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
Over a three-month period, contributed to the Teradata/jupyter-demos repository by developing and enhancing Jupyter Notebooks focused on data analysis, machine learning, and geospatial analytics. Built an end-to-end Travel Insights Demo Notebook using Python and SQL, integrating Teradata ClearScape Analytics and ONNX Runtime for scalable predictive modeling and natural language processing of customer complaints. Improved the Generative Question Answering notebook by refining SQL prompt generation, updating documentation, and maintaining compatibility with evolving dependencies. Addressed machine learning reliability by upgrading scikit-learn and tuning clustering model hyperparameters, resulting in improved model stability and performance across the analytics pipeline and supporting ongoing demo development.
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
Ashwin-apMember
chetan-hiraparaMember
dallasbowdenMember
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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline