Comprehensive YouTube viewing pattern analysis (2020-2025) with temporal insights, content categorization, behavioral detection, and personalized recommendations. Features multi-format exports and Obsidian integration.

2 Open Issues Need Help Last updated: Aug 3, 2025

Open Issues Need Help

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Data Science Machine Learning

AI Summary: Implement a comprehensive test suite for a YouTube data analysis project using pytest, including unit tests for core functions, integration tests for the data pipeline, schema validation tests for JSON inputs, and performance testing. The goal is to achieve at least 75% test coverage and integrate the tests into a CI/CD workflow using GitHub Actions.

Complexity: 4/5
good first issue

Comprehensive YouTube viewing pattern analysis (2020-2025) with temporal insights, content categorization, behavioral detection, and personalized recommendations. Features multi-format exports and Obsidian integration.

Python
Data Science Machine Learning

AI Summary: Refactor the YouTube analysis project to replace hardcoded file paths with a centralized configuration module (`config/settings.py`). This involves creating the configuration module, updating all seven analysis scripts to use it, adding environment variable support for path overrides, and documenting the changes. The goal is to improve maintainability, testability, and portability of the project.

Complexity: 3/5
enhancement good first issue

Comprehensive YouTube viewing pattern analysis (2020-2025) with temporal insights, content categorization, behavioral detection, and personalized recommendations. Features multi-format exports and Obsidian integration.

Python