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auto mpg data regression - exercise

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 file Table Reader 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 file Table Reader

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Created by: Satoru
Created at: 2022-12-12
On NodePit since: 2022-12-12
Last update: 2024-05-18
Created with KNIME version: v4.6.3

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