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Predicting Life Expectancy Using Machine Learning Techniques on Who Data

1R. BHANU SANKAR ,2KALLAKURI SAI TEJA

1Assistant Professor, Department Of MCA, 2MCA Final Semester,

1Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

Abstract:

This project applies machine learning algorithms to predict life expectancy using WHO datasets.
It aims to discover relationships between socio-economic indicators, health expenditure, and life expectancy. Data preprocessing involved handling missing values, encoding categories, and scaling features.  Regression models like Linear, Polynomial, Decision Tree, and Random Forest were used for GDP-expenditure analysis. Model performance was evaluated using R² score, RMSE, and visualized through prediction plots. Logistic Regression was applied to classify countries as 'Developed' or 'Developing' based on normalized features. All models were saved using pickle and deployed via a Streamlit interface for real-time predictions. This study shows how machine learning supports public health insights and data-driven decision-making.

Index Terms: Life Expectancy, Machine Learning, WHO Data, Linear Regression, Random Forest, Logistic Regression, Health Analytics, Predictive Modeling, Data Preprocessing, ROC Curve.

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