📊 A Data Science Project by Vishnu Raj

Life expectancy is a key indicator of a nation’s overall health and development. This project aims to predict life expectancy using Multiple Linear Regression (MLR) based on socio-economic and health factors. Using the WHO Life Expectancy Dataset, we explore correlations, clean the data, visualize relationships, and build regression models for prediction.

✅ Identify significant socio-economic and health predictors of life expectancy ✅ Build and evaluate a Multiple Linear Regression model ✅ Compare performance with Ridge and Lasso Regression ✅ Visualize patterns through EDA for clear interpretation

📄 Dataset: Life Expectancy (WHO) – Kaggle 🌍 Records: 2,930 🧩 Features: 22 (GDP, BMI, Schooling, Status, etc.) 🎯 Target Variable: Life Expectancy

✔ Replaced missing values using median imputation ✔ Encoded categorical variables (Status: Developed/Developing) ✔ Handled outliers using IQR ✔ Split data into 70% Train and 30% Test using train_test_split

Explored patterns between health and economic indicators:

• Correlation heatmaps 📈
• Pairplots for relationships 👥
• Schooling,Diphtheria Immunization as key drivers

💡 Higher education levels and income = higher life expectancy. 💡 Schooling, Health Expenditure, and Diphtheria Immunization strongly correlate. 💡 Regularization (Ridge/Lasso) provided stable, consistent results.

pandas
numpy
matplotlib
seaborn
scikit-learn

✅ Achieved R² ≈ 0.83, showing strong predictive power ✅ Proved education, economy, and healthcare as vital for longer lives ✅ Demonstrated MLR’s simplicity and interpretability in social datasets

👨‍💻 Vishnu Raj 🎓 Data Science Project 💼 GitHub | LinkedIn | 📧 vishnuskillx@gmail.com

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