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Data_​Science

- Apply missing value node to remove missing values.- Partion the data for test and training sets apply arandom seeding within years.- Use a linear regression to predict the years formovies that are popular.-This will evaluate the performance. - Stratified sampling Genre- Train the Decision Tree Learner to predict the genreof movie.- Use the Decision Tree Predictor to apply the modelset.- Evaluate the Scoring Metrics of the Model Node 1Missing Values in Table75% and 25% Random Sampling YearsPredict years for popular moviesApply Prediction ColumnView the Performanceof the regression modelNode 7Stratified sampling GenrePredict Genre of MovieApply the model setScoring MetricsNode 12 CSV Reader Missing Value Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer CSV Reader Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) ROC Curve - Apply missing value node to remove missing values.- Partion the data for test and training sets apply arandom seeding within years.- Use a linear regression to predict the years formovies that are popular.-This will evaluate the performance. - Stratified sampling Genre- Train the Decision Tree Learner to predict the genreof movie.- Use the Decision Tree Predictor to apply the modelset.- Evaluate the Scoring Metrics of the Model Node 1Missing Values in Table75% and 25% Random Sampling YearsPredict years for popular moviesApply Prediction ColumnView the Performanceof the regression modelNode 7Stratified sampling GenrePredict Genre of MovieApply the model setScoring MetricsNode 12CSV Reader Missing Value Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer CSV Reader Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) ROC Curve

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Created by: sammg
Created at: 2023-04-13
On NodePit since: 2023-04-14
Last update: 2024-12-23
Created with KNIME version: v4.7.0

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