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Credit card fraud detection is a crucial task in financial security. This project aims to identify fraudulent credit card transactions using various machine learning algorithms, including Logistic Regression, Isolation Forest, Random Forest, CatBoost, Deep Neural Networks (DNNs), XGBoost, and Long Short-Term Memory (LSTM) networks.
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Data Warehouse: Consolidates and organizes sales data for better accessibility and analysis.
Data Visualization: Utilizes Power BI to create interactive dashboards and reports.
Sales Analysis: Provides insights into sales trends, performance metrics, and customer segmentation.
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The goal is to migrate data from an on-premise SQL Server database to the Azure cloud, transform it, and analyze it using popular Azure tools. The project follows a typical data lakehouse architecture, leveraging Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Gen2, and Power BI to handle different stages of data ingestion, transformation, storage, and reporting.
Published:
Credit card fraud detection is a crucial task in financial security. This project aims to identify fraudulent credit card transactions using various machine learning algorithms, including Logistic Regression, Isolation Forest, Random Forest, CatBoost, Deep Neural Networks (DNNs), XGBoost, and Long Short-Term Memory (LSTM) networks.
Published:
Credit card fraud detection is a crucial task in financial security. This project aims to identify fraudulent credit card transactions using various machine learning algorithms, including Logistic Regression, Isolation Forest, Random Forest, CatBoost, Deep Neural Networks (DNNs), XGBoost, and Long Short-Term Memory (LSTM) networks.