Churn (loss of customers to competition) is a problem for companies because it is more expensive to acquire a new customer than to keep your existing one from leaving. This problem statement is targeted at enabling churn reduction using analytics concepts.
customerID : Customer ID
gender: Whether the customer is a male or a female
SeniorCitizen: Whether the customer is a senior citizen or not (1, 0)
Partner: Whether the customer has a partner or not (Yes, No)
Dependents: Whether the customer has dependents or not (Yes, No)
tenure: Number of months the customer has stayed with the company
PhoneService: Whether the customer has a phone service or not (Yes, No)
MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService: Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup: Whether the customer has online backup or not (Yes, No, No internet service)
DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport: Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract: The contract term of the customer (Month-to-month, One year, Two year)
PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)
PaymentMethod: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges: The amount charged to the customer monthly
TotalCharges: The total amount charged to the customer
Churn: Whether the customer churned or not (Yes or No)
From all the models that we have built, the model with highest accuracy is the Logistic Regression and seems to be the most suitable model to predict the customer churn