Data Science Project - Instacart Market Basket Analysis

Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.


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What will you learn

  • 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

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 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.

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Curriculum For This Mini Project

  Problem Statement Overview
  Import Libraries
  Market Basket Analysis
  Transaction Set
  Association Rules
  Steps for creating Association Rules
  Read the Data Set files
  Explore the Data Set
  Which day receives most orders?
  Which department is purchased most?
  Exploratory Data Analysis
  Recoding the variables
  Prior Orders Placed
  Number of Items ordered
  Association Rule Mining
  Apriori Algorithm
  Creating Association Rules
  Product Recommendations
  Convert Rule to DataFrame
  Remove Redundant Rules