ResumeRover/Main
Apr 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data CleaningData PreprocessingFeature EngineeringMachine LearningNatural Language ProcessingPandas
yugan243
PROFILE
Yugan243
Worked on the ResumeRover/Main repository to deliver four machine learning features over two months, focusing on robust data preprocessing and model training pipelines. Developed advanced feature engineering for extracting experience years from resume data, handling complex date formats and missing values using Python and pandas. Built a notebook-driven workflow to encode job titles into numerical embeddings with sentence transformers, enabling richer input for downstream models. Designed and implemented reproducible Jupyter Notebook pipelines for decision tree regression, including randomized hyperparameter tuning, model evaluation with MSE and R², and model serialization with joblib, supporting efficient experimentation and production-ready candidate ranking solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
1,614
Activity Months2
Your Network
5 peopleWork History
May 2025
3 Commits • 2 Features
May 1, 2025Monthly summary for 2025-05: Two notebook-driven ML pipelines delivered in ResumeRover/Main, enabling repeatable experimentation, hyperparameter tuning, and model persistence. Key features include a Decision Tree Regressor Training Notebook with randomized grid search (data loading, train/validation/test splits, training with specified hyperparameters, evaluation via MSE and R², and saving the trained model) and AI Candidate Ranking Model Training Notebook Enhancements (refined execution counts, model parameters, and validation/test performance metrics, plus saving the best performing model). Impact centers on faster experimentation cycles, improved model quality through robust hyperparameter search, and reproducible, production-ready pipelines. Technologies demonstrated include Python, Jupyter notebooks, scikit-learn, dataset handling, model evaluation (MSE, R²), and model serialization.
3 Commits • 2 Features
May 1, 2025Monthly summary for 2025-05: Two notebook-driven ML pipelines delivered in ResumeRover/Main, enabling repeatable experimentation, hyperparameter tuning, and model persistence. Key features include a Decision Tree Regressor Training Notebook with randomized grid search (data loading, train/validation/test splits, training with specified hyperparameters, evaluation via MSE and R², and saving the trained model) and AI Candidate Ranking Model Training Notebook Enhancements (refined execution counts, model parameters, and validation/test performance metrics, plus saving the best performing model). Impact centers on faster experimentation cycles, improved model quality through robust hyperparameter search, and reproducible, production-ready pipelines. Technologies demonstrated include Python, Jupyter notebooks, scikit-learn, dataset handling, model evaluation (MSE, R²), and model serialization.
May 2025
April 2025
2 Commits • 2 Features
Apr 1, 2025Month: 2025-04 — Performance-oriented feature engineering and NLP feature engineering for ResumeRover/Main, focused on building robust, ML-ready inputs and enabling richer job-title representations.
April 2025
2 Commits • 2 Features
Apr 1, 2025Month: 2025-04 — Performance-oriented feature engineering and NLP feature engineering for ResumeRover/Main, focused on building robust, ML-ready inputs and enabling richer job-title representations.
Activity
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Quality Metrics
Correctness82.0%
Maintainability80.0%
Architecture80.0%
Performance72.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data CleaningData PreprocessingData ScienceData ValidationDecision TreesFeature EngineeringHyperparameter TuningJoblibMachine LearningModel TrainingNatural Language ProcessingPandasPythonScikit-learnSentence Transformers
Repositories Contributed To
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