Predictive Analytics

Predictive Analytics Definition

Predictive Analytics is the practice of employing statistics and modeling techniques to extract information from current and historical datasets in order to predict potential future outcomes and trends.

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FAQs

What is Predictive Analytics?

Predictive analytics utilizes a variety of statistical techniques, such as automated machine learning algorithms, deep learning, data mining, and AI, to create predictive models, which extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes. There are three types of predictive analytics techniques: predictive models, descriptive models, and decision models.

The predictive analytics method begins with defining business objectives and the datasets to be used, followed by the development of a statistical model that is trained to validate assumptions and run them against selected data to generate predictions. Predictive analytics techniques are not always linear -- once a predictive model is developed, deployed, and starts producing actionable results, teams of data scientists, data analysts, data engineers, statisticians, software developers, and business analysts may be involved in its management and maintenance. A myriad of industries and fields use predictive analytics is an important decision-making tool, evaluating patterns in data to identify opportunities and risks.

How to Use Predictive Analytics

Predictive analytics techniques can broadly be classified as regression techniques or machine learning techniques. Some predictive analytics examples include:

Regression Techniques

Regression models focus on establishing a mathematical equation as a method to represent the interactions between the different variables. Predictive analytics software relies heavily on a wide variety of regression models, including linear regression models, discrete choice models, logistic regression, time series models, survival or duration analysis, and decision tree learning.

Machine Learning

Machine learning predictive analytics is a category of algorithm that can receive input data and use statistical analysis to predict outputs while updating outputs as new data becomes available. This allows software applications to become more accurate in predicting outcomes without being explicitly programmed. Examples of machine learning techniques include neural networks, multilayer perceptron, radial basis functions, support vector machines, Naïve Bayes, and geospatial predictive modeling.

Predictive Analytics vs Predictive Modeling

Predictive modeling, a tool used in predictive analytics, is a process that uses data mining and statistics to develop models that examine current and historical datasets for underlying patterns and predict the probability of an outcome. The predictive modeling process starts with data collection, then a statistical model is formulated, predictions are made, and the model is revised as new data becomes available.

Predictive analytics models generally fall within two classes, either parametric or nonparametric. Within these two camps are several different varieties of predictive analytics models, including Ordinary Least Squares, Generalized Linear Models, Logistic Regression, Random Forests, Decision Trees, Neural Networks, and Multivariate Adaptive Regression Splines.

The terms “Predictive Modeling,” “Predictive Analytics,” and “Machine Learning” may sometimes be used interchangeably due to their largely overlapping fields and similar objectives, however there are some differentiating factors, such as practical applications. Predictive modeling is used throughout a range of industries, including meteorology, archaeology, automobile insurance, and algorithmic trading. When deployed commercially, predictive modelling is often referred to as predictive analytics.

Predictive Analytics vs Machine Learning

A common misconception is that predictive analytics and machine learning are the same thing. Some may define predictive analytics as being the umbrella discipline and machine learning as being an extension. While both data science technologies aid in drawing meaningful conclusions from large datasets, each process has unique characteristics.

Machine learning is a category of Artificial Intelligence (AI) and predictive analytics developed to enable computers to learn without being explicitly programmed by building algorithms that can receive input data and use statistics to predict an output while evolving and adapting as new data becomes available. The machine learning process, managed by a data scientist or analyst, involves identifying and preparing relevant dataset for analysis, selecting the type of algorithm to use, building an analytical model based on that algorithm, training and revising the model as needed, and finally running the model to generate scores and other information.

Machine learning algorithms are generally categorized as either Supervised or Unsupervised. Types of machine learning algorithms within these two categories include:

• Decision trees: a learning model that uses observations about a specific item to develop conclusions about the item's target value
• K-means clustering: aggregates a specified number of data points into a specific number of groupings based on certain similarities
• Neural networks: deep learning models that process large amounts of training data to identify correlations between several variables to learn to process future incoming data
• Reinforcement learning: an area of deep learning that concerns models iterating over many attempts, rewarding moves that produce favorable outcomes and penalizing steps that produce undesired outcomes, therefore training the algorithm to learn the optimal process

Machine learning and predictive analytics play a crucial role in companies, education, insurance, investment management, and retail; however, where machine learning is heavily coding-oriented and can make decisions in real time with little or no human intervention, predictive analytics models still rely on human analysts to determine and test the correlations between cause and outcome.

