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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.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
Given a customer's search query and the returned product in text format, your predictive model needs to tell whether it is what the customer was looking for.
Build a machine learning model that will predict which jobs users will apply to given their past applications, demographics and work history.