Enhanced Data Processing with Fuzzy Matching

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📅 2024-08-12 — Session: Enhanced Data Processing with Fuzzy Matching

🕒 00:20–23:30
🏷️ Labels: Python, Fuzzy Matching, Data Processing, Pandas, Scikit-Learn
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance data processing capabilities by refining fuzzy matching techniques and ensuring robust data handling in Python.

Key Activities

  • Updated run_predict_save Function: Added an overwrite argument to manage file creation and loading, ensuring efficient data processing.
  • Handled Scikit-learn Version Inconsistencies: Addressed warnings related to version mismatches, implementing strategies for consistent model saving and loading.
  • Inverted Matcher Datasets: Developed a Python script to invert matcher datasets by comparing voter DataFrames against a merged list of unique names.
  • Custom Merging Strategy: Implemented a strategy using Levenshtein distance for fuzzy matching with a one-character tolerance, utilizing the rapidfuzz library.
  • Threshold in Fuzzy Matching: Modified the find_best_match function to include a threshold, ensuring matches with a score above 95.
  • Chunk Processing with Fuzzy Matching: Utilized the fuzzywuzzy library for chunk processing, saving intermediate results to CSV files.
  • Avoided SettingWithCopyWarning: Used .copy() and .loc[] for safe DataFrame slice modifications, preventing common pandas warnings.

Achievements

  • Enhanced data processing techniques with improved fuzzy matching strategies.
  • Ensured robust handling of data and warnings in Python.

Pending Tasks

  • Further optimization of fuzzy matching algorithms for larger datasets.
  • Explore additional libraries for performance improvements.

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