Life Expectancy Prediction using Machine Learning

Project Overview

This project predicts life expectancy using machine learning techniques based on various health, economic, and social factors.

The analysis helps understand how different indicators such as healthcare, income, immunization, and lifestyle affect life expectancy across countries.

This project was developed as part of my internship learning program.

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Objective

The main objective of this project is to analyze global health-related data and build a machine learning model to predict life expectancy.

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Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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Dataset Features

Some important features used in the dataset include:

• Country
• Year
• Adult Mortality
• Infant Deaths
• Alcohol Consumption
• Hepatitis B
• BMI
• GDP
• Population
• Schooling
• Income Composition of Resources

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Machine Learning Algorithms Used

• Linear Regression
• Random Forest Regressor
• Decision Tree Regressor (Add the algorithm you actually used)

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Project Workflow

1. Data Collection
2. Data Cleaning
3. Exploratory Data Analysis
4. Feature Engineering
5. Data Visualization
6. Model Training
7. Model Evaluation
8. Prediction and Analysis

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Results

The machine learning model predicts life expectancy based on health and economic indicators.

Model Accuracy / R² Score: XX% (Add your actual score)

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Data Visualizations

The project includes:

• Correlation Heatmap
• GDP vs Life Expectancy Graph
• Country-wise Analysis
• Feature Importance Graphs
• Prediction Comparison Charts

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Future Improvements

• Improve prediction accuracy
• Deploy using Streamlit or Flask
• Add interactive dashboard
• Use advanced ensemble models

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Conclusion

This project demonstrates how machine learning can be used to analyze factors affecting life expectancy and support data-driven healthcare insights.

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Author

Jhansi
B.Tech Student | Aspiring Data Analyst