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Cleaning text data is 80% of any NLP project — here's my actual checklist:
Strip HTML, hashtags, special characters
Lowercase + tokenize
Remove stop words (carefully — sometimes they matter)
Lemmatize, don't just stem, if word meaning matters
Most "bad model" complaints I've seen are actually bad-data problems.
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Would you rather inherit a messy dataset with accurate numbers, or a clean-looking dataset where a few numbers are quietly wrong?
I'd take messy every time. Messy at least announces itself, duplicate rows, inconsistent formats, obvious gaps. A clean-looking spreadsheet with a wrong formula three columns over looks exactly as trustworthy as the correct one, right up until someone builds a decision on it.
A sample Python project showing my data cleaning workflow — removing duplicates, handling missing values, standardizing text fields, and summarizing sales by city using Pandas. This reflects the kind of cleanup and analysis I do for client datasets.
Cleaned and formatted a raw dataset in Excel by removing duplicates, fixing formatting issues, handling missing values, and organizing the data into a structured format for analysis.
