📊 Loan Default Analysis Project

This project analyzes loan application data to identify factors that contribute to loan default. It uses real-world datasets to perform data cleaning, EDA, statistical testing, and derive actionable insights.

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📁 Folder Structure

Loan-default-analysis/ │ ├── data/ # Raw CSV files ├── notebooks/ # Jupyter Notebook for EDA & testing ├── output/ # Saved charts/plots ├── docs/ # Summary and documentation ├── requirements.txt # Required libraries └── README.md # Project overview

📌 Objective

To uncover insights from demographic and financial data that influence whether a loan gets approved or not — and support risk assessment in loan applications.

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🔍 Key Steps Performed

Step	Description
✅ Data Cleaning	Handled missing values, verified data types
✅ EDA	Univariate & Bivariate analysis (age, income, etc.)
✅ Visualization	Countplot, Histogram, Boxplot, Heatmap
✅ Statistical Testing	Chi-square, T-test, ANOVA (Scipy)
✅ Documentation	Summary reports in Markdown files
✅ Organized Code	Jupyter Notebook, structured folders

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🧪 Tools & Technologies Used

• Python: pandas, numpy, seaborn, matplotlib, scipy
• Jupyter Notebook
• Markdown
• VS Code / GitHub

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📊 Sample Visuals

✅ Gender Distribution

✅ Education vs Loan Status

✅ Income vs Loan Status

✅ Correlation Heatmap

All visuals are stored in /output/ folder.

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📄 Summary Report

• 📘 EDA Report → docs/eda_summary.md
• 📘 Final Summary → docs/summary.md

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🙋‍♂️ Author

Tarun Kumar Malviya
Data Analyst | Python | SQL | Tableau | EDA | Hypothesis Testing

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📬 Contact

For collaboration or queries:
📧 tarunmalviya804@gmail.com

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✅ Project Status

• ✅ Completed: EDA + Hypothesis Testing
• 🟡 Optional Next Phase: ML Modeling (Logistic Regression)

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