UrbanReflex is an open-source smart city platform that transforms road segments into linked data nodes, integrating environmental monitoring, infrastructure status, and citizen reports. AI-powered classification and prioritization help city teams manage issues efficiently, while NGSI-LD open data ensures transparency and interoperability.

Machine Learning Assignment - RMIT 2023A - COSC2753 Machine Learning

This repo details code for building a text classifier for predicting Bank Transaction categories. I finetune a base version of a DeBERTaV3 model purely on text data, as well as another version using a combination of text and non-text (e.g., categorical, datetime, etc.) data.

AI-powered system for predicting appeal case outcomes using LegalBERT and generating legal briefs with GPT-4o-mini

AI Unsupervised Algorithm

A modular deep learning framework for training and evaluating image classification models on datasets like CIFAR-10 and MNIST. Supports configurable CNN architectures, automated training, and performance visualization using Python and TensorFlow.

Machine learning project for classifying Iris flower species using algorithms like Logistic Regression and Decision Tree. Includes data preprocessing, model training, accuracy comparison, and visualization using Python and scikit-learn.

A machine learning web app that predicts customer churn risk in real-time. Built with Streamlit and Scikit-learn, it helps businesses proactively retain valuable customers.

A machine learning algorithm that are correctly predicts a Somali language fake news

Classification on unknown data for different classes of road segments and driving patterns of different participants based on which inferences were made about the workload regions on different parts of roads.

This project aims to build classification and regression models for housing data using supervised machine learning techniques.

Classifying images using CNNs.

Applied AI/ML lab featuring multiple projects: digit recognition, facial recognition, fracture classification, reviews classification, and weather prediction.

Columbia Data Science Capstone Project

Classify wine type and predict quality using ML models like Random Forest and SVM on Vinho Verde dataset.

This project predicts brain stroke risk using machine learning by analyzing medical and lifestyle factors. It includes data preprocessing, model training, and a simple web interface for real-time predictions. Designed for learning, research, and healthcare analytics, it demonstrates practical ML applications in disease-risk assessment.