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Challenge 23 - Modeling Churn Predictions I

Challenge 23 - Modeling Churn Predictions I
Challenge 23: Modeling Churn Predictions I Description: A telecom company wants you to predict which customers are going to churn (that is, are going to canceltheir contracts) based on attributes of their accounts. To this end, you are expected to use a decision tree classifier. Thecompany gives you two datasets (training and test), both with many attributes and the class ‘Churn’ to be predicted (value0 corresponds to customers that do not churn, and 1 corresponds to those who do). You should train the decision treeclassifier 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 onclassification. You are expected to just apply a decision tree classifier (and get an accuracy of about 92%). A simplesolution should consist of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute orclass in the datasets, and use all available attributes. DATA INPUT TRAIN MODELS & PREDICTION CHECK RESULTS Read Testing Data ~20%ReadTraining Data ~80%Train Modelwith Training DataPredictusing Testing DataTrain Modelwith Training DataPredictusing Testing DataCSV Reader CSV Reader DecisionTree Learner Decision TreePredictor LogisticRegression Learner Logistic RegressionPredictor Component Challenge 23: Modeling Churn Predictions I Description: A telecom company wants you to predict which customers are going to churn (that is, are going to canceltheir contracts) based on attributes of their accounts. To this end, you are expected to use a decision tree classifier. Thecompany gives you two datasets (training and test), both with many attributes and the class ‘Churn’ to be predicted (value0 corresponds to customers that do not churn, and 1 corresponds to those who do). You should train the decision treeclassifier 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 onclassification. You are expected to just apply a decision tree classifier (and get an accuracy of about 92%). A simplesolution should consist of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute orclass in the datasets, and use all available attributes. DATA INPUT TRAIN MODELS & PREDICTION CHECK RESULTS Read Testing Data ~20%ReadTraining Data ~80%Train Modelwith Training DataPredictusing Testing DataTrain Modelwith Training DataPredictusing Testing DataCSV Reader CSV Reader DecisionTree Learner Decision TreePredictor LogisticRegression Learner Logistic RegressionPredictor Component

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Created by: USER
Created at: 2022-07-02
On NodePit since: 2022-07-04
Last update: 2024-11-21
Created with KNIME version: v4.5.0
Tags: #justknimeit-23#justknimeit-23

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