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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

  • Read data from large size files
  • Perform Exploratory Data Analysis (EDA)
  • Apply logic to derive insights
  • Create association rule model
  • Implementation using R

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

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.

Curriculum For This Mini Project

 
  Problem Statement Overview
05m
  Import Libraries
06m
  Market Basket Analysis
04m
  Transaction Set
01m
  Association Rules
08m
  Steps for creating Association Rules
02m
  Read the Data Set files
06m
  Explore the Data Set
01m
  Which day receives most orders?
14m
  Which department is purchased most?
12m
  Exploratory Data Analysis
08m
  Recoding the variables
04m
  Graphs
07m
  Prior Orders Placed
01m
  Number of Items ordered
04m
  Association Rule Mining
18m
  Apriori Algorithm
02m
  Creating Association Rules
09m
  Product Recommendations
09m
  Convert Rule to DataFrame
09m
  Remove Redundant Rules
06m
  Conclusion
00m