Our motive is to predict the features on which the customer attrition depends and predict the whether the customer will continue or switch the service. Dimensions and explanation of the DataSet customerID: Customer ID gender: gender (female, male) SeniorCitizen: Whether the customer is a senior citizen or not (1, 0) Partner: 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) Model_Applied Accuracy 1. Logistic_Regression = 0.785140 2. Model_DecisionTree = 0.712257 3. RandomForest = 0.766209 4. KNN = 0.755797