Loan Default Predictor App Development
Zeeshan Siddiqui
💳 Loan Default Predictor
This is a machine learning web app built using Streamlit that predicts whether a credit card user will default on their payment next month, based on various personal and financial features.
🔗 Live Demo:
👉 Loan Default Predictor Streamlit App
🧠 Problem Statement
Financial institutions need to assess credit risk before issuing credit. This app uses historical data to predict the likelihood of a customer defaulting on their credit card payment the following month.
🚀 Features
Interactive Streamlit UI
Real-time predictions using a trained Random Forest model
Scaled numeric features and properly encoded categorical inputs
Clean, intuitive layout for non-technical users
📊 Model Info
Algorithm: Random Forest Classifier
Preprocessing: StandardScaler for feature scaling
Trained On:
default_of_credit_card_clients.xls dataset (UCI repository)🖥️ Tech Stack
Python
Pandas, Scikit-learn, Joblib
Streamlit for frontend
Git/GitHub for version control and deployment
⚙️ How to Run Locally
1. Clone the repository
git clone https://github.com/zeesh8x/loan-default-predictor.git cd loan-default-predictor
2. Install dependencies
Make sure you have Python 3.8 or above:
pip install -r requirements.txt
3. Train the model (if not already trained)
python train_model.py
4. Run the Streamlit app
python -m streamlit run app.py
🧪 Sample Inputs for Testing Try out combinations like:
Education: University
Marriage: Single
Bill Payments: Positive numbers for debts, 0 for paid
Repayment Status: 0 means paid, 1–9 means delayed payments
Past Payments: Amounts paid in previous months
📤 Deployment The app is deployed using Streamlit Community Cloud. Push changes to GitHub and they’ll automatically reflect live.
🙌 Acknowledgements Dataset from UCI Machine Learning Repository
Streamlit for rapid web UI development
🧑💻 Author Zeeshan Feel free to connect on LinkedIn or raise issues in the repository.
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Posted Jul 1, 2025
Built a Streamlit app to predict credit card payment defaults using Random Forest.
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