{"id":146107,"date":"2025-07-25T10:55:03","date_gmt":"2025-07-25T05:25:03","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146107"},"modified":"2025-07-25T10:55:03","modified_gmt":"2025-07-25T05:25:03","slug":"lifestyle-health-risk-prediction-using-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/lifestyle-health-risk-prediction-using-machine-learning\/","title":{"rendered":"ML Project – Lifestyle Health Risk Prediction using K-means Clustering"},"content":{"rendered":"

Program 1<\/h3>\n

Lifestyle Health Risk Dataset<\/strong><\/a><\/p>\n

Lifestyle Health Risk Dataset 1<\/strong><\/a><\/p>\n

import pandas as pd # For handling tabular data.\r\nimport numpy as np #For numerical operations and random data generation.\r\nimport matplotlib.pyplot as plt # For plotting graphs.\r\nfrom sklearn.cluster import KMeans   # Unsupervised clustering algorithm from sklearn.\r\nfrom sklearn.preprocessing import StandardScaler  #Used to normalize features (mean = 0, std = 1).\r\n\r\nnp.random.seed(42) #  Ensures reproducibility of the random data.\r\nn_people = 200 # Total number of synthetic individuals.\r\n\r\ndata = {\r\n    'PersonID': np.arange(1, n_people + 1),\r\n    'Exercise (hrs\/week)': np.random.normal(3, 1.5, n_people).clip(0, 10),  # Mean (\u03bc) = 3 Standard deviation (\u03c3) = 1.5\r\n    'Sleep (hrs\/day)': np.random.normal(7, 1.0, n_people).clip(3, 10),\r\n    'Junk Food (times\/week)': np.random.randint(0, 8, n_people),\r\n    'Screen Time (hrs\/day)': np.random.normal(6, 2.0, n_people).clip(2, 14)\r\n}\r\ndf = pd.DataFrame(data)\r\ndf\r\n# Creates synthetic values for 4 lifestyle habits:\r\n# Exercise: Normally distributed around 3 hrs\/week (clipped 0\u201310)\r\n# Sleep: Around 7 hrs\/day (clipped 3\u201310)\r\n# Junk Food: Integer from 0\u20137\r\n# Screen Time: Around 6 hrs\/day (clipped 2\u201314)\r\n\r\ndf.to_csv('D:\/\/scikit_data\/KMeans\/lifestyle_health_risk.csv', index=False)\r\n\r\n# Feature Selection & Normalization\r\n\r\nfeatures = ['Exercise (hrs\/week)', 'Sleep (hrs\/day)', 'Junk Food (times\/week)', 'Screen Time (hrs\/day)']\r\nX = df[features]\r\nscaler = StandardScaler()\r\nX_scaled = scaler.fit_transform(X)\r\nX_scaled\r\n# Select only the relevant columns (exclude PersonID).\r\n# Normalize all features so that they contribute equally during clustering.\r\n# Output is a scaled X_scaled array (mean=0, std=1).\r\n\r\n#Apply K-Means Clustering\r\nkmeans = KMeans(n_clusters=3, random_state=42)\r\ndf['Health Risk Category'] = kmeans.fit_predict(X_scaled)\r\ndf.to_csv('D:\/\/scikit_data\/KMeans\/lifestyle_health_risk1.csv', index=False)\r\n\r\n# A  scatter plot showing how people are grouped based on their sleep and diet habits.\r\n# Helps visually identify which group is more or less healthy.\r\n\r\nplt.figure(figsize=(8, 6))\r\nplt.scatter(df['Sleep (hrs\/day)'], df['Junk Food (times\/week)'],\r\n            c=df['Health Risk Category'], cmap='coolwarm', s=60)\r\nplt.scatter(\r\n    scaler.inverse_transform(kmeans.cluster_centers_)[:, 1],\r\n    scaler.inverse_transform(kmeans.cluster_centers_)[:, 2],\r\n    s=200, c='black', marker='X', label='Centroids')\r\n\r\nplt.title('Lifestyle Clustering: Sleep vs. Junk Food')\r\nplt.xlabel('Sleep (hrs\/day)')\r\nplt.ylabel('Junk Food (times\/week)')\r\nplt.legend()\r\nplt.grid(True)\r\nplt.tight_layout()\r\nplt.show()\r\n<\/pre>\n
 <\/p>\n
 
Program 1 Lifestyle Health Risk Dataset Lifestyle Health Risk Dataset 1 import pandas as pd # For handling tabular data. import numpy as np #For numerical operations and random data generation. import matplotlib.pyplot as...<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[19647,34962,34965,34964,34963,8431,34966,33127,33128,20697],"class_list":["post-146107","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-k-means-clustering","tag-lifestyle-health-risk-prediction","tag-lifestyle-health-risk-prediction-project","tag-lifestyle-health-risk-prediction-using-k-means-clustering","tag-lifestyle-health-risk-prediction-using-machine-learning","tag-machine-learning","tag-machine-learning-lifestyle-health-risk-prediction-project","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"\nML Project - 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