An Optimized way to Solve Regression Problems
Jyothi Vishnu Vardhan Kolla1, Poorna Chandra Vemula2, Vanapala Sai Mohit3

1Jyothi Vishnu Vardhan Kola, Pursuing, B Tech, Department of Computer Science and Engineering, Gitam university vishakapatnam, Andhra Pradesh
2Poorna Chandra Vemula*, Pursuing, Bachelors of Technology, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu
3Vanapala Sai Mohit, Pursuing, Bachelors of Technology, Department of Computer Science and Engineering, Gitam Institute of Technology, Visakhapatnam, Andhra Pradesh
Manuscript received on June 20, 2021. | Revised Manuscript received on August 09, 2021. | Manuscript published on August 30, 2021.| PP: 61-65 | Volume-10 Issue-6, August 2021. | Retrieval Number: 100.1/ijeat.E28730610521 | DOI: 10.35940/ijeat.E2873.0810621
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In many real world scenarios, regression is a commonly used technique to predict continuous variables. In case of noisy(inconsistent) and incomplete datasets, a large number of previous works adopted complex non traditional machine learning approaches in order to get accurate predictions. However, compromising on time and space overheads. In this paper, we work with complex data yet by using traditional machine learning regression algorithms by working on data cleaning and data transformation according to the working principle of those machine learning algorithms.
Keywords: Regression, Data processing, Noisy data, Random sampling.