combined_customer_data.csv

customer_id,age,gender,income,location,product_id,product_category,purchase_amount,purchase_date
1,34,Female,75000,New York,101,Electronics,300,2023-01-15
1,34,Female,75000,New York,106,Clothing,70,2023-03-01
2,28,Male,60000,San Francisco,102,Clothing,50,2023-01-20
2,28,Male,60000,San Francisco,107,Grocery,30,2023-03-05
3,45,Female,85000,Chicago,103,Grocery,20,2023-01-25
3,45,Female,85000,Chicago,108,Furniture,200,2023-03-10
4,39,Male,95000,New York,104,Electronics,150,2023-02-10
4,39,Male,95000,New York,109,Electronics,100,2023-03-15
5,30,Female,70000,Los Angeles,105,Furniture,500,2023-02-15
5,30,Female,70000,Los Angeles,110,Clothing,60,2023-03-20