finxter / customer-churn Goto Github PK

Churn prediction app using a dataset sourced by the IBM Developer Platform. The dataset has 7043 rows and 21 features.

There are 17 categorical features:

CustomerID: Customer ID unique for each customer 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) Dependent: Whether the customer has dependents or not (Yes, No) PhoneService: Whether the customer has a phone service or not (Yes, No) MultipeLines: 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 an 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 years) PaperlessBilling: The contract term of the customer (Month-to-month, One year, Two years) PaymentMethod: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))

Next, there are 3 numerical features:

Tenure: Number of months the customer has stayed with the company MonthlyCharges: The amount charged to the customer monthly TotalCharges: The total amount charged to the customer Finally, there’s a prediction feature:

Churn: Whether the customer churned or not (Yes or No)

We used XGBoost model to train the data. It achieved an accuracy score of 80.4%.