What Is Predictive Analytics? Why It Is Important

Predictive Analytics: In this tutorial, we will learn what is predictive analytics, why predictive analytics is, and how to use predictive analytics. By IncludeHelp Last updated : June 09, 2023

What Is Predictive Analytics?

Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events.

Overall, predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.

Predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance. Predictive analytics looks at current and historical data patterns to determine if those patterns are likely to emerge again. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next or to suggest actions to take for optimal outcomes. This allows businesses and investors to adjust where they use their resources to take advantage of possible future events. Predictive analytics has received a lot of attention in recent years due to advances in supporting technology, particularly in the areas of big data and machine learning.

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 as an important decision-making tool, evaluating patterns in data to identify opportunities and risks.

Why Predictive Analytics Is Important?

Predictive analytics is often discussed in the context of big data, engineering data, for example, comes from sensors, instruments, and connected systems out in the world. Business system data at a company might include transaction data, sales results, customer complaints, and marketing information. Increasingly, businesses make data-driven decisions based on this valuable trove of information. With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways.

Companies also use predictive analytics to create more accurate forecasts, such as forecasting the demand.

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.

Deep Learning

Deep Learning techniques are in trend nowadays due to their capability to produce fast and accurate results. Many researchers have developed methods for predictive analytics in the area of data science. As deep learning techniques have an advantage over conventional learning techniques, these techniques are in demand in finding insights into the data.

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