preprosessointina
Preprocessing is a crucial step in data preparation for machine learning and data analysis. It involves transforming raw data into a format that can be effectively used by machine learning algorithms. The primary goal of preprocessing is to clean and organize the data, making it more suitable for analysis and improving the performance of machine learning models.
Common preprocessing techniques include data cleaning, which involves handling missing values, removing duplicates, and correcting errors.
Preprocessing can also involve data reduction techniques, such as dimensionality reduction, to simplify the data while
The choice of preprocessing techniques depends on the nature of the data and the specific requirements of