Your question: Which of the following best describes predictive analytics?

Option C (A predictive analytics is a process that creates a statistical model of future behavior) is correct. While predictive modeling is often used in marketing, banking, financial services, and insurance sector, it also has many other potential uses for predicting future behavior.

What is meant by predictive analytics?

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.

What is predictive analytics in simple words?

Predictive analytics is a way to predict future events based on past behavior. It’s a combination of statistics and data mining; Tools from both areas are applied to existing large data sets to: Identify patterns and trends. Build models to predict what might happen in the future.

Which of the following statements is true about predictive analytics?

The correct answer is the statement (A). Predictive analytics involves data mining from different sources, statistical techniques like regression, classification, and clustering algorithms to predict the most likely outcome in the future.

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Which of the following are features of predictive analytics?

Predictive analytics has been applied to customer/prospect identification, attrition/retention projections, fraud detection, and credit/default estimates. The common characteristic of these opportunities is the varying propensities of individuals displaying a behavior that impacts a business objective.

Where is predictive analytics used?

Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Improving operations. Many companies use predictive models to forecast inventory and manage resources.

What is predictive analytics Where is it used?

Predictive analytics is used in insurance, banking, marketing, financial services, telecommunications, retail, travel, healthcare, pharmaceuticals, oil and gas and other industries.

What are predictive analytics tools?

Here are eight predictive analytics tools worth considering as you begin your selection process:

  • IBM SPSS Statistics. You really can’t go wrong with IBM’s predictive analytics tool. …
  • SAS Advanced Analytics. …
  • SAP Predictive Analytics. …
  • TIBCO Statistica. …
  • H2O. …
  • Oracle DataScience. …
  • Q Research. …
  • Information Builders WEBFocus.

Is K means a predictive model?

K is an input to the algorithm for predictive analysis; it stands for the number of groupings that the algorithm must extract from a dataset, expressed algebraically as k. A K-means algorithm divides a given dataset into k clusters.

What are the possible types of predictive models?

There are many different types of predictive modeling techniques including ANOVA, linear regression (ordinary least squares), logistic regression, ridge regression, time series, decision trees, neural networks, and many more.

What is the goal of predictive analytics?

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 providing a best assessment of what will happen in the future.

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What are the four primary aspects of predictive analytics?

They are:

  • Appropriate sources of data. One of the most fundamental points to consider is whether data is indeed capable of providing an answer to every question that the organisation has. …
  • Data cleanliness and usefulness. …
  • Automation and machine learning. …
  • Meeting business objectives.

Which type of data is used for predictive analytics?

Predictive analytics uses historical data to predict future events. 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.

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