This repository contains a comprehensive portfolio of statistical and visual analyses performed on the Instacart Market Basket dataset. The project leverages multiple data analysis and visualization tools including Pandas, NumPy, SQL, and Power BI to extract insights and present meaningful findings from the data.
Market basket analysis is a popular data mining technique used to understand customer purchasing behavior by analyzing the combinations of products frequently bought together. This project explores the Instacart dataset, which contains detailed information about customer orders, products, and purchasing patterns.
The goal is to uncover patterns, trends, and associations in the data through statistical analysis, SQL queries, and interactive visualizations, enabling better business decisions and marketing strategies.
The dataset used in this project is the Instacart Market Basket Dataset, which includes:
- Customer orders and order sequences
- Product details and categories
- Department and aisle information
- User purchase history
This rich dataset allows for in-depth analysis of shopping behaviors and product affinities.
- Python: Pandas and NumPy for data manipulation and statistical analysis
- SQL: For querying and aggregating data efficiently
- Power BI: For creating interactive dashboards and visualizations
- Jupyter Notebooks: For combining code, analysis, and visual output in a single document
- Data cleaning and preprocessing scripts
- Exploratory data analysis notebooks
- SQL scripts for advanced querying
- Power BI reports and dashboards for visualization
- Documentation and summaries of key findings