🏦 Loan Approval Prediction App

This is a simple and beginner-friendly Machine Learning web app built with Streamlit that predicts whether a loan application is likely to be approved or rejected.

The app uses a Random Forest Classifier trained on a custom loan dataset and predicts approval based on user inputs such as income, CIBIL score, loan amount, and asset values.

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📈 Model Performance

✅ Accuracy: 97.78% on test data

Classification Report:

✅ Accuracy: 0.978

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📊 Classification Report

Class	Precision	Recall	F1-Score	Support
0	0.98	0.99	0.98	536
1	0.98	0.96	0.97	318

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📋 Overall Metrics

Metric	Score
Accuracy	0.98
Macro Avg F1	0.98
Weighted F1	0.98

📂 Features

• Predict loan approval using a trained ML model.
• Interactive UI built with Streamlit.
• Input fields for education, employment status, financial assets, and more.
• Instant results with visual feedback.

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🧠 Machine Learning Model

• Model: RandomForestClassifier
• Preprocessing:
  • Label Encoding for categorical columns.
  • Cleaned and encoded features for training.
• Training/Test split: 80/20
• Accuracy & classification report printed in console during training.

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📝 Input Features

• Education level
• Employment status (self-employed or not)
• CIBIL Score
• Income and Loan Details
• Value of residential, commercial, luxury, and bank assets
• Number of dependents

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🛠️ Tech Stack

• Python
• scikit-learn
• Streamlit
• pandas
• joblib

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🔧 Setup Instructions

1. Clone the repository

   git clone https://github.com/rd9437/predicting_loan_approval.git
   cd predicting_loan_approval