PredictFutureSales_EDA

EDA of PredictFutureSales competition on kaggle 📊 📈 📉

Link: https://www.kaggle.com/code/kseniyavishnevskaya/predict-future-sales-eda

We are provided with daily historical sales data. Our task is to analyse the data and highlight interesting features.

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

Tools: Python🐍 - pandas, numpy, plotly, matplotlib, seaborn