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Scikit-learn VS InsightSquared

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

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Scikit-learn logo Scikit-learn

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

InsightSquared logo InsightSquared

#1 for Salesforce.com Pipeline forecasting, profitability analysis, activity tracking: all the small business intelligence you need. Works using CRM data and automatic syncing.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • InsightSquared Landing page
    Landing page //
    2021-09-29

  learn.insightsquared.comSoftware by InsightSquared

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.

InsightSquared features and specs

  • User-Friendly Interface
    InsightSquared offers an easy-to-navigate dashboard with customizable reports and visualizations, making it accessible for users with varying levels of technical expertise.
  • Comprehensive Analytics
    Provides in-depth sales analytics and KPIs that help sales teams track performance and make data-driven decisions effectively.
  • Integration Capabilities
    Can be easily integrated with popular CRM solutions like Salesforce, ensuring seamless data flow and unified analytics.
  • Customer Support
    InsightSquared is known for its excellent customer support, offering timely assistance and resources for troubleshooting.

Possible disadvantages of InsightSquared

  • Cost
    The platform may be cost-prohibitive for small businesses or startups, making it more suited for mid-size to large enterprises.
  • Learning Curve
    Despite the user-friendly interface, there can be a learning curve for new users to fully utilize all the advanced features and capabilities.
  • Customization Limitations
    While the tool offers many built-in reports, users might find limitations in customizing certain aspects to fit extremely specific organizational needs.
  • Integration Issues
    Occasional issues may arise with data syncing from specific CRM systems or other integrations, necessitating manual adjustments.

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.

Analysis of InsightSquared

Overall verdict

  • InsightSquared is considered a valuable tool for businesses looking for robust sales analytics and performance management solutions.

Why this product is good

  • Comprehensive Analytics: InsightSquared offers deep insights into sales data, helping businesses make informed decisions.
  • User-Friendly Interface: The platform is designed with a user-friendly dashboard that is easy to navigate, even for users who may not be tech-savvy.
  • Integrations: It integrates well with popular CRM systems like Salesforce, allowing for seamless data synchronization.
  • Customizable Reports: Users can create bespoke reports tailored to their specific needs and business goals.
  • Real-Time Data: Provides up-to-the-minute data, enabling businesses to react swiftly to changing conditions and opportunities.

Recommended for

  • Small to Medium Businesses (SMBs) looking to optimize their sales processes.
  • Companies that rely on Salesforce and want to enhance their CRM with powerful analytics.
  • Sales managers who need detailed, actionable insights into team performance.
  • Businesses looking for a cost-effective analytics solution compared to full-scale business intelligence platforms.
  • Organizations aiming to improve forecasting accuracy and sales pipeline management.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

InsightSquared videos

+ Add

InsightSquared Customer Webinar - A Sales VP's Guide to the Quarterly Business Review

More videos:

  • Review - Nate G. Reviews His Experience Using InsightSquared
  • Review - What is InsightSquared?

Category Popularity

0-100% (relative to Scikit-learn and InsightSquared)

Scikit-learn

InsightSquared

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Business Intelligence
0 0%
Business Intelligence
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Data Dashboard
45 45%
Data Dashboard
55% 55

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 InsightSquared

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

InsightSquared Reviews

8 Alternatives to Apache Superset That’ll Empower Start-ups and Small Businesses with BI
Apache Superset can hold a vast universe of SQL data to fit their users' needs. InsightSquared is a revenue intelligence specialised business intelligence tool. Therefore, InsightSquared integrations are limited to that purpose.
Source: trevor.io

Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 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 (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

InsightSquared mentions (0)

We have not tracked any mentions of InsightSquared yet. Tracking of InsightSquared recommendations started around Mar 2021.

What are some alternatives?

When comparing Scikit-learn and InsightSquared, 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.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

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

Clari - Clari is a predictive analytics platform for sales people.

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