The assigned task is to perform Ensemble Learning Methods on provided dataset of Higher Education Students Performance Evaluation Dataset.This Dataset describes 32 attributes in which 1-10 of the data are the personal questions, 11-16. questions include family questions, and the remaining questions include education habits. It was compiled by Dean De Cock.More details of this dataset are described in the paper: Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R.,Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham.*
#Task Description Requirements:
Apply Ensemble Learning Methods on Datasets
In this assignment, you have to implement three different Ensemble Learning Methods on any two of these models:
1) SVM,
2) Logistic Regression,
3) and K-NN.
Visualize the insights derived from these algorithms. Compare and evaluate using appropriate performance metrics.
EL Methods:
1)Bagging.
2)Stacking.
3)Boosting.
You may use any dataset of your choice from UCI Machine Learning Repository.
I have attached a URL to help you understand EL Methods.
Best of luck!
Links Datasets:
https://archive.ics.uci.edu/ml/datasets/
Task-Documentation:
https://machinelearningmastery.com/tour-of-ensemble-learning-algorithms/