Predictive Analytics: Predicting Sales & Customer Lifetime Value

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. In recent years, retailing has changed from a fragmented space into a winner-takes-all sector, in which a key differentiating factor is the ability to tightly predict demand and measure customer lifetime value. Begin this course by attempting to predict the sales for each week in a Walmart store. You will explore and visualize your data, creating an Azure machine learning workspace and a hosted Python notebook to write code. Then, perform regression analysis to predict the sales after one-hot encoding the requisite explanatory variables. You will apply different models as well, including ridge regression, K-nearest neighbors, decision trees, random forests, and extra tree regressors. Next, predict the customer lifetime value using regression analysis, and perform cross-validation and feature selection on the model in order to improve its performance. Finally, experiment with feature selection, including recursive feature elimination, lasso regularization, and linear SVR.