7006SCN_Isha_13275073

Financial Fraud Detection using Machine Learning and Big Data

Project Description

This project focuses on detecting fraudulent financial transactions using Machine Learning and Big Data technologies. The analysis is performed on a large-scale Financial Transactions Dataset containing more than 13 million transaction records and over 30 features related to customers, merchants, and transactions.

PySpark was used for distributed data processing, data cleaning, feature engineering, and machine learning model development. Four classification algorithms were implemented and compared: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosted Trees (GBT). Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and AUC-ROC metrics.

The results showed that Gradient Boosted Trees achieved the highest predictive performance, while Decision Tree provided the fastest training time. SHAP explainability techniques were applied to identify the most influential features affecting fraud predictions.

Interactive Tableau dashboards were created to visualize data quality, fraud patterns, model performance, business insights, and scalability analysis, supporting data-driven decision making.

7006SCN2526MAYJUL/7006SCN_Isha_13275073

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