NLP Project (Sentiment / Text Classification)
Part of the Data Science Roadmap
In short
Companies drown in reviews, tickets and chats. A text classifier that sorts them, and explains what people complain about, is one of the most useful and hireable projects you can show.
A dedicated NLP portfolio piece, ideally distinct from your Phase 10 practice project, demonstrating text preprocessing, feature representation, and model choice on a real text dataset.
Think of it like this
The feedback slip baskets
A sweet-shop owner reads hundreds of feedback slips and sorts them into 'happy' and 'unhappy' baskets. Then she notices most complaints say 'too sweet'. Your NLP project does the sorting at scale, and then tells the owner why people are unhappy.
Watch it happen
The build plan
1/6Pick a real text dataset with labels, like product or movie reviews, ideally a few thousand rows.
The big ideas
What you need to know
A dataset with a story
Pick text you understand, like food delivery or app reviews, so error analysis makes sense.
Baseline before transformers
TF-IDF plus logistic regression is fast and often strong. Any fancy model must beat it.
Error analysis section
Show real misclassified examples and explain the pattern. This is what reviewers remember.
A tiny demo
A Streamlit or Gradio box where anyone can paste a review and see the prediction.
See it in code
Walk through the code
1from sklearn.pipeline import make_pipeline2from sklearn.feature_extraction.text import TfidfVectorizer3from sklearn.linear_model import LogisticRegression4 5texts = ["Delivery was quick, loved it", "Cold food, never again",6 "Great taste and fresh", "Late and the box was broken"]7labels = [1, 0, 1, 0]8 9clf = make_pipeline(TfidfVectorizer(ngram_range=(1, 2)),10 LogisticRegression(max_iter=1000))11clf.fit(texts, labels)12 13print(clf.predict(["fresh and quick", "broken and cold"]))1/3Tiny labelled sample: 1 means positive, 0 means negative.
The classic text baseline in a few lines. Swap the four toy reviews for a real dataset of thousands.
Save yourself the pain
Common mistakes
Oops
Jumping straight to fine-tuning a large transformer.
Do this instead
Start with TF-IDF plus logistic regression. Then show how much, if at all, the bigger model improves.
Oops
Reporting 90% accuracy when 90% of reviews are positive.
Do this instead
Report F1 for the negative class and a confusion matrix. That's where the business value is.
Your mission
First milestone: a baseline and its errors
Quick check
Why does setting ngram_range=(1, 2) help with reviews like 'not good'?
Go deeper
- 1Pick a text dataset and business question different from your Phase 10 exercise.
- 2Compare at least a classic (TF-IDF) and a modern (pretrained model) approach.
- 3Include example predictions, both correct and incorrect, in your write-up.
More projects at this stop
End-to-End ML Project (Problem to Deployment)
Kaggle Competition Submission
Time Series Forecasting Project
Computer Vision Project
Business Case Study With ML Recommendation
Portfolio Website / GitHub Showcase
Stuck on this topic? Ask an Insider
Get 1:1 guidance from people who've walked this exact path — on the InsideEdge app.
