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AI-Based Loan Approval Prediction System
Project Overview
This project is an AI-based Loan Approval Prediction System that predicts whether a loan application should be Approved or Rejected.
Dataset
The project uses a dataset containing 50,000 records.
Dataset distribution:
- Full Dataset: 50,000 rows
- Main Dataset: 5,000 rows
- Training Dataset: 4,000 rows
- Testing Dataset: 1,000 rows
- Unseen Dataset: 150 records
Machine Learning Model
The project uses a Random Forest Classifier.
Input Features
- Age
- Gender
- Married
- Dependents
- Education
- Self Employed
- Applicant Income
- Coapplicant Income
- Loan Amount
- Loan Term
- Credit History
- Property Area
Output
The model predicts:
- Approved
- Rejected
Evaluation
The model is evaluated using:
- Accuracy
- Precision
- Recall
- F1-Score
- Confusion Matrix
Technologies Used
- Python
- Google Colab
- Pandas
- NumPy
- Scikit-learn
- Hugging Face
- Gradio
Project Workflow
50,000 Dataset
↓
5,000 Main Dataset
↓
4,000 Training + 1,000 Testing
↓
Random Forest Model
↓
Model Evaluation
↓
150 Unseen Records
↓
Final Predictions
Author
Manii77
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