk Prediction Dataset\\CVD_cleaned.csv\"\n",
"data = pd.read_csv(dataset_path)\n",
"print(data.shape)\n",
"display(data.head(n=5))"
]
},
{
"cell_type": "markdown",
"id": "6d083bab",
"metadata": {},
"source": [
"## Describing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7d31bcf2",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>308854.000000</td>\n",
" <td>308854.000000</td>\n",
" <td>308854.000000</td>\n",
" <td>308854.000000</td>\n",
" <td>308854.000000</td>\n",
" <td>308854.000000</td>\n",
" <td>308854.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>170.615249</td>\n",
" <td>83.588655</td>\n",
" <td>28.626211</td>\n",
" <td>5.096366</td>\n",
" <td>29.835200</td>\n",
" <td>15.110441</td>\n",
" <td>6.296616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>10.658026</td>\n",
" <td>21.343210</td>\n",
" <td>6.522323</td>\n",
" <td>8.199763</td>\n",
" <td>24.875735</td>\n",
" <td>14.926238</td>\n",
" <td>8.582954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>91.000000</td>\n",
" <td>24.950000</td>\n",
" <td>12.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>163.000000</td>\n",
" <td>68.040000</td>\n",
" <td>24.210000</td>\n",
" <td>0.000000</td>\n",
" <td>12.000000</td>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>170.000000</td>\n",
" <td>81.650000</td>\n",
" <td>27.440000</td>\n",
" <td>1.000000</td>\n",
" <td>30.000000</td>\n",
" <td>12.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>178.000000</td>\n",
" <td>95.250000</td>\n",
" <td>31.850000</td>\n",
" <td>6.000000</td>\n",
" <td>30.000000</td>\n",
" <td>20.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>241.000000</td>\n",
" <td>293.020000</td>\n",
" <td>99.330000</td>\n",
" <td>30.000000</td>\n",
" <td>120.000000</td>\n",
" <td>128.000000</td>\n",
" <td>128.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Height_(cm) Weight_(kg) BMI Alcohol_Consumption \\\n",
"count 308854.000000 308854.000000 308854.000000 308854.000000 \n",
"mean 170.615249 83.588655 28.626211 5.096366 \n",
"std 10.658026 21.343210 6.522323 8.199763 \n",
"min 91.000000 24.950000 12.020000 0.000000 \n",
"25% 163.000000 68.040000 24.210000 0.000000 \n",
"50% 170.000000 81.650000 27.440000 1.000000 \n",
"75% 178.000000 95.250000 31.850000 6.000000 \n",
"max 241.000000 293.020000 99.330000 30.000000 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption \\\n",
"count 308854.000000 308854.000000 \n",
"mean 29.835200 15.110441 \n",
"std 24.875735 14.926238 \n",
"min 0.000000 0.000000 \n",
"25% 12.000000 4.000000 \n",
"50% 30.000000 12.000000 \n",
"75% 30.000000 20.000000 \n",
"max 120.000000 128.000000 \n",
"\n",
" FriedPotato_Consumption \n",
"count 308854.000000 \n",
"mean 6.296616 \n",
"std 8.582954 \n",
"min 0.000000 \n",
"25% 2.000000 \n",
"50% 4.000000 \n",
"75% 8.000000 \n",
"max 128.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe() # print the descriptive statistics"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5ac2c919",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>308854.00</td>\n",
" <td>308854.00</td>\n",
" <td>308854.00</td>\n",
" <td>308854.00</td>\n",
" <td>308854.00</td>\n",
" <td>308854.00</td>\n",
" <td>308854.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>170.62</td>\n",
" <td>83.59</td>\n",
" <td>28.63</td>\n",
" <td>5.10</td>\n",
" <td>29.84</td>\n",
" <td>15.11</td>\n",
" <td>6.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>10.66</td>\n",
" <td>21.34</td>\n",
" <td>6.52</td>\n",
" <td>8.20</td>\n",
" <td>24.88</td>\n",
" <td>14.93</td>\n",
" <td>8.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>91.00</td>\n",
" <td>24.95</td>\n",
" <td>12.02</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>163.00</td>\n",
" <td>68.04</td>\n",
" <td>24.21</td>\n",
" <td>0.00</td>\n",
" <td>12.00</td>\n",
