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https://github.com/aurascoper/instacarteda

https://github.com/aurascoper/instacarteda

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Repository

Basic Info
  • Host: GitHub
  • Owner: aurascoper
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 525 KB
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Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

Instacart Market Basket Analysis

This repository contains an exploratory data analysis (EDA) notebook focused on the Instacart Market Basket Analysis dataset. The goal of this notebook is to understand customer purchasing behavior and extract useful insights to support recommendation systems and market strategy.

📘 Notebook Overview

The notebook EDA.ipynb includes:

  • Data loading and inspection
  • Summary statistics and missing value analysis
  • Customer ordering patterns
  • Product and aisle popularity
  • Visualizations using matplotlib and pandas

📦 Dependencies

Make sure you have the following Python libraries installed:

bash pip install pandas matplotlib

Imports used:

python import pandas as pd import matplotlib.pyplot as plt

📂 Data

The dataset used is the Instacart Market Basket Analysis dataset. You can find it on Kaggle.

Typical files include:

  • orders.csv
  • order_products_prior.csv
  • products.csv
  • aisles.csv
  • departments.csv

Make sure these files are available in the expected directory when running the notebook.

📈 Sample Visuals

The notebook generates plots to illustrate:

  • Distribution of orders by day of week and hour of day
  • Most commonly reordered products
  • Frequency of purchases per department and aisle

🧠 Insights

Initial EDA reveals trends in consumer shopping habits, such as:

  • Peak ordering hours
  • Frequently reordered items
  • Category-wise product preferences

These insights can be foundational for machine learning applications like personalized recommendations.

🔧 How to Use

  1. Clone this repo:

bash git clone https://github.com/aurascoper/instacart-eda.git

  1. Navigate to the folder and open the notebook:

bash jupyter notebook EDA.ipynb

  1. Run all cells to reproduce the analysis.

📝 License

This project is open-source and available under the MIT License.

Owner

  • Login: aurascoper
  • Kind: user

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