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A Machine Learning project using R and RapidMiner to predict the possibility of customer churn using 6 different machine learning algorithms.

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Customer-Churn-Analysis: A Machine Learning Project

Problem Statement:

Customer churn occurs when customers or subscribers stop doing business with a company or service, also known as customer attrition. It is also referred as loss of clients or customers. One industry in which churn rates are particularly useful is the telecommunications industry, because most customers have multiple options from which to choose within a geographic location.

Aim:

To predict the possibility of customer churn for the telecom industry

Methodology:

• This is a binary classification problem- whether a customer will churn- YES/NO

• The problem has been approached using the following ML models both on R and RapidMiner

 Decision Trees

 Logistic Regression

 Random Forest

 Naïve Bayes

 Support Vector Machine

 Neural Networks

• Finally, the best fit model is determined by comparing all the models using ROC and AUC

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A Machine Learning project using R and RapidMiner to predict the possibility of customer churn using 6 different machine learning algorithms.

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