"oxayavongsa/projects" is a public GitHub repository serving as a diverse AI/ML Project Portfolio. Using Python coding and Juptyer notebook for multiple methodologies to model statistical algorithms.

A Machine learning project on Cat v/s Dog image classification using CNN, VGG-16 and VGG-19 in Python

A Machine learning based approach on customer churn analysis in python.

This project is a hands-on exploration of multiple regression modeling techniques applied to real-world challenges in the context of real estate. It aims to understand how different features influence house or property prices. By utilizing multiple regression models, the project provides insights, decision support, and enhances data science skills.

Student performance analysis and prediction using datasets has become an essential component of modern education systems. With the increasing availability of data on student, schools and universities are using advanced analytics and machine learning algorithms to gain insights into student performance and predict future outcomes.

Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make healthy choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood.

Examples of techniques that can be used to optimize neural network models (some techniques can apply more generally).

Module 21 Challenge

Jupiter notebook to predict the outcome of passengers who died or not in the tragical Titanic event.

CMU week 3 - whole process overview, data split, validation and model scoring

The number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.

CMU week 4 - Case Study #1 - Applying Data Science tools on obesity dataset - presentation on 18th October - Requirements: • Between 500 to 100k samples • Tabular file (csv) • Only categorical and/or numerical features (No new topics discussed during week 4)