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.
- 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.
- 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.
- Understand driving factors behind loan default to enhance risk assessment.
- Develop predictive models to identify potential defaulters and optimize lending decisions.
- Data Cleaning: Handle missing values, duplicates, and outliers.
- Exploratory Data Analysis (EDA): Analyze distributions, correlations, and relationships between variables.
- Feature Engineering: Create new features and transform existing ones.
- Model Building: Select and train appropriate classification algorithms.
- Evaluation: Assess model performance using relevant metrics.
- Interpretation: Interpret model results and identify key predictors of loan default.
To run this project on your system, follow these steps:
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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
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Install Dependencies: Navigate to the project directory and install the required dependencies using pip:
cd bank-loan-default-prediction pip install -r requirements.txt
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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
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Execute the Notebook Cells: Execute the cells in the notebook to perform data analysis, model building, and evaluation.
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Explore the Results: Explore the results, visualizations, and insights obtained from the analysis.
- Identified key factors influencing loan default.
- Developed predictive models with satisfactory performance.
- Recommendations for optimizing lending decisions and risk assessment.
This project provides valuable insights into loan default prediction, enabling the company to make informed decisions and mitigate credit risks effectively.