On-Time Delivery

The foundation of this analytical venture is a robust dataset comprising.

@kaggle.willianoliveiragibin_on_time_delivery

Table List

1 table(141.7 KB)

Train New

@kaggle.willianoliveiragibin_on_time_delivery.train_new

141.7 KB
10999 rows
12 columns

id ID	warehouse_block Warehouse Block	mode_of_shipment Mode Of Shipment	customer_care_calls Customer Care Calls	customer_rating Customer Rating	cost_of_the_product Cost Of The Product	prior_purchases Prior Purchases	product_importance Product Importance	gender Gender	discount_offered Discount Offered	weight_in_gms Weight In Gms	reached_on_time_y_n Reached.on.Time Y.N
1	D	Flight	4	2	177	3	low	F	44	1233	1
2	F	Flight	4	5	216	2	low	M	59	3088	1
3	A	Flight	2	2	183	4	low	M	48	3374	1
4	B	Flight	3	3	176	4	medium	M	10	1177	1
5	C	Flight	2	2	184	3	medium	F	46	2484	1
6	F	Flight	3	1	162	3	medium	F	12	1417	1
7	D	Flight	3	4	250	3	low	F	3	2371	1
8	F	Flight	4	1	233	2	low	F	48	2804	1
9	A	Flight	3	4	150	3	low	F	11	1861	1
10	B	Flight	3	2	164	3	medium	F	29	1187	1


CREATE TABLE train_new (
  "id" BIGINT,
  "warehouse_block" VARCHAR,
  "mode_of_shipment" VARCHAR,
  "customer_care_calls" BIGINT,
  "customer_rating" BIGINT,
  "cost_of_the_product" BIGINT,
  "prior_purchases" BIGINT,
  "product_importance" VARCHAR,
  "gender" VARCHAR,
  "discount_offered" BIGINT,
  "weight_in_gms" BIGINT,
  "reached_on_time_y_n" BIGINT
);