Life Expectancy EDA

Project Overview

This repository hosts an in-depth Exploratory Data Analysis (EDA) project focusing on the Life Expectancy (WHO) dataset. The goal is to comprehensively analyze various socio-economic and health factors influencing life expectancy across different countries and years. Through statistical methods and rich visualizations, this project aims to uncover patterns, trends, and relationships within the dataset.

Dataset

The dataset used in this project is the "Life Expectancy (WHO)" dataset, which contains health and economic factors for 193 countries from 2000-2015.

• Source: Kaggle - Life Expectancy (WHO)
• File: Life Expectancy Data.csv (This file should be placed in the root directory of the cloned repository.)

Analysis Covered

The Jupyter notebook Life_Expectancy_EDA.ipynb performs the following key analyses:

1. Data Loading & Cleaning: Initial inspection, handling of column names (stripping whitespace), and imputation of missing numerical values using the mean.
2. Descriptive Statistics: Calculation of measures of central tendency (mean, median) and variability (standard deviation, quartiles, IQR) for all numerical features.
3. Distribution Analysis: Visual exploration of the distribution of all numerical features using histograms, Kernel Density Estimate (KDE) plots, box-whisker plots, and violin plots to identify skewness, outliers, and overall shape.
4. Covariance & Correlation: Quantification and visualization of linear relationships between all numerical features using a correlation matrix heatmap. Specific attention is given to the relationship between 'Life expectancy' and 'GDP'.
5. Time-Series Trends: Analysis of average life expectancy over the years.

Getting Started

To run this EDA project locally, follow these steps:

Prerequisites

Ensure you have Python (3.7+) and pip installed.

Installation

1. Clone the repository:

   git clone [https://github.com/CRMawande/Life-Expectancy-EDA.git](https://github.com/CRMawande/Life-Expectancy-EDA.git)
   cd Life-Expectancy-EDA

2. Install the required Python packages:

   pip install -r requirements.txt

3. Download the dataset: Download the Life Expectancy Data.csv file from the Kaggle dataset page and place it directly into the Life-Expectancy-EDA directory (the root of the cloned repository).

Running the Notebook

1. Launch Jupyter Notebook:

   jupyter notebook

2. Your web browser will open with the Jupyter interface. Navigate to and open Life_Expectancy_EDA.ipynb.
3. Run all cells in the notebook to reproduce the analysis and visualizations.

Key Insights

• Preliminary findings indicate the distribution of Life expectancy is somewhat negatively skewed.
• Strong correlations exist between Life expectancy and factors like Schooling, Income composition of resources, and Adult Mortality.
• Features like infant deaths, under-five deaths, Measles, and HIV/AIDS often show highly skewed distributions with significant outliers.
• The project provides a visual understanding of how various health and economic indicators interact to influence life expectancy trends.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or suggestions, please feel free to reach out.

• GitHub: CRMawande

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