KULDEEP220 / Evaluation_Project_Customer_Churn_Analysis

Aim: Building and comparing several customer churn prediction models.

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Evaluation_Project_Customer_Churn_Analysis

Aim: Building and comparing several customer churn prediction models.

Problem Statement:

Customer churn is when a company’s customers stop doing business with that company. Businesses are very keen on measuring churn because keeping an existing customer is far less expensive than acquiring a new customer. New business involves working leads through a sales funnel, using marketing and sales budgets to gain additional customers. Existing customers will often have a higher volume of service consumption and can generate additional customer referrals.

Customer retention can be achieved with good customer service and products. But the most effective way for a company to prevent attrition of customers is to truly know them. The vast volumes of data collected about customers can be used to build churn prediction models. Knowing who is most likely to defect means that a company can prioritise focused marketing efforts on that subset of their customer base.

Preventing customer churn is critically important to the telecommunications sector, as the barriers to entry for switching services are so low.

You will examine customer data from IBM Sample Data Sets with the aim of building and comparing several customer churn prediction models.

Attribute Information:

customerID : Unique Id of Customer , object
gender : Gender of the customer, object
SeniorCitizen : Where customer is a SeniorCitizen or not, int
Partner : Where customer having Partner or not object
Dependents : Where customer having Dependents
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

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Aim: Building and comparing several customer churn prediction models.


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