Google Professional Machine Learning Engineer
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You work for a retail company and are tasked with developing a predictive model to determine whether a customer will purchase a product on a given day. To aid this, your team has processed sales data and created a table with the following columns: Customer_id, Product_id, Date, Days_since_last_purchase (measured in days), Average_purchase_frequency (measured in 1/days), and Purchase (binary class indicating if the customer purchased the product on the given date). It is essential to interpret your model’s predictions for each individual instance to provide actionable insights. What should you do?
You work for a retail company and are tasked with developing a predictive model to determine whether a customer will purchase a product on a given day. To aid this, your team has processed sales data and created a table with the following columns: Customer_id, Product_id, Date, Days_since_last_purchase (measured in days), Average_purchase_frequency (measured in 1/days), and Purchase (binary class indicating if the customer purchased the product on the given date). It is essential to interpret your model’s predictions for each individual instance to provide actionable insights. What should you do?
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