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Feature Engineering from Multi-Valued Categorical Data

By dotData

Introduction Multi-valued categorical data is ubiquitous and critical to businesses across multiple industries. Most real-world data that businesses store is not numerical but categorical in nature. Examples of categorical data include the level of education (elementary, middle school, bachelor's degree, master's degree, Ph.D.), subscriptions types (basic, premium, business), countries, and countless other examples, that are prevalent in business and necessary for daily operation and analysis of business performance. Although businesses need to rely on categorical data to capture real-world trends, working with such data can be challenging - but there are ways to address such challenges. Overview Categorical columns represent data that can be divided into several different groups.  There are two types of categorical columns: ordinal and nominal, which can be uni-valued (regular) and multi-valued. Multi-valued categorical columns can contain different types of information in a single column. In Fig.1, the payment_type column is a regular categorical column, whereas…

What is Predictive Data Mining?

By dotData

What is Data Mining? Data mining is the process of analyzing data to uncover hidden knowledge; this is also known as knowledge discovery. The data is analyzed to find patterns and other valuable information that can benefit our business. Data mining is performed by analyzing large amounts of usually raw data and applying different techniques to extract patterns and information that can help businesses make decisions, mitigate risks, optimize their processes, better allocate their funds, and many more. Regression Analysis Regression analysis is one of the methods we use in predictive data mining. With the help of statistics, it established the relationship between several variables. The two main types are logistic and simple/multiple linear regression. Logistic regression is used when we try to predict whether an example will fall into one of the two classes. Logistic regression examples: Spam Detection: Predicting if the email is spam, Medicine: Predicting if a…

Feature Engineering for Temporal Data – Part 2: Types of Temporal Data

By dotData

Temporal data is one of the most common and essential data types for enterprise AI applications, such as demand forecasting, sales forecasting, price prediction, etc. Analyzing time-series data helps organizations understand underlying patterns in their business over time and allows them to forecast what will happen in the future (a.k.a. time-series forecasting).  Part one of this series focused on standard time-series models such as AR models, ARIMA, LTSM, and Prophet. While time-series modeling techniques are still widely used, they have limitations, such as the inability to work with heterogeneous data characteristics or time resolutions, lack of support for temporal transactions, and poor model explainability and transparency.  This second part will review an alternative approach, i.e., feature engineering from temporal datasets, that provides many advantages over standard time-series modeling. We will look at three different types of temporal data, the alternative approach of engineering new features from the temporal datasets, an…

Predictive vs. Prescriptive Analytics: What is the Difference?

By dotData

Predictive and prescriptive analytics are two forms of advanced analytics. Predictive analytics helps us predict future outcomes, whereas prescriptive analytics takes those predictions to the next level by identifying the likely outcomes of these predictions and suggesting ways to alter them. Predictive analytics is understanding what will happen, and prescriptive analytics is understanding how to make it happen, prevent it, or change it. What is Predictive Analytics? Predictive analytics is a form of advanced analytics that can help a business predict future outcomes. Predictive analytics can predict customer behavior, future trends, or the likely outcome of current activities. The predictions are derived by analyzing historical data and using statistical methods to predict the outcomes we can expect in the future. Examples Churn prediction detects which customers a business is likely to lose, for example, through the cancelation of a subscription to services. Churn prediction can provide insight into possible future…

Predictive Analytics Use Cases by Industry

By dotData

Predictive analytics is a form of advanced analytics that can help us predict future outcomes and identify risks and opportunities. It can consist of predicting customers' behavior, future market trends, potential risks for our business, profits, and opportunities. Predictive analytics uses machine learning and statistics to analyze historical data and make predictions.  Predictive analytics has a number of useful applications in many industries and for a wide range of use cases. Let’s explore some examples of predictive analytics applications in the banking, manufacturing, retail, insurance, and telecom industries. Examples of Predictive Analytics Use Cases by Industry  Banking Risk and Fraud Prevention Banks can leverage predictive analytics by implementing processes and procedures that inform them of potential risks and fraud. A straightforward example is that the bank will track the customers' spending habits together with the location; therefore, when an unusual activity that stands out from the pattern we have for…

Feature Engineering for Temporal Data – Part 1

By dotData

An overview of common time-series modeling techniques Time-series (or temporal) data are among the most common and essential data types across enterprise AI applications, such as demand forecasting, sales forecasting, price prediction, etc. Analyzing time-series data helps enterprises understand the underlying patterns in their business over time and allows them to forecast what is likely to happen in the future (a.k.a. time-series forecasting). Developing good time-series models is an important and challenging process for enterprise data science and analytics teams.  This blog series will overview different approaches to developing AI and ML models from time-series and temporal datasets. This first blog in a three-part series will review common time-series modeling techniques and discuss their characteristics, advantages, and limitations. Standard Time-series Modeling Techniques Autoregressive Model The autoregressive (AR) model is one of the most straightforward and traditional time-series modeling techniques. The model is named autoregressive because it applies linear regression to…

Types of Predictive Models (& How They Work)

