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language: - en tags: - churn-prediction - customer-retention - customer-service - demographics - telecom - tabular-classification pretty_name: Telco Customer Churn dataset_info: - config_name: default features: - name: Age dtype: int64 - name: Avg Monthly GB Download
dtype: float64 - name: Avg Monthly Long Distance Charges dtype: float64 - name: Churn Category dtype: string - name: Churn Label dtype: string # Target Variable - name: Churn Reason dtype: string - name: Churn Score dtype: int64 - name: Churn Value dtype: int64 - name: City dtype: string - name: CLTV dtype: float64 - name: Contract dtype: string - name: Country dtype: string - name: Customer ID dtype: string - name: Customer Status dtype: string - name: Dependents dtype: bool - name: Device Protection Plan dtype: bool - name: Gender dtype: string - name: Lat Long dtype: string - name: Latitude dtype: float64 - name: Longitude dtype: float64 - name: Married dtype: bool - name: Monthly Charge dtype: float64 - name: Multiple Lines dtype: string - name: Number of Dependents dtype: int64 - name: Number of Referrals dtype: int64 - name: Offer dtype: string - name: Online Backup dtype: bool - name: Online Security dtype: bool - name: Paperless Billing dtype: bool - name: Partner dtype: bool - name: Payment Method dtype: string - name: Phone Service dtype: bool - name: Population dtype: int64 - name: Premium Tech Support dtype: bool - name: Quarter dtype: string - name: Referred a Friend dtype: bool - name: Satisfaction Score dtype: int64
- name: Senior Citizen
dtype: bool - name: State
dtype: string - name: Streaming Movies dtype: bool - name: Streaming Music dtype: bool - name: Streaming TV dtype: bool - name: Tenure in Months dtype: int64 - name: Total Charges dtype: float64 - name: Total Extra Data Charges dtype: float64 - name: Total Long Distance Charges dtype: float64 - name: Total Refunds dtype: float64 - name: Total Revenue dtype: float64 - name: Under 30 dtype: bool - name: Unlimited Data dtype: bool - name: Zip Code dtype: string

Telco Customer Churn

This dataset is a valuable resource for exploring and predicting customer churn in the telecommunications industry. It provides a comprehensive snapshot of customer demographics, service usage patterns, billing information, and churn status, making it ideal for training machine learning models to predict customer churn and develop effective customer retention strategies.

Content and Structure:

The dataset is structured in a tabular format, with each row representing a unique customer and each column containing attributes about that customer.

  • Customer Demographics: Features like gender, age, marital status, and dependents provide insights into customer profiles.
  • Service Usage: Details customer subscriptions to services such as phone, internet, multiple lines, online security, online backup, device protection, tech support, and streaming options.
  • Billing Information: Provides data on tenure, contract type, payment method, monthly charges, and total charges.
  • Churn Information: Includes labels indicating whether a customer churned, the reason for churn (if applicable), and churn scores for analysis.

Data Collection and Curation:

This dataset is a fictional dataset created by IBM data scientists as a sample dataset for exploring customer churn prediction. It is not based on real-world data and should be treated as a simulation for learning and experimentation.

Usage Examples:

  • Customer Churn Prediction: Train classification models to predict churn based on customer demographics, service usage, and billing information.
  • Customer Segmentation: Analyze the dataset to identify customer segments with different churn probabilities, allowing for targeted retention strategies.
  • Feature Engineering: Experiment with feature engineering techniques to improve churn prediction model accuracy.

Additional Information:

  • Industry Relevance: Relevant for businesses in the telecommunications industry and other sectors that deal with customer churn.
  • Ethical Considerations: This is a fictional dataset and does not contain real personal or sensitive information.