Predictive analytics

What Predictive analytics


Predictive analytics is a form of advanced analytics that uses new and historical data to predict future activity, behavior, and trends. It involves applying statistical analysis techniques, analytical queries, and automatic machine learning algorithms to data sets to create predictive models that place a numerical value or score on the probability that a particular event will occur.

Predictive analytics software applications use variables that can be measured and analyzed to predict the likely behavior of individuals, machinery, or other entities. For example, an insurance company is likely to take into account potential driving safety variables such as age, gender, location, vehicle type, and driving history when pricing and writing insurance policies. of automobile. The multiple variables are combined into a predictive model capable of evaluating future probabilities with an acceptable level of reliability. The software relies heavily on advanced algorithms and methodologies such as logistic regressions, time series analysis, and decision trees.

Predictive analytics has grown in prominence along with the emergence of big data, or big data systems. As companies have accumulated larger and broader pools of data in Hadoop clusters and other large data platforms, they have created greater opportunities for them to exploit that data for predictive insights. The increased development and commercialization of machine learning tools by IT vendors has also helped expand predictive analytics capabilities.

Marketing, financial services, and insurance companies have been notable adopters of predictive analytics , as have the large search engines and online service providers. Predictive analytics is also commonly used in industries such as healthcare, retail, and manufacturing.

Business applications for predictive analytics include targeting online advertisements, signaling potentially fraudulent financial transactions, identifying patients at risk of developing particular medical conditions, and detecting impending part failures in industrial equipment before they occur.

The predictive analytics process

Predictive analytics requires a high level of experience with statistical methods and the ability to build predictive models from data. As a result, it is typically the domain of data science, statisticians, and other data analytics experts. They are supported by data engineers, who help collect relevant data and prepare it for analysis, and by software developers and business analysts, who help with data visualization, dashboards, and reports.

The data scientists use predictive models to look for correlations between different data elements in the data clickstream websites, health records of patients and other types of data sets. Once the data to be analyzed is collected, a statistical model is formulated, trained, and modified as necessary to produce accurate results; the model is then run against the selected data to generate predictions. Complete data sets are analyzed in some applications, but in others, analysis teams use data sampling to speed up the process. The predictive model is validated or revised as additional data becomes available.

The predictive analytics process isn’t always linear, and correlations often show up where data scientists aren’t looking. For that reason, some companies are filling data scientist positions by hiring people who have formal backgrounds in physics and other harsh scientific disciplines and, consistent with the scientific method, are comfortable where the data takes them. Even if companies follow the more conventional path of hiring data scientists trained in math, statistics, and computer science, an open mind in exploring data is a key attribute to effective predictive analytics.

Once predictive modeling produces actionable results , the analytics team shares them with business executives, usually with the help of dashboards and reports that present the information and highlight future business opportunities based on the findings. Functional models can also be incorporated into operational applications and data products to provide real-time analytical capabilities, such as a recommendation engine on an online retail website that directs customers to specific products based on their browsing activity and choices. shopping.

Predictive analytics applications

Online marketing is one area in which predictive analytics has had a significant business impact. Predictive analytics tools are used by retailers, marketing service providers, and other organizations to identify trends in a website visitor’s browsing history to personalize ads. Retailers also use customer analytics to make more informed decisions about what types of products the retailer should stock.

Predictive maintenance is emerging as a valuable application for manufacturers monitoring a piece of equipment for signs of something that may be about to break down. As the Internet of Things (IoT) develops , manufacturers are connecting sensors to machinery on the factory floor and to mechatronic products, such as cars. Sensor data is used to forecast when maintenance and repair work must be performed to avoid problems.

IoT also enables similar predictive analytics uses to monitor oil and gas pipelines, drilling rigs, windmill farms, and various other industrial IoT facilities. Localized weather forecasts for farmers, partially based on data collected at sensor-equipped weather data stations installed in farm fields, is another IoT-powered predictive modeling application.

Analysis tools and techniques

A wide range of tools and techniques are used in model prediction and analysis . IBM, Microsoft, the SAS Institute, and many other software vendors offer predictive analytics tools, including machine learning software and related technologies that support deep learning applications.

Additionally, open source software plays an important role in the predictive analytics market. The open source language R is commonly used in predictive analytics applications, as are the Python and Scala programming languages. Several open source machine learning and predictive analytics platforms are also available, including a library of algorithms built into the Spark processing engine.

Analytics teams can use the open source base editions of R and other analytics languages, or pay for commercial versions offered by vendors such as Microsoft. Commercial tools can be expensive, but they come with vendor technical support, whereas users of pure open source releases are often alone when trying to solve problems with the technology.

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