CreditWise Loan Approval System

MINI PROJECT : PREDICTS LOAN APPROVAL

🏦 Project Overview

A MACHINE LEARNING SYSTEM DEVELOPED TO AUTOMATE LOAN APPROVAL DECISIONS FOR SECURETRUST BANK. THIS PROJECT ADDRESESS THE PROBLEM OF MANUAL, BIASED Loan Verification by Implementing An Intelligent Prediction Model.

Business Impact: Reduce False Rejections (Loss of GOOD CUSTOMERS) And False Approvals (financial risk).

📂 FILES IN THIS PROJECT

• docs/CreditWise Loan System.pdf - PROJECT REQUIREMENTS AND PROBLEM STATEMENT
• data/loan_approval_data.csv - HISTORICAL LOAN application DATASET
• notebooks/loan_approval_analysis.ipynb - Complete ML pipeline implementation

🎯 ML PIPELINE

1. DATA EXPLORATION & CLEANING
2. FEATURE ENGINEERING
3. MODEL TRAINING (Logistic Regression, Random Forest, etc.)
4. Model Evaluation & Selection
5. Results Analysis

📊 KEY Results

• **Best Model: loan-approval-predictor
• **Accuracy: 85 %
• **Recall: 70 % (Important for minimizing good customer rejection)
• Key Features: Credit Score, Applicant Income, Loan Amount

🚀 Quick Start

1. Clone this repository
2. Install requirements: pip install -r requirements.txt
3. Open the Jupyter notebook: jupyter notebook notebooks/loan_approval_analysis.ipynb

📝 Author

SHREEKANTH GUTTEDAR : MACHINE LEARNING ENGINEER
Shreekanthashokg@gmail.com
https://www.linkedin.com/in/shreekanth-a-guttedar-81562b384

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NOTE: THIS IS A PORTFOLIO PROJECT DEMONSTRATING END-to-end ML implementation.