Implemented Data Filtering and Greedy Algorithms

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Implemented Data Filtering and Greedy Algorithms

  • Day: 2024-11-03
  • Time: 22:30 to 23:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data Filtering, Greedy Algorithm, Python, Data Manipulation, JSON

Description

Session Goal

The session aimed to enhance data processing capabilities by implementing filtering techniques and greedy algorithms for educational datasets.

Key Activities

  • Dataset Filtering: Filtered datasets to retain records with individuals having more than one exam entry, focusing on those with at least two exam records.
  • Greedy Algorithm Implementation: Developed a greedy algorithm for course selection based on unique contributions to student coverage, with a practical Python code example.
  • Data Conversion and Manipulation: Converted JSON-like strings to Python dictionaries with sets for efficient data manipulation, and improved JSON output from pandas DataFrames to represent id fields as sets.
  • LCD Course Filtering: Filtered datasets to include only courses labeled ‘LCD’ to facilitate a greedy iteration process.

Achievements

  • Successfully filtered datasets for specific criteria.
  • Implemented a greedy algorithm for course selection, enhancing decision-making in course offerings.
  • Improved data manipulation techniques using Python sets and JSON conversion.

Pending Tasks

  • Further optimization of greedy algorithm parameters for different datasets.
  • Exploration of additional filtering criteria to enhance dataset relevance.

Evidence

  • source_file=2024-11-03.sessions.jsonl, line_number=2, event_count=0, session_id=bc1e2c55694a850b898c0b36622d3a70f66ee62aabfa0cb0cf0a2402f25f7fcf
  • event_ids: []

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