World Happiness Report Analysis

This project analyzes the World Happiness Report dataset to uncover insights into global happiness trends and factors contributing to global well-being

Interactive Visualizations

Due to GitHub's limited support for interactive plots, the dropdown visualizations may not display correctly in this repository. To view the interactive elements:

1. Clone this repository and run the notebook locally
2. View the notebook world-happiness-report-analysis.ipynb notebook
3. Check out the static versions of key visualizations in the 'images' folder

Runnning locally

• Clone repo and cd into that directory
• Create a virtual environment: pyenv virtualenv 3.10.6 <environment-name>
• Set the local environment: pyenv local <environment-name>
• Install necessary packages pip install -r requirements.txt
• Open the Jupyter Notebook using your preferred method and select your <environment-name> as the kernel.

Key Features

• Data visualization of happiness scores and contributing factors
• Correlation analysis between happiness and economic/social indicators
• Analysis of happiness trends over time
• K-means clustering to group countries based on happiness factors
• Predictive modeling using Random Forest Regressor

Technologies Used

• Python
• Pandas for data manipulation
• Matplotlib and Seaborn for data visualization
• Scikit-learn for machine learning (K-means clustering, Random Forest)
• Jupyter Notebooks for interactive analysis

Key Insights

• Identified strongest correlations between happiness and economic/social factors
• Discovered worldwide patterns in happiness scores
• Clustered countries into 6 groups based on happiness factors, revealing patterns based on geolocation
• Built a predictive model for happiness scores with feature importance analysis