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A company wants to understandthe customer purchase behavior (dependent variable) against different products using their demographic information as features where most of the features are self-explanatory. This dataset consists of null values, redundant and unstructured data. Machine Learning is one of the most obvious applications in the domain of retail industry. This concept helps to develop a predictor that has a clear commercial value to the store owners as it would help with their financial planning, inventory management, marketing, and advertising. This entire process of developing a model includes preprocessing, modelling, training, testing and evaluating. Therefore, frameworkshave been developed to automate some of this process and hide away its complexity. The algorithm we proposed was Random Forest regressor that performed an average accuracy of 83.6% and with minimum RMSE (Root Mean Squared Error) value of 2829 on the Black Friday sales dataset.

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