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🌍 Life Expectancy Predictor

This project builds a regression model to estimate life expectancy across countries using a range of socioeconomic and health-related factors, including:

  • Gross Domestic Product (GDP)
  • Education level
  • Healthcare expenditure
  • Immunization rates
  • and other development indicators

📂 Dataset Source

This dataset was originally published on Kaggle by KumarRajarshi. It contains global life expectancy data curated from WHO and other development indicators.

📖 Dataset Description

This project aims to predict the life expectancy of a given country based on various health, economic, and demographic parameters. The dataset includes the following variables:

  • Country: Name of the country
  • Year: Year of the data record
  • Status: Development status (Developed or Developing)
  • Life Expectancy: Average expected lifespan in years
  • Adult Mortality: Number of adult deaths per 1000 population
  • Infant Deaths: Number of infant deaths per 1000 live births
  • Alcohol: Alcohol consumption per capita (in liters)
  • Percentage Expenditure: Health expenditure as a percentage of GDP
  • Hepatitis B: Immunization coverage percentage for Hepatitis B
  • Measles: Number of reported measles cases
  • BMI: Average Body Mass Index of the population
  • Under Five Deaths: Number of deaths of children under five years
  • Polio: Immunization coverage percentage for Polio
  • Total Expenditure: Total health expenditure per capita
  • Diphtheria: Immunization coverage percentage for Diphtheria
  • HIV/AIDS: Death rate per 1000 due to HIV/AIDS
  • GDP: Gross Domestic Product per capita
  • Population: Total population of the country
  • Thinness 1-19 years: Prevalence of thinness among children and adolescents aged 1–19
  • Thinness 5-9 years: Prevalence of thinness among children aged 5–9
  • Income Composition of Resources: Indicator of income inequality and distribution
  • Schooling: Average years of schooling in the population

This dataset provides a broad view of factors influencing life expectancy, enabling the development of predictive models to analyze health outcomes globally.

📊 Project Highlights

  • ✅ Data Cleaning & Preprocessing: Handled missing values, encoded categorical features, and normalized continuous variables.
  • 📈 Correlation Analysis & Feature Selection: Used heatmaps and feature importance (via Random Forest) to select relevant predictors.
  • 🧠 Modeling & Evaluation:
    • Algorithms tested: Linear Regression, SVR, RandomForest, and ensemble models.
    • Hyperparameter tuning via GridSearchCV.
    • Final model: VotingRegressor combining RandomForest and LinearRegression.
    • Best RMSE: 1.66 years

🔧 Tools & Libraries

  • pandas, numpy, matplotlib, seaborn, plotly
  • scikit-learn
  • Jupyter Notebook