Loan Prediction System using Python Machine Learning (ML)
“Loan Prediction System” is a web-based application aimed at solving the growing challenge of loan approval automation in financial institutions by combining machine learning techniques with a Django-based web platform. This system is designed to automatically predict whether a loan application is likely to be approved or rejected using machine learning models. Instead of relying solely on traditional manual verification, which is often slow, inconsistent, and prone to human bias, the system provides a scalable, efficient, and accurate solution capable of analyzing large volumes of applications in real time.
🛠️ Tech Stack Used
Frontend / Web Interface:
- Django (Python Web Framework) – Used to create the web interface for user input, displaying predictions, and managing data
- HTML5, CSS3, JavaScript – For rendering and styling web pages
- Bootstrap (optional) – For responsive UI components
- Django Templates – For dynamic web page rendering
🧠 Machine Learning / Backend Logic:
- scikit-learn – Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
- NumPy→ For numerical operations and matrix manipulation
- Pandas → For handling and preprocessing datasets
- joblib → To save and load the trained machine learning model
🗃️ Database:
- SQLite – Lightweight relational database used to store user data and predictions
- Django ORM (Object Relational Mapper) – Handles interaction between Django models and the SQLite database
⚙️ Tools & Environment:
- Python 3.x – Core programming language used
- PyCharm – IDE for development
- Virtualenv / pip – For managing dependencies
✅ Key Features
User Module
- User Registration & Authentication – Secure login and registration system to allow users to track their loan prediction history.
- Loan Application Interface – Users can enter details such as income, loan amount, loan term, dependents, and credit history.
- Preprocessing & Feature Engineering – Encodes categorical variables, normalizes inputs, and prepares data for accurate prediction.
- Machine Learning Model Integration – A trained ML model predicts the probability of loan approval.
- Result Visualization – Displays prediction results (Approved / Not Approved) with probability scores.
- Prediction History – Stores past predictions for each user in the database for easy tracking.
- User-Friendly Interface – Responsive and clean design using HTML, CSS, Bootstrap, and Django templates.
Admin Module
- Dashboard – Administrators can view important system statistics such as total users, number of applications, and prediction outcomes.
- User Management – Admin can view, manage, and control all registered users.
- Prediction History – Admin can track user applications and their corresponding predictions.
- Reports & Analytics – Generates summarized reports of loan predictions over time.
- Role-Based Access – Only authorized administrators can access this panel.
- Real-Time Updates – Dashboard statistics update dynamically as users submit loan applications.
Student Performance Prediction System: Output Screens
Home Page
User Registration
Prediction Form
Admin Dashboard
All Applications
All Predictions
Registered Users
How to run the Loan Prediction System using Python Machine Learning (ML)
1. Download the zip file
2. Extract the file, copy loan_predictor, the folder and paste it on the desktop
3. Open PyCharm and import the project into PyCharm
4. Install four libraries (if not installed)
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1 2 3 4 |
pip install joblib pip install numpy pip install scikit-learn pip install pandas |
5. Run the Project using the following command
python manage.py runserver
Now, click the URL http://127.0.0.1:800,0 and the Project will run
