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KDDCup09_churn

KDDCup09_churn

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  • study_14 study_1 study_284 study_618 study_509 study_113 study_572 study_189 study_378 study_189 study_201
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Author: Orange Telecom Source: [ACM KDD Cup](http://www.sigkdd.org/kddcup/index.php) - 2009 Please cite: The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn). Churn (wikipedia definition): Churn rate is also sometimes called attrition rate. It is one of two primary factors that determine the steady-state level of customers a business will support. In its broadest sense, churn rate is a measure of the number of individuals or items moving into or out of a collection over a specific period of time. The term is used in many contexts, but is most widely applied in business with respect to a contractual customer base. For instance, it is an important factor for any business with a subscriber-based service model, including mobile telephone networks and pay TV operators. The term is also used to refer to participant turnover in peer-to-peer networks. The training set contains 50,000 examples. The first predictive 190 variables are numerical and the last 40 predictive variables are categorical. The last target variable is binary {-1,1}.

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10 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: CHURN
0 runs - estimation_procedure: 10% Holdout set - target_feature: CHURN
0 runs - estimation_procedure: Leave one out - target_feature: CHURN
0 runs - estimation_procedure: Test on Training Data - target_feature: CHURN
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: CHURN
0 runs - estimation_procedure: 33% Holdout set - target_feature: CHURN
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: CHURN
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: CHURN
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: CHURN
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: CHURN
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