In this project we analyzed the World Happiness Report to gain insights into the factors affecting the happiness of countries around the world. We used Python and several data analysis libraries such as Seaborn Pandas Matplotlib Plotly Pyspark and Anaconda to clean analyze and visualize the data.
The World Happiness Report dataset includes happiness scores and rankings for 156 countries, as well as several socio-economic, health, and environmental factors that are believed to contribute to happiness. We used this data to perform a detailed analysis and gain insights into the factors affecting happiness across different countries.
We analyzed the data using various statistical and data visualization techniques. We used Pandas to clean and manipulate the data, Seaborn and Matplotlib to create visualizations, and Plotly to create interactive maps. We also used Pyspark to process large datasets efficiently.
We looked at factors such as GDP per capita, social support, life expectancy, freedom to make life choices, generosity, and perceptions of corruption to identify the key drivers of happiness across different countries. We visualized the data using various graphs and charts, including scatter plots, bar charts, and heatmaps.
Our analysis shows that the factors that contribute to happiness vary widely across different countries. For example, while social support and freedom to make life choices are important factors for overall happiness, perceptions of corruption have a greater impact on happiness in some countries than others.
Overall, our analysis provides valuable insights into the complex and multifaceted nature of happiness and highlights the importance of considering a range of factors when studying happiness.
1. To run this project you will need to install the required libraries and dependencies listed in the requirements.txt file. You can do this using pip or conda.
2. Once you have installed the dependencies you can run the world_happiness_report.ipynb notebook to reproduce our analysis and visualizations.
Technologies used in the project:
- Python
- Seaborn
- Pandas
- Matplotlib
- Plotly
- Pyspark
- Anaconda
- Pycharm
- LaTex
This project was completed by [your name] as part of a [data analytics course/bootcamp]. We would like to acknowledge the World Happiness Report for providing the dataset used in this project.
This project is licensed under the GNU General Public License v3.0