I'm an undergraduate student passionate about Software Engineering, Data Science, and Machine Learning. I enjoy building projects that combine practical coding skills with data-driven insights to solve real-world problems.
Programming & Data Science: Python, PyTorch, Scikit-Learn, NumPy, Pandas, Matplotlib, SQL
Web Development: React, TailwindCSS, Django, Node.js, REST APIs
Data Visualization & BI: PowerBI, Matplotlib
Tools & Other Technologies: Git/GitHub, Jupyter, APIFY, OpenAI API
- Model training using classical ML algorithms (Logistic Regression)
- Deep learning models implemented in PyTorch (RNNs, LSTMs, Transformers)
- Model evaluation with accuracy, F1-score, and ROC-AUC
- Explainability with SHAP or attention visualization
- Fine-tuned a pretrained ResNet18 using PyTorch to classify 10 image categories.
- Implemented data preprocessing and augmentation for improved generalization.
- Trained and evaluated the model with GPU acceleration, tracking accuracy and loss metrics.
- Designed and implemented a responsive UI with React + TailwindCSS to plan meals, track groceries, and view consumption history.
- Developed a secure REST API with Django + SQLite for user authentication and meal/grocery management.
- Integrated the OpenAI API to generate personalized weekly meal plans, reducing planning time by ~90%.
- Built a Random Forest Classifier to predict Titanic passenger survival, achieving 81% accuracy and AUC of 0.88.
- Engineered features like
FamilySizeand preprocessed categorical variables for better model performance. - Performed grid search hyperparameter tuning and visualized feature importance.
Always excited to collaborate on innovative projects in software engineering and data science!