← Back to Projects

Instacart — EDA & Data Cleaning

Exploratory analysis using multiple related tables to understand customer purchase patterns and reorder behavior. This project highlights data validation, cleaning decisions, and insight-driven analysis.

Context & goal

Explore a retail grocery dataset and answer questions about ordering behavior, focusing on patterns that could inform product and customer decisions.

Approach

• Data validation: checked keys, joins, and schema assumptions across tables
• Cleaning: handled missing values and duplicates with documented rationale
• EDA: explored ordering time patterns, basket behavior, and reorder-related signals
• Communication: summarized insights clearly, including limitations

Links

• Repository: github.com/Raul-Dom/Instacart_eda
• Notebook: instacart_eda_clean.ipynb

Last updated: