Which algorithms are used in this credit scoring project?

The system implements and compares two algorithms: XGBoost and Logistic Regression. XGBoost achieved superior performance with 93.2% accuracy and 96.52% ROC-AUC.

What is the accuracy of the credit default prediction model?

The XGBoost model achieves 93.2% accuracy with an F1-Score of 91.87% and ROC-AUC of 96.52%, demonstrating excellent predictive capability for credit risk assessment.

Who can benefit from this credit scoring system?

Financial institutions, banks, credit unions, lending platforms, and risk managers can use this system for loan underwriting, risk assessment, and portfolio management.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Intelligent Financial Health and Credit Scoring System - Complete Project Demo Video

Watch Demo Video

Machine Learning

Intelligent Financial Health and Credit Scoring System

The Intelligent Financial Health and Credit Scoring System is a comprehensive machine learning solution that predicts credit default risk using the 'Give Me Some Credit' dataset. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements XGBoost and Logistic Regression with SMOTE for class imbalance, achieving 93.2% accuracy and 96.52% ROC-AUC.

The system utilizes the 'Give Me Some Credit' dataset from Kaggle containing 150,000 borrower records with 10 financial features including revolving utilization, payment history, and debt ratios. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time credit risk assessment, enabling financial institutions to make data-driven lending decisions.

Python 3.8+ Machine Learning XGBoost Logistic Regression Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Credit Default Prediction
XGBoost (93.2% Accuracy)
Logistic Regression (89.7% Accuracy)
Feature Engineering (income_to_debt_ratio, total_late_payments)
SMOTE for Class Imbalance Handling
Interactive Visualizations
Real-time Risk Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with L2 regularization, class_weight='balanced', max_iter=1000

🎯 Accuracy: 89.7%

⚡ XGBoost

Gradient boosting with n_estimators=150, max_depth=6, learning_rate=0.1, regularization

🎯 Accuracy: 93.2%

🔧 Feature Engineering

income_to_debt_ratio, utilization_per_line, total_late_payments, age_squared

📊 Top Feature: Revolving Utilization

🔄 SMOTE Oversampling

Synthetic minority oversampling for class imbalance (93:7 ratio)

📊 CV Mean: 96.28%

Methodology & Workflow

1 Data Collection
150,000 borrower records with 10 financial features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
XGBoost and Logistic Regression with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time risk assessment

Model Performance Comparison

Metric	Logistic Regression	XGBoost	Best
Test Accuracy	89.7%	93.2%	XGBoost
Test Precision (Weighted)	89.6%	93.2%	XGBoost
Test Recall (Weighted)	89.7%	93.2%	XGBoost
Test F1-Score (Weighted)	88.3%	91.9%	XGBoost
Test ROC-AUC	92.3%	96.5%	XGBoost
CV Mean ROC-AUC	91.95%	96.28%	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Give Me Some Credit Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

Check Payment Status

LIMITED TIME OFFER -70%

Complete Project Package Lifetime Access

Original Price

₹9,999

Today's Price ₹2,999 💎 Save ₹7,000

You Save ₹7,000 (70% OFF)

Complete Source Code

Documentation & PPT

Video Tutorial

24/7 Expert Support

Scan & Pay with UPI

SECURE

Payee Thirumalai Kumar

UPI ID 9600095045@icici

Amount ₹2,999

Submit Your Payment

100% SECURE

Full Name *

Email *

Phone Number *

College/University *

Degree

Passout Year

Department

Payment Details

Enter your UPI Transaction ID and upload payment screenshot for verification.

Transaction ID *

Payment Screenshot *

Click to upload screenshot JPG, PNG, GIF, PDF • Max 5MB

📚 Academic & Research Support Services

Need help with Thesis, Dissertation, Assignments, or PhD Research? We've got you covered!

📝

Thesis & Dissertation

Complete thesis writing, research guidance, and formatting support

Expert Help

📄

Assignment Help

Quality assignment writing, editing, and proofreading services

100% Original

🔬

PhD Research

Research proposal, literature review, data analysis & publication

PhD Level

📊

Project Guidance

Academic projects, mini projects, and final year project support

Hands-on

📞 Need custom support? Contact us directly!

Chat on WhatsApp → Email Us

Final Year Projects