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Instacart Market Basket Analysis

In this project, we are going to build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.
Event Date
Jul - 2017
07:00am - 12:00pm PST
What are the prerequisites for this project?
  • Jupyter Notebook from Anaconda installation
  • R (3.3.3) and R-Studio (1.4) installation
  • At least 4 GB RAM Machine
  • This project assumes that you have a good knowledge of Data Science and the R language. If not - we recommend you to take the Data Science in R Programming course first.

What will you learn

  • Read data from large size files
  • Perform EDA
  • Apply logic to derive insights
  • Create association rule model
  • Implementation using either R or Python

Project Description

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 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.



A Data Scientist with 10 + years experience into Predictive Modeling, Machine Learning and Text Mining and Sentiment Analysis in Healthcare, Retail and E-Commerce Domain. Holds a Patent as lead Inventor (current status-Pending) for developing a new algorithm to address the issues in assortment optimization and stor see more...

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