Divya Patel

Built content recommendation engine

Python, scikit-learn, pandas, and NumPy.

• Developed a content-based recommendation system to generate personalized recommendations from user preferences and item attributes. Applied TF-IDF vectorization and cosine similarity to calculate content relevance and rank recommendations.
• Cleaned, transformed, and analyzed [X]+ records to prepare features for the recommendation pipeline.
• Built an end-to-end recommendation workflow from data preprocessing through recommendation generation and evaluation.

End-to-End Machine Learning & MLOps Pipeline

Python, Scikit-learn, MLflow, Docker, FastAPI, Git/GitHub

• Developed an end-to-end machine learning pipeline covering data preprocessing, feature engineering, model training, evaluation, and deployment.
• Trained and evaluated ML models using Scikit-learn and tracked experiments, parameters, metrics, and model versions with MLflow.
• Built a REST API using FastAPI to serve real-time model predictions and containerized the application using Docker.
• Implemented a reproducible workflow with Git/GitHub, including automated testing and structured project organization.
• Evaluated model performance using [Accuracy/F1/ROC-AUC/RMSE] and improved performance from [X] to [Y] through feature engineering and hyperparameter tuning.

• Developed user-friendly interfaces for seamless navigation and improved user experience.
• Conducted automated trial testing of root-cause failures to detect and verify defects.
• Assisted with development of automation software by supporting application engineers with Java-based code, Git workflows, and framework integration.
• Developed software applications using Java and Python, enhancing functionality and user experience.
• Collaborated with cross-functional teams to gather requirements and deliver project milestones on time.