I have customer buying data with each row specifying an item bought by customer. The problem is that even if at the same time customer buys five items then there are five different rows for it and as a result the total number of rows in data have gone too much to train. what can i do to reduce the size of data so that i can train it effectively.Just to give the context of the problem, i want to recommend products to the customers based on their buying data.
Dataset size: (7981262, 16)
Variable Description customerID unique customer ID DOB date of birth of customer Gender gender State customer's state PinCode pincode of area where customer lives transactionDate date of transaction store_code unique code of store store_description description of store till_no counter no. in the store transaction_number_by_till unique transaction number by counter, transactionDate, store_code promo_code if promotional code (offer) used in the transaction promo_description description of the offer product_code unique code of the product purchased product_description description of the product purchased sale_price_after_promo sale price of the product after applying promotion discountUsed after promo, customer used this discount(s) on transaction