Icon

auto mpg data regression - solution

Auto MPG data-Fuel economy of 398 cars (in terms of miles per gallon, or MPG)-6 numerical features in addition-Goal of this analysis to predict the fuel ecomony (MPG) based on other available features.Source: https://archive.ics.uci.edu/ml/datasets/auto+mpg Auto MPG data regression exercise1. Reading the data set -Read the file "auto-mpg.table" with the Table Reader node2. Explore the data -Use the Data Explorer node to examine statsitics and distributions of the features -Use the Scatterplot node to plot various attributes3. Missing value treatment -Any rows with missing value are removed by Missign Value node4. Partitioning -Use the Partitioning node to split the data set into the training (70%) & testing (30%) data sets Model 1: Linear Regression5. Train a linear regression model -Train a linear regression model on the training data using the Linear Regression Learner node -Examine the data and the prediction scatter plot view6. Predictions based on the trained mode -Generate predicted outcomes on the testing data using the Regression Predictor node7. Model performance -Evaluate the model performance with the Numeric Scorer node Model 2: Regression Tree5. Train a regression tree model -Train a regression tree model on the training data using theSimple Regression Tree Learner node -Set the minimus split to 10, minimum node (leaf) size to 5 -Examine the generated regression tree6. Predictions based on the trained mode -Generate predicted outcomes on the testing data using the Simple Regression Tree Predictor node7. Model performance -Evaluate the model performance with the Numeric Scorer node Readingthe data fileExploring featuresPlotting MPGvs other featuresRemovingrows withmissing valuesTraining alinear regressionmodelPrediction basedon the trained modelTraining - 70%Testing - 30%Evaluatingthe modelTraining aregression treemodel on thetraining dataPredictions basedon the trained modelEvaluatingthe model Table Reader Data Explorer Scatter Plot Missing Value Linear RegressionLearner RegressionPredictor Partitioning Numeric Scorer Simple RegressionTree Learner Simple RegressionTree Predictor Numeric Scorer Auto MPG data-Fuel economy of 398 cars (in terms of miles per gallon, or MPG)-6 numerical features in addition-Goal of this analysis to predict the fuel ecomony (MPG) based on other available features.Source: https://archive.ics.uci.edu/ml/datasets/auto+mpg Auto MPG data regression exercise1. Reading the data set -Read the file "auto-mpg.table" with the Table Reader node2. Explore the data -Use the Data Explorer node to examine statsitics and distributions of the features -Use the Scatterplot node to plot various attributes3. Missing value treatment -Any rows with missing value are removed by Missign Value node4. Partitioning -Use the Partitioning node to split the data set into the training (70%) & testing (30%) data sets Model 1: Linear Regression5. Train a linear regression model -Train a linear regression model on the training data using the Linear Regression Learner node -Examine the data and the prediction scatter plot view6. Predictions based on the trained mode -Generate predicted outcomes on the testing data using the Regression Predictor node7. Model performance -Evaluate the model performance with the Numeric Scorer node Model 2: Regression Tree5. Train a regression tree model -Train a regression tree model on the training data using theSimple Regression Tree Learner node -Set the minimus split to 10, minimum node (leaf) size to 5 -Examine the generated regression tree6. Predictions based on the trained mode -Generate predicted outcomes on the testing data using the Simple Regression Tree Predictor node7. Model performance -Evaluate the model performance with the Numeric Scorer node Readingthe data fileExploring featuresPlotting MPGvs other featuresRemovingrows withmissing valuesTraining alinear regressionmodelPrediction basedon the trained modelTraining - 70%Testing - 30%Evaluatingthe modelTraining aregression treemodel on thetraining dataPredictions basedon the trained modelEvaluatingthe modelTable Reader Data Explorer Scatter Plot Missing Value Linear RegressionLearner RegressionPredictor Partitioning Numeric Scorer Simple RegressionTree Learner Simple RegressionTree Predictor Numeric Scorer

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Satoru
Created at: 2022-12-12
On NodePit since: 2022-12-12
Last update: 2024-05-01
Created with KNIME version: v4.6.3

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!