Goal of the Competition

In this “getting started” competition, you’ll use time-series forecasting to forecast store sales on data from Corporation Favorite, a large Ecuadorian-based grocery retailer.Specifically, you'll build a model that more accurately predicts the unit sales for thousands of items sold at different Favorite stores. You'll practice your machine learning skills with an approachable training dataset of dates, store, and item information, promotions, and unit sales.

link : https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview

Data

train: The data that the model will be trained on.

• The training data, comprising time series of features store_nbr, family, and onpromotionas well as the target sales.

• store_nbr identifies the store at which the products are sold.

• family identifies the type of product sold.

• sales give the total sales for a product family at a particular store at a given date. Fractional values are possible since products can be sold in fractional units (1.5 kg of cheese, for instance, as opposed to 1 bag of chips).

• onpromotion gives the total number of items in a product family that were being promoted at a store at a given date.

• Shape —> (3000888, 6)

  

test: The data on which we will make predictions for the competition.

The test data, having the same features as the training data. You will predict the target sales for the dates in this file.

• The dates in the test data are for the 15 days after the last date in the training data.
• Shape —> (28512, 5)


stores.csv

• Store metadata, including city, state, type, and cluster.
• cluster is a grouping of similar stores.
• Shape —> (54, 5)