Implemented svr model to predict sale in a shop on friday Problem Statement A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month. Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products. Data Variable Definition User_ID User ID Product_ID Product ID Gender Sex of User Age Age in bins Occupation Occupation (Masked) City_Category Category of the City (A,B,C) Stay_In_Current_City_Years Number of years stay in current city Marital_Status Marital Status Product_Category_1 Product Category (Masked) Product_Category_2 Product may belongs to other category also (Masked) Product_Category_3 Product may belongs to other category also (Masked) Purchase Purchase Amount (Target Variable)
It takes so much time to train data on svr so I fit model only on 20000 examples and then later on 40000 examples and my score increased from 0.19 to 0.21 so its definitely gonna be good if u train on whole data.
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