By dotData

Predictive analytics is an umbrella term for the use of data to predict future outcomes. The basic idea is that predictive analytics models can analyze historical data and find patterns. Patterns discovered by these models can predict future behavior. Some predictive analytics models are easier to understand than others, but they all have their place in business and data science. In this article, we'll outline the major types of predictive analytics models so you know what kind of model could help your organization achieve its goals. What is a Predictive Analytics Model? A predictive analytics model is essentially a set of algorithms that discovers patterns in data and uses those patterns to predict beneficial outcomes. Predictive Analytics Models can predict two main outputs:  The probability of an individual case given known characteristics (i.e., how likely is this person who walked into the bank today to default on the loan they…

AI in Healthcare: Improving Medical Debt Recovery with dotData

By dotData

Case Study: Identifying and Addressing Risk of Non-Payment Amongst Patient Groups. How a healthcare provider used data technology to significantly reduce the non-payment of medical bills by their patients. 400% improvement in the correct identification of non-paying patients. 25% reduction in non-payments. Significant enhancements to the data pool and analytic processes. Challenge Low correlation between risk scoring and bill default rate of patients, along with millions of data points across multiple variables, made predicting payment issues difficult. Solution dotData’s Feature Factory technology was applied to the data, allowing an increase in data size and the inclusion of multivariate data. ML was used to identify risk parameters. Results With dotData, the company reduced non-payment by 25% and increased the accuracy of default prediction 400%. The client was able to stratify risk profiles, allowing a targeted outreach. The Problem with Healthcare Payments It can be difficult to accurately predict ongoing medical billing…

How to Improve Customer Targeting to Increase Cross-Selling

By dotData

Case Study: Increasing Cross-Selling Conversion Rates Tenfold Challenge A major telecommunications company needed to identify customers most likely to upgrade services. Their legacy methodology yielded a conversion rate of only 1%. Solution dotData’s AI-powered feature discovery and evaluation platform identified correlated behaviors that were not previously obvious or easily discovered Results With dotData’s platform, the client was able to increase the conversion rate by 1,000%  and delivered precise targeting criteria for marketing campaigns In 2021, the global market for mobile payments exceeded US$ 1.78 trillion. Accelerated by the worldwide pandemic, customers have been increasingly keen to find alternative, cashless, contactless payment methods. A Research and Markets study predicts that this trend will accelerate, with a CAGR of 22% from 2022 and a total market value of US $6 trillion by 2027. As global network infrastructure sees additional investment and expansion, customers respond by adopting mobile payment platforms as a regular…

The Top 5 AI & Machine Learning Trends

By dotData

Updated The past couple of years have seen dramatic changes as the pandemic first drove the need for more online transactions and communications, then became the cause of worldwide shortages that have increased inflation and forced organizations around the globe to rethink their strategies. Machine Learning stands at the epicenter of these changes, but the long-term success of ML projects will be impacted by technology trends that continue to alter and shape the market for Machine Learning products and enabling technologies. This post is an updated version of an older post. Here are the latest trends we see as critical to ML practitioners: Augmented Analytics Transforming Business Intelligence Augmented Analytics will transform Business Intelligence – Augmented Analytics uses AI and ML technologies to assist with data preparation, insight generation, and explanation to expand how people explore and analyze data in analytics and BI platforms. AI is a critical enabling technology,…

Predictive Analytics Techniques for Getting Started

By dotData

Predictive analytics, often also referred to as advanced analytics, has grown from a niche practice to an essential part of any company’s analytics arsenal. According to markets and research, the analytics industry will be worth $309 billion by 2026, a CAGR of almost 40%. As new predictive analytics techniques and algorithms become available, the value of predictive analytics becomes more evident and more far-reaching. As a recent Forbes article points out, the predictive power of AI allows companies to get companies back on track following the disruptions of the pandemic and the ongoing supply chain issues. Predictive analytics has powerful use-cases across many industries, and is proving especially useful for inventory management and delivery optimization, helping companies operate more leanly and with less waste. What is Predictive Analytics? From a business perspective, Predictive Analytics is the science of analyzing large volumes of historical data to identify scenarios where action can…

How a Multinational Industrial Supplier Saved $40M Annually Through AI-Powered Customer Churn Prediction

By dotData

Challenge This US Manufacturer of Industrial Equipment needed to predict customer churn, but with 10,000+ clients, and pressure to implement a solution, they needed to accelerate their development. Solution dotData’s Managed Predictive Analytics program gave their BI team the training and software to build ML models using a 100% no-code approach and a two-step iterative process. Results With dotData, the company built an analytics process in 14 days, identified more than 50 churn predictors, and built a model that will save over US$40 Million annually. 10X Faster analytics process50 Churn Predictors Identified$200M in Annual Revenue Recovered Annually Learn more about dotData Enterprise: Predictive Analytics Automation for BI & Analytics Teams The Problem of Client Churn According to a Harvard Business Review article, “acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one. Research done by Frederick Reichheld of Bain & Company (the…