Software Alternatives, Accelerators & Startups
Register | Login

Cal.com VS Scikit-learn

Compare Cal.com VS Scikit-learn and see what are their differences

Monitask
Employee Monitoring Software with Screenshots, Internet, Activity and Time Tracking featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Cal.com logo Cal.com

Cal.com (formerly Calendso) is the open source Calendly alternative.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Cal.com Landing page
    Landing page //
    2023-10-08
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Cal.com features and specs

  • Customizable
    Cal.com allows extensive customization to fit various branding and scheduling needs, which makes it adaptable for different types of users including businesses and individuals.
  • Open-source
    Being an open-source platform, Cal.com provides the flexibility for developers to modify and extend the software as per their specific needs, fostering a collaborative development environment.
  • Integrations
    Cal.com offers a wide range of integrations with other software tools like Google Calendar, Microsoft Outlook, and Zoom, enhancing its functionality and making it easier to fit into existing workflows.
  • User-friendly Interface
    Cal.com has an intuitive and clean interface that makes it easy for users of all technical skill levels to set up and manage their scheduling.
  • Privacy-focused
    Cal.com emphasizes data privacy, ensuring user information is handled securely, which is crucial for users who need to comply with regulations like GDPR.

Possible disadvantages of Cal.com

  • Learning Curve
    Although it is highly customizable, the plethora of options and features may result in a steeper learning curve for new users who are not familiar with such scheduling tools.
  • Limited Free Version
    The free version of Cal.com comes with limitations that may not be sufficient for growing businesses or advanced users who require more comprehensive features.
  • Dependency on Integrations
    Cal.com's effectiveness heavily depends on its integrations. Without these integrations, some users might find the tool less useful or incomplete, especially if their primary tools are not supported.
  • Support
    While open-source has many benefits, it may also mean that immediate, personalized support could be limited compared to fully commercial solutions. This might pose a challenge for users needing quick resolutions.
  • Performance
    As an open-source platform, the performance might vary depending on how it is hosted and managed. Suboptimal configurations could lead to slower performance or downtimes.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Analysis of Cal.com

Overall verdict

  • Cal.com is generally considered a good option for scheduling and calendar management.

Why this product is good

  • Cal.com is praised for its open-source nature, allowing for greater customization and integration flexibility. It offers a user-friendly interface and supports various calendar integrations, making it a versatile tool for individuals and businesses alike.

Recommended for

  • Freelancers who need a simple yet effective scheduling tool.
  • Small businesses looking for a customizable scheduling solution.
  • Developers who appreciate open-source software and need a tool they can modify.
  • Businesses seeking a platform that can integrate with existing tools and workflows.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Cal.com videos

+ Add

What can you do with Cal? | Cal.com Version 1.1 Launch | 10 new languages

More videos:

  • Review - Cal.com Version 1.0 Launch Event

Scikit-learn videos

+ Add

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 Cal.com and Scikit-learn)

Cal.com

Scikit-learn

Productivity
100 100%
Productivity
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Appointments and Scheduling
100 100%
Appointments and Scheduling
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Cal.com and Scikit-learn. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

Cal.com Reviews

I've poked around a while ago at some Calendly alternatives (specifically was lo... | Hacker News
I tried using https://cal.com for a bit but ended up just switching over to https://zcal.co and it has been great so far. All these other scheduling tools end up trying to do too much and always seem to end up a bit clunky and charge absurd amounts for it
Source: news.ycombinator.com

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...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Cal.com should be more popular than Scikit-learn. It has been mentiond 56 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.

Cal.com mentions (56)

  • 5 Side Project Ideas for Developers to Monetize as Micro-SaaS in 2025
    Take Cal.com (https://cal.com/), formerly known as Calendso. It started as an open source alternative to Calendly which offers a free, self-hostable version for users. - Source: dev.to / 3 months ago
  • Using Clerk SSO to access Google Calendar and other service data
    BookMate is an open-source, publicly accessible, lightweight clone of popular booking services like cal.com or Calendly. - Source: dev.to / 6 months ago
  • My Journey into Open Source: First Contributions and Lessons Learned
    Then, I came across Cal.com, a fantastic open-source project for scheduling meetings and managing tasks (super useful for productivity!). I knew the basics of Git but wasn’t quite there with forking, merging branches, and all the intricate Git processes. After some YouTube tutorials, I started to get the hang of things. 😅. - Source: dev.to / 7 months ago
  • Start your own (side) business with open-source in mind
    Cal.com is an open-source event-juggling scheduler for everyone, and is free for individuals. - Source: dev.to / over 1 year ago
  • Fellow HSP entrepreneurs, how do you manage your energy and stress?
    I force clients who want to talk to me to book a call. I use cal.com (free) and my Google Calendar (which its linked to) only allows calls on specific days/times. I have a few "Call Blocks" where they can book. That let's me do calls in a small section of my week, with ample downtime to recover the rest of the week. I'm still learning how many calls a day I can handle. Currently anything more than 2 is too much. Source: over 1 year ago
View more

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 months ago
  • 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 / 12 months 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 / over 1 year 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 / almost 2 years ago
View more

What are some alternatives?

When comparing Cal.com and Scikit-learn, you can also consider the following products

Calendly - Say goodbye to phone and email tag for finding the perfect meeting time with Calendly. It's 100% free, super easy to use and you'll love our customer service.

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

TidyCal - Optimize your schedule with custom booking pages and calendar integrations

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

SavvyCal - A scheduling tool both the sender and the recipient will love.

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

Loading...