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📊 Dataset Overview

The dataset contains information on happiness scores across different countries worldwide and their correlation with economic, health, and social factors.

🛠️ Technologies Used

✅ Python 🐍
✅ Pandas (for data manipulation)
✅ Seaborn & Matplotlib (for visualization)
✅ NumPy (for numerical operations)
✅ Jupyter Notebook

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📊 Key Analysis & Insights

📌 1. Data Cleaning & Preprocessing

✔️ Handled missing values (used mean/median based on skewness).
✔️ Fixed string encoding issues (\xa0) and trimmed unwanted spaces.
✔️ Converted non-numeric columns to numerical for correlation analysis.

📌 2. Exploratory Data Analysis (EDA)

✔️ Descriptive statistics using .describe(), .info().
✔️ Checked correlation between happiness score & other factors.
✔️ Identified data distribution & skewness using histograms.

📌 3. Correlation Analysis

✔️ Found that GDP per capita (0.80) and Social Support (0.75) are the strongest predictors of happiness.
✔️ Discovered that higher corruption (-0.40 correlation) lowers happiness levels.
✔️ Freedom to make life choices (0.54 correlation) significantly impacts happiness.

📌 4. Data Visualization

📊 Correlation Matrix Heatmap – Showed strong relationships between variables.
📉 Scatter Plot (Happiness Score vs GDP per Capita) – Showed a positive correlation.
📈 Box Plot – Helped identify outliers in happiness scores.

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📊 Visualizations

📌 Correlation Matrix Heatmap

📌 Happiness Score vs GDP per Capita

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💡 Next Steps:

• Exploring interactive dashboards using Tableau/Plotly Dash 📊.

• Applying Machine Learning for Happiness Prediction 🤖.

🚀 How to Run the Project

🔹 1. Clone the Repository

git clone https://github.com/yourusername/world_happiness_report.git
cd world_happiness_report