oracle/accelerated-data-science
Jan 2026 – Jun 2026
5 Months active
Languages Used
Python
Technical Skills
Pythondata analysisdata sciencemachine learningmodel explainabilitytesting
Sumit Kumar Jangir
PROFILE
Sumit Kumar Jangir
Contributed to the oracle/accelerated-data-science repository by building and enhancing forecasting and regression capabilities for data science workflows. Developed time-series forecasting features including Theta and ETS models with automated seasonality detection, explainability via SHAP for LGBM/XGBoost, and robust anomaly detection testing. Improved reporting by implementing dynamic, spec-driven report titles and expanded evaluation metrics with SMAPE integration. Introduced a Regression Operator Framework supporting multiple models such as KNN, XGBoost, and Random Forest, enabling configurable preprocessing and model transparency. Leveraged Python, machine learning, and model deployment skills to deliver maintainable, testable solutions that improve forecast accuracy, reliability, and reporting flexibility.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
7
Lines of code
8,386
Activity Months5
Your Network
886 peopleSame Organization
@oracle.com866
Work History
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for repository oracle/accelerated-data-science. This month delivered foundational forecasting enhancements and expanded evaluation metrics to improve cross-series forecast accuracy and reliability. 1) Key features delivered - Forecasting: Per-series auto model selection introduced with a choice between meta-learning and backtesting strategies. This enables automatic, per-series model selection to optimize forecasting performance across heterogeneous time series. Commits: 8f2a71ba0504ff2591579f2888a00755ecb80702 ("Added forecasting auto-select-series-basic (#1382)"). - SMAPE metric: Added SMAPE (Symmetric Mean Absolute Percentage Error) as an evaluation metric in the regression operator, including enum updates, computation logic, and tests. Commit: 6652c73818913c27e72ac6171ec4778117f91bf6 ("Added SMAPE error metric in regression operator (#1384)"). 2) Major bugs fixed - No major bugs fixed reported for this repo in June 2026. 3) Overall impact and accomplishments - Strengthened forecasting automation and evaluation by enabling per-series model selection and robust SMAPE-based evaluation, improving forecast accuracy, reliability, and user confidence. - The changes lay groundwork for broader adoption of meta-learning/backtesting strategies in production forecasting workflows and clearer performance signaling through SMAPE. 4) Technologies/skills demonstrated - Python-based ML workflow enhancements, per-series model selection architecture, and integration of meta-learning/backtesting strategies. - Implementation of SMAPE with enum updates, computation logic, and test coverage. - Emphasis on code quality, testing, and maintainability to support scalable forecasting pipelines.
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for repository oracle/accelerated-data-science. This month delivered foundational forecasting enhancements and expanded evaluation metrics to improve cross-series forecast accuracy and reliability. 1) Key features delivered - Forecasting: Per-series auto model selection introduced with a choice between meta-learning and backtesting strategies. This enables automatic, per-series model selection to optimize forecasting performance across heterogeneous time series. Commits: 8f2a71ba0504ff2591579f2888a00755ecb80702 ("Added forecasting auto-select-series-basic (#1382)"). - SMAPE metric: Added SMAPE (Symmetric Mean Absolute Percentage Error) as an evaluation metric in the regression operator, including enum updates, computation logic, and tests. Commit: 6652c73818913c27e72ac6171ec4778117f91bf6 ("Added SMAPE error metric in regression operator (#1384)"). 2) Major bugs fixed - No major bugs fixed reported for this repo in June 2026. 3) Overall impact and accomplishments - Strengthened forecasting automation and evaluation by enabling per-series model selection and robust SMAPE-based evaluation, improving forecast accuracy, reliability, and user confidence. - The changes lay groundwork for broader adoption of meta-learning/backtesting strategies in production forecasting workflows and clearer performance signaling through SMAPE. 4) Technologies/skills demonstrated - Python-based ML workflow enhancements, per-series model selection architecture, and integration of meta-learning/backtesting strategies. - Implementation of SMAPE with enum updates, computation logic, and test coverage. - Emphasis on code quality, testing, and maintainability to support scalable forecasting pipelines.
