This repository contains the code and data for analyzing unemployment patterns in India before and after the outbreak of the COVID-19 pandemic. The goal of this project is to identify trends, patterns, and underlying factors contributing to changes in unemployment rates over time.

The primary dataset used in this analysis is sourced from kaggle. Further info id provided in the notebook, kindly refer to it.

• Datasets/: Contains raw datasets used in the analysis.
• UnempEDA.ipynb: Jupyter notebook containing data extraction, wrangling, and exploratory data analysis.
• README.md: Overview of the project and instructions for usage.
• LICENSE: The MIT License file.
• requirements.txt: File listing necessary dependencies.

To set up and run this project locally, follow these steps:

1. Clone the repository:

git clone https://github.com/asvcodes/Covid19-Unemp-Analysis.git

2. Navigate to the project directory:

cd Covid19-Unemp-Analysis

3. Install the necessary dependencies:

pip install -r requirements.txt

The data extraction process involves collecting relevant datasets related to unemployment in India. Detailed documentation and scripts for retrieving data from various sources are provided in the data_extraction/ directory.

In the data wrangling phase, the raw data is cleaned, preprocessed, and transformed into a suitable format for analysis. The data_wrangling/ directory contains scripts and notebooks demonstrating these data preparation steps.

Exploratory Data Analysis involves visually exploring the dataset, identifying patterns, correlations, and trends. Jupyter notebooks and visualizations showcasing the EDA process are available in the exploratory_data_analysis/ directory.

The analysis results, insights, and key findings are summarized in the Jupyter notebook. Additionally, visualizations and statistical analysis outputs provide a comprehensive understanding of unemployment trends in India before and after the COVID-19 pandemic.

Contributions to this project are welcome. If you have suggestions for improvements or new features, please open an issue or submit a pull request.

This project is licensed under the MIT License - see the LICENSE file for details.

• Gokul Raj Kuppan for providing valuable insights and support. LinkedIn Profile
• CMIE for providing the data on Unemployment.