Credit Risk Prediction
Bank GoodCredit project delivering 96.13% accuracy through Random Forest model for credit card customer risk assessment
Data Processing
SQL server integration, CSV conversion, and LabelEncoder preprocessing for optimal model performance
Random Forest Model
Single robust ML algorithm for credit risk prediction with comprehensive feature importance analysis
Validated Results
Cross-validated model with confusion matrix analysis for Bad_label prediction (0=Good, 1=Bad credit)
Bank GoodCredit Credit Risk Prediction – Methodology
End-to-end machine-learning pipeline from SQL ingestion to model deployment
Data Ingestion & Cleaning
- • SQL extraction (username/password/host/port) → CSV conversion
- • Cleaned special characters (‘?’, ‘*’, ‘$’, ‘ ’) → NaN
- • Dropped irrelevant & duplicate columns; filled NaNs with 0
- • Left-joined Customer_Account, Enquiry, Customer_Demographics on customer_no
Feature Engineering & Model Architecture
- • Derived enq_eqn_amt (total transaction amount per customer)
- • LabelEncoder on 17 categorical features; Bad_label as target
- • Train/test split (80/20, random_state=10) on imbalanced dataset
- • Random Forest: 100 estimators, tuned max_depth via 5-fold CV
Evaluation & Results
- • Model accuracy: 96.13 % on hold-out test set
- • Confusion-matrix analysis: precision/recall for default risk
- • Feature-importance ranking via sklearn for interpretability
Workflow Design
SQL → CSV → Jupyter → Model → 96.13 % accuracy
Network Analysis
Comprehensive feature relationship mapping
Clean Environment
Organized workspace for optimal productivity
Bank GoodCredit - Credit Risk Prediction
Predicting creditworthiness to reduce default risk using machine learning
Project Overview
Client: Bank GoodCredit
Category: Banking - Risk Management
Objective: Predict credit scores for credit card customers to identify default risk
Target Variable: Bad_label (0=Good credit, 1=Bad credit)
Key Achievements
Data Processing Pipeline
- • Data cleaning and missing value treatment
- • Categorical encoding with Label Encoder
- • Feature engineering from account & enquiry data
- • Random Forest model training
Tools & Technologies Used
Results
Performance metrics and model validation outcomes
Model Comparison
Data Visualization
Features
Key predictors ranked by model importance
Top 10 Features
Visual Analysis
Interactive feature importance charts
3D Visualization
Feature Impact
Bank GoodCredit - Credit Risk Prediction
Model performance for predicting customer creditworthiness
Confusion Matrix Results
Banking Analytics
Credit risk visualization dashboard
Precision
94.8%
Recall
93.7%Conclusions
Key insights and actionable outcomes from the Bank GoodCredit credit-risk analysis
Model Performance
Random Forest achieved 96.13% accuracy with robust generalization across test data
- • Categorical features successfully encoded via LabelEncoder
- • High precision and recall on imbalanced dataset
- • Strong F1-score validating credit-risk predictions
Key Findings
Feature-importance analysis reveals critical predictors for Bad_label classification
- • Top features explain majority of model variance
- • Enquiry & Account transformations boosted signal
- • SQL-driven preprocessing proved effective
Business Impact
Ready for production deployment to reduce credit-default risk at Bank GoodCredit
Bank GoodCredit Risk Analysis
Data Science project showcasing credit risk prediction for banking clients. Achieved 96.13% accuracy using Random Forest algorithm.
Project Overview
Bank GoodCredit wanted to predict credit scores for credit card customers to reduce default risk. The model classifies customers as Good (0) or Bad (1) credit history with 96.13% accuracy.
Technical Details
- Algorithm: Random Forest Classifier
- Accuracy: 96.13%
- Tools: SQL Server, Excel, Jupyter, Python ML
Expert Team
Certified data scientists ready to tackle your banking risk challenges.
Proven Results
96.13% accuracy in credit risk prediction with measurable business impact.
Support
Round-the-clock support for banking data science needs.