Machine Learning-based Sales Forecasting for a Leading Russian Software Firm: A Case Study of 1C Company
In this project, I worked with a challenging time-series dataset consisting of daily sales data, provided by one of the largest Russian software firms - 1C Company.
Competition Link: https://www.kaggle.com/competitions/competitive-data-science-predict-future-sales/overview
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