This project aims to predict world happiness based on various socio-economic factors. It utilizes machine learning techniques to analyze and model the relationship between different variables and the happiness scores reported by countries around the world. Its focus lies on a subset of countries and data in the range of 2015 to 2021.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/thirdcoffee/predicting-world-happiness.git- Navigate to the project directory:
cd predicting-world-happiness- Install the required dependencies:
pip install -r requirements.txt- Run the Jupyter notebook
As this is an university assignment, this project is not maintained. If you would like to create your own project based on this one, feel free to fork the repository and use it as a starting point.
The dataset used in this project is the World Happiness Report, which contains happiness scores and various indicators for 156 countries. The data is collected annually and provides a comprehensive view of the factors that contribute to happiness. The report can be found here. We used this dataset from kaggle.
Additional data similiar to the original factors in the WHI:
- GDP per capita (current US$) from https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
- Life expectancy at birth from https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)
- World giving index (% of people donating to charity) from https://www.cafonline.org/about-us/publications/2022-publications/caf-world-giving-index-2022
- Human rights data from https://ourworldindata.org/grapher/distribution-human-rights-vdem
- Corruption perception index from https://www.transparency.org/en/cpi/
Additional data not included in the WHI:
- Rural population % of total population from https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS
- Power consumption per capita from https://ourworldindata.org/grapher/per-capita-energy-use
- Inflation of consumer prices from https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG
- Military expenditure % of GDP from https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS
- Population growth % from https://data.worldbank.org/indicator/SP.POP.GROW
- CO2 emissions per capita from https://edgar.jrc.ec.europa.eu/report_2022
This project is licensed under the MIT License. Feel free to modify and distribute it as needed.
By thirdcoffee