Jingyong14/HPDP02
May 2025 – Jul 2025
3 Months active
Languages Used
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Big DataBig Data ProcessingCode OrganizationDaskData AnalysisData Cleaning
Ong Yi Yan
PROFILE
Ong Yi Yan
Over three months, Ong Yan contributed to the Jingyong14/HPDP02 repository by developing an end-to-end sentiment analysis pipeline for Malaysia tourism, integrating Reddit data collection, VADER sentiment scoring, and model training with Naive Bayes and LSTM. He established a reproducible workflow with robust error handling and clear documentation, enabling reliable data processing and model comparison. Ong also standardized artifact management and improved onboarding through detailed technical writing and lifecycle updates. His work leveraged Python, Pandas, and Elasticsearch, demonstrating depth in big data engineering, natural language processing, and cross-library benchmarking, while ensuring transparency, reproducibility, and audit readiness across the project.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
79Total
Bugs
5
Commits
79
Features
10
Lines of code
42,565
Activity Months3
Your Network
74 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for Jingyong14/HPDP02: Delivered an end-to-end Malaysia Tourism Sentiment Analysis Pipeline, with data collection from Reddit, VADER-based sentiment scoring, training Naive Bayes and LSTM models, and visualization in Elasticsearch and Kibana dashboards. Implemented robust error handling and logging for reliability; performed model performance comparison and surfaced results in dashboards; established a reproducible architecture in HPDP02 with clear commit history.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for Jingyong14/HPDP02: Delivered an end-to-end Malaysia Tourism Sentiment Analysis Pipeline, with data collection from Reddit, VADER-based sentiment scoring, training Naive Bayes and LSTM models, and visualization in Elasticsearch and Kibana dashboards. Implemented robust error handling and logging for reliability; performed model performance comparison and surfaced results in dashboards; established a reproducible architecture in HPDP02 with clear commit history.
July 2025
June 2025
8 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for Jingyong14/HPDP02: Delivered critical documentation and artifact-management improvements that increase transparency, reproducibility, and accessibility of the big data processing workflow across Pandas, Dask, and Polars. Implemented precise documentation updates, standardised logbook artifacts, and streamlined lifecycle processes. Business value includes faster onboarding, audit readiness, and more reliable cross-library comparisons.
June 2025
8 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for Jingyong14/HPDP02: Delivered critical documentation and artifact-management improvements that increase transparency, reproducibility, and accessibility of the big data processing workflow across Pandas, Dask, and Polars. Implemented precise documentation updates, standardised logbook artifacts, and streamlined lifecycle processes. Business value includes faster onboarding, audit readiness, and more reliable cross-library comparisons.
May 2025
70 Commits • 7 Features
May 1, 2025May 2025 performance summary for Jingyong14/HPDP02 (Group 6 HDDP). The month focused on establishing a solid project foundation, cleaning and standardizing artifacts, and improving documentation to enable a smooth final submission. Key outcomes include the initial scaffolding and bulk asset uploads for Group 6 HDDP, removal of obsolete tooling and directory cleanup, and comprehensive renaming/standardization of final reports and notebooks. Additionally, Readme and big_data documentation were updated across multiple batches to improve reproducibility and stakeholder clarity, while new Group 6 assets were added to support delivery readiness.
70 Commits • 7 Features
May 1, 2025May 2025 performance summary for Jingyong14/HPDP02 (Group 6 HDDP). The month focused on establishing a solid project foundation, cleaning and standardizing artifacts, and improving documentation to enable a smooth final submission. Key outcomes include the initial scaffolding and bulk asset uploads for Group 6 HDDP, removal of obsolete tooling and directory cleanup, and comprehensive renaming/standardization of final reports and notebooks. Additionally, Readme and big_data documentation were updated across multiple batches to improve reproducibility and stakeholder clarity, while new Group 6 assets were added to support delivery readiness.
May 2025
Activity
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Quality Metrics
Correctness97.4%
Maintainability97.4%
Architecture97.6%
Performance97.2%
AI Usage20.6%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
API IntegrationBig DataBig Data ProcessingCode OrganizationDaskData AnalysisData CleaningData CollectionData EngineeringData ProcessingData TransformationData VisualizationDockerDocumentationElasticsearch
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