d2cml-ai/CausalAI-Course
Nov 2024 – Dec 2024
2 Months active
Languages Used
JuliaJupyter NotebookMarkdownPythonRSQL
Technical Skills
Academic ResearchCausal InferenceCode OrganizationData AnalysisData PreprocessingData Science
Pepix2345
PROFILE
Pepix2345
Contributed to the d2cml-ai/CausalAI-Course repository by developing research artifacts and analytical tools for machine learning and causal inference workflows. Delivered end-to-end studies such as an Airbnb price prediction report and advanced wage data analysis, leveraging Python, R, and Julia for modeling, data preprocessing, and visualization. Implemented double machine learning and propensity score matching, and enhanced reproducibility through notebook execution fixes and kernel updates. Improved project organization by restructuring assignment directories and managing asset lifecycles. Developed program impact analysis workflows using econometric and machine learning models, emphasizing robust data analysis, technical writing, and clear documentation to support academic and applied research.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
17Total
Bugs
1
Commits
17
Features
7
Lines of code
20,547
Activity Months2
Your Network
91 peopleWork History
December 2024
9 Commits • 3 Features
Dec 1, 2024Concise monthly summary for 2024-12: Delivered feature-rich enhancements and analytical capabilities in the CausalAI-Course repository, strengthening asset lifecycle, data availability, and causal inference tooling. The work focused on business value through scalable asset management and robust program impact analysis.
9 Commits • 3 Features
Dec 1, 2024Concise monthly summary for 2024-12: Delivered feature-rich enhancements and analytical capabilities in the CausalAI-Course repository, strengthening asset lifecycle, data availability, and causal inference tooling. The work focused on business value through scalable asset management and robust program impact analysis.
December 2024
November 2024
8 Commits • 4 Features
Nov 1, 2024November 2024 focused on delivering research artifacts and enabling reproducible ML/causal inference work in the CausalAI-Course repo. Key features delivered include the Airbnb price prediction study report and advanced wage data analysis notebooks (Julia, R, Python) with causal ML components; major bug fix: notebook execution issues and Julia kernel updated; organizational improvements: Group5_Assignment3_4 directory restructure; and a comprehensive Double Machine Learning report.
November 2024
8 Commits • 4 Features
Nov 1, 2024November 2024 focused on delivering research artifacts and enabling reproducible ML/causal inference work in the CausalAI-Course repo. Key features delivered include the Airbnb price prediction study report and advanced wage data analysis notebooks (Julia, R, Python) with causal ML components; major bug fix: notebook execution issues and Julia kernel updated; organizational improvements: Group5_Assignment3_4 directory restructure; and a comprehensive Double Machine Learning report.
Activity
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Quality Metrics
Correctness77.6%
Maintainability78.8%
Architecture77.6%
Performance74.0%
AI Usage24.8%
Skills & Technologies
Programming Languages
JuliaJupyter NotebookMarkdownPythonRSQL
Technical Skills
Academic ResearchCausal InferenceCode OrganizationData AnalysisData PreprocessingData ScienceData VisualizationDataFramesDouble Machine LearningEconMLFile ManagementJupyter NotebooksMachine LearningMatplotlibPandas
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