Predictive Modeling and Analytics - Regression

This module shall define predicting, modeling and analyzing and discuss predictive modeling in the first half. The course then speaks about how these predictions can prescribe what tasks to perform to drive the motive of an organization.

This section begins by telling you what a model is. It then continues by helping you understand how simple linear regression is performed with assumption and simplification steps. You will also learn to model an equation based on the given scenario.

This section tells you how one dataset influences the other depending upon the requirements and time. It also talks about how the estimated data can predict the progress and output in the future, through testing and training, by describing a real-life business scenario.

This section describes a supervised learning technique where an outcome results from multiple predictions. You will understand how the result is obtained by deriving the solution to the scenario through multiple linear regression methods.

Here in this module, you will understand some linear regression assumptions, such as the assumption of linearity and the assumption of normality of the error distribution. Lastly, you will understand various dimensions to plot the linear regression models.