Implemented entity resolution with Dedupe in Jupyter

📅 2025-03-04 — Session: Implemented entity resolution with Dedupe in Jupyter

🕒 22:05–22:40
🏷️ Labels: Entity Resolution, Dedupe, Data Cleaning, Jupyter, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to implement robust entity resolution techniques using Dedupe.io within Jupyter Notebooks, focusing on deduplication and data cleaning processes.

Key Activities

• Explored various algorithms and methods for entity resolution, including both probabilistic and deterministic techniques.
• Implemented Dedupe.io, a probabilistic entity resolution algorithm, to handle large datasets with missing values using active learning, fuzzy matching, and clustering.
• Addressed command-line argument conflicts in Jupyter Notebooks caused by the optparse library when using the dedupe library.
• Adapted a Python script for deduplication within Jupyter, detailing steps from data loading to saving cleaned output.
• Corrected syntax for variable definitions in Dedupe 3.0, transitioning from dictionary definitions to direct variable objects.
• Resolved a ZeroDivisionError in Dedupe by converting empty strings to None before processing.

Achievements

• Successfully implemented entity resolution techniques in Jupyter using Dedupe.io.
• Resolved technical issues related to command-line arguments and variable definitions in Dedupe 3.0.
• Provided a complete working example for deduplication in Jupyter.

Pending Tasks

• Further testing and validation of the deduplication process on diverse datasets to ensure robustness and accuracy.