PERSONALIZED ONLINE LEARNING RECOMMENDATIONS USING PYTHON

Abstract

The project Personalized Online Learning Recommendations Using Python aims to transform the online education experience by providing tailored course suggestions that cater to individual learning needs. With the rapid growth of digital education platforms, the demand for personalized learning solutions has surged, necessitating advanced recommendation systems. This project leverages Python’s powerful libraries for data analysis, machine learning, and natural language processing to build a robust system capable of analyzing user preferences, browsing history, performance metrics, and feedback. By utilizing machine learning techniques such as collaborative filtering, content-based filtering, and hybrid approaches, the system generates accurate and relevant recommendations that align with users' learning goals. Additionally, natural language processing is employed to analyze course content and user-generated data for improved content matching. The solution incorporates scalable data pipelines to manage large datasets efficiently and integrates an intuitive, user-friendly interface to enhance user engagement. By dynamically adapting to user behavior and inputs, the system improves learning efficiency and satisfaction while addressing the growing need for individualized learning pathways. This project offers a scalable and intelligent framework adaptable to various e-learning platforms, contributing to the evolution of personalized education in the digital era.