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Understanding the problem statement
Importing a training dataset and testing from AWS
Installing necessary libraries and understanding its use
Standard MBA or Market basket analysis
Using a predictive model to estimate the demand for a particular product
Product recommendation engine using collaborative filtering
Merging the relevant CSV files
Applying the minimum support criteria to identify most frequent item set
Eclat algorithm and Apriori algorithm
Visualizing the target variable with variation in time
Converting the variables to suitable datatypes
Visualization of a time series
Visualization using ggplot
Convert the available information to a transactional dataset
Converting the rules into a data frame
Sorting the values before recommending it to the company
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.