This repository contains an analysis and model development for optimizing the loan approval process at LoanTap. The primary objective is to determine creditworthiness by analyzing customer data and improving precision and recall metrics in the underwriting process.
LoanTap aims to enhance its underwriting strategy by accurately assessing whether to extend credit lines to individuals. The goal is to minimize financial risks associated with loan defaults while maximizing the approval of reliable borrowers. Understanding customer demographics and performance metrics is crucial for effective decision-making.
The dataset includes customer demographic and financial information relevant to the loan approval process. Key features include:
- User_ID: Unique identifier for each customer
- Annual_Income: Annual income of the customer
- Credit_History: History of credit repayment
- Loan_Amount: Amount of loan requested
- Loan_Term: Duration of the loan
- Age: Age of the customer
- Gender: Gender of the customer
- Marital_Status: Marital status of the customer
- Employment_Status: Employment status of the customer
- Import the dataset and examine its structure and characteristics.
- Handle missing values and outliers using descriptive statistics.
- Utilize logistic regression to predict loan approval based on key features.
- Prioritize improving Precision (e.g., 85%) and Recall (e.g., 90%) to enhance model performance.
- Analyze model performance using metrics like Precision, Recall, F1 Score, and ROC AUC.
- Experiment with different sampling strategies and thresholds to optimize precision-recall trade-offs.
- Identify critical features affecting loan approval, including Annual Income and Credit History.
- Provide actionable insights for improving loan approval strategies.
- Precision Focus: High precision ensures accurate identification of creditworthy customers, minimizing potential defaults.
- Demographic Influence: Geographical factors (e.g., zip code) significantly affect creditworthiness predictions.
- Ongoing Monitoring: Continuous analysis of precision-recall metrics is recommended to adapt to changing customer behaviors.
This analysis and model development provide valuable insights into customer creditworthiness during the loan approval process. The results will aid LoanTap in optimizing its underwriting strategy, improving risk management, and enhancing customer targeting.
git clone https://github.com/yourusername/loan-tap-optimization.gitEnsure you have Python and the necessary libraries installed. Use the following command to install required packages:
pip install -r requirements.txtOpen the Jupyter notebooks provided in the repository to explore the data and perform the analysis step-by-step.
Contributions to this project are welcome. Feel free to fork the repository, make improvements, and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See the LICENSE file for details.