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Unemployment Analysis using Python

This project analyzes the unemployment scenario before and after the lockdown using Python. It includes data analysis, visualizations, and insights derived from the provided dataset.

Features

  • Analyze the unemployment rates, employment, and labor participation for different states and regions.
  • Visualize the unemployment rates through various plots and charts.
  • Compare the average unemployment rates before and after the lockdown.
  • Explore the impact of lockdown on employment in different states.
  • Deployed as a web application using Streamlit for a better user interface.

Dataset

The dataset used for this analysis is available in the data.csv file. It contains information about unemployment rates, employment, labor participation, and other relevant factors for different states and regions.

Code

  • The unemployment-analysis.ipynb file contains the Jupyter Notebook code used for data analysis and visualization.
  • The app.py file contains the Streamlit code for deploying the project as a web application.
  • The requirements.txt file lists the dependencies required for running the Streamlit app.

Deployed Application

The project has been deployed as a web application using Streamlit. You can access the deployed application here.

Project Structure

The project repository has the following structure:

  • app.py
  • data.csv
  • requirements.txt
  • unemployment-analysis.ipynb
  • README.md Feel free to explore the repository and run the project locally.

Additional Notes

In this project, I performed an in-depth analysis of unemployment rates using Python as part of the data science internship at Oasis Infobyte. I explored various visualizations to understand the trends and patterns in unemployment data. The project includes descriptive statistics, heatmaps, box plots, bar plots, scatter plots, and geographical plots to gain insights into the impact of lockdown on employment.

I also deployed the project as a web application using Streamlit, which provides an interactive and user-friendly interface for exploring the analysis results.

Please refer to the Jupyter Notebook file (unemployment-analysis.ipynb) for a detailed step-by-step analysis and visualization code.

If you have any questions or suggestions, feel free to reach out.