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Student Linear Regression

Exercise 4: Decision Tree and Multiple Linear Regression Description: The dataset involves students' performance in secondary education of two Portuguese schools in the Mathematics (mat) subject. Theprediction task aims to predict the student's final grade (G3) based on the factors of (1) first-period grade (G1), (2) second-period grade (G2),(3) workday alcohol consumption (Dalc), (4) weekend alcohol consumption (Walc), (5) weekly study time (studytime), and (6) home to schooltravel time (traveltime). The model to be evaluated in this prediction task is the Multiple Linear Regression Model. Steps: 1) Read student-mat.csv2) Delete rows that has missing values3) Partition the data into a training set4) Train a multiple linear regression model on the training set to predict the correlation of the selected variables to the final grade5) Apply the model to the test set6) Evaluate the performance of the multiple linear regression with the Numeric Scorer node. Read Students RecordsTop: Training Set (80%)Bottom: Test Set (20%)Take from TopTrain the Model to Predict the Final GradeApply the Model to the Test SetEvaluate Model PerformanceFilter out 0 G3 rowsNode 11Node 13Node 14 CSV Reader Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Row Filter Bar Chart Pie chart (local) Pie/Donut Chart component Exercise 4: Decision Tree and Multiple Linear Regression Description: The dataset involves students' performance in secondary education of two Portuguese schools in the Mathematics (mat) subject. Theprediction task aims to predict the student's final grade (G3) based on the factors of (1) first-period grade (G1), (2) second-period grade (G2),(3) workday alcohol consumption (Dalc), (4) weekend alcohol consumption (Walc), (5) weekly study time (studytime), and (6) home to schooltravel time (traveltime). The model to be evaluated in this prediction task is the Multiple Linear Regression Model. Steps: 1) Read student-mat.csv2) Delete rows that has missing values3) Partition the data into a training set4) Train a multiple linear regression model on the training set to predict the correlation of the selected variables to the final grade5) Apply the model to the test set6) Evaluate the performance of the multiple linear regression with the Numeric Scorer node. Read Students RecordsTop: Training Set (80%)Bottom: Test Set (20%)Take from TopTrain the Model to Predict the Final GradeApply the Model to the Test SetEvaluate Model PerformanceFilter out 0 G3 rowsNode 11Node 13Node 14 CSV Reader Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Row Filter Bar Chart Pie chart (local) Pie/Donut Chart component

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Created by: Y2
Created at: 2021-08-16
On NodePit since: 2023-02-18
Last update: 2024-04-27
Created with KNIME version: v4.7.1

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