Credit Card Approval Prediction System

This project is a machine learning-based web application that predicts the likelihood of credit card approval for a customer based on their personal and financial details. It leverages a Random Forest Classifier trained on historical credit and application records.

---

🔍 Project Overview

Many financial institutions face challenges in assessing credit card applications quickly and accurately. This project provides a predictive solution using machine learning to automate and improve this process.

The application offers:

• A user-friendly web interface (built using Flask)
• Real-time predictions
• Visual insights into model performance

---

📁 Directory Structure

├── Dataset/
│   ├── application\_record.csv
│   └── credit\_record.csv
│ 
├── Images/
│   ├── Decision Tree\_plot.png
│   ├── Logistic Regression\_plot.png
│   ├── Random Forest\_plot.png
│   ├── XGBoost\_plot.png
│   └── model\_comparison\_metrics.png
│ 
├── models/
│   ├── Random\_Forest\_best\_model.pkl
│   ├── best\_threshold.txt
│   └── train\_columns.pkl
│ 
├── notebooks/
│   ├── 1\_Visualizing\_and\_analyzing\_data.ipynb
│   ├── 2\_Data\_preprocessing.ipynb
│   ├── 3\_Model\_building.ipynb
│   └── 4\_Prediction.ipynb
│ 
├── static/
│   ├── credits-card.jpg
│   ├── landing_page.jpg
│   ├── input_page.png
│   ├── result_page.png
│ 
├── templates/
│   ├── landing\_page.html
│   ├── form.html
│   └── result.html
│ 
├── app.py
├── requirements.txt
├── LICENSE
├── .gitignore
└── README.md

🚀 How to Run the Project

1. Clone the repository

   git clone https://github.com/your-username/credit-card-approval-prediction.git
   cd credit-card-approval-prediction
   
   

2. Create a virtual environment (optional but recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the Flask app

   python app.py

5. Visit in browser: Navigate to http://127.0.0.1:5000 to use the application.

---

🧠 Machine Learning Details

• Model Used: Random Forest Classifier
• Evaluation Metrics: Accuracy, F1 Score, Confusion Matrix
• Preprocessing: Handled using SMOTE, encoding, feature scaling
• Threshold Selection: Based on optimized F1 score

---

📊 Visualizations

Located in the Images/ folder, these plots help analyze the performance of:

• Logistic Regression
• Decision Trees
• Random Forests
• XGBoost
• Model comparison metrics

---

🖥️ Web Interface Using Flask

• landing_page.html: Landing page with a call-to-action to initiate prediction
• form.html: Takes user inputs for prediction
• result.html: Displays prediction results and approval probability

🔥 Project Screenshots

🚀 Landing Page

🧾 Input Form

✅ Result Prediction

---

👨‍💻 Author

• Rutuja Kumbhar - GitHub

---

📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

---

If you have any questions or suggestions, feel free to reach out via GitHub or email.