Exploratory Data Analysis: Suicide Rates Overview (1985 - 2016)
INTRODUCTION
Suicide is a major cause of death across the world. Self-harm refers to a deliberate non-fatal act, whether physical, drug overdose, or poisoning, carried out in the knowledge that it is potentially harmful. Self-harm is strongly associated with the risk of suicide but is an important public health problem in its own right. Here in this Case Study we shall look into the various factors associated with Suicides in various countries & in different age gaps so as to derive the motive & finally work towards Suicide Prevention over the coming years.
Overview - Dataset
Dataset source: Kaggle
URL: https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
The Dataset consists of the following information:
- Country
- Year
- Sex
- Age group
- Count of suicides
- Population
- Suicide rate
- Country-year
- Composite key
- HDI for year
- GDPforYear
- GDPperCapita
- Generation (based on age grouping average)
References include:
United Nations Development Program. (2018). Human development index (HDI).
Retrieved from http://hdr.undp.org/en/indicators/137506
World Bank. (2018). World development indicators: GDP (current US$) by country:1985 to 2016.
Retrieved from http://databank.worldbank.org/data/source/world-development-indicators#
[Szamil]. (2017). Suicide in the Twenty-First Century [dataset].
Retrieved from https://www.kaggle.com/szamil/suicide-in-the-twenty-first-century/notebook
World Health Organization. (2018). Suicide prevention.
Retrieved from http://www.who.int/mental_health/suicide-prevention/en/
# Importing the Pandas library for exploring the dataset
import pandas as pd
# Loading the dataset using pd.read_csv()
suicide_info_df = pd.read_csv("master.csv")