OpenBB-finance/OpenBB
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisEconometricsPandasPythonStatistical ModelingStatsmodels
Hemu
PROFILE
Hemu
Hemanth M worked on the OpenBB-finance/OpenBB repository, where he developed a Variance Inflation Factor (VIF) calculation feature for the econometrics module. This addition enables users to assess multicollinearity in regression models by computing VIF values and returning them as a pandas DataFrame. He implemented the function using Python and Statsmodels, ensuring robust integration with existing data analysis workflows. Hemanth updated unit tests and documentation to cover usage scenarios, edge cases, and interpretation, supporting user adoption and reliability. The work demonstrated a solid grasp of statistical modeling and disciplined Git-based release practices, delivering a focused and well-documented enhancement.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
94
Activity Months1
Your Network
28 peopleSame Organization
@buffalo.edu16
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024In 2024-11, delivered the Variance Inflation Factor (VIF) calculation in the econometrics module for OpenBB, enabling users to quantify multicollinearity in regression models. The feature introduces a new function that computes VIF values and returns them as a DataFrame, with updated tests and accompanying documentation to reflect usage, edge cases, and interpretation. No major bugs were reported this month. Impact: enhances model diagnostics and reliability of econometric analyses, empowering users to build more robust financial models and make better-informed decisions. Tech stack and skills demonstrated: Python, pandas DataFrame handling, unit testing, documentation, and disciplined Git-based release practices. Key commit: 6fa993d15a3176aaf3053de15ec4a56e9ef1d164
1 Commits • 1 Features
Nov 1, 2024In 2024-11, delivered the Variance Inflation Factor (VIF) calculation in the econometrics module for OpenBB, enabling users to quantify multicollinearity in regression models. The feature introduces a new function that computes VIF values and returns them as a DataFrame, with updated tests and accompanying documentation to reflect usage, edge cases, and interpretation. No major bugs were reported this month. Impact: enhances model diagnostics and reliability of econometric analyses, empowering users to build more robust financial models and make better-informed decisions. Tech stack and skills demonstrated: Python, pandas DataFrame handling, unit testing, documentation, and disciplined Git-based release practices. Key commit: 6fa993d15a3176aaf3053de15ec4a56e9ef1d164
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisEconometricsPandasPythonStatistical ModelingStatsmodels
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