TrustGate.AI

Gatekeeper for model quality, production guardrails, trust, bias, risk, and anomaly detection, . README. Scanned on 2025-11-06 18:08._

Overview

Models for Customer Loan Purchase Prediction

This case study will help us to understand the stages in the AI/ML project lifecycle with loans data set to predict whether potential customer's will be targeted for loans. We will focus on the following stages namely -

• Preprocess the data.
• Handle missing values.
• Perform feature engineering.
• Machine learning classification model.
• Build and evaluate classification models.
• Provide insights based on the model's performance.

Models validation

Data Validation and Preprocessing Tests Model Training Validation ML Classification evaluation matrix (Confusion): Accuracy, Precision, Recall, F1-Score, ROC-AUC Regression Matrix : RMSE, MAE, R² Bias, Fairness & Explainability Tests Performance & Scalability Testing Integration & Pipeline Testing Monitoring & Continuous Testing in Production

Project Structure

+---.idea
�   �   .gitignore
�   �   AIML.iml
�   �   misc.xml
�   �   modules.xml
�   �   workspace.xml
�   �   
�   +---inspectionProfiles
�           profiles_settings.xml
�           
+---Loan Pediction Modal Evaluation
�   �   Customer_Financial_Info.csv
�   �   Loan Approval Prediction.py
�   �   loan_approval_best_model.joblib
�   �   Loan_Approval_test.csv
�   �   Loan_Approval_train.csv
�   �   model_comparison_summary.csv
�   �   model_test.py
�   �   model_validation.py
�   �   
�   +---__pycache__
�           model_validation.cpython-39.pyc
�           
+---Reinforcement Learning
    �   dino_run.py
    �   
    +---aigym_env
            setup.py

Data

Data/file paths:

• /content/Customer_Financial_Info.csv

Environment & Requirements

Install dependencies (adjust versions as needed):

pip install matplotlib numpy pandas scikit-learn seaborn

How to Run

1. Run Loan Approval Prediction.py
   • loan_approval_best_model.joblib file generated.
2. Run model_validation.
3. Run the model_test

Modeling (auto-detected)

• Algorithms referenced: DecisionTreeClassifier, KNeighborsClassifier, LogisticRegression, RandomForestClassifier
• Metrics computed: classification_report, roc_auc_score
• Target variable: Not detected from the notebook code
• Random state(s): 42

Notes

• For production use, consider promoting and consuming it.