📅 2024-07-12 — Session: Implemented Many-to-Many Search with Fuzzy Matching
🕒 03:05–03:55
🏷️ Labels: Fuzzy Matching, Elasticsearch, Python, Data Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to implement a many-to-many search mechanism linking individuals to relevant pages using Elasticsearch and fuzzy matching techniques.
Key Activities
- Developed a structured approach for many-to-many search using data preparation, NLP feature extraction, and Elasticsearch indexing and querying.
- Implemented fuzzy matching between names in DataFrames using Python’s pandas and fuzzywuzzy libraries, enabling efficient entity recognition and matching.
- Created Python code snippets to handle lists of tuples in DataFrames, focusing on matching entities from two datasets.
- Utilized Elasticsearch for optional indexing and efficient querying of matched entities.
Achievements
- Successfully implemented a fuzzy matching function in Python to search for names within a cleaned text column of a DataFrame.
- Completed the workflow for loading data, defining a matching function, applying it, and displaying results.
Pending Tasks
- Further optimization of the Elasticsearch indexing process for improved performance.
- Exploration of additional NLP techniques to enhance feature extraction and matching accuracy.