Data processing and preprocessing are critical steps in any machine learning workflow. Before training a model, data must be prepared in a structured and clean format to ensure accuracy and efficiency. Raw data is often noisy, incomplete, or inconsistent, making preprocessing a crucial step to improve the quality of input data.
Preprocessing involves several techniques, such as handling missing values, normalizing features, encoding categorical data, and feature selection. These steps help transform raw data into a suitable format for machine learning algorithms, reducing biases and improving model performance. Properly processed data ensures that machine learning models generalize well to new, unseen data, preventing overfitting and underfitting.