Project Description: Jun 2021 – Sept 2022 Developed a fraud detection system for Wells Fargo Bank using Machine Learning models like Random Forest, XGBoost, and Neural Networks to detect fraudulent transactions. Preprocessed data handled class imbalance with SMOTE, and optimized models using Grid Search. Split data into training and test sets. Deployed the model using FastAPI for real-time fraud detection and integrated Tableau for financial data visualization and anomaly detection. This solution enhances fraud detection accuracy, reduces financial risks, and strengthens transaction security by identifying suspicious transactions efficiently and in real-time. Project Responsibilities:• Preprocessing financial transaction data, handling class imbalance with SMOTE, and feature engineering for anomaly detection. • Fraud detection for Wells Fargo Bank transactions using Machine Learning models such as Random Forest, XGBoost, and Neural Networks. • Loading, summarizing &amp; visualizing the data for exploration analysis. • Built Regression, Classification, and Clustering models to detect fraudulent patterns in financial data. • Identified and mitigated multi-collinearity issues in feature selection. • Performed residual analysis and visualized data distributions to enhance model accuracy. • Optimized hyperparameters using Grid Search and Randomized Search for improved fraud detection. • Analyzing the given data sets by splitting it into Training &amp; Test data. • Deployed the model using FastAPI for real-time fraud detection in banking transactions. • Utilized Tableau for financial data visualization and anomaly pattern recognition. • Trained new team members in fraud detection techniques and machine learning workflows.&nbsp;

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