June 2026
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 monthly summary for developer work at oracle/accelerated-data-science. Delivered a Regression Operator Framework for Tabular Supervised Learning, enabling rapid experimentation with multiple regression models and built-in explainability. This work strengthens the platform’s capabilities for tabular data modeling and reporting, driving faster time-to-insight for data science teams.
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 monthly summary for developer work at oracle/accelerated-data-science. Delivered a Regression Operator Framework for Tabular Supervised Learning, enabling rapid experimentation with multiple regression models and built-in explainability. This work strengthens the platform’s capabilities for tabular data modeling and reporting, driving faster time-to-insight for data science teams.
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for oracle/accelerated-data-science focusing on forecasting enhancements, configuration normalization, and performance improvements to enable faster, more reliable business decisions.
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for oracle/accelerated-data-science focusing on forecasting enhancements, configuration normalization, and performance improvements to enable faster, more reliable business decisions.
March 2026
February 2026
2 Commits • 1 Features
Feb 1, 2026Month 2026-02: Delivered targeted fixes and configurability enhancements in oracle/accelerated-data-science to improve forecast reliability and reporting accuracy. Key features delivered include a bug fix to AutoMLX and Theta forecaster for frequency normalization and forecast value handling, and a dynamic report title mechanism for Anomaly and Forecaster Operators driven by the specification. These changes reduce manual intervention, increase confidence in forecasts, and enable better business decisions through more accurate, configurable reports. Technologies demonstrated include Python-based forecasting components (AutoMLX, Theta), frequency normalization, spec-driven configuration, and Git-based collaboration. Overall impact: improved forecast stability, reduced risk in planning, and increased maintainability of reporting pipelines.
February 2026
2 Commits • 1 Features
Feb 1, 2026Month 2026-02: Delivered targeted fixes and configurability enhancements in oracle/accelerated-data-science to improve forecast reliability and reporting accuracy. Key features delivered include a bug fix to AutoMLX and Theta forecaster for frequency normalization and forecast value handling, and a dynamic report title mechanism for Anomaly and Forecaster Operators driven by the specification. These changes reduce manual intervention, increase confidence in forecasts, and enable better business decisions through more accurate, configurable reports. Technologies demonstrated include Python-based forecasting components (AutoMLX, Theta), frequency normalization, spec-driven configuration, and Git-based collaboration. Overall impact: improved forecast stability, reduced risk in planning, and increased maintainability of reporting pipelines.
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for oracle/accelerated-data-science: Delivered major time-series forecasting enhancements, added model explainability, and strengthened testing to improve reliability and business value. Implemented Theta Forecaster with seasonal period detection, frequency normalization, and explanation reporting; introduced ETS Forecaster for traditional forecasting; integrated SHAP explainability for LGBM/XGBoost forecasting models; and expanded anomaly detection tests to ensure robust coverage. The changes enhance forecast accuracy, interpretability for business users, and testing rigor, enabling faster, more confident decision-making and more maintainable code.
4 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for oracle/accelerated-data-science: Delivered major time-series forecasting enhancements, added model explainability, and strengthened testing to improve reliability and business value. Implemented Theta Forecaster with seasonal period detection, frequency normalization, and explanation reporting; introduced ETS Forecaster for traditional forecasting; integrated SHAP explainability for LGBM/XGBoost forecasting models; and expanded anomaly detection tests to ensure robust coverage. The changes enhance forecast accuracy, interpretability for business users, and testing rigor, enabling faster, more confident decision-making and more maintainable code.
January 2026
Activity
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Quality Metrics
Correctness85.0%
Maintainability81.6%
Architecture83.4%
Performance81.6%
AI Usage43.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ScienceMachine LearningModel DeploymentPythonPython programmingRegression Analysisdata analysisdata scienceforecastingmachine learningmodel explainabilityreport generationtestingtime series forecastingtime-series forecasting
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