Which algorithms are used in this life expectancy prediction project?

The system implements and compares two ensemble learning algorithms: XGBoost and Random Forest. XGBoost achieved the best performance with 91.5% accuracy and an F1-score of 0.915.

What features are used for life expectancy prediction?

The system uses 20 features including adult mortality, infant deaths, alcohol consumption, hepatitis B immunization, measles, BMI, polio immunization, diphtheria immunization, HIV/AIDS prevalence, GDP, thinness, income composition of resources, and schooling years.

What is the accuracy of this life expectancy prediction model?

The XGBoost model achieves 91.5% test accuracy with precision of 91.6%, recall of 91.5%, F1-score of 0.915, and ROC-AUC of 0.961, making it highly reliable for healthcare policy support.

Who can benefit from this life expectancy prediction system?

Policymakers can use it for resource allocation, healthcare administrators for intervention planning, international organizations for monitoring health progress, and researchers for studying health determinants.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Life Expectancy Prediction Using Machine Learning

The Life Expectancy Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts whether a country's life expectancy is High or Low using WHO data with 20 health and socioeconomic features. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced ensemble algorithms like XGBoost and Random Forest to achieve 91.5% accuracy.

The system leverages features including adult mortality, BMI, GDP, schooling, immunization rates, and income composition of resources. It provides interactive visualizations, feature importance analysis, model comparison, and real-time life expectancy prediction to help policymakers and healthcare administrators make data-driven decisions.

Python 3.8+ Machine Learning XGBoost Random Forest Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Binary Life Expectancy Classification
XGBoost (91.5% Accuracy)
Random Forest (90.8% Accuracy)
20 Feature Analysis
Feature Importance Analysis
Real-time Country Prediction
Model Performance Comparison
Interactive Visualizations
5-Fold Stratified CV
Flask Web Application

Algorithms Used

⚡ XGBoost Classifier

Optimized gradient boosting with regularization, handles missing values effectively

🎯 Accuracy: 91.5%

🌲 Random Forest Classifier

Ensemble learning with 100 trees, robust to outliers and noise

🎯 Accuracy: 90.8%

📊 Feature Engineering

StandardScaler, binary target creation, categorical encoding

📊 Features: 20

🔍 5-Fold Stratified CV

Cross-validation for model stability and generalization

📊 XGB: 89.9% ± 1.5%

Methodology & Workflow

1 Data Loading & Inspection
WHO dataset with 2,938 records across 193 countries

2 Data Preprocessing
Missing value imputation, outlier removal, feature scaling

3 Feature Engineering
Binary target creation, status encoding

4 Model Training
XGBoost & Random Forest with optimized parameters

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time life expectancy prediction

Model Performance Comparison

Metric	XGBoost	Random Forest	Best
Accuracy	0.915	0.908	XGBoost
Precision	0.916	0.909	XGBoost
Recall	0.915	0.908	XGBoost
F1-Score	0.915	0.908	XGBoost
ROC-AUC	0.961	0.949	XGBoost
CV Mean (5-Fold)	0.899	0.891	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (2,938 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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