This project has been inspired by well known fact that there is a strong correlation between climate and suicide ratio around the world. The idea of the project is to check if there is a similar correlation with happiness ratio using data for the period 2015-2018.
Raw data has been downloaded from various sources in csv format and then stored in SQL database.
Cleaning and analysis has been performed by Python and Tableau
Presentation prepared in Tableau Public https://public.tableau.com/profile/svetlana.gruzdeva#!/vizhome/Happiness_Project_16057811399910/start
SQL database (MySQL)
Jupyter Notebook
Tableau
Python 3.8.3
- os
- re
- math
- pandas
- numpy
- matplotlib.pyplot
- seaborn
- sqlalchemy
- pymysql
- requests
- bs4
- sklearn.linear_model
- statsmodels.tsa.ar_model
- statsmodels.tools.eval_measures
- scipy import stats
World Happiness Report - https://www.kaggle.com/unsdsn/world-happiness
Suicide Rates Overview - https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
Suicide Rates Overview (2015-2016) - https://data.worldbank.org/indicator/SH.STA.SUIC.P5?view=map
Suicide Rates Overview (2017-2018) - https://data.oecd.org/healthstat/suicide-rates.htm
Average Temperature by Country - https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data?select=GlobalLandTemperaturesByCountry.csv
General assumption has been made that collected data is reliable and representative.
Average annual temperature available in data set didn't contain reqiured period so linear regression model has been used to extrapolate available data to required period.