Life Expectancy & Socio-Economic Analysis

This project explores the relationship between socio-economic factors and life expectancy across global regions using data analysis and predictive modeling techniques in R.

Dataset

The dataset, sourced from Kaggle, contains 3,306 observations and 16 variables, including:

• Life Expectancy (World Bank)
• Prevalence of Undernourishment
• CO2 Emissions
• Health and Education Expenditures
• Corruption Levels
• Income Classifications

Objectives

The primary objective of this project is to analyze and model socio-economic indicators that influence life expectancy. Specific goals include:

1. Investigating how factors like corruption, unemployment, and CO2 emissions impact life expectancy.
2. Examining patterns in income classifications by region.
3. Developing a predictive model to estimate undernourishment rates based on life expectancy.

Methodology

1. Exploratory Data Analysis (EDA)

• Created visualizations using ggplot2 to analyze regional and income-based patterns.
• Examples:
  • Stacked bar charts to explore income distribution by region.
  • Boxplots comparing life expectancy across European, Central Asian, and Sub-Saharan African countries.

2. Predictive Modeling

• Built a K-Nearest Neighbors (KNN) regression model using the tidymodels framework.
• Performed data preprocessing, including normalization and handling missing values.
• Tuned the model to minimize RMSE, achieving optimal performance with k = 49.

3. Results

• Key Insights:
  • Regions with higher life expectancy exhibit lower rates of undernourishment.
  • Socio-economic disparities, such as income level and corruption, are significant predictors of quality of life.
• Predictive Model Performance:
  • Training RMSE: 7.4
  • Testing RMSE: 8.13

Technologies Used

• R Programming: Data wrangling, visualization, and modeling
• Packages: ggplot2, tidymodels, gridExtra, dplyr, tidyverse

Files

• Computational Statistics Project.Rmd: The RMarkdown file containing all code and analysis.
• Computational Statistics Project.pdf: The compiled report.

How to Run

1. Clone the repository:

   git clone https://github.com/your-username/life-expectancy-analysis.git  
   cd life-expectancy-analysis

2. Open Computational Statistics Project.Rmd in RStudio

3. Run all code chunks to reproduce the analysis and visualizations.