A microservice for performing OCR annotation on images using Tesseract.js, containerized by Docker.
Hello guys, welcome to my Data Science Portfolio. I include some knowledges I earn in my journey. I included some case study, papers, and code. Please check the readme.
🏠 California Housing Price Prediction using Machine Learning. Performed Data Cleaning, Exploratory Data Analysis (EDA), and built a Regression model to predict house prices. 🚀
Advanced data science project for Netflix stock price prediction and analysis using Python, Pandas, NumPy, Matplotlib, Seaborn, and Plotly with interactive visualizations
A machine learning project predicting suicide risk based on multiple socio-economic and environmental factors using data mining techniques.
Contains multiple small projects on Deep Neural Network from scratch to intermediate level.
Evaluates the wpd real-space topological invariant for finite non-hermitian chains by first predicting the crop length parameter l_star using Random-forest predictors. Follows the principles provided in arXiv:2607.05900
Open-data API for expert-verified Philippine snake sightings (SnaKédex)
Predicts and visualizes weather data using Machine Learning and OpenWeather API.
Udacity's Machine Learning Nanodegree - Project 1- Deploy a Sentiment Analysis Model
Scalable vehicle telemetry platform with real-time data ingestion, ML-based anomaly detection, and automated alerting.
AI-powered contractor intake & estimation platform - FastAPI, Next.js, PostgreSQL, and a custom-trained PyTorch image classifier, with OpenAI-driven scope/estimate generation.
Time Series Forecasting project using the SARIMA (Seasonal AutoRegressive Integrated Moving Average) model to predict future airline passenger traffic. The project includes data preprocessing, trend and seasonality analysis, stationarity testing, SARIMA model development, forecasting, and performance evaluation using Python.
This project built a binary classifier to help Alphabet Soup predict applicant success using machine learning and feature selection.
Explainable AI for simulated cognitive profiles, interactive React/TypeScript tool with a from-scratch, interpretable logistic regression (educational, non-diagnostic)
Runs multiple.arff files through SMO and prepares results for R
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