Work place: Banasthali University, Jaipur, 304022, India



Research Interests: Artificial Intelligence, Computational Learning Theory, Data Mining, Information Retrieval, Data Structures and Algorithms


Dr. Manisha is a former Associate Professor at Banasthali University, Jaipur, India. She received her Doctorate Degree from Banasthali University in Computer Science. She has more than 18 years of experience in academia and is guiding many research students under her supervision for Doctorate Degree. She has more than 30 research papers to her credit in reputed National and International Journals. Her major field of research includes data mining, machine learning, artificial intelligence and information retrieval.

Author Articles
A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction

By Pooja Thakar Anil Mehta Manisha

DOI:, Pub. Date: 8 Sep. 2017

Data Mining is gaining immense popularity in the field of education due to its predictive capabilities. But, most of the prior effort in this area is only directed towards prediction of performance in academic results only. Nowadays, education has become employment oriented. Very little attempt is made to predict students’ employability. Precise prediction of students’ performance in campus placements at an early stage can identify students, who are at the risk of unemployment and proactive actions can be taken to improve their performance.
Existing researches on students’ employability prediction are either based upon only one type of course or on single University/Institute; thus is not scalable from one context to another. With this necessity, the conception of a unified model of clustering and classification is proposed in this paper.
With the notion of unification, data of professional courses namely Engineering and Masters in Computer Applications students are collected from various universities and institutions pan India. Data is large, multivariate, incomplete, heterogeneous and unbalanced in nature. To deal with such a data, a unified predictive model is built by integrating clustering and classification techniques. Two- Level clustering (k-means kernel) with chi-square analysis is applied at the pre-processing stage for the automated selection of relevant attributes and then ensemble vote classification technique with a combination of four classifiers namely k-star, random tree, simple cart and the random forest is applied to predict students’ employability. Proposed framework provides a generalized solution for student employability prediction. Comparative results clearly depict model performance over various classification techniques. Also, when the proposed model is applied up to the level of the state, classification accuracy touches 96.78% and 0.937 kappa value.

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