Overview
What this challenge is about.
Build a sentiment model from 10,000 cosmetics reviews using NLP. Hit 80% accuracy and get your verifiable certificate.
The scenario
GlowUp Naturals is a fast-growing D2C cosmetics brand in Tel Aviv, selling vegan and cruelty-free products. They have a strong social media presence but lack automated tools to analyze customer feedback at scale.
The Brief
What you'll do, and what you'll demonstrate.
How can GlowUp Naturals automatically extract sentiment from customer reviews to identify strengths and weaknesses of their new lipstick line?
Earning criteria — what you'll demonstrate
- Apply text preprocessing techniques (tokenization, stopword removal, stemming/lemmatization)
- Implement and compare different feature extraction methods (BoW, TF-IDF)
- Train and evaluate a supervised classification model (e.g., Logistic Regression, Naive Bayes)
- Interpret model results to derive actionable business insights
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Text Preprocessing
Apply text preprocessing to solve real industry problems and demonstrate production-level capability.
- Sentiment Analysis
Apply sentiment analysis to solve real industry problems and demonstrate production-level capability.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Nltk
Apply nltk to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles: