Churn Prediction Using Machine Learning

One of the most famous and useful case studies of churn prediction is in the telecom industry. It is important for telecom companies to analyze all relevant customer data and develop a robust and accurate Churn Prediction model to retain customers and to form strategies for reducing customer attrition rates. In this project, Telco Customer Churn Dataset which is available at Kaggle is used. Two numerical columns: 1. MonthlyCharges: The amount charged to the customer monthly 2. TotalCharges: The total amount charged to the customer Eighteen categorical columns: 1. CustomerID: Customer ID unique for each customer 2. gender: Whether the customer is a male or a female 3. SeniorCitizen: Whether the customer is a senior citizen or not (1, 0) 4. Partner: Whether the customer has a partner or not (Yes, No) 5. Dependents: Whether the customer has dependents or not (Yes, No) 6. Tenure: Number of months the customer has stayed with the company 7. PhoneService: Whether the customer has a phone service or not (Yes, No) 8. MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service) 9. InternetService: Customer's internet service provider (DSL, Fiber optic, No) 10. OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service) 11.