Suicides and Happinesss Data Analysis

Libraries Used

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Plotly
• Cufflinks

Project motivation

Year 2020 have been a difficult year for all of us. Since the depression rates might spike in the current situation due to economy and health, they can lead to more number of suicides in coming times. I wanted to learn what factors in past years affected the number of suicides. Also, to see other side of the spectrum I wanted to understand how happinessof a country depends on different factors.

Files in the repository

• happiness_data.csv: Happiness dataset for the year 2015.
• suicide_data.csv: Suicides dataset for the years 1985 to 2016.
• suicides_happiness_data_analysis.ipynb: Notebook file contaning the exploration and analysis of the work.
• readme.md: The readme file for write-up.

Summary of the results

• Country with highest number of suicides Russian Federation, United States and Japan.

• GDP per capita might or might not have inverse effect on suicides number, we will see that later. But from these two graphs there is slight hint that there can be.

• Russian Federation has highest number of suicides between the age range of 35-54 years. It is not that surprising since country also registers levels of depression above the regional levels.

• There is a big gap between female and male suicides rate. Female suicide rate is less as compared to Male.

• Between 2011 and 2015 highest number of suicides are constantly in United States followed by Russian Federation which has highest number overall from 1985.

• Happiness Score seems to be directly correlated with GDP per capita.

Acknowledgements

• Kaggle
• Udacity
• stackoverflow

Additional Resource

You can find the accompanying blog post here: https://medium.com/@shwets1503/facts-about-suicides-and-happiness-7d0e733ae054