Developed Product Taxonomy and Data Processing Pipeline

• Day: 2024-08-28
• Time: 21:30 to 22:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Processing, Product Taxonomy, Feature Engineering, Python, Web Scraping

Description

Session Goal

The session aimed to develop a comprehensive product taxonomy and establish a robust data processing pipeline for product comparison and analysis.

Key Activities

• Merging Data Files: Implemented Python scripts to merge price, product, and store data files, ensuring outputs are saved in the correct directories.
• Disk Usage Analysis: Utilized the du command to assess disk usage across directories, optimizing storage management.
• Web Scraping Optimization: Applied limits on store counts in the PreciosClarosSpider class to enhance data scraping efficiency.
• Product Data Management: Addressed challenges in managing product data using techniques like text normalization, fuzzy matching, and clustering.
• Product Taxonomy Development: Planned and executed strategies for developing a product taxonomy using classification techniques and KNN, focusing on feature engineering and data preparation.

Achievements

• Successfully merged diverse data files, optimizing data management.
• Enhanced web scraping processes by implementing store count limits.
• Developed a structured approach for product taxonomy and feature engineering, setting the stage for effective product comparison.

Pending Tasks

• Further refinement of the product taxonomy and feature engineering processes.
• Implementation and evaluation of the KNN model for product comparison.

Evidence

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• event_ids: []