Exploratory data analysis with detailed visualizations in a top-down manner, exploring every attribute with respect to sales and revenue and performed a time-series predictive analysis model and plot using Auto Regressive Integrated Moving Average (ARIMA) modelling. The dataset contains records from Jan 2013 to October 2015. I have predicted the approximate sales line for November 2015 using historical data.
Predict Future Sales - Kaggle https://www.kaggle.com/c/competitive-data-science-predict-future-sales
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.
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