" <td>4.00</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>170.00</td>\n",
" <td>81.65</td>\n",
" <td>27.44</td>\n",
" <td>1.00</td>\n",
" <td>30.00</td>\n",
" <td>12.00</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>178.00</td>\n",
" <td>95.25</td>\n",
" <td>31.85</td>\n",
" <td>6.00</td>\n",
" <td>30.00</td>\n",
" <td>20.00</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>241.00</td>\n",
" <td>293.02</td>\n",
" <td>99.33</td>\n",
" <td>30.00</td>\n",
" <td>120.00</td>\n",
" <td>128.00</td>\n",
" <td>128.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Height_(cm) Weight_(kg) BMI Alcohol_Consumption \\\n",
"count 308854.00 308854.00 308854.00 308854.00 \n",
"mean 170.62 83.59 28.63 5.10 \n",
"std 10.66 21.34 6.52 8.20 \n",
"min 91.00 24.95 12.02 0.00 \n",
"25% 163.00 68.04 24.21 0.00 \n",
"50% 170.00 81.65 27.44 1.00 \n",
"75% 178.00 95.25 31.85 6.00 \n",
"max 241.00 293.02 99.33 30.00 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption \\\n",
"count 308854.00 308854.00 \n",
"mean 29.84 15.11 \n",
"std 24.88 14.93 \n",
"min 0.00 0.00 \n",
"25% 12.00 4.00 \n",
"50% 30.00 12.00 \n",
"75% 30.00 20.00 \n",
"max 120.00 128.00 \n",
"\n",
" FriedPotato_Consumption \n",
"count 308854.00 \n",
"mean 6.30 \n",
"std 8.58 \n",
"min 0.00 \n",
"25% 2.00 \n",
"50% 4.00 \n",
"75% 8.00 \n",
"max 128.00 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"pd.options.display.float_format = ' {:.2f}'.format # set the format for two decimal places\n",
"data.describe() # print the descriptive statistics"
]
},
{
"cell_type": "markdown",
"id": "dbb71f48",
"metadata": {},
"source": [
"## Dataset information"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "97b392bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 308854 entries, 0 to 308853\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 General_Health 308854 non-null object \n",
" 1 Checkup 308854 non-null object \n",
" 2 Exercise 308854 non-null object \n",
" 3 Heart_Disease 308854 non-null object \n",
" 4 Skin_Cancer 308854 non-null object \n",
" 5 Other_Cancer 308854 non-null object \n",
" 6 Depression 308854 non-null object \n",
" 7 Diabetes 308854 non-null object \n",
" 8 Arthritis 308854 non-null object \n",
" 9 Sex 308854 non-null object \n",
" 10 Age_Category 308854 non-null object \n",
" 11 Height_(cm) 308854 non-null float64\n",
" 12 Weight_(kg) 308854 non-null float64\n",
" 13 BMI 308854 non-null float64\n",
" 14 Smoking_History 308854 non-null object \n",
" 15 Alcohol_Consumption 308854 non-null float64\n",
" 16 Fruit_Consumption 308854 non-null float64\n",
" 17 Green_Vegetables_Consumption 308854 non-null float64\n",
" 18 FriedPotato_Consumption 308854 non-null float64\n",
"dtypes: float64(7), object(12)\n",
"memory usage: 44.8+ MB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c6456a07",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"General_Health 5\n",
"Checkup 5\n",
"Exercise 2\n",
"Heart_Disease 2\n",
"Skin_Cancer 2\n",
"Other_Cancer 2\n",
"Depression 2\n",
"Diabetes 4\n",
"Arthritis 2\n",
"Sex 2\n",
"Age_Category 13\n",
"Height_(cm) 99\n",
"Weight_(kg) 525\n",
"BMI 3654\n",
"Smoking_History 2\n",
"Alcohol_Consumption 31\n",
"Fruit_Consumption 77\n",
"Green_Vegetables_Consumption 75\n",
"FriedPotato_Consumption 69\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the number of unique values in each column\n",
"unique_value_counts = data.nunique()\n",
"\n",
"display(unique_value_counts)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "bdf0600c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['No' 'Yes' 'No, pre-diabetes or borderline diabetes'\n",
" 'Yes, but female told only during pregnancy']\n"
]
}
],
"source": [
"# Print the unique values in the column 'Gender'\n",
"unique_values = data['Diabetes'].unique()\n",
