- The target of this research analysis is to manipulate and interpret meaningful patterns in the given data that could give us the ability to make precise, data-driven sales management decisions. Hence maximize the sales capacity, and also meet customer need.
- Behavior data for 2 month (from October to November 2019 ) of a large multi-category online store (13.67 GB)
Because of the large dataset, Dask dataframes is used to read and write the csv data in Parquet formats, to facilitate computation speed and memory.
Each row in the file represents an event. All events are related to products and users. There are different types of events.
- event_time
- Time when event happened at (in UTC).
- event_type
- view - a user viewed a product
- cart - a user added a product to shopping cart
- removefromcart - a user removed a product from shopping cart
- purchase - a user purchased a product
- product_id
- ID of a product
- category_id
- Product's category ID
- category_code
- Product's category taxonomy (code name)
- brand
- Downcased string of brand name. Can be missed.
- price
- Float price of a product.
- user_id
- Permanent user ID.
- user_session
- Temporary user's session ID. Same for each user's session. Changed every time user come back to online store from a long pause.
- ADM-HW2.ipynb
- Jupyter notebook script that contains the solutions to all the research questions