AI Engineer | ML Engineer | Building Production Grade Engines | Currently Autonomous Research Agent

github

Abdul Moiz  • Peerlist

<h3>Project Overview</h3>An end-to-end fraud detection pipeline built on the IEEE-CIS Fraud Detection Kaggle competition dataset. This repository demonstrates a complete machine learning workflow for detecting fraudulent transactions in real-time financial systems. Starting with 590K+ transactions and 434 raw features, we engineer intelligent risk signals, benchmark multiple gradient boosting algorithms, and maintain a rigorous experiment log — all designed for production-ready deployment.<h3>What Makes This Different?</h3>

Feature Engineering
Guess &amp; pray
Hypothesis-driven with adversarial validation

Model Selection
Single algorithm
LGBM · XGBoost · CatBoost benchmark

Validation
Naïve train/test
Stratified splits + early stopping + threshold tuning

Experiment Tracking
Scattered notebooks
Versioned experiment log with F1 deltas

Categorical Handling
One-hot explosion (485+ features)
Native LightGBM category support

Model Analysis
Basic accuracy
SHAP · Calibration · PR Curves · Confusion Matrix

<h3>Live Experiment Dashboard</h3>

0.84370.25CatBoost442+40.1% over baseline

The project utilizes advanced machine learning techniques to predict customer risk levels. Key features include:<ul><li>Comprehensive Feature Engineering: Developed over 400 features, including time-based, count-based, and identity-based features, to capture complex fraud patterns.</li><li>Model Benchmarking: Evaluated and compared multiple gradient boosting models (LightGBM, XGBoost, CatBoost) to identify the best performing algorithm.</li><li>Rigorous Validation: Employed stratified splits, early stopping, and threshold tuning to ensure robust model performance.</li><li>Explainable AI (XAI): Integrated SHAP values for model interpretability, allowing for detailed analysis of individual predictions and feature importance.</li><li>Production-Ready Pipeline: Designed for real-time deployment, with optimized data processing and model serving capabilities.</li></ul><h3>Engineered Features</h3>The project focuses on creating meaningful features that capture user behavior and transaction anomalies. Notable engineered features include:<ul><li>UID-based features: Aggregations and statistics derived from user identifiers (e.g., uid_transaction_count, uid_mean_amount) to identify suspicious patterns.</li><li>Temporal features: Analysis of transaction timing (e.g., uid_mean_hour) to detect unusual activity.</li><li>Match indicators: Features capturing consistency between different data points (e.g., card address vs. billing address).</li></ul><h3>Model Performance &amp; Visualizations</h3>The project provides extensive visualizations and performance metrics to demonstrate the effectiveness of the fraud detection models. This includes:<ul><li>Confusion Matrix Analysis: Visualizes true positives, true negatives, false positives, and false negatives to assess classification accuracy.</li><li>Precision-Recall Curve: Illustrates the trade-off between precision and recall, crucial for imbalanced datasets.</li><li>Model Calibration: Assesses how well the predicted probabilities align with actual fraud rates.</li><li>Feature Importance: Identifies the most influential features driving fraud predictions across different models.</li><li>SHAP Analysis: Provides both global and local explanations for model predictions, enhancing transparency and trust.</li></ul>