Analyzing the Generalized Association between Attribute Selection and Data Handling

This paper explores the association between attribute selection methods and data handling techniques in machine learning models. The primary objectives are to identify a suitable dataset and perform data cleaning, determine the most significant attributes using a custom model, and evaluate the performance of models using cleaned and processed datasets. Through comprehensive experiments and analysis, we demonstrate the impact of different attribute selection methods on data quality and model performance. The results provide insights into optimizing data preprocessing for enhanced predictive accuracy.