zeshan22/Student-Performance-Data-Set

The objective of this model is predict student performance in secondary education. First i visualize the grades of each student by all categorical andthe numeric columns. I dropped the columns that does not have great impact on performance. Then i encoding categorical data and applied backward elimination for finding optimal features. I split the data into training and test sets. I trained data on Random forest classifier, Kernel SVM and KNeighborsClassifier. So by confusion matrix and f-score we find out that random forest(77% accuracy) is best classifier for this problem. I plotted feature importance plot we find out absences is the important features for determining the grades of students.