CSST104-Final-Project
CSST 104: Advanced Machine Learning
Welcome to the CSST 104: Advanced Machine Learning Repository!
This repository is a central hub for all the files for the final project, including comprehensive documentation and datasets for analyzing student exam performance.
Group Information
Members: Renz Dexter M. Perci, Favielle Anne O. Reyes, Justin John Mico Villaflor
Section: BSCS 3A IS
Course: CSST 104
Department: College of Computer Studies
School: Laguna State Polytechnic University - Santa Cruz (Main) Campus
Professor: Mark P. Bernardino, MSCS
Table of Contents
Introduction
This repository contains all the materials and resources for analyzing student exam performance based on demographic and socio-economic factors. Inside, you will find key files, including:
- Analysis Document: A comprehensive report detailing our project’s methodology, findings, and recommendations on student performance.
- Dataset: The complete dataset used for the analysis provides the raw data underpinning our findings.
- Google Colab Link: This link provides access to the Google Colab notebook with the code used for data processing, analysis, and visualization. It allows for easy replication and further exploration of our project.
Course Description
The Advanced Machine Learning course covers the theoretical foundations and advanced methods used in modern machine learning. Students delve into the mathematical and statistical aspects of machine learning, gaining a deeper understanding of the algorithms and their properties. This course introduces students to a wide range of statistical and machine-learning techniques. They will also become acquainted with data analysis and the instruments required to examine datasets realistically in their many dimensions.
Beyond theory, the course explores cutting-edge research and practical applications. Students learn about advanced machine learning methods, frameworks, and techniques. They gain proficiency in designing effective solutions for real-world problems, leveraging their understanding of both theory and practice. Overall, the course aims to provide a solid foundation for conducting research, solving complex challenges, and contributing to machine learning.