Dataset Cleaning Tips
Handling Categorical and Text Data During Dataset Cleaning
This prompt helps data science teams develop effective techniques for cleaning categorical and text data. It focuses on standardizing labels, encoding categories, and processing text for better integration into machine learning models.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Engineering
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Identify the dataset’s categorical and text features and their roles in analysis or modeling.
Review inconsistencies in category names or common issues in text entries.
Define the objectives for processing categorical and text data, such as feature integration or topic modeling.
THE PROMPT
Help create a detailed plan for cleaning categorical and text data in [specific dataset or domain, e.g., customer feedback dataset]. Focus on:
Categorical Data Standardization: Recommending strategies, such as, ‘Identify inconsistent labels and standardize category names, removing typos or irrelevant entries.’
Encoding Methods: Suggesting suitable techniques, like, ‘Apply label encoding or one-hot encoding for categorical features, ensuring compatibility with machine learning models.’
Handling Rare Categories: Including recommendations, such as, ‘Group rare categories into an "Other" category or remove them if they lack significant representation.’
Text Preprocessing: Proposing text cleaning methods, such as, ‘Remove special characters, normalize case, and perform tokenization or lemmatization for text data.’
Missing Values in Categorical Features: Recommending imputation strategies, such as, ‘Replace missing values with the mode, a placeholder category, or domain-specific estimates.’
Provide actionable guidance for cleaning and preparing categorical and text data, ensuring compatibility with downstream analysis or modeling tasks. If additional details about the dataset or goals are needed, ask clarifying questions to refine the guidance.
Bonus Add-On Prompts
Propose strategies for handling multilingual text data during preprocessing.
Suggest methods for identifying and merging similar categories in large datasets.
Highlight techniques for reducing dimensionality in datasets with many categorical features.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on text data cleaning for sentiment analysis or natural language processing (NLP).
Include tips for handling hierarchical or nested categorical features.
Propose ways to visualize category distributions for identifying inconsistencies.
Highlight tools like spaCy, NLTK, or FastText for advanced text preprocessing.
Add suggestions for using libraries like CategoryEncoders for complex categorical encoding tasks.
WHEN TO USE
During the preprocessing of datasets with significant categorical or text data.
To prepare text and categorical data for use in machine learning or statistical models.
When analyzing customer feedback, survey data, or other mixed-format datasets.
WHEN NOT TO USE
For datasets without categorical or textual features.
If data cleaning focuses solely on numerical data preprocessing.