CourseCompanion

In this step, we will focus on collecting and preprocessing the data required for building the course recommendation system. This involves gathering course information, student data, and enrollment records from various sources. The collected data will then be cleaned, transformed, and formatted to prepare it for further processing and analysis.

In this step, we will construct a course-student matrix that represents the relationships between courses and students. This matrix will serve as the foundation for building the recommendation algorithm. We will use the preprocessed data to create a sparse matrix that captures course enrollments and student interactions.

In this step, we will design and implement a knowledge graph-based recommendation algorithm. This algorithm will leverage the course-student matrix and knowledge graph to provide personalized course recommendations. We will use NetworkX to create and manipulate the knowledge graph.

In this step, we will implement a deep learning-based recommendation algorithm using TensorFlow. This algorithm will learn complex patterns in the course-student matrix and provide accurate course recommendations. We will design and train a neural network that takes the course-student matrix as input and outputs a list of recommended courses.

In this step, we will integrate the knowledge graph-based and deep learning-based recommendation algorithms to create a hybrid recommendation system. This system will leverage the strengths of both approaches to provide accurate and diverse course recommendations.

In this step, we will deploy the CourseCompanion system and perform maintenance tasks. This includes setting up a production environment, monitoring system performance, and updating the system as necessary.