Cereal Recommender

• Tech Stack: Python, Selenium, BeautifulSoup, NLTK, Spacy, Sentiment Analysis
• Github URL: Project Link

Scraped data of over 1500 cereals from Mrbreakfast.com, including user reviews, and employed user requirements of desirable attributes or similar cereals.

Recommends the top-3 niche products based on results from Sentiment Analysis and Word2Vector.

Output looks like this :

For product Sweet Crunch Cereal:

• Attribute `crunchy` occurs in 6.30% of the reviews

• Attribute `fruity` occurs in 0.00% of the reviews

• Attribute `sweet` occurs in 29.17% of the reviews

Please feel free to check the GitHub for full analysis and the code.