# Model Information

## Overview

The Loan Approval Predictor uses a Random Forest machine learning model trained on loan application data to predict whether a loan application will be approved or rejected.

## Model Details

- **Model Type**: Random Forest Classifier
- **Features Used**:
  - Gender
  - Married
  - Dependents
  - Education
  - Self_Employed
  - ApplicantIncome
  - CoapplicantIncome
  - LoanAmount
  - Loan_Amount_Term
  - Credit_History
  - Property_Area
- **Output**: Binary prediction (Approved/Not Approved)
- **Model File**: `random_forest_model.pkl`
- **Scaler File**: `scaler.pkl`

## Dataset

The model was trained on the Loan Approval Prediction dataset, which contains historical loan application data with features about applicants and whether their loans were approved.

## Feature Importance

The most important features for loan approval prediction typically include:
1. Credit History
2. Loan Amount
3. Applicant Income
4. Property Area
5. Loan Amount Term

## Limitations

- The model is trained on historical data and may not capture recent changes in lending policies
- The model assumes that the input data follows the same distribution as the training data
- Extreme outlier values may lead to unreliable predictions