Instacart Market Basket Analysis

This project was completed as part of PG Level Advanced Certification Programme in Computational Data Science coursework at Centre for Continuing Education - Indian Institute of Science in collaboration with Talent Sprint

A special thanks to Prof. Shashi Jain & Mentor Mr. Sachin Sharma

Problem Statement: Extract association rules and find groups of frequently purchased items from a large-scale grocery orders dataset.

Module: Business Analytics

Project Type: Team

Project Overview

A comprehensive analysis of Instacart's customer purchase patterns using market basket analysis techniques. The project analyzes over 3 million grocery orders from 200,000+ Instacart users to uncover shopping patterns, product associations, and temporal trends.

Dataset

• Over 3 million grocery orders
• 200,000+ Instacart users
• Data spread across multiple files:
  • orders.csv
  • products.csv
  • aisles.csv
  • departments.csv
  • order_products_train.csv

Analysis Components

1. Data Integration & Preprocessing

• Merged multiple data sources into a unified dataset
• Handled missing values and data transformations
• Created purchase frequency matrices

2. Exploratory Data Analysis

• Product frequency analysis
• Department-wise purchase patterns
• Temporal analysis (day of week, hour of day)
• Reorder behavior analysis

3. Market Basket Analysis

• Implemented Apriori algorithm
• Generated association rules
• Analyzed product co-occurrence patterns

Key Findings

Shopping Patterns

• Most popular product: Bananas
• Peak ordering hours: 10 AM - 4 PM
• Higher order frequencies during weekends
• Clear patterns in reorder behavior

Product Associations

• Generated frequent itemsets with minimum support of 0.01
• Identified strong product associations using lift metric
• Discovered valuable product grouping patterns

Technologies Used

• Python
• Pandas
• NumPy
• Seaborn
• Matplotlib
• MLxtend (for Apriori algorithm)
• Scipy
• Apriori Algorithm

Business Applications

• Inventory optimization
• Store layout recommendations
• Targeted marketing strategies
• Product recommendation systems
• Staffing optimization
• Reorder prediction

Acknowledgments

• Instacart for providing the dataset

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Note: This project is for educational purposes and uses a public dataset from Instacart - Please download this by yourself.