The Essential Steps in Data Preprocessing for Different Data Formats

This post details essential data preprocessing steps for various data formats (structured, textual, temporal, image) within the context of exploratory data analysis. It provides code snippets for handling missing values, normalization, one-hot encoding, feature selection for structured data; tokenization, stop word removal, stemming/lemmatization, and text vectorization for textual data; parsing dates, handling time gaps, and feature engineering for temporal data; and resizing, normalization, and augmentation for image data. It emphasizes the importance of these steps for accurate analysis and model training.