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This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
Whether you shop from meticulously planned grocery lists or let whimsy guide your grazing, our unique food rituals define who we are. Instacart, a grocery ordering and delivery app aim to make it easy to fill your refrigerator and pantry with your personal favorites and staples when you need them. After selecting products through the Instacart app, personal shoppers review your order and do the in-store shopping and delivery for you.
Instacart’s data science team plays a big part in providing this delightful shopping experience. Currently, they use transactional data to develop models that predict which products a user will buy again, try for the first time, or add to their cart next during a session. Recently, Instacart open-sourced this data - see their blog post on 3 Million Instacart Orders, Open Sourced.
In this data science project, we are going to use this anonymized data on customer orders over time to predict which previously purchased products will be in a user’s next order.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.
In this machine learning project, we will predict which coupons a customer will buy.