Predicting Student Performance for Early Intervention using Classification Algorithms in Machine Learning
Subject Areas : Machine learningKalaivani K 1 * , Ulagapriya K 2 , Saritha A 3 , Ashutosh Kumar 4
1 - Vels Institute of Science, Technology and Advanced Studies, India
2 - Vels Institute of Science, Technology and Advanced Studies, India
3 - Vels Institute of Science, Technology and Advanced Studies, India
4 - Vels Institute of Science, Technology and Advanced Studies, India
Keywords:
Abstract :
Predicting Student’s Performance System is to find students who may require early intervention before they fail to graduate. It is generally meant for the teaching faculty members to analyze Student's Performance and Results. It stores Student Details in a database and uses Machine Learning Model using i. Python Data Analysis tools like Pandas and ii. Data Visualization tools like Seaborn to analyze the overall Performance of the Class. The proposed system suggests student performance prediction through Machine Learning Algorithms and Data Mining Techniques. The Data Mining technique used here is classification, which classifies the students based on student’s attributes. The Front end of the application is made using React JS Library with Data Visualization Charts and connected to a backend Database where all student’s records are stored in MongoDB and the Machine Learning model is trained and deployed through Flask. In this process, the machine learning algorithm is trained using a dataset to create a model and predict the output on the basis of that model. Three different types of data used in Machine Learning are continuous, categorical and binary. In this study, a brief description and comparative analysis of various classification techniques is done using student performance dataset. The six different machine learning Classification algorithms, which have been compared, are Logistic Regression, Decision Tree, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine and Random Forest. The results of Naïve Bayes classifier are comparatively higher than other techniques in terms of metrics such as precision, recall and F1 score. The values of precision, recall and F1 score are 0.93, 0.92 and 0.92 respectively.