This project is about analyzing world happiness report from the year 2015-2021, to gain insights from the data and to understand which factors effects the 'happiness score and economy' i.e GDP of a country and how the countries have performed over the years. Furthermore 'kmeans clustering' have been performed on the '2021 world happiness data' based on several factors such as GDP per capita, life expectancy, corruption,social support etc to form clusters of countries according to these factors
The dataset is obtained from kaggle and it consists of 'World happiness report' of the year 2015,2016,2017,2018,2019,2020,2021.
This heatmap represents the correlation between various features of the data.It can be seen that the ladder score i.e the happiness score mostly depends on features like 'GDP per capita,social support,healthy life expectancy and freedom to make life choices'.It is least correlated with ''generosity' and 'Perception of corruption'.
Higher GDP Per Capita, Social Support, Healthy life Expectancy, Freedom To Choose life decisions & Generosity leads to higher happiness score of a country.
A web app for this project was built and deployed on streamlit.