Feature engineering-based predictive modelling using a non-imputed dataset Online publication date: Thu, 24-Feb-2022
by Partha Sarathi Mishra; Debabrata Nandi
International Journal of Data Science (IJDS), Vol. 6, No. 3, 2021
Abstract: This paper presents random investigations of different regression algorithms of machine learning techniques by considering some of the feature engineering principles using a major nutrient-based soil dataset containing missing values and noisy instances. Here, one of the feature engineering techniques like imputation is considered and emphasised for the efficient random investigation analysis using seven regression machine learning algorithms. The subsequent dataset then participates without losing its basic features and negotiating accuracy results. Finally, the transformed input dataset tuned by feature engineering principles is used by the different machine learning algorithms for random investigations. The result obtained from the different experiments ensures that the feature engineering-based linear regression, Ridge regression, and Classification and Regression tree regression algorithms powered by explanatory are one of the significant approaches in the field of machine learning in building a potent explanatory model with improved performances.
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