International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Double Blind Reviewed

Downloads: 163 | Weekly Hits: ⮙2 | Monthly Hits: ⮙4

Research Paper | Computer Science & Engineering | India | Volume 9 Issue 11, November 2020

Artificial Intelligence for Hiring

Ishan Borker | Ashok Veda ^[2]

Abstract: Artificial Intelligence (AI) for hiring is a very important category under HR to reduce time-consuming activities like manually screening of resumes. AI in HR provides a simple way not only to reach out to possible candidates, but also screen, rank, and shortlist their resumes based on the traits most relevant to your company. This paper gives us the idea of predicting the number of students placed using machine learning algorithms. We have used various machine learning algorithms like XGBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, Decision Tree Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Multi-Layer Perceptron (MLP) Classifier, and Extra Trees Classifier. The algorithms were used based on the number of features and for predicting students placed or not. We also compare the accuracy of different algorithms.

Keywords: XGBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, Decision Tree Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Multi-Layer Perceptron, MLP, Classifier, Extra Trees Classifier, Machine Learning, Artificial Intelligence, Accuracy

Edition: Volume 9 Issue 11, November 2020,

Pages: 457 - 461

How to Cite this Article?

Ishan Borker, Ashok Veda, "Artificial Intelligence for Hiring", International Journal of Science and Research (IJSR), Volume 9 Issue 11, November 2020, pp. 457-461, https://www.ijsr.net/get_abstract.php?paper_id=SR201104153250

How to Share this Article?

Click below to Watch Video Lecture of Above Article

Similar Articles with Keyword 'Random Forest Classifier'