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KNIME_​project C0S10022

Assignment 1 Task 2 Asssignment 1 Task 1 accident_2025_train2_73.csvloads a CSV file into KNIME as a table.accident_2025_test2_73.txtloads a CSV file into KNIME as a table.trains a logistic regression model on the training data to learn the relationship between the attributes and Survived.compares the predicted values against the actual Survived values and outputs the accuracy and confusion matrix.reads and combines every matching file in a folder in one gosplits rows into two outputs based on a condition written, example, Survived = Yes.splits the Yes branch into an exact 350 row subset and a 28 row remainder, using Absolute modesplits the No branch into an exact 350-row subset and a 472 row remainder, using Absolute modestacks the two 350-row subsets into a 700-row training setstacks the two remainders into a 500-row test setrandomises row order of the 700 row training set before modellingrandomises row order of the 500 row test set before modellingtrains a logistic regression model, using Survived as the target and all other attributes as predictorsapplies the trained logistic regression model to the test data to predict Survivedcompares predicted Survived against actual Survived for the Logistic Regression model, outputs accuracy and confusion matrixtrains a Naive Bayes classifier on Survived, assuming predictors are independent given the classapplies the trained Naive Bayes model to the test data to predict Survivedcompares predicted Survived against actual Survived for the Naive Bayes model, outputs accuracy and confusion matrixtrains an ensemble of decision trees on Survived, using all attributes as predictorsapplies the trained forest to the test data to predict Survived, with prediction confidencecompares predicted Survived against actual Survived for the Random Forest model, outputs accuracy and confusion matrixapplies the trained model to the test data to generate a predicted Survived value for each row. CSV Reader CSV Reader LogisticRegression Learner Scorer CSV Reader Rule-basedRow Splitter Partitioning Partitioning Concatenate Concatenate Shuffle Shuffle LogisticRegression Learner Logistic RegressionPredictor Scorer Naive Bayes Learner Naive BayesPredictor Scorer Random ForestLearner Random ForestPredictor Scorer Logistic RegressionPredictor Assignment 1 Task 2 Asssignment 1 Task 1 accident_2025_train2_73.csvloads a CSV file into KNIME as a table.accident_2025_test2_73.txtloads a CSV file into KNIME as a table.trains a logistic regression model on the training data to learn the relationship between the attributes and Survived.compares the predicted values against the actual Survived values and outputs the accuracy and confusion matrix.reads and combines every matching file in a folder in one gosplits rows into two outputs based on a condition written, example, Survived = Yes.splits the Yes branch into an exact 350 row subset and a 28 row remainder, using Absolute modesplits the No branch into an exact 350-row subset and a 472 row remainder, using Absolute modestacks the two 350-row subsets into a 700-row training setstacks the two remainders into a 500-row test setrandomises row order of the 700 row training set before modellingrandomises row order of the 500 row test set before modellingtrains a logistic regression model, using Survived as the target and all other attributes as predictorsapplies the trained logistic regression model to the test data to predict Survivedcompares predicted Survived against actual Survived for the Logistic Regression model, outputs accuracy and confusion matrixtrains a Naive Bayes classifier on Survived, assuming predictors are independent given the classapplies the trained Naive Bayes model to the test data to predict Survivedcompares predicted Survived against actual Survived for the Naive Bayes model, outputs accuracy and confusion matrixtrains an ensemble of decision trees on Survived, using all attributes as predictorsapplies the trained forest to the test data to predict Survived, with prediction confidencecompares predicted Survived against actual Survived for the Random Forest model, outputs accuracy and confusion matrixapplies the trained model to the test data to generate a predicted Survived value for each row. CSV Reader CSV Reader LogisticRegression Learner Scorer CSV Reader Rule-basedRow Splitter Partitioning Partitioning Concatenate Concatenate Shuffle Shuffle LogisticRegression Learner Logistic RegressionPredictor Scorer Naive Bayes Learner Naive BayesPredictor Scorer Random ForestLearner Random ForestPredictor Scorer Logistic RegressionPredictor

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Created by: User
Created at: 2026-09-06
On NodePit since: 2026-09-20
Last update: 2026-09-20
Created with KNIME version: v5.4.0

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