Predict Demand

Demand Prediction Problem

@kaggle.veeralakrishna_predict_demand

Table List

2 tables(128.68 KB)

Train

@kaggle.veeralakrishna_predict_demand.train

104.07 KB
7560 rows
12 columns

id Id	date Date	city City	lat Lat	long Long	pop Pop	shop Shop	brand Brand	container Container	capacity Capacity	price Price	quantity Quantity
	31/01/12	Athens	37.97945	23.71622	672130	shop_1	kinder-cola	glass	500ml	0.96	13280
1	31/01/12	Athens	37.97945	23.71622	672130	shop_1	kinder-cola	plastic	1.5lt	2.86	6727
2	31/01/12	Athens	37.97945	23.71622	672130	shop_1	kinder-cola	can	330ml	0.87	9848
3	31/01/12	Athens	37.97945	23.71622	672130	shop_1	adult-cola	glass	500ml	1	20050
4	31/01/12	Athens	37.97945	23.71622	672130	shop_1	adult-cola	can	330ml	0.39	25696
5	31/01/12	Athens	37.97945	23.71622	672130	shop_1	orange-power	glass	500ml	1	15041
6	31/01/12	Athens	37.97945	23.71622	672130	shop_1	orange-power	can	330ml	0.43	34578
7	31/01/12	Athens	37.97945	23.71622	672130	shop_1	gazoza	glass	500ml	0.49	44734
8	31/01/12	Athens	37.97945	23.71622	672130	shop_1	lemon-boost	glass	500ml	0.7	18623
9	31/01/12	Athens	37.97945	23.71622	672130	shop_1	lemon-boost	plastic	1.5lt	2.21	9645


CREATE TABLE train (
  "id" DOUBLE,
  "date" VARCHAR,
  "city" VARCHAR,
  "lat" DOUBLE,
  "long" DOUBLE,
  "pop" DOUBLE,
  "shop" VARCHAR,
  "brand" VARCHAR,
  "container" VARCHAR,
  "capacity" VARCHAR,
  "price" DOUBLE,
  "quantity" DOUBLE
);