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Sales Analytics: How to Use Machine Learning to Predict and Optimize Product Backorders

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Sales, customer service, supply chain and logistics, manufacturing… no matter which department you’re in, you more than likely care about backorders. Backorders are products that are temporarily out of stock, but a customer is permitted to place an order against future inventory. Back orders are both good and bad: Strong demand can drive back orders, but so can suboptimal planning. The problem is when a product is not immediately available, customers may not have the luxury or patience to wait. This translates into lost sales and low customer satisfaction. The good news is that machine learning (ML) can be used to identify products at risk of backorders. In this article we use the new H2O automated ML algorithm to implement Kaggle-quality predictions on the Kaggle dataset, “Can You Predict Product Backorders?”. This is an advanced tutorial, which can be difficult…
Original Post: Sales Analytics: How to Use Machine Learning to Predict and Optimize Product Backorders