5 Best Predictive Modelling Tools and Software

IBM Watson Studio

IBM Watson Studio is a powerful data science and machine learning platform that provides decision-makers with valuable tools to enhance their business strategies. By offering a collaborative environment for data scientists and developers, Watson Studio streamlines the process of data preparation, model development, and deployment. Its automated machine-learning capabilities make it accessible to a wide range of professionals, even those without extensive machine-learning expertise.IBM Watson Studio offers a versatile and comprehensive set of features that make it a standout platform for data science and machine learning. Its AutoAI capability automates much of the machine-learning process, making it accessible to users with varying levels of expertise.

Collaboration tools support real-time teamwork and knowledge sharing, while open-source support allows flexibility in tool choices. The platform also excels in model deployment, integration with other IBM Watson services, and data preparation. Its enterprise-ready features ensure data security and compliance, and it provides prebuilt models, experiment tracking, deployment flexibility, model explainability, and access to a range of deployment options. Decision makers can leverage Watson Studio to gain actionable insights from their data, make data-backed decisions, and deploy predictive models to improve business outcomes. This platform empowers organisations to harness the full potential of their data, driving innovation and informed decision-making in a rapidly evolving digital landscape.

Microsoft Azure Machine Learning

Microsoft Azure Machine Learning (Azure ML) is a cloud-based platform that equips decision-makers with the tools and capabilities to accelerate decision-making, drive predictive analytics, and optimise costs.

It enables rapid model development and deployment, offering insights into future trends and customer behaviour. With a pay-as-you-go pricing model and seamless integration with Azure services, Azure ML allows decision-makers to allocate resources efficiently, scale operations as needed, and build end-to-end solutions across various domains. 

Automated features optimise credit risk and save time, while stringent security and compliance standards mitigate data-related risks. Decision-makers can foster collaboration, monitor model performance, and adapt strategies for continuous improvement, making Azure ML an invaluable asset for data-driven decision-making in today’s competitive business environment.

Model.ai by Corestrat

Corestrat’s Model.ai is a no-code, enterprise-ready solution, that allows you to build and deploy classification and/or regression predictive models with just a few clicks. With Model.ai, decision-makers are empowered to make data-driven decisions based on hidden patterns in data. Model.ai serves as an innovative platform that requires no training, programming, or intricate formulas, empowering both data scientists and non-data experts to effortlessly create predictive models with a few simple clicks. Leveraging the power of AutoML, Model.ai automates the construction of high-performing models while continuously learning from historical data to yield enhanced results. Organisations, spanning from lending institutions to the retail sector, can use Model.ai to develop predictive models, enabling them to make decisions based on data.

For example, lending institutions, fintech companies and anyone entering the lending business seeking meaningful insights from extensive sets of both conventional and non-traditional data can leverage Model.ai to obtain a holistic understanding of a borrower’s financial well-being. Just like a prominent tech company based in Singapore, which lacked expertise in lending, it utilised Model.ai to establish its lending business successfully.

Corestrat’s Model.ai played a pivotal role in assisting this prominent tech company based in Singapore in devising a data-centric approach for segmenting their customer base, enabling them to offer unsecured credit products to millions of existing users within their Super App Ecosystem. The decision-makers within the organisation can utilise the Ecosystem Risk Score, driven by Model.ai, to assess an array of both conventional and unconventional data sources. This enables the client to build multi-objective optimisation strategies in their portfolio management to forecast actions that increase lending margin while limiting credit loss risk.

Tensorflow

TensorFlow is an open-source machine learning framework developed by the Google Brain team. It is widely used in predictive modelling and machine learning applications. TensorFlow provides a flexible and comprehensive ecosystem for building and deploying predictive models. It offers a range of features, including model customisation, deep learning capabilities, and scalability, allowing organisations to build predictive models tailored to their specific needs. 

TensorFlow’s deep learning capabilities enable the discovery of complex patterns within large datasets, enhancing predictive accuracy and reducing uncertainty in decision-making. It also provides tools for automated feature engineering and transfer learning, streamlining model development and saving time. The framework supports real-time insights, model interpretability, and continuous learning, empowering decision-makers to adapt to changing conditions and make informed, dynamic decisions. 

TensorFlow equips decision-makers with the means to create accurate and scalable predictive models, fostering data-backed decision-making across various industries and domains. Its flexibility and capabilities ensure organisations can harness the full potential of their data, gaining valuable insights to drive strategic and informed decisions.

H2O Driverless AI

H2O Driverless AI is an advanced artificial intelligence (AI) and machine learning platform developed by H2O.ai. It is designed to automate many of the complex and time-consuming tasks associated with building, deploying, and managing predictive models. H2O Driverless AI is known for its AutoML (Automatic Machine Learning) capabilities, which enable data scientists, analysts, and domain experts to create high-performing machine learning models with minimal manual intervention.

H2O Driverless AI is highly scalable and can handle large datasets, making it suitable for organisations dealing with big data challenges. It places a strong emphasis on producing interpretable models, a critical aspect for industries with regulatory requirements. The platform also excels in time series analysis, Natural Language Processing (NLP) tasks, and Graphics processing unit (GPU) acceleration. Furthermore, its model explainability tools help users understand and trust the predictions made by the models.

With deployment options and collaboration features, H2O Driverless AI streamlines the entire machine learning lifecycle, making it a valuable asset for organizations across various industries seeking to leverage AI and predictive modelling for data-driven decision-making.