All Projects
A collection of 8 projects. Each one has a write-up covering the problem, the approach, the results and the tech stack, and you can filter them by technology.
RAG Chatbot Assistant
Ask questions about your own documents and get short, grounded answers. Files are chunked and embedded into a vector store, and a Gemini model answers using only the retrieved passages and the conversation so far.
- RAG
- LLM Applications
- LangChain
- FastAPI
- Vector Databases
- NLP
Product Advisor — LLM Assistant
This project combines a large language model with structured product data to build a conversational shopping assistant. It uses intent classification and entity extraction to separate recommendation requests, product comparisons and general questions. Fuzzy product matching resolves product names in comparisons, and prompt-based LLM responses turn the matched specifications into practical advice on price, RAM, storage, camera and battery.
- LLMs
- LangChain
- Prompt Engineering
- Natural Language Processing
- Intent Classification
- Fuzzy Matching
Genetic Algorithm — Defense Scheduling Optimization
This project uses a genetic algorithm to automate the scheduling of academic defenses. It reads students, supervisors and rooms from Excel files, builds conflict-free candidate schedules, and provides selection, crossover and mutation operators to improve them. A custom evaluation function penalizes resource conflicts and scores how compactly each professor's sessions are grouped across days and time slots.
- Genetic Algorithms
- Optimization
- Python
- Constraint Satisfaction
- Scheduling
- Data Processing
Hand Sign Classifier
The notebook loads a class-per-folder image dataset (133 training and 32 validation images), applies random augmentation, and trains a small classification head on top of a frozen MobileNetV2 for 20 epochs. It plots training and validation accuracy and loss, saves the model as an HDF5 file, and passes a webcam frame captured in Colab through the model to get a two-class softmax output. Validation accuracy reaches 1.0, but on a 32-image split that is augmented like the training data, so it is a sanity check and not a measure of real-world accuracy.
- Transfer Learning
- Image Classification
- Data Augmentation
- TensorFlow And Keras
- Computer Vision
Face Verification with a Siamese Network
Instead of classifying faces into fixed identities, this project learns a face embedding and compares two images by the distance between their embeddings, an approach designed to verify a face against a single reference photo without retraining a classifier. Built with TensorFlow and a custom training loop.
- Deep Learning
- Computer Vision
- Siamese Networks
- Data Augmentation
- TensorFlow
Billing Fraud Detection
The notebook merges client records with invoice history, cleans and encodes the data, and undersamples the majority class to a 3:1 ratio. It then trains and compares three models on a stratified 75/25 split. All three reach 100% accuracy, because the label column was left among the input features, so the notebook demonstrates the full pipeline but does not yet measure real detection performance.
- Fraud Detection
- Feature Engineering
- Imbalanced Data Handling
- Scikit-Learn
- Neural Networks
- Exploratory Data Analysis
Brain Tumor Detection from MRI Images
Medical imaging datasets are often tiny, so training a network from scratch rarely works well. This project builds a binary MRI classifier and compares three approaches: a CNN trained from scratch, MobileNetV2 transfer learning, and a ResNet50-based model. Transfer learning with MobileNetV2 clearly outperformed the from-scratch baseline.
- Deep Learning
- Computer Vision
- Transfer Learning
- CNN
- Medical Imaging
- TensorFlow
Titanic Survival Prediction
A classic classification problem used to show the full workflow: exploring the data, cleaning missing values, engineering new features and training a model. The exploration looked at how sex, class, family size and port of embarkation relate to survival, and the trained model's predictions were exported in Kaggle submission format.
- Machine Learning
- Data Analysis
- Feature Engineering
- Classification
- Data Visualization