"print(unique_values)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "58837710",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 9542 rows with abnormal data in the Diabetes column.\n"
]
}
],
"source": [
"# Check the number of rows with abnormal data in the Diabetes column\n",
"abnormal_count = data[data['Diabetes'].isin(['No, pre-diabetes or borderline diabetes', 'Yes, but female told only during pregnancy'])].shape[0]\n",
"print(f'There are {abnormal_count} rows with abnormal data in the Diabetes column.')"
]
},
{
"cell_type": "markdown",
"id": "99fb3e47",
"metadata": {},
"source": [
"## Checking missing values"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5a8c5c5a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the missing values matrix\n",
"msno.matrix(data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d37cbb66",
"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>Missing Data Ratio (%)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>General_Health</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Checkup</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exercise</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heart_Disease</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Skin_Cancer</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other_Cancer</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Depression</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diabetes</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arthritis</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age_Category</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Height_(cm)</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weight_(kg)</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BMI</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Smoking_History</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alcohol_Consumption</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fruit_Consumption</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FriedPotato_Consumption</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Missing Data Ratio (%)\n",
"General_Health 0.00\n",
"Checkup 0.00\n",
"Exercise 0.00\n",
"Heart_Disease 0.00\n",
"Skin_Cancer 0.00\n",
"Other_Cancer 0.00\n",
"Depression 0.00\n",
"Diabetes 0.00\n",
"Arthritis 0.00\n",
"Sex 0.00\n",
"Age_Category 0.00\n",
"Height_(cm) 0.00\n",
"Weight_(kg) 0.00\n",
"BMI 0.00\n",
"Smoking_History 0.00\n",
"Alcohol_Consumption 0.00\n",
"Fruit_Consumption 0.00\n",
"Green_Vegetables_Consumption 0.00\n",
"FriedPotato_Consumption 0.00"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import pandas as pd\n",
"import pandas as pd\n",
"\n",
"# Import display from IPython.display\n",
"from IPython.display import display\n",
"\n",
"# Calculate the missing data ratio for each column as percentages\n",
"missing_data_ratio = data.isnull().mean() * 100\n",
"\n",
"# Convert the Series to a DataFrame for better display\n",
"missing_data_df = pd.DataFrame({'Missing Data Ratio (%)': missing_data_ratio})\n",
"\n",
"# Display the missing data ratio for each column as percentages in a scrollable DataFrame\n",
"with pd.option_context('display.max_rows', None):\n",
" display(missing_data_df)"
]
},
{
"cell_type": "markdown",
"id": "942db3a6",
"metadata": {},
"source": [
"## Checking for duplicates"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ea8d9b5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of duplicates: 80\n"
]
}
],
"source": [
"# Count duplicates\n",
"duplicate_count = data.duplicated().sum()\n",
"\n",
"# Print the count\n",
"print(\"Number of duplicates:\", duplicate_count)"
]
},
{
"cell_type": "markdown",
"id": "394bdfc0",
"metadata": {},
"source": [
"## Checking outliers"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9175462b",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1600x1000 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import matplotlib.pyplot\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# List of numerical columns\n",
