ADS Capstone Chronicles Revised

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transformation are crucial for maintaining the currency of the data and ensuring it is accurate and reliable for subsequent analytical tasks. Once the data has been processed, it is uploaded into Azure SQL Database. This integration process supports the centralization of data from multiple sources, allowing for comprehensive analysis and reporting. Azure’s robust security features, such as encryption, and role-based access control, play a critical role in maintaining data integrity and ensuring compliance with regulatory standards throughout the data lifecycle. These security measures protect the data from unauthorized access and breaches, ensuring it remains secure and compliant with industry regulations. While the data is currently manually being pulled, the intention is to automate this process periodically to enhance efficiency, reduce the potential for human error, and ensure the most up-to-date information is always available. ​ 4.1.1 Exploratory Data Analysis. Exploratory Data Analysis (EDA) was performed to explore and understand the characteristics of the dataset, which was collected from various operational sources within the e-commerce platform. This dataset includes critical transactional details such as Trasaction_ID, Customer_ID, Year, Total_Purchases, Amount, and Total_Amount. By examining these elements, the EDA aimed to uncover patterns,

trends, and anomalies that could provide valuable insights into customer behavior and transaction dynamics. The descriptive statistics derived from the dataset reveal that it contains approximately 301,702 transactions. The average amount spent per transaction is about $255.16, with a standard deviation of $141.39, indicating a significant To further visualize the data, Figure 2 illustrates a histogram depicting the distribution of the average amount spent per customer. This graphical representation is instrumental in identifying spending patterns and variations among customers. The histogram provides a clear visual summary of how transaction amounts are distributed, allowing for a more intuitive grasp of underlying data trends and aiding in the identification of any potential outliers or clusters in spending behavior. variation in transaction values. The dataset also highlights a wide range of transaction amounts, from a minimum of $10.00 to a maximum of $499.99. These statistics offer a foundational understanding of spending behaviors and the scale of financial transactions within the e-commerce platform. Figure 2 Histogram Showing the Distribution of the Average Amount Spent per Customer

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