August 30, 2021 Journal article Open Access

Jyothi Vishnu Vardhan Kolla; Poorna Chandra Vemula; Vanapala Sai Mohit

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    <subfield code="a">Regression, Data processing, Noisy data, Random  sampling.</subfield>
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    <subfield code="u">Pursuing, Bachelors of Technology, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu</subfield>
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    <subfield code="u">Pursuing, Bachelors of Technology, Department of Computer Science and Engineering, Gitam Institute of Technology, Visakhapatnam, Andhra Pradesh</subfield>
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    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT</subfield>
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    <subfield code="u">Pursuing, BTech, Department of Computer Science and Engineering, Gitam university vishakapatnam, Andhra Pradesh</subfield>
    <subfield code="a">Jyothi Vishnu Vardhan Kolla</subfield>
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    <subfield code="a">An Optimized way to Solve Regression Problems</subfield>
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    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;</subfield>
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