"numerical_cols = ['Height_(cm)', 'Weight_(kg)', 'BMI', 'Alcohol_Consumption', \n",
" 'Fruit_Consumption', 'Green_Vegetables_Consumption', \n",
" 'FriedPotato_Consumption']\n",
"\n",
"# Create histograms\n",
"plt.figure(figsize=(16, 10))\n",
"for i, column in enumerate(numerical_cols, 1):\n",
" plt.subplot(3, 3, i)\n",
" plt.hist(data[column], bins=20) # Plot a histogram with 20 bins\n",
" plt.xlabel(column) # Add a label for the x-axis\n",
"\n",
"plt.tight_layout() # Adjust the spacing between subplots\n",
"plt.show() # Display the plots"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2d409dd5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x1000 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# List of numerical columns\n",
"numerical_cols = ['Height_(cm)', 'Weight_(kg)', 'BMI', 'Alcohol_Consumption', \n",
" 'Fruit_Consumption', 'Green_Vegetables_Consumption', \n",
" 'FriedPotato_Consumption']\n",
"\n",
"# Create box plots\n",
"plt.figure(figsize=(16, 10))\n",
"for i, column in enumerate(numerical_cols, 1):\n",
" plt.subplot(3, 3, i)\n",
" sns.boxplot(y=data[column])\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2d5f84e5",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>Fair</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
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" <td>4.00</td>\n",
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" <td>Within the past 2 years</td>\n",
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" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
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" <td>5.00</td>\n",
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" <td>1.00</td>\n",
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" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
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" <td>75-79</td>\n",
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" <td>86.18</td>\n",
" <td>20.36</td>\n",
" <td>No</td>\n",
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" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>70-74</td>\n",
" <td>193.00</td>\n",
" <td>98.88</td>\n",
" <td>26.54</td>\n",
" <td>No</td>\n",
" <td>0.00</td>\n",
" <td>60.00</td>\n",
" <td>60.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>Fair</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Male</td>\n",
" <td>65-69</td>\n",
" <td>198.00</td>\n",
" <td>70.31</td>\n",
" <td>17.91</td>\n",
" <td>Yes</td>\n",
" <td>0.00</td>\n",
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" <td>2.00</td>\n",
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" <th>308660</th>\n",
" <td>Good</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>35-39</td>\n",
" <td>175.00</td>\n",
" <td>172.37</td>\n",
" <td>56.12</td>\n",
" <td>No</td>\n",
" <td>0.00</td>\n",
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" <td>8.00</td>\n",
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" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
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" <td>No</td>\n",
" <td>No</td>\n",
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" <td>25-29</td>\n",
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" <td>48.55</td>\n",
" <td>No</td>\n",
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" <td>1.00</td>\n",
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" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>25-29</td>\n",
" <td>147.00</td>\n",
" <td>90.72</td>\n",
" <td>41.80</td>\n",
" <td>No</td>\n",
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" <td>4.00</td>\n",
" <td>8.00</td>\n",
