🏦 Bank Loan Default Prediction Project

📝 Overview

This project aims to develop a predictive model to identify potential loan defaulters for a consumer finance company. By analyzing historical loan data, the company seeks to understand the factors influencing loan defaults and mitigate credit losses.

💼 Business Understanding

• The company specializes in providing various types of loans to urban customers.
• Two types of risks associated with loan decisions:
  • Loss of business if a reliable applicant is rejected.
  • Financial loss if a defaulter is approved.
• Objectives include minimizing credit losses by identifying risky loan applicants and optimizing lending strategies.

📊 Data Understanding

• The dataset contains loan data from 2007 to 2011.
• Detailed data dictionary describing the meaning of variables is available.
• Various attributes such as applicant demographics, loan terms, and repayment status are included.

🎯 Business Objectives

• Understand driving factors behind loan default to enhance risk assessment.
• Develop predictive models to identify potential defaulters and optimize lending decisions.

📈 Analysis Approach

1. Data Cleaning: Handle missing values, duplicates, and outliers.
2. Exploratory Data Analysis (EDA): Analyze distributions, correlations, and relationships between variables.
3. Feature Engineering: Create new features and transform existing ones.
4. Model Building: Select and train appropriate classification algorithms.
5. Evaluation: Assess model performance using relevant metrics.
6. Interpretation: Interpret model results and identify key predictors of loan default.

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🚀 How to Run the Project

To run this project on your system, follow these steps:

1. Clone the Repository: Clone this repository to your local machine using the following command:

   git clone https://github.com/your-username/bank-loan-default-prediction.git
   

2. Install Dependencies: Navigate to the project directory and install the required dependencies using pip:

   cd bank-loan-default-prediction
   pip install -r requirements.txt
   

3. Run the Jupyter Notebook: Launch Jupyter Notebook and open the main notebook file (bank_loan_default_prediction.ipynb)

   jupyter notebook bank_loan_default_prediction.ipynb
   

4. Execute the Notebook Cells: Execute the cells in the notebook to perform data analysis, model building, and evaluation.

5. Explore the Results: Explore the results, visualizations, and insights obtained from the analysis.

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📊 Results

• Identified key factors influencing loan default.
• Developed predictive models with satisfactory performance.
• Recommendations for optimizing lending decisions and risk assessment.

🏁 Conclusion

This project provides valuable insights into loan default prediction, enabling the company to make informed decisions and mitigate credit risks effectively.