Unemployment Analysis in India using Python

CodeAlpha Data Science Internship – Task 3 **Author - Rawal JayKumar NarendraKumar

📌 Project Overview

This project analyzes unemployment trends across India using Python. The analysis focuses on identifying unemployment patterns across different states, understanding the impact of the COVID-19 pandemic, comparing rural and urban unemployment, and generating insights that can support economic and social policy decisions.

The project demonstrates the complete data analysis workflow, including data cleaning, exploratory data analysis (EDA), feature engineering, statistical analysis, and data visualization.

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🎯 Problem Statement

Analyze unemployment rate data representing the percentage of unemployed people in India.

The project aims to:

• Analyze unemployment trends using Python.
• Perform data cleaning and preprocessing.
• Explore unemployment patterns across different states.
• Study the impact of COVID-19 on unemployment.
• Compare Rural vs Urban unemployment.
• Identify seasonal unemployment trends.
• Generate insights useful for policymakers.

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📂 Dataset

Dataset Source - https://www.kaggle.com/datasets/gokulrajkmv/unemployment-in-india

This project uses two datasets.

Dataset 1

Unemployment in India.csv

Contains:

• State
• Date
• Frequency
• Estimated Unemployment Rate (%)
• Estimated Employed
• Estimated Labour Participation Rate (%)
• Area (Urban / Rural)

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Dataset 2

Unemployment_Rate_upto_11_2020.csv

Contains:

• State
• Date
• Frequency
• Estimated Unemployment Rate (%)
• Estimated Employed
• Estimated Labour Participation Rate (%)
• Region
• Latitude
• Longitude

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Plotly
• Jupyter Notebook
• VS Code
• Git
• GitHub

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📁 Project Structure

CodeAlpha-Unemployment-Analysis/

│
├── data/
│   ├── Unemployment in India.csv
│   └── Unemployment_Rate_upto_11_2020.csv
│
├── notebook/
│   └── unemployment_analysis.ipynb
│
├── src/
│   ├── data_cleaning.py
│   ├── analysis.py
│   ├── visualization.py
│   └── main.py
│
├── images/
│
├── report/
│   └── Project_Report.pdf
│
├── README.md
├── requirements.txt
└── LICENSE

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⚙️ Installation

Clone the repository

git clone https://github.com/JayRawal316/CodeAlpha-Unemployment-Analysis.git

Move inside the project folder

cd CodeAlpha-Unemployment-Analysis

Create Virtual Environment

python -m venv venv

Activate Virtual Environment

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

Install dependencies

pip install -r requirements.txt

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▶️ Running the Project

Execute

python src/main.py

Or explore the project interactively using

jupyter notebook

Open

notebook/unemployment_analysis.ipynb

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🔄 Project Workflow

1. Import Libraries
2. Load both datasets
3. Data Cleaning
4. Handle Missing Values
5. Remove Duplicates
6. Feature Engineering
7. Exploratory Data Analysis (EDA)
8. Statistical Analysis
9. COVID-19 Impact Analysis
10. State-wise Analysis
11. Rural vs Urban Comparison
12. Data Visualization
13. Business Insights
14. Conclusion

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📊 Exploratory Data Analysis

The project performs:

• Dataset inspection
• Missing value analysis
• Duplicate analysis
• Feature engineering
• Descriptive statistics
• Correlation analysis
• Monthly unemployment trends
• State-wise unemployment comparison
• Regional analysis
• COVID impact analysis

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📈 Visualizations

The project generates multiple visualizations including:

• Distribution of Unemployment Rate
• Monthly Trend Analysis
• State-wise Unemployment
• Top 10 States with Highest Unemployment
• Top 10 States with Lowest Unemployment
• COVID-19 Impact Comparison
• Rural vs Urban Comparison
• Labour Participation Trend
• Employment Trend
• Correlation Heatmap
• Boxplots
• Scatter Plots
• Interactive Plotly Visualizations
• Region-wise Analysis
• Time Series Analysis

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📌 Key Insights

The analysis identifies:

• States with the highest unemployment rates.
• States with the lowest unemployment rates.
• Impact of COVID-19 lockdown on employment.
• Monthly unemployment fluctuations.
• Rural vs Urban employment differences.
• Labour participation trends.
• Regional employment disparities.
• Relationships between employment, labour participation, and unemployment.

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📈 Skills Demonstrated

• Data Cleaning
• Data Preprocessing
• Feature Engineering
• Exploratory Data Analysis
• Statistical Analysis
• Data Visualization
• Time Series Analysis
• Business Intelligence
• Data Storytelling
• Python Programming
• Git & GitHub

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🚀 Future Improvements

• Build an interactive Streamlit Dashboard.
• Deploy the project on Streamlit Cloud.
• Create Power BI dashboard.
• Predict future unemployment trends using Machine Learning.
• Develop an interactive web application using Flask.

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📸 Project Output

The project generates:

• Cleaned datasets
• Statistical summaries
• Insightful visualizations
• Business insights
• Professional report

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📚 Learning Outcomes

Through this project, the following concepts were applied:

• Pandas Data Manipulation
• NumPy Operations
• Feature Engineering
• Data Cleaning
• Statistical Analysis
• Exploratory Data Analysis
• Data Visualization
• Correlation Analysis
• Time Series Analysis
• Python Best Practices

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👨‍💻 Author

Rawal JayKumar NarendraKumar

Aspiring Data Scientist

• Python
• Data Analytics
• Machine Learning
• Data Visualization

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📄 License

This project is licensed under the MIT License.

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⭐ Acknowledgements

• CodeAlpha Data Science Internship
• Author - Rawal JayKumar NarendraKumar
• github - https://github.com/JayRawal316
• LinkedIn - www.linkedin.com/in/jay-rawal-68462028b
• Kaggle Datasets
• Dataset Source - https://www.kaggle.com/datasets/gokulrajkmv/unemployment-in-india
• Python Open Source Community

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⭐ If you found this project useful, consider giving it a Star on GitHub.