Predicting Behavior with Logistic Regression

Customer churn occurs when customers stop doing business with a company. Retaining existing customers is less expensive than it is to acquire new customers and hence, building a good predictive model for customer churn is of importance to many companies. Download the dataset Telco.customer.csv. Through this dataset, we attempt to predict behavior to retain customers using logistic regression.

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  1. Using R, partition the dataset into training and testing sets by using the code:
    (YOURDATA is the name of your dataset in R.)

    intrain- createDataPartition(YOURDATA$Churn,p=0.7,list=FALSE)
    set.seed(2017)
    training- YOURDATA[intrain,]
    testing- YOURDATA[-intrain,]

  2. Fit a logistic regression model by using the code:

    glm(Churn ~ .,family=binomial(link=”logit”),data=training)

  3. Examine the resulting fitted model. What are the significant factors that affect customer churn? Explain how and why they are significant.
  4. Now, let’s examine how the model fits using the following code.

    testing$Churn – as.character(testing$Churn)
    testing$Churn[testing$Churn==”No”] – “0”
    testing$Churn[testing$Churn==”Yes”] – “1”
    fitted.results – predict(LogModel,newdata=testing,type=’response’)
    fitted.results – ifelse(fitted.results 0.5,1,0)
    misClasificError – mean(fitted.results != testing$Churn)
    print(paste(‘Logistic Regression Accuracy’,1-misClasificError))

    This provides the accuracy of the model.

  1. How can you make a customer churn prediction from the model you fitted? Explain. Although it is not required, the actual calculation may help your understanding.

Provide the output from each set of code and your answer and/solution to the questions in an MS PowerPoint presentation. Your presentations should have 8-12 slides. Every slide must contain a “Notes Section” with full explanation of the slide’s content.

Follow APA format. Include a title slide and a slide citing references. These 2 slides are in addition to the 8 -12 used in the presentation. Cite at least one outside academic source other than the textbook, course materials, or other information provided as part of the course materials.

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