The best predictive analytics software and tools

Software and tools for predictive analytics

  • Alteryx

Alteryx Analytic Process Automation Platform is a no-code platform that specializes in building repeatable workflows using low-code and high-code analytics blocks. This platform is for companies who want to offer self-service analytics and data sciences for all departments. Alteryx uses augmented machine-learning to assist citizen data workers in building predictive models.

Cloud platform from the company makes it easy to share workflows online as well as at your desk. It also integrates seamlessly with many modern cloud ecosystem apps. Analytic Process Automation Platform combines data science, process automation, and data quality. It combines data preparation, analytics, machine learning, and data science into one service. This automation service has more than 80 data sources that are natively integrated. Alteryx’s Designer Service makes it simple to combine data sets, use codeless and code-friendly tools, and create visual workflows or reports.

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Alteryx offers training and educational information on machine learning through its Data Science Portal.

Alteryx provides a 30-day free license for Designer to business users. The company also offers a one-year, renewable Designer license for students, educators, and career changers. For more information on pricing, please contact the company.

  • Azure Machine Learning

Microsoft’s cloud platform provides business analytics services to support the machine learning process. These services include data preparation, training and validation of models, deployment and management, and monitoring. Microsoft claims that the platform can improve the ROI of machine-learning products, reduce the time required to train models by 70%, and reduce by 90% the number of lines of code needed to run pipelines. Azure Machine Learning offers PyTorch Enterprise as a support program for open-source deep-learning framework. This allows service providers to create and provide tailored support to their customers.

Azure ML offers responsible AI capabilities that make models more transparent, reliable and trustworthy. Visualizations, what-if analysis, and model explanation graphs are just a few of the features offered by Azure ML. It includes algorithms that test models for fairness, and an error analysis toolkit that debugs errors and improves accuracy.

Microsoft offers 60 compliance certificates, as well as tutorials for beginners and advanced. Azure is available for a free trial. Azure Machine Learning is free. However, users must pay for Azure Blob Storage and Azure Key Vault. Azure Container Registry, Azure Application Insights, Azure Container Registry, Azure Container Registry, and Azure Blob Storage. You can customize pricing options by service type, region, currency, and time period.

  • Databricks

The Lakehouse Platform is a combination of a data warehouse platform and a data lake. Databricks Lakehouse combines data warehouse and AI use cases onto one platform. It also provides a single platform for cloud deployments. The structured transactional layer of the warehouse is created using open-source technology Delta Lake. This open format storage layer, according to the company, provides reliability, security, and performance for streaming and batch operations. It can replace data silos by providing a single home for structured and semi-structured data.

The high-performance query engine Delta Engine. It has SQL and performance capabilities. This includes indexing, caching, and MPP processing. Direct file access is possible and native support for Python and data science frameworks can be obtained. AWS, Azure, and Google Cloud are cloud partners.

Databricks Data Science Workspace allows everyone to use a notebook environment. Existing notebooks can either be imported into the Databricks environment for a company or to the community edition.

  • DataRobot

DataRobot’s AI Cloud Platform allows for collaboration between all users, from data scientists and analysts to IT and DevOps teams, executives, and information workers. It includes data engineering, machine-learning, decision intelligence, and trusted AI services. The service supports decision intelligence by offering a no-code app creator, AI apps, and Decision Flows, which allow users to create rules that automate decisions. With the no-code app maker, users can convert a model into an AI program without needing to code. According to the company, this makes it easier to take AI-driven business decisions.

These apps include detailed explanations of predictions to help users understand any model’s decision. The no-code app creator allows users to perform what-if analyses by changing any one or more inputs in order to create new scenarios. Users can then compare the results. Companies can incorporate feedback from stakeholders and end users into their model revisions thanks to this transparency.

