This is a basic student performance analysis with predefined data using basics of machine learning. We used a predetermined dataset consisting of students marksheets. The output analysis is visualized using graphs for each subject and its chapters. It is done using K-Clustering algorithom which is an unsupervised learning algorithm. Here a subject mark's labels are grouped into one single cluster. This helps to analyse the student's performance and help him do better in upcoming exams

Libraries Used

We have used the below libraries to implement data analysis and to visualise student's data

Kit Solution Source

As you can see it in the given examples, Student's Maths marks are represented graphically according to his or her performance. It shows the tests in which the student scored marks which fall under Safe zone and a few test scores are in the danger zone, that is, he/she obtained less than average marks. By this labels we can predict the marks of upcoming exams for that student.

Kit Deployment Instructions

Deployed Application: