1-844-696-6465 (US)        +91 77600 44484        help@dezyre.com
instacart-market-basket-analysis.jpg

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.
4.84.8

Users who bought this project also bought

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.

Prerequisites

  • Jupyter Notebook from Anaconda installation
  • R (3.3.3) and R-Studio (1.4) installation
  • At least 4 GB RAM Machine

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.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Problem Statement Overview
00:05:23
  Import Libraries
00:06:05
  Market Basket Analysis
00:04:45
  Transaction Set
00:01:32
  Association Rules
00:08:29
  Steps for creating Association Rules
00:02:06
  Read the Data Set files
00:06:04
  Explore the Data Set
00:01:08
  Which day receives most orders?
00:14:14
  Which department is purchased most?
00:12:28
  Exploratory Data Analysis
00:08:52
  Recoding the variables
00:04:46
  Graphs
00:07:39
  Prior Orders Placed
00:01:34
  Number of Items ordered
00:04:32
  Association Rule Mining
00:18:16
  Apriori Algorithm
00:02:37
  Creating Association Rules
00:09:38
  Product Recommendations
00:09:17
  Convert Rule to DataFrame
00:09:47
  Remove Redundant Rules
00:06:39
  Conclusion
00:00:15