Data Description

You are provided with daily historical sales data. The task is to forecast the total amount of products sold in every shop for the test set. Note that the list of shops and products slightly changes every month. Creating a robust model that can handle such situations is part of the challenge.

File descriptions

sales_train.csv - the training set. Daily historical data from January 2013 to October 2015.

test.csv - the test set. You need to forecast the sales for these shops and products for November 2015.

sample_submission.csv - a sample submission file in the correct format.

items.csv - supplemental information about the items/products.

item_categories.csv - supplemental information about the items categories.

shops.csv- supplemental information about the shops.

Data fields

ID - an Id that represents a (Shop, Item) tuple within the test set

shop_id - unique identifier of a shop

item_id - unique identifier of a product

item_category_id - unique identifier of item category

item_cnt_day - number of products sold. You are predicting a monthly amount of this measure

item_price - current price of an item

date - date in format dd/mm/yyyy

date_block_num - a consecutive month number, used for convenience. January 2013 is 0, February 2013 is 1,...,
October 2015 is 33

item_name - name of item

shop_name - name of shop

item_category_name - name of item category

1. import data

import os
from pathlib import Path as path

root = path("./data/competitive-data-science-predict-future-sales")

os.listdir(root)

['sample_submission.csv',
 'item_categories.csv',
 'sales_train.csv',
 'items.csv',
 '.~lock.items.csv#',
 'test.csv',
 'shops.csv']