• Suhani Arora

This study shows the impact of feature scaling on the performance of classification algorithms using the Wine dataset. Five classifiers were evaluated with and without applying standardization- Logistic Regression, KNN, SVM, Decision Tree, and Random Forest. The results show that distance-based classifiers show huge improvement in performance after feature scaling, but the tree-based models remain largely unaffected. The findings show that the effect of feature scaling depends on what classification algorithm is used.

Feature scaling, Classification, Distance-based algorithms, Tree-based algorithms

"Impact of feature scaling on the performance of classification algorithms: a comparative study on wine dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.b76-b78, January-2026, Available :http://www.jetir.org/papers/JETIR2601112.pdf

"Impact of feature scaling on the performance of classification algorithms: a comparative study on wine dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppb76-b78, January-2026, Available at : http://www.jetir.org/papers/JETIR2601112.pdf