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
This dataset was originally published on Kaggle by KumarRajarshi. It contains global life expectancy data curated from WHO and other development indicators.
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.
- ✅ 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
pandas,numpy,matplotlib,seaborn,plotlyscikit-learn- Jupyter Notebook