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KNIME_​challenge23_​solution

KNIME_challenge23_solution
Version 1: Simple prediction model using decision tree Partition of dataset for testing modelaccuracy Partition of dataset meant for trainingmodel Challenge 23: Modeling Churn Predictions - Part 1Level: EasyDescription: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts) based onattributes of their accounts. To this end, you are expected to use a decision tree classifier. The company gives you two datasets (training and test), bothwith many attributes and the class ‘Churn’ to be predicted (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do).You should train the decision tree classifier with the training data, and assess its quality over the test data (calculate the accuracy, precision, recall, andconfusion matrix for example). Note 1: This challenge is a simple introduction to predictive problems, focusing on classification. You are expected tojust apply a decision tree classifier (and get an accuracy of about 92%). A simple solution should consist of 5 nodes. Note 2: In this challenge, do notchange the statistical distribution of any attribute or class in the datasets, and use all available attributes. Note 3: Need more help to understand theproblem? Check this blog post out.Author: Aline Bessa Version2: Advanced version leveraging AutoML-node tobenchmark several AA-models and choose the one withhighest accuracy learn and predictchurnReadchurn_problem_training_data.csvtrainpredictchurnformatchurn as strAccuracy = 93%formatchurn as strReadchurn_problem_test_data.csvpredictchurnAccuracy = 95% AutoML CSV Reader DecisionTree Learner Decision TreePredictor Table Manipulator Scorer (JavaScript) Table Manipulator CSV Reader Workflow Executor Scorer (JavaScript) Version 1: Simple prediction model using decision tree Partition of dataset for testing modelaccuracy Partition of dataset meant for trainingmodel Challenge 23: Modeling Churn Predictions - Part 1Level: EasyDescription: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts) based onattributes of their accounts. To this end, you are expected to use a decision tree classifier. The company gives you two datasets (training and test), bothwith many attributes and the class ‘Churn’ to be predicted (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do).You should train the decision tree classifier with the training data, and assess its quality over the test data (calculate the accuracy, precision, recall, andconfusion matrix for example). Note 1: This challenge is a simple introduction to predictive problems, focusing on classification. You are expected tojust apply a decision tree classifier (and get an accuracy of about 92%). A simple solution should consist of 5 nodes. Note 2: In this challenge, do notchange the statistical distribution of any attribute or class in the datasets, and use all available attributes. Note 3: Need more help to understand theproblem? Check this blog post out.Author: Aline Bessa Version2: Advanced version leveraging AutoML-node tobenchmark several AA-models and choose the one withhighest accuracy learn and predictchurnReadchurn_problem_training_data.csvtrainpredictchurnformatchurn as strAccuracy = 93%formatchurn as strReadchurn_problem_test_data.csvpredictchurnAccuracy = 95% AutoML CSV Reader DecisionTree Learner Decision TreePredictor Table Manipulator Scorer (JavaScript) Table Manipulator CSV Reader Workflow Executor Scorer (JavaScript)

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Created by: MMU
Created at: 2022-06-30
On NodePit since: 2022-07-01
Last update: 2024-04-26
Created with KNIME version: v4.5.2
Tags: justknimeitjustknimeit-23

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