Unlocking Future Transactions: Predicting Customer's Next Purchase in E-commerce through Machine Learning Analysis

Computer Engineering

The exponential growth of the E-commerce industry has created a need for accurate sales predictions to drive effective business strategies. This abstract presents a data-driven approach to predict e-commerce sales by leveraging machine learning techniques. To develop the prediction model, historical sales data from various e-commerce platforms was collected and preprocessed to ensure data quality. Feature engineering techniques were applied to extract relevant information from the data, including customer demographics, product attributes, pricing, promotions, and seasonal trends. Several machine learning algorithms, including regression models, ensemble methods, and deep learning models, were implemented and evaluated to identify the most accurate prediction model.

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