Every Day to day passing by the competition is high in the market for the Telecom Industry and losing the customers from its customer base gives a lot of loss to the company and other the other hand acquiring new customers is difficult and costly. The Telecom Company wants to know the customers who gonna churn and want a model that classifies the customers which are going to churn so that the company can run measures to retain them.

I will be classifying the customers based on the various features we collected from the Telecom Company and will be given output if the customer will churn or not.

The data is from the IBM Watson Of Telecom Churn, Thanks to IBM providing real-life scenario data so that like me aspiring Data scientists can learn and perform the task which can be in future replicated in Real Industry.

customerID : Customer Identification Gender : the customer is a male or a female SeniorCitizen : the customer is a senior citizen or not (1, 0) Partner : customer a partner or not (Yes, No) Dependents : customer dependents or not (Yes, No) Tenure : Number of months the customer stayed with the company PhoneService : a phone service or not (Yes, No) MultipleLines : customer multiple lines or not (Yes, No, No phone service) InternetService : Customer’s internet service provider (DSL, Fiber optic, No) OnlineSecurity : customer online security or not (Yes, No, No internet service) OnlineBackup : customer online backup or not (Yes, No, No internet service) DeviceProtection : customer device protection or not (Yes, No, No internet service) TechSupport : customer tech support or not (Yes, No, No internet service) StreamingTV : customer streaming TV or not (Yes, No, No internet service) StreamingMovies : customer streaming movies or not (Yes, No, No internet service) Contract : The contract term of the customer (Month-to-month, One year, Two years) PaperlessBilling : 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)

We have successfully solved the Business problem and have given insightful Mesures while Exploratory Analysis of data to help retain Customers at Company Level and successfully proposed a model which is 86% accurate in predicting the Customers who are going to churn.