Fullstack developer & data enthusiast crafting web apps, interactive data visualizations, and machine learning.
In this project, I took on the role of System Designer and Fullstack Developer to build the official platform for the IAPA Web Conference. I developed the frontend using Vue JS and implemented backend services with Spring Boot. The system featured modules for live registration, session tracking, and user management, designed to ensure a seamless virtual conference experience.
Vue JS (FE) & Springboot (BE)
In this project, I developed a digital music platform that enables users to browse, stream, and visualize songs through an interactive interface. I used Django to implement the backend and handle music metadata, playlists, and user sessions. This platform also included data visualizations to enhance user engagement and deliver a personalized music experience.
Django (Python)
In this project, I built a mobile-oriented pharmacy management system to streamline daily operations. I was responsible for both frontend development using Vue JS and backend logic with Spring Boot. The system supported inventory tracking, prescription processing, and transaction logging, aiming to reduce operational errors and improve efficiency in pharmaceutical workflows.
Vue JS (FE) & Springboot (BE)
In this project, I apply the Classification and Regression Tree (CART) algorithm to predict students' final grades based on various academic and demographic features. I begin by exploring the dataset through visualizations of age distribution, grade frequency, attendance, and stress levels. I then clean the data by removing irrelevant columns, filling missing values with median or mode depending on the feature type, and encoding categorical variables using LabelEncoder.
Pandas, NumPy, AI Model
In this project, I focus on building a classification model using the k-Nearest Neighbors (k-NN) algorithm. I begin by exploring and preprocessing the dataset, which includes handling missing values using distribution-based imputation, where I fill missing numerical values based on the feature's skewness, using either the mean or the median.
Pandas, NumPy, AI Model
In this project, I work on building a regression model to predict a continuous target variable, real efficiency, using feature-rich production data. I begin by exploring the dataset, identifying and imputing missing values using distribution-based strategies (mean or median depending on skewness), and checking for duplicate records.
Pandas, NumPy, AI Model
JavaScript