Predictive Analytics Model for Term Deposit Investment in R

Learn Predictive Analytics Model using R from a case study

Description

Predictive analytics is an emerging strategy across many business sectors and they are used to improve the performance of the companies. Predictive modeling is a part of predictive analytics which is used to create a statistical model to predict the future behaviour. The predictive modeling can be used on any type of event regardless of its occurrence. The predictive model to be used for a particular situation is often selected on the basis of the detection theory. This chapter includes an overview of predictive analytics and predictive modeling. This chapter also includes examples of predictive modeling.

Predictive Analytics and Modeling is the process of creating, testing and validating a model. It uses statistics to predict the outcomes. Predictive modeling has different methods like machine learning, artificial intelligence and others. This model is made up of number of predictors which are likely to affect the future results. Predictive modeling is most widely used in information technology.

Uses of Predictive Analytics and Modeling

Predictive modeling is the most commonly used statistical technique to predict the future behaviour. Predictive modeling analyzes the past performance to predict the future behaviour.

Features in Predictive Analytics and Modeling

• Data Analysis and Manipulation
• Visualization
• Statistics
• Hypothesis Testing

Pre requisites for taking this course

The pre requisites for this course includes a basic statistical knowledge and details on software like SPSS or SAS or STATA.

Target Audience for this course

This course is more suitable for students or researchers who are interested in learning about predictive analytics.

Predictive Modeling Course Objectives

After the completion of this course you will be able to

• Understand how to use predictive analytics tools to solve real time business problems
• Learn about predictive models like regression, clustering and others
• Use predictive analytics techniques to interpret model outputs

Who this course is for:

• This course is more suitable for students or researchers who are interested in learning about predictive analytics.