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  • ai

According to H2O.ai, the automated machine-learning capabilities of H2O.ai make artificial intelligence more accessible with high levels accuracy, speed, and transparency. It offers options to build models and apps, as well as monitor performance and adapt to changing conditions. These services can be used by data scientists, developers and machine learning engineers as well as IT professionals and business users.

The platform offers services such as data visualization, preprocessing transformers and dataset splitting. Outlier detection and feature encoding are also available. Automated validation and cross validation are also offered

These services are called automated machine learning

  • Hyperparameter autotuning
  • Modelling and ensembling
  • Automated label assignment
  • Automated model documentation
  • Handling of unbalanced data
  • Model leaderboards
  • Unsupervised machine learning

The platform includes a low-code framework for application development (Python/R), which allows you to create user interfaces and integrate machine learning.

A model repository, model deployment, and model monitoring are all available for machine learning operations.

IBM SPSS

IBM’s Statistical Package for the Social Sciences allows complex statistical data analysis through a library that includes text analysis, machine learning algorithms and open source extensibility. It can be integrated with big data and easily deployed into applications. It includes a statistical component that can be used for ad-hoc analysis, as well as a modeler with algorithms ready for immediate use. There is also a modeler in cloudpak for data and an AI and data container that can be used to build and run predictive models on premises or in the cloud. A number of related products are available to support predictive analytics software for students and teachers, as well as an analytical server that makes predictive analytics more accessible.

The statistics component can be used by business analysts to:

  • All aspects of the analytic process, from data preparation and management to analysis & reporting, are covered
  • Automated methods for identifying anomalies and statistical transformations in order to correct outliers
  • Visualize and deliver tables
  • Make sure to classify the cases and use predictor variables to predict the values of target variables.
  • Allows for accurate modeling of non-linear and linear relationships
  • Improve forecasts and plans by imputing missing values with expected values using regression and expectation-maximization

IBM has launched an early access program to assist intermediate and beginner users in statistics. The learning modules include a simplified UI and a guided tour of the software. They also have a dashboard that provides data overviews. This beta service is free for 60 days. IBM offers SPSS subscription plans and on-premise licensing editions. There are four levels: premium, standard, professional, and base. For pricing information, contact IBM

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IBM Watson Studio

This platform is IBM’s data science platform, formerly Data Science Experience. This platform offers a workspace, collaboration, and open-source data science tools. Cloud Pak Data as a Service’s core offering is Watson Studio. This service provides tools for analyzing and visualizing data, cleaning it up and shaping it into machine learning models.

Watson Studio’s architecture is built around a project, which includes collaborators, assets, and tools. The studio offers the following software:

  • Data Refinery: Prepare and visualize data
  • Jupyter notebook editor: Code Jupyter notebooks
  • RStudio Code Jupyter notebooks for R and R Shiny applications
  • SPSS Modeler: Automate data flow through models with SPSS algorithms
  • Decision Optimization model builder: Optimize solving business problem scenarios
  • Watson Machine Learning Services provides Deployment Spaces and Watson Knowledge Catalog services for projects.

RapidMiner

This data science platform offers an integrated environment for data preparation and machine learning. It also supports text mining, predictive analytics, deep learning, text analysis, and text mining. It can be used for business applications, as well as research, education and training, rapid prototyping, application development, and research. The company claims that the platform is robust enough to support data scientists, but user-friendly enough for all users. Data scientists will find the following features:

  • 500+ native algorithms, data prep, and data science functions
  • Support for many machine learning libraries from third parties
  • Notebooks and integration of custom Python and R
  • Platform services and advanced analytics
  • Business users have the following features:
  • Use case templates
  • Online certification that you can do yourself by a person

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Fully automated options

RapidMiner AI Cloud is designed for all users. It offers an augmented and guided user experience, a visual interface with minimal learning curves and explanations of the data and modeling process.

RapidMiner Academy, training and certification services are available to the company. Global partners are available for support and integrations that speed up data access and the deployment of machine-learning models. For pricing information for enterprises, please contact the company.

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