• Dwarampudi Tejo Madhuri
• ALUGOLU AVINASH

This paper explores novel preprocessing techniques for numerical data analysis, aimed at improving model performance in machine learning tasks. We introduce advanced data transformation methods, including adaptive scaling [1], robust outlier handling, and feature synthesis techniques, which enhance data quality and reduce noise. Additionally, we discuss innovative approaches to feature selection and dimensionality reduction to optimize competitional efficiency. Experimental results demonstrate significant improvements in predictive accuracy across various datasets, highlighting the potential of these techniques to advance data-driven decision-making.

Preprocessing , Scaling, Imputation, Dimensionality.

"Novel Preprocessing Techniques for Numerical Data Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.g123-g128, October-2024, Available :http://www.jetir.org/papers/JETIR2410616.pdf

"Novel Preprocessing Techniques for Numerical Data Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppg123-g128, October-2024, Available at : http://www.jetir.org/papers/JETIR2410616.pdf