HappyLens

Project Overview

HappyLens is a data science project designed to explore happiness levels across countries and visualize patterns. Using real data from the World Happiness Report, the project combines data analysis, cluster analysis, correlation analysis and a user-friendly CLI tool for country recommendation.

Objectives

• Understand global happiness trends using historical data.
• Explore and visualize factors influencing happiness across the world.
• Cluster countries based on key well-being indicators.
• Recommend top countries to users based on customized priorities.

Project Structure

HappyLens/
├── cli/                       # CLI tool for country recommendation
│   └── main.py
├── dashboard/                 # Streamlit dashboard code
│   └── app.py
├── data/                      # Raw dataset and preprocessing notebook
│   ├── World Happiness Report.csv
│   ├── happiness_data.csv
│   └── data_preprocessing.ipynb
│   └── clustered_happiness.csv
├── data_analysis/             # EDA and clustering
│   └── data_analysis.ipynb
├── README.md
├── requirements.txt           # Python dependencies

Features

• Exploratory Data Analysis (EDA): PCA, correlation matrix, visual summaries.
• Clustering: KMeans-based grouping of countries by happiness-related features.
• Interactive CLI Tool: Users assign importance (weights) to happiness factors and receive a ranked list of recommended countries.
• Feature Engineering: Removal of irrelevant features. Standardization via StandardScaler. PCA applied before clustering.
• Streamlit Dashboard: Visualizes clustered countries on an interactive map, shows cluster descriptions, and allows personalized ranking via sliders.

Tech Stack

• Language: Python 3
• Jupyter Notebook
• Data: World Happiness Report 2024
• Libraries: pandas, matplotlib, seaborn, plotly, scikit-learn, streamlit

Results

• Clustered countries into 4 groups based on well-being metrics.
• Interactive map of happiness: created with the help of Plotly, it allows to visually explore the level of happiness in countries around the world.
• The CLI tool personalizes recommendations based on user preferences and subjective factor weights.
• Streamlit dashboard offers intuitive interface for exploring clusters and customizing rankings.

How to Use

Streamlit Dashboard (Recommended)

You can explore the dashboard either by:

• Opening the hosted app via this link: Happy Map

• Or running it locally from code:

cd HappyLens/dashboard
streamlit run app.py

• Explore the interactive world map showing country clusters by happiness level.
• View detailed descriptions of each cluster, including average scores and example countries.
• Customize factor weights (GDP, Social Support, Life Expectancy, Freedom, Generosity, Corruption) via sliders to generate a personalized happiness ranking.
• Browse countries by cluster with a clean table interface.
• The dashboard combines visual insights and personalized recommendations in one easy-to-use web app.

Country Recommendation CLI

cd HappyLens/cli
python main.py

• Input your preferences (weights between 0 and 1) for six key factors.
• The system ranks countries based on your customized happiness score.
• Returns the Top-10 countries best aligned with your values.

GDP: 1
SocialSupport: 1
LifeExpectancy: 1
Freedom: 1
Generosity: 1
Corruption: 1

Your personalized Top-10 countries based on your weights:
    Country  HappinessScore  Score
  Singapore           6.565  6.064
     Norway           7.262  6.031
    Denmark           7.521  5.986
     Sweden           7.345  5.959
    Finland           7.736  5.954
    Ireland           6.889  5.937
 Luxembourg           7.122  5.895
Switzerland           6.935  5.888
    Iceland           7.515  5.857
Netherlands           7.306  5.723

Explore EDA

Open the notebooks in Jupyter:

• data_analysis/data_analysis.ipynb: Explore global patterns and clustering.

Inspiration

This project aims to go beyond generic happiness rankings by making the analysis personal, interactive, and actionable. "There is no universal formula for happiness — but there can be a data-driven guide to help find your own."