Teradata/jupyter-demos
Oct 2025 – Apr 2026
3 Months active
Languages Used
PythonSQL
Technical Skills
Data AnalysisDatabase ManagementGeospatial AnalysisMachine LearningNatural Language ProcessingONNX Runtime
Mohd-Hassan
PROFILE
Mohd-hassan
Developed a Travel Insights Demo Notebook for the Teradata/jupyter-demos repository, delivering an end-to-end analytics workflow that combines geospatial analysis, predictive modeling, and natural language processing to explore the relationship between travel distance and customer dissatisfaction. Leveraged Teradata ClearScape Analytics for in-database computation and ONNX Runtime for efficient, scalable inference within the notebook. The solution, implemented in Python and SQL, enables data-driven prediction of potential customer complaints and supports customer-experience optimization. This work established a reusable demo pattern, addressed specific stakeholder requirements, and demonstrated the integration of advanced analytics techniques for actionable insights in travel-related customer data.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
1,122,255
Activity Months3
Your Network
20 peopleShared Repositories
20
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Daniel HerreraMember
db255058Member
JasonD13Member
Jason DsouzaMember
JanethLMember
Nitin SreeramMember
Work History
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 highlights for Teradata/jupyter-demos: Improved ML reliability and model quality through two targeted changes. 1) Fixed RandomForest-related errors by upgrading scikit-learn to 1.5.2 (commit 5693e927bbb5d3b5d2bb12fefbcabf8fdc61d978), enhancing stability and throughput for ML tasks. 2) Refined clustering model performance by adjusting rank hyperparameters after the auto-cluster step (commit 5479c0a65629180350ccf1d6041acf5ade04cf57), leading to measurable gains in evaluation metrics.
2 Commits • 1 Features
Apr 1, 2026April 2026 highlights for Teradata/jupyter-demos: Improved ML reliability and model quality through two targeted changes. 1) Fixed RandomForest-related errors by upgrading scikit-learn to 1.5.2 (commit 5693e927bbb5d3b5d2bb12fefbcabf8fdc61d978), enhancing stability and throughput for ML tasks. 2) Refined clustering model performance by adjusting rank hyperparameters after the auto-cluster step (commit 5479c0a65629180350ccf1d6041acf5ade04cf57), leading to measurable gains in evaluation metrics.
April 2026
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025: Focused on validating and enhancing the Generative Question Answering notebook in Teradata/jupyter-demos. Implemented SQL prompt improvements, updated documentation references, and refreshed dependencies to improve reliability and alignment with Teradata docs.
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025: Focused on validating and enhancing the Generative Question Answering notebook in Teradata/jupyter-demos. Implemented SQL prompt improvements, updated documentation references, and refreshed dependencies to improve reliability and alignment with Teradata docs.
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on Teradata/jupyter-demos. Delivered a Travel Insights Demo Notebook that showcases in-database geospatial analysis, predictive modeling, and NLP on customer complaints to analyze correlation between travel distance and customer dissatisfaction and to predict potential complaints. The notebook uses Teradata ClearScape Analytics for analytics and ONNX Runtime for efficient inference, and includes the commit 10c07ad80a8a52ca42e3f5cfcb3abb4bc34234aa implementing Improving_Customer_Satisfaction_Travel_Insights per Tatiana's request. This work advances our data-to-insights capabilities and provides a reusable demo for customer-experience optimization.
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on Teradata/jupyter-demos. Delivered a Travel Insights Demo Notebook that showcases in-database geospatial analysis, predictive modeling, and NLP on customer complaints to analyze correlation between travel distance and customer dissatisfaction and to predict potential complaints. The notebook uses Teradata ClearScape Analytics for analytics and ONNX Runtime for efficient inference, and includes the commit 10c07ad80a8a52ca42e3f5cfcb3abb4bc34234aa implementing Improving_Customer_Satisfaction_Travel_Insights per Tatiana's request. This work advances our data-to-insights capabilities and provides a reusable demo for customer-experience optimization.
October 2025
Activity
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Quality Metrics
Correctness92.0%
Maintainability84.0%
Architecture88.0%
Performance84.0%
AI Usage48.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisDatabase ManagementGeospatial AnalysisJupyter NotebookJupyter NotebooksMachine LearningNatural Language ProcessingONNX RuntimePredictive ModelingPythonSQLTeradata ClearScape Analyticsdata analysisdocumentationmachine learning
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