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The training and test sets in this data science project contain transaction history for customers that ended up purchasing a policy. For each customer_ID, you are given their quote history. In the training set you have the entire quote history, the last row of which contains the coverage options they purchased. In the test set, you have only a partial history of the quotes and do not have the purchased coverage options. These are truncated to certain lengths to simulate making predictions with less history (higher uncertainty) or more history (lower uncertainty).
For each customer_ID in the test set, you must predict the seven coverage options they end up purchasing.
Each customer has many shopping points, where a shopping point is defined by a customer with certain characteristics viewing a product and its associated cost at a particular time.
In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.
The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.
In this data science project, we will predict internal failures of Bosch using thousands of measurements and tests made for each component along the assembly line.