Luzmo
Geckoboard
Metabase
Vizzly
Embeddable
Microsoft Power BI
Livedocs
Sisense
FuzzyWuzzy
Amazon Comprehend
spaCy
Microsoft Bing Spell Check API
Google Cloud Natural Language API
OpenNLP
NLTK
TextBlob
Luzmo is an embedded analytics platform, purpose-built for SaaS companies. We bring complex data to life with beautiful, easy-to-use dashboards, embedded seamlessly in any SaaS or web platform. With Luzmo, product teams can add impactful insights to their SaaS product in days, not months. And take their product users from data to decisions, rapidly fast.
Luzmo FuzzyWuzzyNo FuzzyWuzzy videos yet. You could help us improve this page by suggesting one.
Based on our record, FuzzyWuzzy should be more popular than Luzmo. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Recently one of our Cumul.io Ambassadors shared a company Strava dashboard they built with Cumul.io and I had to build something similar for us. It's a nice way to keep motivated to go out for runs and great for those who are competitive when it comes to exercising (NOT me). And it's a fun was to use a data visualization tool like Cumul.io. So anyway, I followed the lead of Olivier de Lamotte who gave us the idea... - Source: dev.to / over 2 years ago
Cumul.io has a number of SDKs available for people to install and use, but we were missing one in Python. So I built one! It's a simple one that provides interaction with our Core API (For those of you who don't know I'll add some info about Cumul.io at the end of this post). This might not be surprising to a lot of you but as it was my first go, I soon discovered there are a plethora of routes you can take to... - Source: dev.to / about 3 years ago
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: about 2 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago
Geckoboard - Get to know Geckoboard: Instant access to your most important metrics displayed on a real-time dashboard.
Amazon Comprehend - Discover insights and relationships in text
Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...
spaCy - spaCy is a library for advanced natural language processing in Python and Cython.
Vizzly - Customer-facing dashboards for your app. Build in days, not months.
Microsoft Bing Spell Check API - Enhance your apps with the Bing Spell Check API from Microsoft Azure. The spell check API corrects spelling mistakes as users are typing.
