Icon

KNIME_​heartKhushiEdited

SVM Naive Bayes KNN Logistic Regression Decision Tree Output This node reads the fileHeart_Disease_PredictionNormalizes the values of all (numeric) columns.partitioning the databaseon the ratio of 80% and 20%.Node 7Trains a support vector machine on the input data.uses a SVM model generated by the SVM learnernode to predict the output for given value.Compares two columns by their attribute value pairs and shows theconfusion matrixPrincipal component analysis (PCA)Converting numbers into string of column(sex,chest pain,Bp,etc)creates a Bayesian modelfrom the given training dataPredicts the class per row based on the learned modelCompares two columns by their attribute value pairs and shows theconfusion matrixClassification based on K-nearest neighbour.Compares two columns by their attribute value pairs and shows theconfusion matrixPerforms a multinomial logistic regressionPredicts the response using a logisticregression model.Compares two columns by their attribute value pairs and shows theconfusion matrixinduces a classification decision tree in main memoryA plot of the provided decision tree usinga JavaScript based libraryuses an existing decision tree to predict the class value for new patternsConverts (a single) decision tree model to PMML Rule Set model Compares two columns by their attribute value pairs and shows theconfusion matrixFiltering the Accuracy columnFiltering the Accuracy columnFiltering the overall row.Filtering the overall row.Filtering the Accuracy columnFiltering the overall row.Filtering the Accuracy columnFiltering the overall row.Filtering the Accuracy columnFiltering the overall row.concatenates two tablesconcatenates two tablesconcatenates two tablesconcatenates two tablesFinal accuracy rates of all the classification algorithms.Writing the concatenated data from all the classification alogrithm to a csv file.This node reads the fileHeart_Disease_Prediction CSV Reader Normalizer Partitioning Normalizer (Apply) SVM Learner SVM Predictor Scorer PCA Number To String Naive Bayes Learner Naive BayesPredictor Scorer K Nearest Neighbor Scorer LogisticRegression Learner Logistic RegressionPredictor Scorer DecisionTree Learner Decision Tree View Decision TreePredictor Decision Treeto Ruleset Scorer Column Filter Column Filter Row Filter Row Filter Column Filter Row Filter Column Filter Row Filter Column Filter Row Filter Concatenate Concatenate Concatenate Concatenate Bar Chart CSV Writer CSV Reader SVM Naive Bayes KNN Logistic Regression Decision Tree Output This node reads the fileHeart_Disease_PredictionNormalizes the values of all (numeric) columns.partitioning the databaseon the ratio of 80% and 20%.Node 7Trains a support vector machine on the input data.uses a SVM model generated by the SVM learnernode to predict the output for given value.Compares two columns by their attribute value pairs and shows theconfusion matrixPrincipal component analysis (PCA)Converting numbers into string of column(sex,chest pain,Bp,etc)creates a Bayesian modelfrom the given training dataPredicts the class per row based on the learned modelCompares two columns by their attribute value pairs and shows theconfusion matrixClassification based on K-nearest neighbour.Compares two columns by their attribute value pairs and shows theconfusion matrixPerforms a multinomial logistic regressionPredicts the response using a logisticregression model.Compares two columns by their attribute value pairs and shows theconfusion matrixinduces a classification decision tree in main memoryA plot of the provided decision tree usinga JavaScript based libraryuses an existing decision tree to predict the class value for new patternsConverts (a single) decision tree model to PMML Rule Set model Compares two columns by their attribute value pairs and shows theconfusion matrixFiltering the Accuracy columnFiltering the Accuracy columnFiltering the overall row.Filtering the overall row.Filtering the Accuracy columnFiltering the overall row.Filtering the Accuracy columnFiltering the overall row.Filtering the Accuracy columnFiltering the overall row.concatenates two tablesconcatenates two tablesconcatenates two tablesconcatenates two tablesFinal accuracy rates of all the classification algorithms.Writing the concatenated data from all the classification alogrithm to a csv file.This node reads the fileHeart_Disease_Prediction CSV Reader Normalizer Partitioning Normalizer (Apply) SVM Learner SVM Predictor Scorer PCA Number To String Naive Bayes Learner Naive BayesPredictor Scorer K Nearest Neighbor Scorer LogisticRegression Learner Logistic RegressionPredictor Scorer DecisionTree Learner Decision Tree View Decision TreePredictor Decision Treeto Ruleset Scorer Column Filter Column Filter Row Filter Row Filter Column Filter Row Filter Column Filter Row Filter Column Filter Row Filter Concatenate Concatenate Concatenate Concatenate Bar Chart CSV Writer CSV Reader

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: Mriganka Samadder
Created at: 2023-02-23
On NodePit since: 2023-02-26
Last update: 2024-11-21
Created with KNIME version: v4.7.1

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

Try NodePit Runner!