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Titanic_​Project

KAGGLE Competion Title - Titanic - Machine Learning from DisasterUse KNIME Analytics Platform to make predictions from the Titanic Data set.Tested with different classification algorithms, Tuned to get the maximumaccuracy and minimum error rates from each one.Selected the algorithm with the best result - i.e. Gradient Boosted Trees.It produced a score of 0.73684 with the test.csv on kaggle Read train.csvReview datasetand remove unwantedcolumnsConvert numbers to stringReplacemissing valueswith median values.Note that this optiongave the bestresultReview datasetPartitiondatasetTrain ModelTest modelAccuracy 0.75373Error 0.24627Partition DatasetTrain ModelTest ModelAccuracy 0.61940Error 0.380597Partition DatasetTrain ModelTest ModelAccuracy 0.74626Error 0.25373PartitionDatasetTrain ModelTest ModelAccuracy 0.813432Error 0.186567Train ModelTest Model with30% of train.csv and100% of test.csvPartition Dataset Top ScorerAccuracy 0.83955Error 0.160447Train ModelTest ModelPartitionDatasetAccuracy 0.828358Error 0.17164Node 32Node 33Node 34Node 35Filter Output for Kaggle SubmissionRename column headersTitanic_predictionGB.csvCSV Reader Data Explorer Number To String Missing Value Data Explorer Partitioning Naive Bayes Learner Naive BayesPredictor Scorer Partitioning SVM Learner SVM Predictor Scorer Partitioning LogisticRegression Learner Logistic RegressionPredictor Scorer Partitioning DecisionTree Learner Decision TreePredictor Scorer Gradient BoostedTrees Learner Gradient BoostedTrees Predictor Partitioning Scorer Tree EnsembleLearner Tree EnsemblePredictor Partitioning Scorer CSV Reader Data Explorer Number To String Missing Value Column Filter Column Rename CSV Writer KAGGLE Competion Title - Titanic - Machine Learning from DisasterUse KNIME Analytics Platform to make predictions from the Titanic Data set.Tested with different classification algorithms, Tuned to get the maximumaccuracy and minimum error rates from each one.Selected the algorithm with the best result - i.e. Gradient Boosted Trees.It produced a score of 0.73684 with the test.csv on kaggle Read train.csvReview datasetand remove unwantedcolumnsConvert numbers to stringReplacemissing valueswith median values.Note that this optiongave the bestresultReview datasetPartitiondatasetTrain ModelTest modelAccuracy 0.75373Error 0.24627Partition DatasetTrain ModelTest ModelAccuracy 0.61940Error 0.380597Partition DatasetTrain ModelTest ModelAccuracy 0.74626Error 0.25373PartitionDatasetTrain ModelTest ModelAccuracy 0.813432Error 0.186567Train ModelTest Model with30% of train.csv and100% of test.csvPartition DatasetTop ScorerAccuracy 0.83955Error 0.160447Train ModelTest ModelPartitionDatasetAccuracy 0.828358Error 0.17164Node 32Node 33Node 34Node 35Filter Output for Kaggle SubmissionRename column headersTitanic_predictionGB.csvCSV Reader Data Explorer Number To String Missing Value Data Explorer Partitioning Naive Bayes Learner Naive BayesPredictor Scorer Partitioning SVM Learner SVM Predictor Scorer Partitioning LogisticRegression Learner Logistic RegressionPredictor Scorer Partitioning DecisionTree Learner Decision TreePredictor Scorer Gradient BoostedTrees Learner Gradient BoostedTrees Predictor Partitioning Scorer Tree EnsembleLearner Tree EnsemblePredictor Partitioning Scorer CSV Reader Data Explorer Number To String Missing Value Column Filter Column Rename CSV Writer

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Created by: demol
Created at: 2022-02-25
On NodePit since: 2022-02-26
Last update: 2024-04-27
Created with KNIME version: v4.5.1

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