https://github.com/syedzaheerabbas/loantap-logistic-regression

A credit risk prediction project for LoanTap using machine learning to classify loan repayment behavior. It focuses on data preprocessing, handling imbalance, and optimizing model performance for real-world lending decisions.
https://github.com/syedzaheerabbas/loantap-logistic-regression

data-balancing eda f1-score logistic-regression precision recall smote visualization

Last synced: 4 months ago
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A credit risk prediction project for LoanTap using machine learning to classify loan repayment behavior. It focuses on data preprocessing, handling imbalance, and optimizing model performance for real-world lending decisions.

• Host: GitHub
• URL: https://github.com/syedzaheerabbas/loantap-logistic-regression
• Owner: Syedzaheerabbas
• Created: 2025-06-18T08:03:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-06-18T08:11:31.000Z (4 months ago)
• Last Synced: 2025-06-18T09:23:26.950Z (4 months ago)
• Topics: data-balancing, eda, f1-score, logistic-regression, precision, recall, smote, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 16.7 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
![image](https://github.com/user-attachments/assets/a63fdc21-09e3-4f3f-be5d-64cc0f751e46)

# 💼 LoanTap Credit Risk Modeling Project

## 📌 Introduction

**LoanTap** is a digital lending platform that provides flexible loan products to salaried professionals. With the rise of fintech-driven credit solutions, underwriting accuracy becomes crucial to minimize default risk while ensuring timely loan disbursement. This project builds a predictive model to assess credit risk and assist LoanTap in making data-driven lending decisions.

---

## 🧠 Project Overview

The objective of this project is to develop a machine learning model that predicts whether a borrower is likely to **repay the loan (Fully Paid)** or **default (Charged Off)**. The model supports LoanTap’s credit risk team in automating and improving the efficiency of their underwriting process.

---

## 📊 Dataset

The dataset includes borrower-level and loan-level features such as:

- **Loan Amount**

- **Annual Income**

- **Interest Rate**

- **EMI**

- **Credit Score**

- **Loan Tenure**

- **Purpose of Loan**

- **Employment Details**

- **Repayment Status (Target Variable)**

**Target Variable:**

- `Fully Paid` → 1  

- `Charged Off` → 0  

The dataset was imbalanced, with a majority of loans marked as “Fully Paid.”

---

## 🔬 Methodology

### 1. Exploratory Data Analysis (EDA)

- Identified skewness and outliers in numeric variables.

- Detected important patterns between features and repayment behavior.

- Handled missing values and ensured clean formatting.

### 2. Data Preprocessing

- Encoded categorical variables.

- Normalized numerical features.

- Addressed data imbalance using:

  - **SMOTE (Synthetic Minority Over-sampling Technique)**

  - **Class Weighting**

### 3. Model Building

Built multiple Logistic Regression models:

- Baseline Logistic Regression

- Logistic Regression with Class Weights

- Logistic Regression with SMOTE

- SMOTE + Class Weights

- Threshold-tuned model for best F1-score

- Reglurazed model

### 4. Model Evaluation

- Evaluated using **Confusion Matrix**, **F1-Score**, **Precision**, **Recall**, **ROC-AUC**, and **PR Curve**.

- Tuned the classification threshold using F1 optimization to improve performance on minority class.

---

## 📈 Results and Insights

- **Best Model**: Logistic Regression with SMOTE + Class Weighting + Threshold Tuning

- **Key Features Impacting Default Risk**:

  - **Zip Code**(Geographical presence)

  - High **EMI** relative to income

  - Low **Credit Score**

  - High **Interest Rate**

  - Purpose categories like “Debt Consolidation” showed higher risk

- **F1-score improved** significantly after addressing imbalance and threshold tuning.

---

## ✅ Recommendations

- **Prioritize 36-Month Loan Terms**: Given the higher default rates on 60-month loans, encourage 36-month loans by offering slightly better terms (e.g., lower interest or processing fees) to reduce long-term risk exposure.

- Implement regional risk scoring by incorporating pincode-level default trends. High-risk areas could be subjected to stricter eligibility or additional checks.

Limit Loan Size in Risk Bands

- Incorporate external credit bureau data for enhanced accuracy.

- Regularly retrain the model to account for shifts in applicant behavior and economic conditions.

---

## 🔭 Future Improvements

- Experiment with advanced models like **XGBoost**, **Random Forest**, and **LightGBM**.

- Deploy the model using **Flask** or **Streamlit** to create an interactive loan approval dashboard.

- Integrate explainability tools like **SHAP** or **LIME** for transparent decision-making.

- Monitor model drift and performance using a feedback loop from live loan outcomes.

---

## Colab Notebook

- You can access the full Python analysis on Google Colab using the following link: [View the notebook](https://colab.research.google.com/drive/11MP_rUCVyKrtoH_NQa3tq6GFzMe9Xq8T#scrollTo=WTCNvu7F-D68)

## PDF Report

A detailed analysis report is available in the following PDF file: [View Report](Loan_Tap.pdf).

## Contact

[SYED ZAHEER ABBAS] - [SYEDZAHEER.C@GMAIL.COM]