Abstract: Churn analysis is an important tool for companies as it can reduce the costs that are related to customer churn. Churn prediction is the process of identifying users before they churn, this is done by implementing methods on collected data in order to ﬁnd patterns that can be helpful when predicting new churners in the future.The objective of this report is to identify churners with the use of surveys collected from diﬀerent golfclubs, their members and guests. This was accomplished by testing several diﬀerent supervised machine learning algorithms in order to ﬁnd the diﬀerent classes and to see which supervised algorithms are most suitable for this kind of data.The margin of success was to have a greater accuracy than the percentage of major class in the datasetThe data was processed using label encoding, ONE-hot encoding and principal component analysis and was split into 10 folds, 9 training folds and 1 testing fold ensuring cross validation when iterated 10 times rearranging the test and training folds. Each algorithm processed the training data to create a classiﬁer which was tested on the test data.The classiﬁers used for the project was K nearest neighbours, Support vector machine, multi-layer perceptron, decision trees and random forest.The diﬀerent classiﬁers generally had an accuracy of around 72% and the best classiﬁer which was random forest had an accuracy of 75%. All the classiﬁers had an accuracy above the margin of success.K-folding, confusion-matrices, classiﬁcation report and other internal crossvalidation techniques were performed on the the data to ensure the quality of the classiﬁer.The project was a success although there is a strong belief that the bottleneck for the project was the quality of the data in terms of new legislation when collecting and storing data that results in redundant and faulty data.
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