Abstract: Data Mining in Educational System has increased tremendously in the past and still increasing in present era. This study focusses on the academic stand point and the performance of the student is evaluated by various parameters such as Scholastic Features, Demographic Features and Emotional Features are carried out. Various Machine learning methodologies are adopted to extract the masked knowledge from the educational data set provided, which helps in identifying the features giving more impact to the student academic performance and there by knowing the impacting features, helps us to predict deeper insights about student performance in academics. Various Machine learning workflow starting from problem definition to Model Prediction has been carried out in this study. The supervised learning methodology has been adopted and various Feature engineering methods has been adopted to make the ML model appropriate for training and evaluation. It is a prediction problem and various Classification algorithms such as Logistic Regression, Random Forest, SVM, KNN, XGBOOST, Decision Tree modelling has been done to fit the student data appropriately. Keywords: Scholastic, Demographic, Emotional, Logistic Regression, Random Forest, SVM, KNN, XGBOOST, Decision Tree.
Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments