ResumeRover/Main
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
AIF360Data AnalysisData CleaningData PreprocessingMachine LearningNatural Language Processing
ShanilPraveen
PROFILE
Shanilpraveen
Worked on enhancing candidate ranking in the ResumeRover/Main repository by introducing education data normalization and a fairness-aware machine learning model. The approach involved standardizing degree names and fields, then combining them into a single feature to improve data quality. Implemented cosine similarity-based matching to better align candidate education with job requirements, and integrated a decision-tree regression model to refine ranking relevance. Leveraged Python, Scikit-learn, and the AIF360 toolkit to monitor and mitigate bias, ensuring fairer outcomes. The work established a scalable foundation for future improvements, focusing on explainability and bias reduction within the candidate ranking 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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