Name: Sales Forecasting WOMart Store
Creator: Kaggle
License: http://opendatacommons.org/licenses/dbcl/1.0/

About this Dataset

Sales Forecasting WOMart Store

Supplement Sales Prediction
Your Client WOMart is a leading nutrition and supplement retail chain that offers a comprehensive range of products for all your wellness and fitness needs.

WOMart follows a multi-channel distribution strategy with 350+ retail stores spread across 100+ cities.

Effective forecasting for store sales gives essential insight into upcoming cash flow, meaning WOMart can more accurately plan the cashflow at the store level.

Sales data for 18 months from 365 stores of WOMart is available along with information on Store Type, Location Type for each store, Region Code for every store, Discount provided by the store on every day, Number of Orders everyday etc.

Your task is to predict the store sales for each store in the test set for the next two months.

Train Data

ID: Unique Identifier for a row
Store_id: Unique id for each Store
Store_Type: Type of the Store
Location_Type: Type of the location where Store is located
Region_Code: Code of the Region where Store is located
Date: Information about the Date
Holiday: If there is holiday on the given Date, 1 : Yes, 0 : No
Discount: If discount is offered by store on the given Date, Yes/ No
Orders: Number of Orders received by the Store on the given Day
Sales: Total Sale for the Store on the given Day

Test Data

ID: Unique Identifier for a row
Store_id: Unique id for each Store
Store_Type: Type of the Store
Location_Type: Type of the location where Store is located
Region_Code: Code of the Region where Store is located
Date: Information about the Date
Holiday: If there is holiday on the given Date, 1 : Yes, 0 : No
Discount: If discount is offered by store on the given Date, Yes/ No

Tables

Test Final

@kaggle.shelvigarg_sales_forecasting_womart_store.test_final

195.65 kB
22,265 rows
8 columns

CREATE TABLE test_final (
  "id" VARCHAR,
  "store_id" BIGINT,
  "store_type" VARCHAR,
  "location_type" VARCHAR,
  "region_code" VARCHAR,
  "date" TIMESTAMP,
  "holiday" BIGINT,
  "discount" VARCHAR
);

Train

@kaggle.shelvigarg_sales_forecasting_womart_store.train

2.28 MB
188,340 rows
10 columns

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  -- #Order,
  "sales" DOUBLE
);