This repository contains an exploratory data analysis of the unemployment situation in India. The data was obtained from the National Sample Survey (NSS) database, which is a large-scale household survey conducted by the Government of India.
The EDA was conducted using Python and the following libraries: pandas, numpy, matplotlib, and seaborn. The EDA covered the following topics:
- Data Cleaning and Preprocessing: The raw data was cleaned and preprocessed to handle missing values, inconsistent entries, and outliers.
- Unemployment Trends: The overall unemployment trend in India was analyzed over the period from 2011 to 2018, and the trends by state, education level, age group, and gender were also examined.
- Regional Differences: The regional differences in unemployment were analyzed by comparing the unemployment rates across different states and union territories.
- Gender Differences: The gender differences in unemployment were analyzed by comparing the unemployment rates between male and female respondents.
The results of the EDA are presented in the Jupyter notebook Unemployment_EDA.ipynb.
The EDA revealed some interesting insights into the unemployment situation in India. The analysis showed that unemployment rates varied significantly across different states, education levels, age groups, and gender. The analysis also highlighted some of the challenges faced by different groups in the labor market, such as young people and those with lower levels of education. Overall, the EDA provides a valuable starting point for further research and policy analysis on unemployment in India.