Recent Advancements in Various Machine Learning Techniques
Jaspreet Kaur1, Dilbag Singh2, Manjit Kaur3

1Jaspreet Kaur, Apex Institute of Technology, Chandigarh University, Gharuan, Punjab, India.

2Dilbag Singh, Apex Institute of Technology, Chandigarh University, Gharuan, Punjab, India.

3Manjit Kaur, Apex Institute of Technology, Chandigarh University, Gharuan, Punjab, India.

Manuscript received on 21 September 2019 | Revised Manuscript received on 30 September 2019 | Manuscript Published on 01 October 2019 | PP: 10-19 | Volume-8 Issue-9S4 July 2019 | Retrieval Number: I11030789S419/19©BEIESP | DOI: 10.35940/ijitee.I1103.0789S419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: The field of machine learning is witnessing its golden era as it turnout to be the leader in this field of artificial intelligence. This paper presents a comprehensive study of recently developed machine learning techniques especially for house price prediction. Due to the lack of sufficient knowledge required to train the machine learning models, existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired results. The primary objective of this review paper is to provide a structured outline of some well-known machine learning techniques. This paper also focuses on the methods which can assign optimal values to the existing techniques. The review has revealed that the meta-heuristic techniques can attain the optimistic parameters for the machine learning techniques. However, metaheuristic techniques still suffer from the poor convergence speed and stuck in local optima kind of issues. Finally, this paper describes the various issues and challenges of image machine learning techniques, which are required to be further studied. The various challenges with existing machine learning techniques are as: parameter tuning, ensembling, over/under-fitting, etc.

Keywords: Regression, Linear Regression, Non-Linear Regression, Random Forest Regression.
Scope of the Article: Machine Learning