Update retail_sales_dataset.csv

retail_sales_dataset.csv

@@ -158,25 +158,6 @@ Transaction ID,Date,Customer ID,Gender,Age,Product Category,Quantity,Price per U
 166,2023-04-02,CUST166,Male,34,Clothing,4,500,2000
 167,2023-09-17,CUST167,Female,43,Clothing,3,50,150
 168,2023-02-24,CUST168,Male,53,Clothing,1,300,300
-169,2023-11-17,CUST169,Male,18,Beauty,3,500,1500
-170,2023-06-02,CUST170,Female,25,Clothing,2,25,50
-171,2023-11-24,CUST171,Female,52,Clothing,3,300,900
-172,2023-09-17,CUST172,Male,32,Beauty,2,25,50
-173,2023-11-08,CUST173,Male,64,Electronics,4,30,120
-174,2023-04-12,CUST174,Female,39,Beauty,1,300,300
-175,2023-03-20,CUST175,Female,31,Electronics,4,25,100
-176,2023-07-11,CUST176,Female,43,Beauty,2,50,100
-177,2023-03-24,CUST177,Male,45,Beauty,2,50,100
-178,2023-10-04,CUST178,Male,40,Clothing,2,30,60
-179,2023-09-29,CUST179,Male,31,Electronics,1,300,300
-180,2023-01-01,CUST180,Male,41,Clothing,3,300,900
-181,2023-11-03,CUST181,Male,19,Electronics,4,300,1200
-182,2023-06-15,CUST182,Male,62,Beauty,4,30,120
-183,2023-09-08,CUST183,Female,43,Beauty,3,300,900
-184,2023-01-10,CUST184,Male,31,Electronics,4,50,200
-185,2023-02-27,CUST185,Male,24,Clothing,1,25,25
-186,2023-07-05,CUST186,Male,20,Clothing,4,50,200
-187,2023-06-07,CUST187,Female,64,Clothing,2,50,100
 188,2023-05-03,CUST188,Male,40,Clothing,3,25,75
 189,2023-01-30,CUST189,Male,63,Beauty,1,50,50
 190,2023-05-04,CUST190,Female,60,Beauty,3,30,90