44 lines
1.2 KiB
Markdown
44 lines
1.2 KiB
Markdown
# Model Information
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## Overview
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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.
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## Model Details
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- **Model Type**: Random Forest Classifier
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- **Features Used**:
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- Gender
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- Married
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- Dependents
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- Education
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- Self_Employed
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- ApplicantIncome
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- CoapplicantIncome
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- LoanAmount
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- Loan_Amount_Term
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- Credit_History
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- Property_Area
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- **Output**: Binary prediction (Approved/Not Approved)
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- **Model File**: `random_forest_model.pkl`
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- **Scaler File**: `scaler.pkl`
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## Dataset
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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.
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## Feature Importance
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The most important features for loan approval prediction typically include:
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1. Credit History
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2. Loan Amount
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3. Applicant Income
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4. Property Area
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5. Loan Amount Term
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## Limitations
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- The model is trained on historical data and may not capture recent changes in lending policies
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- The model assumes that the input data follows the same distribution as the training data
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- Extreme outlier values may lead to unreliable predictions
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