ResumeRover/Main
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
AIF360Data AnalysisData CleaningData PreprocessingMachine LearningNatural Language Processing
ShanilPraveen
PROFILE
Shanilpraveen
In May 2025, Praveen enhanced the ResumeRover/Main candidate ranking pipeline by developing a fairness-aware machine learning model that normalizes and combines education data for improved relevance. Using Python, Pandas, and Scikit-learn, he standardized degree names and fields, engineered a combined_education feature, and implemented cosine similarity to better match candidate education with job requirements. He integrated a decision-tree regression model and applied the AIF360 toolkit to monitor and mitigate bias, supporting more equitable rankings. The work established a scalable foundation for future improvements, demonstrating depth in data preprocessing, feature engineering, and fairness evaluation within a production-ready ML pipeline.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
1,127
Activity Months1
Your Network
5 peopleShared Repositories
5
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 – ResumeRover/Main: Delivered a production-ready enhancement to candidate ranking with education data normalization and a fairness-aware ML model. Implemented standardization of education attributes (degree names and fields) and created a combined_education feature to improve feature quality. Added cosine similarity-based matching between candidate education and job requirements to improve ranking relevance. Introduced a decision-tree regression model with AI fairness evaluation using the AI Fairness 360 (AIF360) toolkit to monitor and mitigate bias. The work was integrated into the ranking pipeline with two commits focused on education combination and the model with fairness checks. No major bugs fixed this month. Impact: better alignment of candidate rankings with job needs, reduced bias potential, and a scalable design for future improvements. Technologies/skills demonstrated: Python, data preprocessing and feature engineering, cosine similarity, decision-tree regression, AI fairness tooling (AIF360), ML pipeline integration.
2 Commits • 1 Features
May 1, 2025May 2025 – ResumeRover/Main: Delivered a production-ready enhancement to candidate ranking with education data normalization and a fairness-aware ML model. Implemented standardization of education attributes (degree names and fields) and created a combined_education feature to improve feature quality. Added cosine similarity-based matching between candidate education and job requirements to improve ranking relevance. Introduced a decision-tree regression model with AI fairness evaluation using the AI Fairness 360 (AIF360) toolkit to monitor and mitigate bias. The work was integrated into the ranking pipeline with two commits focused on education combination and the model with fairness checks. No major bugs fixed this month. Impact: better alignment of candidate rankings with job needs, reduced bias potential, and a scalable design for future improvements. Technologies/skills demonstrated: Python, data preprocessing and feature engineering, cosine similarity, decision-tree regression, AI fairness tooling (AIF360), ML pipeline integration.
May 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AIF360Data AnalysisData CleaningData PreprocessingMachine LearningNatural Language ProcessingPandasPythonScikit-learn
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