Abstract:
Personal realization is one of the best things for a
successful life. Sometimes, one needs help to realize about bad
habits, career goals and accomplish mental health as well as to
overcome other problems. This help is generally known as
“Counselling”. To ensure effectiveness of counselling service,
prime concern is to find out the target group of instances. Many
researchers worked with student performance prediction based on
academic attributes moreover students’ counselling is also needed
to increase their performance. We addressed this issue for this paper
work. Here, a model is proposed to predict a student who needs
counselling. This study was mainly motivated by two main steps.
The first was to investigate university students who feels an urge
about having counselling for psychological help from their
circumstances and second was to predict efficiently which group
of students really needs counselling. This paper work was
established with 498 instances and each comprised of 6 attributes.
In the case of evaluate the result, paper shows superiority over
state- of-the-art methods to predict student counselling through
machine learning and factor scoring method. We applied 10 fold
cross-validation and 66% dataset splits evaluation method to find
out better algorithm among selected 5 algorithms which are Ibk,
Naive Bayes, Multilayer, SMO and Random Forest. Weka 3.8.0
have been used for machine learning algorithms where Ibk
(Instance Based Learning) was found best for our approach with
95.38% accuracy.