EDA_Happiness_Project 🌍✨

Understanding Happiness Through Data Analytics

📌 Project Overview

This project explores the drivers of happiness using data from 2018 and 2019, focusing on key factors like GDP per capita, freedom to make life choices, social support, generosity, and perceptions of corruption.

Using a self-reported happiness score as the outcome variable, the goal is to identify what influences happiness across countries and offer insights for individuals, governments, and policymakers.

🎯 Objectives

✔️ Perform data cleaning, handling missing values & outliers
✔️ Conduct hypothesis testing on freedom and happiness
✔️ Analyze correlations between economic and social indicators
✔️ Visualize trends across countries and years

📊 Key Findings

1️⃣ Global Happiness Trends

• Happiness scores ranged from 2.9 (least happy) to 7.8 (happiest), with a global mean of 5.5.
• GDP per capita averaged ~0.9, showing significant economic disparity across nations.
• Freedom to make life choices averaged 0.4 (on a 0–1 scale), highlighting differing levels of autonomy worldwide.
• Perceptions of corruption had a low global average of 0.1, reflecting widespread distrust in governance.
• Social support averaged 1.1 (on a 0–2 scale), suggesting moderate support systems globally.

2️⃣ Outlier Detection & Insights

Outliers were found in:
✅ Generosity → Some nations, despite economic struggles, rank high in generosity (e.g., Myanmar, Haiti).
✅ Social Support → Nordic countries stand out due to strong welfare models (e.g., Finland, Denmark).
✅ Corruption Perception → Some countries exhibit high levels of distrust in governance.

3️⃣ Hypothesis Testing: Does Freedom Affect Happiness?

A one-tailed t-test (α = 0.05) comparing high vs. low-freedom countries revealed:
📌 p-value = 0.158655
📌 z-score = 1.0
📌 Conclusion: Statistically significant difference—personal freedom meaningfully impacts happiness.
📌 Visualization: High-freedom countries show higher median happiness & more variation, while low-freedom nations have tighter, lower happiness distributions.

4️⃣ Correlation Analysis

• GDP per capita & Happiness Score → Strong correlation (0.80) → Wealth boosts happiness, but money alone is not enough.
• Freedom to make life choices & Happiness Score → Moderate correlation (0.5) → Autonomy matters!
• GDP per capita & Freedom to make life choices → Weak correlation (0.34) → Economic wealth doesn’t necessarily mean greater personal liberty.
• Corruption perception & Happiness Score → Limited direct link → Dropped from deeper analysis due to inconsistencies.

5️⃣ Happiness Model: Who’s the Happiest?

🏆 Top Happiest Countries → Finland, Iceland, Denmark, Norway
❌ Least Happy Countries → Afghanistan, Syria, South Sudan
📌 Nordic Model: Combines economic strength, autonomy, social safety nets, and strong communities.

📊 Data Visualizations & Techniques

✅ Scatter plots with trendlines (Linear regression models)
✅ Box plots to detect outliers
✅ Heatmaps for correlation analysis
✅ Histograms & distribution plots for variable analysis

🚀 Next Steps

🔹 Improve handling of missing values
🔹 Apply machine learning models to predict happiness scores
🔹 Investigate clustering nations based on happiness metrics

🖥️ Setup & Installation

1️⃣ Clone the repository:

git clone https://github.com/your-username/EDA_Happiness_Project.git

2️⃣ Install dependencies:

pip install pandas numpy matplotlib seaborn plotly

3️⃣ Run analysis script:

python happiness_analysis.py

🤝 Contributing

🔹 Fork the repository
🔹 Make improvements & submit a pull request
🔹 Let's make happiness analytics better together! 🚀

📜 Presentation

📎 Download the slides: Happiness_Project_Slides.pptx.pdf

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Contact: ivanaloveraruiz@gmail.com