Dataset Cleaning Tips
Cleaning Unstructured Data for Analysis
This prompt helps data science teams develop a plan for cleaning unstructured data, such as logs, social media posts, or raw text, for analysis. It focuses on extracting valuable information, handling inconsistencies, and preparing data for modeling.
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 type of unstructured data and its relevance to the analysis or modeling goal.
Review domain-specific challenges, such as abbreviations, slang, or formatting irregularities.
Gather tools or libraries for preprocessing unstructured data, such as NLTK, spaCy, or BeautifulSoup.
THE PROMPT
Help create a cleaning plan for preparing unstructured data in [specific dataset or domain, e.g., social media sentiment analysis]. Focus on:
Text Normalization: Recommending steps, such as, ‘Standardize text data by converting to lowercase, removing special characters, and correcting spelling errors using tools like TextBlob.’
Noise Removal: Suggesting cleaning techniques, like, ‘Eliminate irrelevant data such as stop words, HTML tags, or session logs to focus on meaningful content.’
Tokenization and Lemmatization: Proposing preprocessing steps, such as, ‘Break text into tokens and reduce words to their base forms to improve consistency and reduce dimensionality.’
Feature Extraction: Including transformation strategies, such as, ‘Convert unstructured data into structured formats using techniques like term frequency-inverse document frequency (TF-IDF) or embedding models.’
Inconsistency Resolution: Recommending validation checks, such as, ‘Address inconsistencies in date-time formats, language variations, or other non-textual elements in the dataset.’
Provide actionable guidance for cleaning unstructured data and transforming it into analyzable formats for downstream tasks. If additional details about the dataset or goals are needed, ask clarifying questions to refine the plan.
Bonus Add-On Prompts
Propose strategies for handling multilingual unstructured data during preprocessing.
Suggest methods for visualizing cleaned unstructured data to identify remaining inconsistencies.
Highlight techniques for integrating cleaned unstructured data with structured datasets.
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 cleaning specific unstructured data types, like logs, images, or video transcripts.
Include tips for extracting metadata or tags from raw unstructured datasets.
Propose ways to handle unstructured data in real-time pipelines using tools like Apache NiFi.
Highlight tools like Regex or advanced NLP models for handling diverse data cleaning needs.
Add suggestions for documenting transformations to maintain traceability.
WHEN TO USE
When preparing raw unstructured data for machine learning, sentiment analysis, or exploratory tasks.
To standardize data cleaning workflows across projects or teams.
During data integration to align unstructured and structured datasets.
WHEN NOT TO USE
For structured data that requires minimal cleaning.
If tools or resources for handling unstructured data are unavailable.