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Synthetic Customer Churn Prediction Dataset
Retail & Consumer Behavior
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£79.99
About
This Synthetic Customer Churn Prediction Dataset has been designed as an educational resource for exploring data science, machine learning, and predictive modelling techniques in a customer retention context. The dataset simulates key attributes relevant to customer churn analysis, such as service usage, contract details, and customer demographics. It allows users to practice data manipulation, visualization, and the development of models to predict churn behaviour in industries like telecommunications, subscription services, or utilities.
Dataset Features:
- Customer_Id: Unique identifier for each customer (not included in this dataset for privacy).
- Gender: Gender of the customer (e.g., "Male," "Female").
- Partner: Whether the customer has a partner (e.g., "Yes," "No").
- Dependents: Whether the customer has dependents (e.g., "Yes," "No").
- Tenure (Months): The number of months the customer has been with the company.
- PhoneService: Whether the customer has a phone service (e.g., "Yes," "No").
- MultipleLines: Whether the customer has multiple phone lines (e.g., "Yes," "No phone service").
- InternetService: Type of internet service (e.g., "DSL," "Fiber optic," "No").
- OnlineSecurity: Whether the customer has online security services (e.g., "Yes," "No," "No internet service").
- OnlineBackup: Whether the customer has online backup services (e.g., "Yes," "No," "No internet service").
- DeviceProtection: Whether the customer has device protection services (e.g., "Yes," "No," "No internet service").
- TechSupport: Whether the customer has tech support services (e.g., "Yes," "No," "No internet service").
- StreamingTV: Whether the customer has streaming TV services (e.g., "Yes," "No," "No internet service").
- StreamingMovies: Whether the customer has streaming movies services (e.g., "Yes," "No," "No internet service").
- Contract: Type of contract the customer has (e.g., "Month-to-month," "One year," "Two year").
- PaperlessBilling: Whether the customer uses paperless billing (e.g., "Yes," "No").
- PaymentMethod: The payment method used by the customer (e.g., "Electronic check," "Credit card," "Bank transfer").
- MonthlyCharges: Monthly charges billed to the customer.
- TotalCharges: Total charges incurred by the customer over their tenure.
- Churn: Whether the customer has churned (e.g., "Yes," "No").
Distribution:
Usage:
This dataset is useful for a variety of applications, including:
- Customer Behavior Analysis: To understand factors influencing customer retention and churn.
- Educational Training: To practice data cleaning, feature engineering, and visualization techniques in customer analytics.
- Predictive Modeling: To build machine learning models for predicting customer churn based on service usage patterns and demographic information.
Coverage:
This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.
License:
CCO (Public Domain)
Who can use it:
- Data scientists and enthusiasts: For developing customer analytics skills and predictive modelling expertise.
- Business analysts: To understand customer churn drivers and improve retention strategies.
- Educators and students: For teaching and learning applications in data science and machine learning.
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