HealthBud is a cross-platform mobile application designed to simplify nutrition tracking and promote long-term wellness. The app enables users to log meals, view detailed nutritional breakdowns, and track personalized health metrics through an intuitive dashboard. By combining FDA-aligned dietary guidelines, weighted health scoring, and actionable insights, HealthBud helps users move beyond basic calorie counting to better understand the quality of their daily food choices and build healthier habits over time.

The daily Health Score is calculated using FDA dietary guidelines and scientifically established nutrition standards. Recommended Dietary Allowances (RDA) and Adequate Intakes (AI) for vitamins, minerals, water, and macronutrients are sourced from the Institute of Medicine’s Dietary Reference Intake tables, available here and here. Nutrients are weighted to reflect their relative impact on overall dietary quality, producing a single, interpretable score that represents the healthfulness of a user’s daily food intake.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

During the internship, I was in charge of building and refining scalable features for NutriverseAI’s product. I started by running black-box tests and fixing bugs, then went on to design and implement a chatbot UI, social modules, and personalized nutrition tools, all while ensuring stability and clear communication with the team.

This application allows users to plan out their meals using an AI assistant, post and save their recipes, and find groceries nearby.

This project features automated activity tracking, API integration, dynamic SVG generation, GitHub Actions workflows, data caching, CI/CD automation, token-based authentication, and responsive formatting. The aim, hereby, is to showcase the power of workflow orchestration and developer analytics in visualizing coding habits and maintaining up-to-date developer insights directly within version-controlled environments.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

This project features ingredient list tokenization, fuzzy matching, alias retrieval, semantic filters, keyword matching, web scraping, summary generation, evaluation metrics, and much more. The aim, hereby, is to showcase the power of preprocessing and task specialization in reducing hallucination and increasing despite decreased model size and complexity.

This project report features a detailed description of the project, including abstract, introduction, related work, methodology, experimental setup, results, and conclusion and future work.

The ingredient list was first tokenized into individual ingredients and normalized via fuzzy matching. Then, aliases were retrieved by using inflect for pluralization and singularization and by using the Mistral-7B-Instruct-GPTQ model to generate common name and scientific name aliases. These were then filtered and normalized for use during data retrieval.

Data was retrieved from trusted sites, such as PubMed, OpenFDA, RxNorm, Europe PubMed Central, PubMed Central, and via web scraping using Google Custom Search Engine (CSE). During web scraping, NER entities were extracted from the retrieved data. The search included only matches for health-related keywords and ingredient aliases. Non-human studies, non-relevant NER-labeled data, product-related information, and more was discarded using filters to obtain only relevant data.

The data was parsed into sentences and filtered to only contain those featuring results and conclusion-related terms, an alias, and health-related terms. Sentences were then normalized by removing duplicates and replacing all aliases with the originally matched ingredient name. Then, summaries of health effects and dietary restrictions were generated for each ingredient using the Mistral-7B-Instruct-GPTQ-Model and AutoGPTQ. These were then post-processed to remove duplicates and irrelevant information. The top 5 summaries were selected and displayed.

Both human and automatic evaluations show our model performs competitively with ChatGPT-4o. This supports the potential of smaller, specialized models for domain tasks. Future directions include RLHF, larger models, improved aliasing, sentiment analysis, and hybrid systems. Click here to view the repo.

This project combined ML models using stacking (LogReg, Decision Trees, Random Forests, Gradient Boosting). Bias-variance analysis was included. The best combo, LogReg + Random Forest, achieved 98.66% accuracy with a bias² + variance of 0.5. Final output: bankruptcy predictions in CSV format. For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository.

In this step, the dataset is loaded using Pandas, and features are extracted. A StandardScaler is applied to normalize the data for better model performance.

The class imbalance in the dataset is handled using SMOTE (Synthetic Minority Oversampling Technique), ensuring the minority class has adequate representation. Stacking models used include Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and Support Vector Classifier.

The function computes the bias^2 and variance of different model combinations using K-fold cross-validation, giving insights into model performance trade-offs.

This section generates combinations of the base models and evaluates them using cross-validation. It calculates the mean accuracy and performs bias-variance decomposition to help select the best model combination based on accuracy and bias-variance score.

Once the best model combination is selected, it is trained on the resampled dataset. Predictions are then made on the test set, and the results are saved to a CSV file for submission.

The HackerNews Flask application delivers the latest news from Hacker News, updated hourly. It uses nginx and gunicorn for deployment and SQLite for data management. Features include pagination, likes/dislikes for logged-in users, and Google-based Auth0 authentication. Users can access profiles and an admin interface to manage roles. The clean interface supports continuous updates and user interaction, keeping users engaged and informed.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

This comprehensive web application offers robust law firm management tools, facilitating efficient handling of clients, projects, and staff. It supports full CRUD operations and features advanced utilities like a windowed timer, mass-billing capabilities, customizable interface options, and secure data deletion protocols.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

This Android application provides comprehensive note management and customization features. Users can adjust text color, background color, font size, and text alignment. It supports saving, deleting, and undoing changes to notes, which can be displayed individually or in a card/recycle view format.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

ProFessUp is a platform modeled after Rate My Professor, designed to facilitate CRUD operations for professors, reviews, courses, and user profiles. The application enables users to efficiently search for professors and filter reviews by course. It presents professor profiles and reviews in a card-based layout, utilizing sliders, drop-down menus, buttons, and checkboxes to enhance usability and comprehension. The interface employs a user-friendly color scheme to optimize the user experience. Due to the deletion of the MongoDB, note functionality is demonstrated exclusively through video in this portfolio project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

This program facilitates the manipulation of FAT32 images through a shell-like interface, supporting commands such as ls, cd, mkdir, creat, open, close, read, append, lseek, lsof, rm, and rm -r.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

This program is designed to simulate a shell environment. It incorporates a comprehensive set of functionalities, including both internal and external command processing, as well as the capability to execute an external program. These features collectively emulate the typical operations of a conventional shell.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

This program utilizes an advanced elevator scheduling algorithm that interfaces seamlessly with a custom kernel module. It incorporates system calls, efficient scheduling techniques, kernel operations, mutexes/locks, and multithreading to optimize performance and ensure robust system integration.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.

For a comprehensive demonstration of the development skills utilized in this project, please visit the project repository. These code snippets offer a glimpse into the various capabilities employed throughout the project.