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Big Data Group Project (ML)

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DATA PREPARATION: Missing Values Treatment DATA PREPARATION:Features Selection, Outliers, and Ordinal Variables BIVARIATE ANALYSIS ALGORITHMS + OUTCOMES UNIVARIATE ANALYSIS Node 1 SatisfiedNode 5DissatisfiedNode 7Node 8Node 10Features SelectionNode 50Node 61Node 116Node 119Node 120# of records in trainset by satisfaction# of records in testset by satisfactionNode 364Node 365Node 367Node 368CSV Reader Row Filter Missing Value Row Filter Missing Value Concatenate Linear Correlation Column Filter Data Exploration Multilayer Perceptron (withhyperparameter optimization) Random Forest withParameter Optimization Ordinal VariablesTreatment Partitioning Value Counter Value Counter Gradient Boostingoptimization loop Gradient boosting with optimizedparameteres + features selection Rule Engine Outlier Treatment Pie Charts +Bar Graphs DATA PREPARATION: Missing Values Treatment DATA PREPARATION:Features Selection, Outliers, and Ordinal Variables BIVARIATE ANALYSIS ALGORITHMS + OUTCOMES UNIVARIATE ANALYSIS Node 1 SatisfiedNode 5DissatisfiedNode 7Node 8Node 10Features SelectionNode 50Node 61Node 116Node 119Node 120# of records in trainset by satisfaction# of records in testset by satisfactionNode 364Node 365Node 367Node 368CSV Reader Row Filter Missing Value Row Filter Missing Value Concatenate Linear Correlation Column Filter Data Exploration Multilayer Perceptron (withhyperparameter optimization) Random Forest withParameter Optimization Ordinal VariablesTreatment Partitioning Value Counter Value Counter Gradient Boostingoptimization loop Gradient boosting with optimizedparameteres + features selection Rule Engine Outlier Treatment Pie Charts +Bar Graphs

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Created by: annmy
Created at: 2023-11-12
On NodePit since: 2023-12-01
Last update: 2024-11-18
Created with KNIME version: v5.1.2

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