World Happiness Report 2023 Analysis

This project analyzes the World Happiness Report 2023 dataset to understand factors contributing to happiness across different countries.

Project Structure

├── config/                 # Configuration files
├── data/                   # Data directory
│   ├── raw/               # Raw data files
│   └── processed/         # Processed data files
├── notebooks/             # Jupyter notebooks
├── src/                   # Source code
│   ├── data/             # Data processing scripts
│   ├── features/         # Feature engineering
│   ├── models/           # Model training and evaluation
│   └── visualization/    # Visualization scripts
└── results/              # Output results and visualizations
    ├── models/           # Trained models
    └── visualizations/   # Generated visualizations

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Setup

1. Clone the repository:

git clone <repository-url>
cd world-happiness

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Usage

Running the Complete Pipeline

To run the entire pipeline (download data, preprocess, visualize, and train model):

python src/main.py

Running Individual Components

1. Download the dataset:

python src/data/download_data.py

2. Preprocess the data:

python src/data/preprocess.py

3. Create visualizations:

python src/visualization/create_visualizations.py

4. Train the model:

python src/models/train.py

Jupyter Notebook Analysis

For interactive analysis:

jupyter notebook notebooks/01_exploratory_data_analysis.ipynb

Project Components

Data Preprocessing

• Data cleaning and validation
• Missing value handling
• Feature scaling
• Train-test split

Exploratory Data Analysis (EDA)

• Statistical summaries
• Distribution analysis
• Correlation analysis
• Feature importance visualization

Feature Engineering

• Feature scaling
• Feature selection
• Feature importance analysis

Predictive Modeling

• Random Forest model
• Hyperparameter optimization using Optuna
• Model evaluation metrics
• Feature importance analysis

Visualization

• Happiness score distribution
• Correlation heatmap
• Top/bottom countries analysis
• Regional analysis
• Feature importance plots

Results

The project generates various outputs in the results/ directory:

• results/models/: Trained models and feature importance
• results/visualizations/: Generated plots and interactive visualizations
• pipeline.log: Detailed logging of the pipeline execution

Troubleshooting

1. Data Download Issues

   • Check your internet connection
   • Verify the URL in src/data/download_data.py
   • Ensure write permissions in the data directory

2. Preprocessing Errors

   • Verify the raw data file exists in data/raw/
   • Check for correct column names in the configuration
   • Ensure sufficient disk space

3. Model Training Issues

   • Verify processed data exists
   • Check for memory constraints
   • Adjust model parameters in config/config.yaml

4. Visualization Problems

   • Ensure all required packages are installed
   • Check for write permissions in the results directory
   • Verify data format and column names

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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