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:
- Does the demographic feature (gender, Senior Citizen, Partners, Dependents) have influence on the customers to churn?
- Does the customer who churn using all of the services that telco gives?
- 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)?
- Does expensive charges makes customers churn?
In order to know the answer, please check the notebook!