harsh782patel / data-analysis-and-visualization-of-world-happiness-report-2020

This project analyzes the World Happiness Report 2020 dataset using Python. It explores factors contributing to happiness levels across countries through data visualization and correlation analysis.

Data-Analysis-and-Visualization-of-World-Happiness-Report-2020

This repository contains Python code to analyze the World Happiness Report 2020 dataset. The World Happiness Report is a landmark survey of the state of global happiness. The report ranks 156 countries by their happiness levels, based on factors such as GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption.

Dataset

The dataset used in this analysis is sourced from Kaggle and contains the data used to create Figure 2.1 of the World Happiness Report 2020. You can find the dataset here.

Analysis

The Python script in this repository loads the dataset, explores its structure, checks for missing values, calculates summary statistics of numeric columns, visualizes the data using histograms, and creates a correlation heatmap to explore relationships between variables.

Usage

1. Download the dataset from the provided Kaggle link.
2. Clone or download this repository to your local machine.
3. Ensure you have Python installed on your machine.
4. Install the required Python libraries:

   pip install pandas numpy matplotlib seaborn

5. Run the Python script:

   python analyze_happiness_report.py

Results

The analysis provides insights into the factors that contribute to happiness levels across different countries. Through visualizations and correlation analysis, patterns and relationships within the dataset are revealed.

Contributing

Contributions to improve the analysis or add new features are welcome. Please fork the repository, make your changes, and submit a pull request.