Life Expectancy Analysis Project Overview This project aims to analyze life expectancy data across various countries, examining the impact of socio-economic factors, health indicators, and other relevant variables. The goal is to identify key determinants of life expectancy and understand how these factors interplay to influence the health outcomes of populations.

Dataset The dataset used in this analysis includes:

Country-specific data on life expectancy. Socio-economic indicators such as GDP, income levels, and education. Health-related metrics such as immunization rates, mortality rates, and health expenditures. Environmental factors like pollution levels and access to clean water. The dataset is publicly available and has been cleaned and preprocessed for the purpose of this analysis.

Methodology The analysis is conducted using Python, leveraging various data science libraries including pandas, numpy, matplotlib, and seaborn. The project follows these steps:

Data Cleaning and Preprocessing: Handling missing values, data normalization, and other preprocessing steps to ensure data quality. Exploratory Data Analysis (EDA): Visualizing the data to uncover patterns, trends, and relationships between variables. Statistical Analysis: Applying statistical methods to identify significant factors affecting life expectancy. Modeling: Building predictive models to estimate life expectancy based on the identified factors. Reporting: Summarizing findings and providing actionable insights. Files in the Repository dataset.csv: The cleaned dataset used for the analysis. life_expectancy_analysis.ipynb: Jupyter notebook containing the Python code for the analysis. report.pdf: A comprehensive report detailing the methodology, analysis, and findings of the project.

Key Findings Significant correlations were found between life expectancy and various socio-economic and health indicators. Higher GDP and education levels are strongly associated with increased life expectancy. Health expenditure and access to medical services also play a crucial role in improving life expectancy. Environmental factors, such as pollution levels, have a negative impact on life expectancy.

Conclusion This project provides valuable insights into the determinants of life expectancy across different countries. The findings can inform policy decisions and help target interventions aimed at improving health outcomes and extending life expectancy.