Data preprocessing is a critical step in any machine learning or data analysis pipeline. Here are some common tools and techniques:

  • Text Cleaning: Remove noise, normalize case, and handle special characters

    text_cleaning

  • Data Standardization: Scale numerical features to a standard range (e.g., 0-1)

    data_standardization

  • Missing Value Handling: Impute or remove missing data points

    missing_value_handling

  • Feature Encoding: Convert categorical variables into numerical formats

    feature_encoding

For advanced strategies, check our guide on Data Preprocessing Best Practices. 📘✨