nikbearbrown/INFO_7390_Art_and_Science_of_Data
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Backend DevelopmentCLIPCausal ForestCausal InferenceComputer VisionData Analysis
Sai Suraj Suravajhala
PROFILE
Sai Suraj Suravajhala
Contributed to the INFO_7390_Art_and_Science_of_Data repository by delivering targeted enhancements in causal analytics and generative AI. Developed a wine quality analysis feature using Python and Jupyter Notebook, applying propensity score matching and causal forest methods to estimate heterogeneous treatment effects. Introduced generative AI capabilities for image analysis, leveraging CLIP and image-to-image diffusion on datasets such as CIFAR-10 and Fashion-MNIST. Initiated the setup for an Intelligent Financial Insights Platform, establishing the project structure and initial application skeleton. Improved repository clarity by removing outdated notebooks, ensuring a more maintainable codebase and streamlined collaboration for future data science and machine learning projects.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
3
Lines of code
39,048
Activity Months1
Your Network
159 peopleShared Repositories
18
Work History
April 2025
9 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for nikbearbrown/INFO_7390_Art_and_Science_of_Data. Delivered targeted causal analytics enhancements, introduced Generative AI capabilities for image analysis, kicked off a scalable Intelligent Financial Insights Platform, and improved repository clarity through cleanup of outdated notebooks. Focused on features with clear business value and robust implementation across data science and platform setup.
9 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for nikbearbrown/INFO_7390_Art_and_Science_of_Data. Delivered targeted causal analytics enhancements, introduced Generative AI capabilities for image analysis, kicked off a scalable Intelligent Financial Insights Platform, and improved repository clarity through cleanup of outdated notebooks. Focused on features with clear business value and robust implementation across data science and platform setup.
April 2025
Activity
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Quality Metrics
Correctness84.4%
Maintainability82.2%
Architecture84.4%
Performance82.2%
AI Usage36.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Backend DevelopmentCLIPCausal ForestCausal InferenceComputer VisionData AnalysisData ScienceData VisualizationDeep LearningEconMLGenerative AIImage AnalysisJupyter NotebookMachine LearningMatplotlib
Repositories Contributed To
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