🌎 World Happiness Predictor

A machine learning project that predicts the happiness score of countries based on factors like GDP, social support, life expectancy, freedom, and more.

📚 Table of Contents

• Features

• Getting Started

• How It Works

• Tech Stack

• Contributing

• License

• Contact

✨ Features

📊 Predicts World Happiness Score based on real-world indicators

🔍 Understand key drivers behind national happiness

🎯 High-accuracy machine learning regression models

🌐 Visualize happiness distribution globally

📈 Explainable model with feature importance

🚀 Getting Started

Follow these steps to set up the project locally.

Prerequisites

• Python 3.8+
• poetry (package and dependency manager)

Installation

Clone this repository

git clone https://github.com/vgauss07/happiness_prediction.git

How it Works

• Uses historical World Happiness Report datasets.

Features include:

• GDP per capita

• Social support

• Healthy life expectancy

• Freedom to make life choices

• Generosity

• Perceptions of corruption

• Trains a machine learning model (e.g., Random Forest, XGBoost, or Gradient Boosting) to predict the Happiness Score.

• Hyperparameter tuning using GridSearchCV.

• Visualizes feature importance and country predictions.

Tech Stack

• Python
• Pandas
• Scikit-learn
• Flask

🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project

2. Create your Feature Branch (git checkout -b feature/YourFeature)

3. Commit your Changes (git commit -m 'Add some feature')

4. Push to the Branch (git push origin feature/YourFeature)

5. Open a Pull Request

📜 License

Distributed under the MIT License. See LICENSE for more information.

📬 Contact

Created by Jeffrey Voke Ojuederhie — feel free to connect or collaborate!