Predictive analytics

predictiveCARTpredictive analysispredictive intelligenceand predictive analyticsbusiness intelligenceBusiness Intelligence softwaredata analyticsforecastingFraud detection in predictive analytics
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.wikipedia
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Machine learning

machine-learninglearningstatistical learning
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
In its application across business problems, machine learning is also referred to as predictive analytics.

Data mining

data-miningdataminingknowledge discovery in databases
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.

Prediction

predictpredictionspredictive
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
When these and/or related, generalized set of regression or machine learning methods are deployed in commercial usage, the field is known as predictive analytics.

Prescriptive analytics

prescriptive
In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.
Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics.

Credit score

credit scoringcredit checkcredit scores
One of the best-known applications is credit scoring, which is used throughout financial services.
Although logistic (or non-linear) probability modelling is still the most popular means by which to develop scorecards, various other methods offer powerful alternatives, including MARS, CART, CHAID, and random forests.

Predictive modelling

predictive modelingpredictive modelpredictive models
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
When deployed commercially, predictive modelling is often referred to as predictive analytics.

Big data

big data analyticsbig data analysisbig-data
Big data is a collection of data sets that are so large and complex that they become awkward to work with using traditional database management tools.
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.

Industrial Internet Consortium

Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Using predictive analytics, the Asset Efficiency Testbed aims to collect real-time asset information efficiently and accurately and run analytics to make the right decisions in terms of operations, maintenance, overhaul and asset replacement.

Text mining

text analyticsTextminingtext and data mining
Thanks to technological advances in computer hardware—faster CPUs, cheaper memory, and MPP architectures—and new technologies such as Hadoop, MapReduce, and in-database and text analytics for processing big data, it is now feasible to collect, analyze, and mine massive amounts of structured and unstructured data for new insights.
Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics.

Customer attrition

Customer Churnattritionlapsed customer
With the number of competing services available, businesses need to focus efforts on maintaining continuous customer satisfaction, rewarding consumer loyalty and minimizing customer attrition.
More sophisticated predictive analytics software use churn prediction models that predict customer churn by assessing their propensity of risk to churn.

Fraud

defraudfraudsterfraudulent
Fraud is a big problem for many businesses and can be of various types: inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and false insurance claims.
For detection of fraudulent activities on the large scale, massive use of (online) data analysis is required, in particular predictive analytics or forensic analytics.

Unstructured data

unstructuredunstructured text(unstructured)
Thanks to technological advances in computer hardware—faster CPUs, cheaper memory, and MPP architectures—and new technologies such as Hadoop, MapReduce, and in-database and text analytics for processing big data, it is now feasible to collect, analyze, and mine massive amounts of structured and unstructured data for new insights.
The emergence of Big Data in the late 2000s led to a heightened interest in the applications of unstructured data analytics in contemporary fields such as predictive analytics and root cause analysis.

Forecasting

Customer relationship management

CRMcustomer relationscustomer-relationship management
Analytical customer relationship management (CRM) is a frequent commercial application of predictive analysis.
In 2017, artificial intelligence and predictive analytics were identified as the newest trends in CRM.

Cross-selling

cross-sellcross sellingcross-
Analytical customer relationship management can be applied throughout the customers' lifecycle (acquisition, relationship growth, retention, and win-back).

Credit card fraud

skimmingfraudcredit card skimming
Some examples of likely victims are credit card issuers, insurance companies, retail merchants, manufacturers, business-to-business suppliers and even services providers.

Decision tree learning

decision treesdecision treeClassification and regression tree
Classification and regression trees (CART) are a non-parametric decision tree learning technique that produces either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively.

Predictive policing

before they can harm anyonepredictpredict police requirements
Predictive policing refers to the usage of mathematical, predictive analytics, and other analytical techniques in law enforcement to identify potential criminal activity.

Multivariate adaptive regression spline

Multivariate adaptive regression splinesHinge functionsMARS
Multivariate adaptive regression splines (MARS) is a non-parametric technique that builds flexible models by fitting piecewise linear regressions.
decision trees, or CART;

Underwriting

underwriterunderwritersunderwrite
Underwriting (see below) and other business approaches identify risk management as a predictive method.

In-database processing

in-database
Thanks to technological advances in computer hardware—faster CPUs, cheaper memory, and MPP architectures—and new technologies such as Hadoop, MapReduce, and in-database and text analytics for processing big data, it is now feasible to collect, analyze, and mine massive amounts of structured and unstructured data for new insights.
It is an important step towards improving predictive analytics capabilities.

Geospatial predictive modeling

Conceptually, geospatial predictive modeling is rooted in the principle that the occurrences of events being modeled are limited in distribution.

Criminal Reduction Utilising Statistical History

Criminal Reduction Utilising Statistical History is an IBM predictive analytics system that attempts to predict the location of future crimes.

Autoregressive model

autoregressiveautoregressionAutoregressive process
Two commonly used forms of these models are autoregressive models (AR) and moving-average (MA) models.

Autoregressive–moving-average model

ARMAautoregressive moving average modelautoregressive moving average
The Box–Jenkins methodology (1976) developed by George Box and G.M. Jenkins combines the AR and MA models to produce the ARMA (autoregressive moving average) model, which is the cornerstone of stationary time series analysis.