Student Grade Prediction Using C4.5 Decision Tree

Student Grade Prediction Using C4.5 Decision Tree is a project report that emphasizes the necessity of the student grade prediction easily. The necessary details of how the easy management of student grade prediction is possible are available here. The report is available in either word document or PDF format. The necessary information related to the easy management of the synopsis, abstract report on student grade prediction using C4.5 decision tree can help the users easily. The download mini project, synopsis, abstract report on student grade prediction using C4.5 decision tree available easily.

Study on Student Grade Prediction Using C4.5 Decision Tree, What We Know About How Well Students Did C4.5 Decision Tree is used in the classroom to utilize machine learning and data analysis. The C4.5 method is a decision tree system that looks at a lot of different inputs and past results to guess what a student’s grade will be. The way can help schools and teachers by giving them information about how well students are doing and letting them make choices based on facts that will improve the learning process.

C4.5 decision tree method

The initial stage in developing this system is data collecting. It entails collecting data that might affect a student’s academic success, such as their attendance records, test scores, assignment completion rates, and so on. The C4.5 decision tree method is trained using this data as input.

When using the C4.5 decision tree method, the parts that split the data set over and over again are the best. Assign nodes to traits and lines to possible values in a tree model. The method chooses characteristics that make letters different. This keeps happening until a tree can correctly guess student grades.

Using the decision tree, you can guess a student’s grade by looking at various traits. Students’ information moves through the tree until it reaches a leaf point, which shows an expected score. Such a grade forecast could mean that the student did well in school.

There are several advantages to employing the C4.5 decision tree to forecast students’ grades. By knowing which factors, such attendance or assignment completion, affect students’ grades, educators may better target interventions for struggling students. Additionally, it predicts grades objectively and step by step, removing human assessors’ subjectivity and bias.

The quality and quantity of training data determines the C4.5 decision tree model’s ability to predict student grades. A restricted dataset, lack of officially spoken samples, noise, or missing variables may influence the model’s accuracy and ability. To show Education development, the decision tree model must be updated constantly.

Topics Covered:

01)Introduction
02)Objectives, ER Diagram
03)Flow Chats, Algorithms used
04)System Requirements
05)Project Screenshots
06)Conclusion, References