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We are asking you to predict total sales for every product and store in the next month. By solving this competition you will be able to apply and enhance your data science skills.

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.

Data fields description:

  • 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
  • date_block_num - a consecutive month number, used for convenience. January 2013 is 0, February 2013 is 1,..., October 2015 is 33
  • date - date in format dd/mm/yyyy
  • item_cnt_day - number of products sold. You are predicting a monthly amount of this measure
  • item_price - current price of an item
  • item_name - name of item
  • shop_name - name of shop
  • item_category_name - name of item category

Dependencies

import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import plotly.express as px
!pip install opendatasets --upgrade --q
import opendatasets as od
import os
import seaborn as sns

%matplotlib inline
sns.set_style('darkgrid')
import warnings