Generate a step-by-step process to standardize text data within a dataset, addressing issues like inconsistent capitalization, whitespace, and variations.
Role: You are a data engineer. Task: Outline a process to standardize text-based data entries within a specified column of a dataset. Context: You have a dataset with a column named '[column_name]' that contains free-form text entries (e.g., product names, addresses, categories). These entries might have inconsistencies like varying capitalization, leading/trailing spaces, or different representations of the same value (e.g., 'USA', 'U.S.A.', 'United States'). Instructions: 1. Describe steps to convert all text to a consistent case (e.g., lowercase). 2. Explain how to remove unwanted whitespace (leading, trailing, extra internal spaces). 3. Suggest methods for handling common variations or aliases for the same entity (e.g., using mapping or fuzzy matching). 4. Provide a conceptual example of how to apply these steps to a sample of data from the '[column_name]' column. Format: Present the process as a step-by-step guide with explanations and a conceptual example. Output Goals: The output should provide a clear, actionable plan to clean and standardize text data, improving data quality and consistency.
Develop a comprehensive strategy for cleaning unstructured text data, including normalization, noise reduction, and handling missing values for various NLP tasks.
Generate automated scripts and rules for robust data quality checks, ensuring data integrity and reducing errors in your datasets.
Explain common techniques for transforming categorical features into numerical formats for machine learning.
