Loan status prediction using machine learning is an important application in financial analytics.
This project analyzes applicant information such as income, employment status, credit history, and loan amount to estimate whether a loan will be approved.
Using Python and machine learning, the model classifies each application as approved or not approved based on historical patterns.
This helps banks automate loan screening, reduce risk, and make more consistent decisions.
Project Overview
- Machine learning based loan approval prediction system
- Uses Support Vector Machine (SVM) as the classification model
- Built using Python and its scientific libraries
Libraries Used
- Pandas for data loading and manipulation
- NumPy for numerical operations
- Scikit Learn for preprocessing, model training, and evaluation
- LabelEncoder for converting categorical values
- Train Test Split for model validation
Dataset Details
The dataset contains applicant demographic and financial attributes.
Key features include
- Gender
- Married status
- Education level
- Applicant and co-applicant income
- Loan amount and loan term
- Credit history
- Property area
The target column Loan_Status indicates
- 1 → Loan approved
- 0 → Loan not approved
Preprocessing Steps
- Checked and handled missing values where required
- Encoded categorical entries into numeric format using LabelEncoder and replace()
- Split data into input features (X) and target labels (Y)
- Standardized shape of inputs for model training
Model Building
- Support Vector Machine (SVM) selected as the main algorithm
- Linear kernel used for efficient classification
- Dataset split into training and testing sets
- Model learns patterns linking applicant details with loan outcomes
Performance and Accuracy
- Predictions generated using the trained SVM model
- Model accuracy calculated using test dataset
- Typical results show reliable performance for binary classification
Prediction Flow
User provides applicant details
Inputs are transformed into a numeric array
Model predicts loan status
- 1 → Loan likely approved
- 0 → Loan likely rejected
Deployment Possibilities
- Can be deployed using Flask or Streamlit for real-time predictions
- Users can enter loan applicant details and instantly receive a prediction
Key Takeaways
- Complete ML pipeline: preprocessing, encoding, model training, and evaluation
- Demonstrates financial decision prediction using real-world attributes
- Supports risk management and improves loan processing decisions
Future Enhancements
- Compare performance with Random Forest, Gradient Boosting, or Neural Networks
- Apply hyperparameter tuning and cross-validation for optimization
- Build a user-friendly interface for online loan approval prediction