Posts on machine learning, predictive analytics, the role of data in business, and much more!
April 2024 marks our 1.13.1 release, containing a new ML Solution Template, new business-friendly features for templates, and a new database...
The adoption of predictive analytics in the insurance industry is spearheading rapid evolution, leading to improved efficiency, and streamlined...
You might have heard the phrase “positionless” being used in the NBA. I wanted to dig deeper into the data with some help from machine learning.
Clustering is an unsupervised machine learning method, where datapoints are organized into groups, or clusters, consisting of similar datapoints.
Manufacturing companies have embraced “smart manufacturing” and are adopting predictive analytics to maximize efficiency and save on costs.
Banks are adopting predictive analytics to gain an advantage, using it to predict customer behavior, forecast market trends, and assess risk...
Predictive analytics uses machine learning modeling to predict future outcomes, and provides a way for businesses to make data-driven strategic...
Marketers are making full use of AI tools available in all kinds of ways. In this blog, we’ll go over the most powerful AI use cases in marketing.
The 2022-23 NBA season is one of the tightest races for the MVP award ever. We'll use machine learning to predict if Jokić, Embiid or Giannis will...
A customer win-back strategy focuses on rectifying mistakes and generating goodwill, in order to regain lost customers, so they return to being...
Customer churn analysis is the process of finding trends in customer data in order to uncover who, when and why customers are leaving.
AI isn't just for data scientists, tools that use AI can be used for many marketing tasks. In this article we'll go over the best AI tools for...
Customer churn is the most important metric to get right in business growth. We’ll explore how to reduce churn, by predicting the customers at risk.
Classification is a supervised machine learning method, where a trained model predicts the class of an unknown observation, based on its features.
Predicting the 2022 FIFA World Cup results using classification machine learning models
This blog demonstrates the clustering feature in the AI & Analytics Engine, applied on a database of Pokémon character statistics.
In this comparison article, we look at two powerful Machine Learning tools, Obviously.AI and PI.EXCHANGE'S AI & Analytics Engine.
A list of the 10 best no-code AutoML tools and solutions, that make it easy for non-technical users to innovate with ML, and build & deploy ML models.
In this comparison article, we look at two powerful Machine Learning solutions, Dataiku and PI.EXCHANGE'S AI & Analytics Engine.
Understand the top AI and Machine Learning use cases across different industries like banking, logistics and manufacturing, to build a competitive...
Ready to build your own recommendation system? Find out how you can do so with the AI & Analytics Engine, in just a few simple steps!
How does the H2O Driverless AI add up when compared to the AI & Analytics Engine? Let’s find out in this comparison blog looking at both AutoML...
Calculate your Net Promoter Score (NPS) with ease with the AI & Analytics Engine. Let us take you through the steps!
We use an Amazon Product Review Dataset to show how you can use the Engine to prepare and visualize your data to identify if customer ratings are...
With many AutoML platforms in the market, it can be hard to pick one. In this blog, we compare Trifacta and The AI & Analytics Engine.
Analyzing prediction results is critical in evaluating the performance of trained models. We demonstrate how you can use Excel to evaluate your model
A comparison article of the Amazon SageMaker Canvas and the AI & Analytics Engine. We go through their similarities and differences as ML platforms
What are no-code AutoML solutions? We dive deep into machine learning platforms and take you through how they can help you pursue your ML-driven...
Learn how to calculate time elapsed between two dates with the click of a button. The time could be in seconds, minutes, or hours!
If the data quality is poor, so will be the model and consequently the predictions. In the world of computers, it’s called GARBAGE IN, GARBAGE OUT.
Multi-sheet Excel datasets are commonly used by many businesses. However, they are tricky to use for predictive analytics due to their...
Does machine learning model deployment have to be that hard for businesses to extract value from their data? The answer is NO!
You, as a data scientist, will have to bear the pain of acquiring data from multiple systems & hence must be acquainted with the basics of data...
Introducing a new advanced option on the Engine, when applying an action to a column, extending the ability to apply actions to multiple columns at...
Let's take a deep dive & try to understand Support Vector Classifier, a popular supervised machine learning classification algorithm with the help of...
The Engine’s formula editor lets you easily use custom-made data wrangling formulas, to transform datasets in your data preparation step.
GPUs are empowering data scientists and machine learning engineers to train their models in minutes which could potentially take days or weeks.
Do you often find preparing time-series data from raw sources complex? With a newly built recipe action, the Engine makes this process accessible for...
A Regular Expression (Regex) is simply a string of text that permits a user to create a pattern that aids in matching, locating, and managing text.
Working with multiple datasets is a must in any ML project. Join tables or lookup info from tables easily with the Join and Lookup actions on the...
Comparison of AWS' recently announced data preparation tool, AWS Glue DataBrew, and The AI & Analytics Engine's Smart Data Preparation feature.
Train, validation, and test sets. Is this the normal way to split your data? What could possibly go wrong if one of them is omitted?
Ever wanted a feature to aggregate values on one or multiple columns in a large dataset to be fed into an ML algorithm? With easy-to-use actions...
Spending hours preparing tabular data by splitting and extracting patterns from text columns? Perform these tasks easily with 5 new actions in the...
Guide on using the tools in the AI & Analytics Engine for working with complex and nested data types, such as nested JSON arrays in a simple manner.
Don't know whether AI/ML is a right fit for your business? Getting enticed by AI/ML is easy but don't jump on the bandwagon without having a clear...
In this article, you will learn how to leverage PI.EXCHANGE's AI and Analytics Engine to deploy your AI application so it can be consumed by other...
Occasionally we would need to change a value from one data type to another and this article explains why and how we do so when we cast data types.
Having discussed in detail supervised machine learning algorithms, we focus on an unsupervised machine learning algorithms: K-Means clustering.
In this article, you will learn how to leverage PI.EXCHANGE's AI and Analytics Engine to build your first AI application using a Machine Learning...
Here, you will learn how to leverage the AI & Analytics Engine for data preparation to be consumed by an ML model in a streamlined and repeatable way.
In this article, you will learn how to leverage PI.EXCHANGE's AI and Analytics Engine to Create your first data project.
Feature selection is one of the most important steps in building a model. We show you using a Python implementation, and then with The Engine
AutoML is the future of machine learning that will empower start-ups & SMBs to leverage data to solve problems and build models from scratch.
Introduction to Natural Language Processing: The objective of NLP is to read, decipher, understand, and make sense of human languages.
A list of the most commonly used machine learning and data science concepts and terms without any technical jargon to help you get started.
In this blog, we implement the decision tree classifier. What takes hours in Python, it only takes a few minutes on the AI & Analytics Engine...
70-80% of a data scientist's time might be spent on data cleaning and wrangling. What if you could automate the data cleaning & wrangling process?
Can't code? Is that keeping you from getting started with data science? Leverage no code machine learning, to help you get started on your journey.
Learn how data science emerged, a general definition of data science, and how machine learning and data science can play a role within businesses.
This article answers questions on machine learning, through the 3 ML types: supervised learning, unsupervised learning, and reinforcement learning.
One of the most popular fields of AI, machine learning is the field of study that gives computers the ability to learn without being explicitly...
The field of AI aims to understand how humans interact and make decisions. This understanding expands to create machines that rival human competence.
Data wrangling, also referred to as data munging is the process of converting and mapping data from one raw data form into another format.
Data cleaning, also referred to as data cleansing and data scrubbing is one of the most important steps in quality decision-making
How do you upscale your current product offering affordably with ml, for small businesses? There are a few basic problems a startup will encounter.