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" <td>Fair</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>18-24</td>\n",
" <td>173.00</td>\n",
" <td>125.65</td>\n",
" <td>42.12</td>\n",
" <td>No</td>\n",
" <td>3.00</td>\n",
" <td>30.00</td>\n",
" <td>16.00</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308825</th>\n",
" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>35-39</td>\n",
" <td>175.00</td>\n",
" <td>136.08</td>\n",
" <td>44.30</td>\n",
" <td>No</td>\n",
" <td>2.00</td>\n",
" <td>30.00</td>\n",
" <td>7.00</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>34442 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease \\\n",
"108 Fair Within the past year Yes Yes \n",
"137 Good Within the past 2 years Yes No \n",
"181 Good Within the past year Yes No \n",
"318 Poor Within the past year Yes Yes \n",
"353 Fair Within the past year No Yes \n",
"... ... ... ... ... \n",
"308660 Good Within the past 2 years No No \n",
"308679 Very Good Within the past year Yes No \n",
"308716 Poor Within the past year No No \n",
"308807 Fair Within the past year Yes No \n",
"308825 Good Within the past year No No \n",
"\n",
" Skin_Cancer Other_Cancer Depression Diabetes Arthritis Sex \\\n",
"108 Yes No No Yes Yes Male \n",
"137 No Yes Yes No Yes Male \n",
"181 Yes No No No No Male \n",
"318 No No No Yes No Male \n",
"353 No No No No Yes Male \n",
"... ... ... ... ... ... ... \n",
"308660 No No No No No Female \n",
"308679 No No No Yes No Female \n",
"308716 No No No No No Female \n",
"308807 No No No No No Female \n",
"308825 No No No No No Male \n",
"\n",
" Age_Category Height_(cm) Weight_(kg) BMI Smoking_History \\\n",
"108 75-79 193.00 125.19 33.60 Yes \n",
"137 65-69 196.00 68.04 17.79 Yes \n",
"181 75-79 206.00 86.18 20.36 No \n",
"318 70-74 193.00 98.88 26.54 No \n",
"353 65-69 198.00 70.31 17.91 Yes \n",
"... ... ... ... ... ... \n",
"308660 35-39 175.00 172.37 56.12 No \n",
"308679 25-29 170.00 140.61 48.55 No \n",
"308716 25-29 147.00 90.72 41.80 No \n",
"308807 18-24 173.00 125.65 42.12 No \n",
"308825 35-39 175.00 136.08 44.30 No \n",
"\n",
" Alcohol_Consumption Fruit_Consumption Green_Vegetables_Consumption \\\n",
"108 1.00 8.00 1.00 \n",
"137 0.00 5.00 1.00 \n",
"181 0.00 30.00 1.00 \n",
"318 0.00 60.00 60.00 \n",
"353 0.00 4.00 2.00 \n",
"... ... ... ... \n",
"308660 0.00 30.00 16.00 \n",
"308679 0.00 8.00 12.00 \n",
"308716 0.00 4.00 8.00 \n",
"308807 3.00 30.00 16.00 \n",
"308825 2.00 30.00 7.00 \n",
"\n",
" FriedPotato_Consumption \n",
"108 4.00 \n",
"137 1.00 \n",
"181 0.00 \n",
"318 0.00 \n",
"353 8.00 \n",
"... ... \n",
"308660 8.00 \n",
"308679 1.00 \n",
"308716 12.00 \n",
"308807 3.00 \n",
"308825 8.00 \n",
"\n",
"[34442 rows x 19 columns]"
]
},
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"output_type": "display_data"
}
],
"source": [
"# Import pandas library\n",
"import pandas as pd\n",
"\n",
"# Define a function to find outliers using mean and standard deviation\n",
"def find_outliers(data, col):\n",
" # Calculate mean and standard deviation\n",
" mean = data[col].mean()\n",
" std = data[col].std()\n",
" # Calculate lower and upper bounds\n",
" lower_bound = mean - 2 * std\n",
" upper_bound = mean + 2 * std\n",
" # Filter out the outliers\n",
" outliers = data[(data[col] < lower_bound) | (data[col] > upper_bound)]\n",
" # Return the outliers\n",
" return outliers\n",
"\n",
"# Apply the function to the three variables and store the results in a list\n",
"outlier_list = [find_outliers(data, col) for col in ['Height_(cm)', 'Weight_(kg)', 'BMI']]\n",
"\n",
"# Concatenate the results into a single dataframe\n",
"outlier_df = pd.concat(outlier_list)\n",
"\n",
"# Display the dataframe\n",
"display(outlier_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d5bd443",
"metadata": {},
"outputs": [],
"source": []
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