# Building a RapidMiner Process with Linear Regression Model

1 minute read

This post shows how to construct a simple predictive learning process in RapidMiner Studio by using the linear regression model to predict a continuous value.

Simple linear regression is explained at the post Linear Regression Analysis.

What is predictive learning? Read the post [Predictive Learning from an Operational Perspective]({{ref “2017-02-10-predictive-learning.md” >}})

Linear regression model explains the relationship between a quantitative label and one or more predictors(regular attributes) by fitting a linear equation to observed objects (with labels). The developed linear model will predict the label for unlabeled objects.

Launch RapidMiner Studio. Start a new process.

Download the dataset LR-dataset.csv

The dataset is made artificially from the following linear model

1y = 2 + 3x + (random Gaussian noise with sd=20)

Add the operators that are included in the following process picture. Connect the ports to enable data flows. Each operator requires specific parameter settings.

### Download the Sample Process

The sample process: right click to download linear-regression-sample-rm-process.xml

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