Motivated graduate student currently pursuing a Master's in Data Science, with a strong foundation in statistical analysis, machine learning, and data visualization. Adept at leveraging Python, R, SQL, and data science tools to derive actionable insights from complex datasets. Demonstrated ability to work on real-world data projects. Eager to contribute data-driven solutions in a collaborative and innovative environment.
AutoML System for Model Selection and Hyperparameter Optimization
Technologies: Python, Scikit-learn, Optuna, Pandas, NumPy, Matplotlib
•Automated the end-to-end ML pipeline including data preprocessing, model selection (classification/r regession), and hyperparameter
•Integrated Optuna for dynamic hyperparameter optimization, reducing manual effort and improving model performance by up to 15%.
•Implemented performance visualization and reporting modules for quick model evaluation and comparison.
AI-Powered Environmental Monitoring and Prediction System
Technologies: Python, TensorFlow/Keras, Scikit-learn, Pandas, Matplotlib
•Developed supervised learning models to predict environmental parameters like temperature and humidity from historical sensor data.
•Designed a data ingestion and cleaning pipeline to handle real-time environmental data inputs.
•Achieved accurate short-term forecasting with time-series models, aiding early warnings and environmental planning.