The Difference Between Descriptive and Predictive Analytics

Descriptive analytics is the preliminary stage of data analysis, answering the question, “What happened?” Descriptive analytics precedes diagnostic analytics (Why did it happen?), followed by predictive analytics (What could happen in the future?), and prescriptive analytics (a combination of descriptive analytics and predictive analytics that answers, “How should we respond to potential future events?).

Where predictive analytics models look at historical data to determine the likelihood of particular future outcomes, descriptive analytics models analyze historical data to determine how a unit may respond to a set of variables.

Descriptive analytics examines decisions and outcomes after the fact to better understand the causes of events. Data aggregation and data mining are employed in descriptive analytics to organize data and identify patterns. Querying, reporting, and data visualization may also be applied to gain further insight. Both descriptive analytics and predictive analytics play crucial roles in finance, manufacturing, and operational activities.

The Difference Between Prescriptive and Predictive Analytics

Prescriptive analytics is a more advanced, abstract form of data analytics that enables users to create hypothetical scenarios and extrapolate outcomes based on variables. Prescriptive analytics is the combination of the descriptive analytics process, which provides insight on what happened, and predictive analytics process, which provides insight on what might happen, providing a process by which users can anticipate what will happen, when it will happen, and why it will happen.

Prescriptive analytics relies heavily on machine learning in order to continually take in, understand, and advance new data and adapt without additional human input, automatically improving prediction accuracy and prescribing better suggestions on how to take advantage of a future opportunity or mitigate a future risk.

Prescriptive analytics adds value to a variety of industries -- it is used by the gas and oil industry for pricing decisions and oilfield equipment maintenance optimization, by the healthcare industry for population health management optimization, and by airlines for ticket pricing optimization. Techniques include simulation, optimization, decision-analysis, and game theory methods.

The Difference Between Business Intelligence and Predictive Analytics

The fundamental difference between Business Intelligence and predictive analytics is the questions they answer, with business intelligence answering “What happens now?” and predictive analytics answering “What could happen in the future?”

Business intelligence focuses on identifying patterns in current and historical data in order to enable organizations to draw conclusions from data analysis, discover patterns, and forecast future patterns in business operations. Business intelligence systems combine data gathering, data storage, and knowledge management with advanced statistics and predictive analytics strategies in order to evaluate and transform complex data into meaningful, actionable information, which can be used to support more effective strategic, tactical, and operational insights and decision-making.

Predictive analytics software, which plays a complementary role in many business intelligence systems, builds analytic models at the individual level of a business and identifies predictable behaviors and propensities that can be used to predict the likelihood of particular future outcomes. Business intelligence looks for trends at the macro level of a business in order to identify and eliminate business problems and inefficiencies.

Forecasting vs Predictive Analytics

Predictive analytics is often defined as predicting at a highly detailed level of granularity, generating probabilities for individual organizational elements. This distinguishes it from forecasting.

Forecasting pertains to out-of-sample observations, whereas prediction pertains to in-sample observations. Predicted values are calculated for observations in the sample used to estimate the regression. However, forecasting is made for the same dates beyond the data used to estimate the regression, so the data on the actual value of the forecasted variable are not in the sample used to estimate the regression.

How do you Deal With Outliers in Predictive Analytics?

An outlier in predictive analytics is a single data point that lies an abnormal distance outside the average value in a random sample from a population. Ranging from mild to extreme, Outliers may be the result of disinformation or recording and measurement errors, sometimes indicating inaccurate methods of sample gathering. Outliers are one of the common pitfalls which predictive analytics avoids with proper techniques.

There is a certain degree of ambiguity -- in some cases, an outlier is clearly an error and should be removed, while other cases may require an analyst or model to make a judgement call as to where outliers are a natural deviation. Statisticians may mitigate the effects of outliers by employing data visualization tools such as scatter plots and box plots in order to easily identify what makes an outlier.

There are several ways to deal with outliers in data. Some common strategies include: set up a filter in the testing tool, change or remove outliers during post-test analysis, changing the value of an outlier, consider underlying distributions, perform a separate analysis with only the outliers, or consider the value of mild outliers. Whether an outlier is excluded or not, it can serve as an opportunity for predictive analytics development and should be examined.

Does HEAVY.AI Offer Predictive Analytics Solutions?

As enterprises continue to amass ever growing pools of data, so too grows the need for advanced analytics practices such as predictive analytics. HEAVY.AI's Data Science Platform offers solutions for accelerating the human work behind artificial intelligence and machine learning, making feature engineering faster, monitoring models in production, and explaining black-box models.