Rahayu Putri Andini

Detail information

Data preprocessing refers to the process of preparing and cleaning raw data before it can be used for analysis or modeling. It involves transforming and organizing the data in a way that makes it suitable for further processing. Data preprocessing techniques are applied to address common issues such as missing values, outliers, noise, inconsistencies, and irrelevant or redundant features in the dataset. The goal of data preprocessing is to improve the quality, accuracy, and reliability of the data, and to ensure that it is in a format that can be effectively utilized by machine learning algorithms or other analytical techniques. Some common data preprocessing techniques include data cleaning, data transformation, normalization, feature selection or extraction, and handling missing data.