Software Alternatives, Accelerators & Startups

productboard VS Scikit-learn

Compare productboard VS Scikit-learn and see what are their differences

productboard logo productboard

Beautiful and powerful product management.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • productboard Landing page
    Landing page //
    2023-05-05
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

productboard videos

ProductBoard Review | Project Management Tool | Pearl Lemon Review

More videos:

  • Review - Welcome to productboard!
  • Review - ProductBoard Helps You Make the Right Thing at Disrupt SF Startup Battlefield

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to productboard and Scikit-learn)
Project Management
100 100%
0% 0
Data Science And Machine Learning
Customer Feedback
100 100%
0% 0
Data Science Tools
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare productboard and Scikit-learn

productboard Reviews

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Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than productboard. It has been mentiond 29 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.

productboard mentions (4)

  • Do you use an additional tool aside from JIRA?
    Admittedly, this is an issue with organization and can be solved with thorough cleanups, but I suspect that may disrupt the usual flow of non-PM people more. I am thinking of using a separate tool like craft.io or productboard.com to highlight strategies, roadmaps, cross-team initiatives, discoveries, etc. With a possible link to JIRA somehow. Has anyone ever tried this? Source: about 2 years ago
  • Think twice before using AGE in PotgreSQL
    Recently my friend at Productboard noticed an interesting bug in one of our services. For some reason our code responsible for calculating how many days our customers' features spend in certain states (Idea, Discovery, Delivery, etc) in some cases would give us wrong results. - Source: dev.to / about 2 years ago
  • Which tools you use in your role of PM?
    ProductboardProductboard helps us capture user feedback from email, Slack, Zendesk, our public-facing product portal etc. And see what users need the most. We also use it for prioritizing product objectives, release planning, roadmapping…. Source: almost 3 years ago
  • Ask HN: What software do you use to gather requirements?
    I use ProductBoard. It's fairly expensive but pretty great. I gather requirements into PB and use the inbuilt editor to flesh them out. When a story is ready I push a button and it ends up in Trello (but you can add your own integrations; there's one for github for example). The integrations aren't perfect but I love it. Used it in my last job and brought it in at my current job. https://productboard.com. - Source: Hacker News / about 3 years ago

Scikit-learn mentions (29)

  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 22 days ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 4 months ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago
  • WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
    The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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What are some alternatives?

When comparing productboard and Scikit-learn, you can also consider the following products

Aha - Aha! is the new way to create visual product roadmaps. Web-based product management tools and roadmapping software for agile product managers.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Canny - Canny helps you collect and organize feature requests to better understand customer needs and prioritize your roadmap.

OpenCV - OpenCV is the world's biggest computer vision library

ProdPad - ProdPad helps your team gather ideas, surface the best ones and turn them into product specs, and then put it all on a product roadmap.

NumPy - NumPy is the fundamental package for scientific computing with Python