Regression analysis of life expectancy in 4 West African countries.
# π Life Expectancy and Socio-Economic Conditions in West Africa
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## π Project Overview
This project investigates the relationship between life expectancy and key socio-economic indicators in four West African countries. Using regression analysis, it explores how economic development and access to essential services influence population health outcomes.
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## π― Objective
The goal of this study is to evaluate how selected economic and health-related factors impact life expectancy.
Using data from the **World Development Indicators (WDI)**, this project provides insights that can support policymakers in improving public health and socio-economic conditions.
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## π Dataset
- **Source:** World Bank β World Development Indicators (WDI)
- **Type:** Macroeconomic & Development Indicators
### π Selected Variables
- **Life Expectancy** *(Dependent Variable)*
- **GDP per Capita**
- **Health Expenditure**
- **Access to Electricity**
- **Mobile Subscriptions**
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## βοΈ Methodology
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Linear regression modeling
- Model diagnostics (residual analysis, Durbin-Watson test)
- Comparison with a quadratic model
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## π Key Findings
- The linear model explains approximately **69% of the variation** in life expectancy
- Predictors show **strong statistical significance**
### β οΈ Diagnostic Limitations
- Autocorrelation detected (Durbin-Watson test)
- Residual skewness indicates non-normality
- The quadratic model **did not improve performance**
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## π How to Run the Project
### 1. Clone the repository
```bash
git clone
github.com
cd life-expectancy-regression
```
### 2. Install dependencies
**Make sure you have Python 3 installed, then run:**
```bash
pip install -r requirements.txt
```
### 3. Run the notebook
```bash
jupyter notebook
```
## π Visualizations
This section highlights key relationships between socio-economic factors β¦