Sales Forecasting WOMart Store

Forescast Sales with Exhaustive Retail Store Data

@kaggle.shelvigarg_sales_forecasting_womart_store

Table List

2 tables(2.36 MB)

Train

@kaggle.shelvigarg_sales_forecasting_womart_store.train

2.17 MB
188340 rows
10 columns

id ID	store_id Store Id	store_type Store Type	location_type Location Type	region_code Region Code	date Date	holiday Holiday	discount Discount	n__order #Order	sales Sales
T1000001	1	S1	L3	R1	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	9	7011.839999999999
T1000002	253	S4	L2	R1	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	60	51789.12
T1000003	252	S3	L2	R1	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	42	36868.2
T1000004	251	S2	L3	R1	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	23	19715.16
T1000005	250	S2	L3	R4	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	62	45614.52
T1000006	249	S1	L3	R2	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	39	34211.22
T1000007	248	S1	L1	R2	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	40	35352.66
T1000008	247	S1	L1	R3	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	64	52650
T1000009	246	S3	L1	R3	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	62	42633.78
T1000010	254	S4	L1	R1	Mon Jan 01 2018 00:00:00 GMT+0000 (Coordinated Universal Time)	1	Yes	87	62572.8


CREATE TABLE train (
  "id" VARCHAR,
  "store_id" BIGINT,
  "store_type" VARCHAR,
  "location_type" VARCHAR,
  "region_code" VARCHAR,
  "date" TIMESTAMP,
  "holiday" BIGINT,
  "discount" VARCHAR,
  "n__order" BIGINT,
  "sales" DOUBLE
);