https://github.com/yashksaini-coder/bank-loan-default
Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.
https://github.com/yashksaini-coder/bank-loan-default
clustering clustering-algorithm exploratory-data-analysis financial-analysis lgbm lgbmclassifier machine-learning machine-learning-algorithms
Last synced: about 2 months ago
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Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.
- Host: GitHub
- URL: https://github.com/yashksaini-coder/bank-loan-default
- Owner: yashksaini-coder
- Created: 2024-02-06T18:52:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-14T19:16:37.000Z (over 1 year ago)
- Last Synced: 2025-03-25T11:01:33.191Z (2 months ago)
- Topics: clustering, clustering-algorithm, exploratory-data-analysis, financial-analysis, lgbm, lgbmclassifier, machine-learning, machine-learning-algorithms
- Language: Jupyter Notebook
- Homepage:
- Size: 14.9 MB
- Stars: 16
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏦 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.
---
## 🚀 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.
---
## 📊 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.