Customer Churn Analysis and Prediction Using Machine Learning

Hello everyone!

In this data science project, I am gonna try to analyze and predict churn customer from Telco Customer Churn dataset (https://www.kaggle.com/blastchar/telco-customer-churn/). Churn is phenomenon where customers of a business no longer purchase or interact with the business. A high churn means that higher number of customers no longer want to purchase goods and services from the business.

Data Information:

• 7043 rows
• 21 columns with 19 features

Description:

• customerID: Customer ID
• gender: Whether the customer is a male or a female (Male, Female)
• SeniorCitizen: Whether the customer is a senior citizen or not (Yes, No)
• Partner: Whether 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 a multiple lines or not (Yes, No)
• InternetService: Customer’s internet service provider (Yes, No)
• OnlineSecurity: Whether the customer has a online security or not (Yes, No)
• DeviceProtection: Whether the customer has a device protection or not (Yes, No)
• OnlineBackup: Whether the customer has a online backup or not (Yes, No)
• StreamingMoies: Whether the customer has a streaming movies services or not (Yes, No)
• StreamingTV: Whether the customer has streaming TV or not (Yes, No)
• TechSupport: Whether the customer has tech support or not (Yes, No)
• Contract: Customer's contract (Month-to-month, One year, Two year)
• PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)
• PaymentMethod: 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, No)

To extract actionable insights from the dataset. I listed all the questions that came to mind below after assessing the dataset, and I tried to investigate all of them to find the insights:

1. Does the demographic feature (gender, Senior Citizen, Partners, Dependents) have influence on the customers to churn?
2. Does the customer who churn using all of the services that telco gives?
3. For two groups of those customers who churn and not, how long did they usually stay in the service? and what was their average LTV(Life Time Value)?
4. Does expensive charges makes customers churn?

In order to know the answer, please check the notebook!