Loan_ml_project/docs/model-info.md

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