Authors - Janwale Asaram Pandurang, Minal Dutta, Savita Mohurle Abstract - Black Friday shopping event is one of the most awaited events worldwide now a day, it offers huge discounts and promotions of various products categories. For sellers, it’s important to know the customer purchasing behaviors during this period to predict sales, manage inventory and planning for marketing strategies. This research paper will focus on developing a machine learning model that will predict customer expenses capacity based on previous data from Black Friday, by considering factors such as demographics, product types and previous purchases. After collecting and processing a different dataset, exploratory data analysis was conducted to find important trends. Different machine learning models, like linear-regression, K-nearest-Neighbors (KNN) Regression, Decision-Tree-Regression and Random-Forest-Regression, were applied and tested. The Regression Forest Model with R2 value of 0.81, was found with strong predictive accuracy among those models. This study focuses on machine learning models which will help sellers to improve their productivity and will increase revenue.
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