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04_​Analyze_​Linear_​Regression

Analyze Data by Training a Linear Regression model for House Price Prediction
Transform: - Partition the data into train and test set Evaluation Apply and score Model Train Model Linear Regression Read Data - The data contains various attributesabout the house and the house price How to train Linear RgeressionModel? Step 1: Drag the "Linear Regression Learner"node and double click to open the dialog Step 2: Select the "Target" Column as"SalesPrice" Step 3: RIght Click on the node and select"Execute and Open View" to train the modeland to get a view of the Regression Co-efficients How to evaluate Classification Model? Step 1: Drag the "Regression Predictor" node. Step 2: Connect the output of "Linear Regression Learner"node to model input port and Test Dataset to data input port.Execute the nodeStep 3: Connect the Predictor Output to "Missing Value" nodeto remove rows with missing prediction and then connect it to"Scorer" node to evaluate the model on various evaluationmeasures Port 0: Train Set (70%)Port 1: Test Set (30%)AmesHousing_simple.csvdataset Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor Missing Value File Reader Transform: - Partition the data into train and test set Evaluation Apply and score Model Train Model Linear Regression Read Data - The data contains various attributesabout the house and the house price How to train Linear RgeressionModel? Step 1: Drag the "Linear Regression Learner"node and double click to open the dialog Step 2: Select the "Target" Column as"SalesPrice" Step 3: RIght Click on the node and select"Execute and Open View" to train the modeland to get a view of the Regression Co-efficients How to evaluate Classification Model? Step 1: Drag the "Regression Predictor" node. Step 2: Connect the output of "Linear Regression Learner"node to model input port and Test Dataset to data input port.Execute the nodeStep 3: Connect the Predictor Output to "Missing Value" nodeto remove rows with missing prediction and then connect it to"Scorer" node to evaluate the model on various evaluationmeasures Port 0: Train Set (70%)Port 1: Test Set (30%)AmesHousing_simple.csvdataset Partitioning Numeric Scorer Linear RegressionLearner RegressionPredictor Missing Value File Reader

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2022-08-21
Last update: 2024-11-21
Created with KNIME version: v4.5.2
Tags: regressionLinear RegressionBeginnerBeginner Cheat SheetBeginners Space

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