Software Alternatives, Accelerators & Startups

Scikit-learn VS Causal App

Compare Scikit-learn VS Causal App and see what are their differences

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Causal App logo Causal App

Causal replaces your spreadsheets and slide decks with a better way to perform calculations, visualise data, and communicate with numbers. Sign up for free.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Causal App Landing page
    Landing page //
    2023-07-23

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

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

Causal App videos

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Category Popularity

0-100% (relative to Scikit-learn and Causal App)
Data Science And Machine Learning
Productivity
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Fintech
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 Scikit-learn and Causal App

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...

Causal App Reviews

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Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Causal App. 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.

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 / 17 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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Causal App mentions (17)

  • My Thoughts on Python in Excel
    IMO the better paradigm is coming from enterprise applications like Anaplan. Cells are not the right abstraction to work with numbers. Most of the time you work with multi-dimensional quantities (eg revenue by product, geography, month). We’re working on a more approachable implementation of that paradigm at https://causal.app. - Source: Hacker News / 19 days ago
  • Show HN: Type-safe feature flags with Git versioning, local fallbacks, GraphQL
    We're using Hypertune at https://causal.app for a few months now and it's been great! We have a few feature flags in there but also some more complex typed data for our onboarding modals. - Source: Hacker News / about 1 year ago
  • Show HN: Hypertune – Visual, functional, statically-typed configuration language
    Congrats on the launch! We've been using Hypertune at Causal (https://causal.app) for the last few months and it's saved tonnes of engineering cycles letting me and our PM iterate directly on custom onboarding copy for different Causal templates, alongside more typical feature flag use-cases :). - Source: Hacker News / about 1 year ago
  • Enterprise AE - Career Path (Advice needed)
    If you're particularly keen go onto some of the prep courses there are out there. wall street prep is one, there are other PE prep courses hawked on here for as little as 10 bucks. All are built around excel skills and learning DCFs. I recommend causal.app if you want to try to skip some of this and get forced into a tool. Source: about 1 year ago
  • Minimum Viable Finance: The Guide for Seed/Series A Startups
    Hi HN, I'm the founder of https://causal.app and the author of this post — Most of the finance content online is very textbook-y and overkill for early stage cos, so wanted this to be a 'no-nonsense' guide for founders/ops people that have to juggle a bit of finance stuff alongside everything else. We've helped lots of startups across different stages with finance stuff over the last few years through Causal, so... - Source: Hacker News / over 1 year ago
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What are some alternatives?

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

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

Pry Financials - Finance for Founders

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

Finmark - Financial planning software for startups

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

Sturppy - Helping founders around the world create investor-ready financial models & forecasts