All Projects
ACMGEN Center
Developed a prediction platform for optimizing 3D printing outputs. Introduced two core prediction modes: (1) automated forecasting of a target property by selecting 10 or more input features, and (2) manual value entry to generate a “best fit” percentage match for print accuracy. Built comprehensive reporting tools, archiving previous predictions, dashboard, and user management. Also, Designed and integrated a gcode ingestion pipeline using scheduler that preprocesses print instructions and continuously expands the dataset, enabling more accurate and scalable predictions over time.
Figma LinkRAG Chatbot for Barangay Ordinances in Manila
A Chatbot System that uses Optuna for hypertuning, LLM + RAG for chatbot query, and Codebert analyzing the input by citizens
Figma LinkOnline Class and Activity Management for Python Learners using Code-Bert and Vector Embeddings
Figma LinkOnline Ordering and Inventory Management using Time Series and Analysis with ARIMA Model for Hardware Stores in Manila
Leveraging machine learning to optimize the ordering and inventory management processes for a hardware store in Manila. The following features include: preprocessed data using Pandas and NumPy to enhance time series analysis and forecasting accuracy; fine-tuned the ARIMA model using scikit-learn to optimize forecasting results; created recommendation systems to predict in-demand stocks and notify users; streamlined the ordering and inventory management processes for improved efficiency.
Figma LinkCourse Management System with hybrid recommender system for SEAMAN Institute in Manila
Utilizing machine learning to enhance course management and student learning for SEAMAN Institute in Manila. The following features include: designed a course management module that allows instructors to evaluate each student’s course; constructed an examination module to enable instructors to create exams and students to complete them seamlessly; incorporated a recommendation module to help students learn topics more efficiently.
Figma LinkUnderstanding the Cause and Effect of Student Debt through ML
Leveraging artificial intelligence to analyze and better understand the scope of the student debt crisis. The following features include: generated detailed insights into public sentiment and financial trends related to student debt; built predictive models to identify risk factors and potential solutions for loan management; enabled policymakers, educational institutions, and financial stakeholders to understand the crisis from a data-driven perspective.
Figma LinkCreating a Career Recommendation System to Make Suggestion for Students
Utilizing machine learning to recommend and suggest careers for students. The following features include: created a career suggestion machine learning model using the available public dataset; provided information about how the student population in Lagos State is distributed across the country; based on public data, made recommendations for areas in which students should improve to eliminate a lack of preparation and poor career choices for tertiary institutions.
Figma Link