README.md

language:

• en license: other license_name: Telco Customer Churn License license_link: LICENSE license_details: This dataset is provided under the Telco Customer Churn License, which allows for free use and distribution for non-commercial purposes. Commercial use requires explicit permission from the creators. tags:
• tabular-classification
• churn-prediction
• telecom
• customer-retention
• demographics
• customer-service annotations_creators:
• no-annotation language_creators:
• found language_details:
• en pretty_name: Telco Customer Churn size_categories:
• 10K<n<100K source_datasets:
• original task_categories:
• tabular-classification task_ids:
• multi-class-classification paperswithcode_id: null configs:
• config_name: default data_files:
  • split: train path: telco_customer_churn.csv dataset_info: features:
    • name: Customer ID dtype: string
    • name: Gender dtype: string
    • name: Age dtype: int32
    • name: Under 30 dtype: bool
    • name: Senior Citizen dtype: bool
    • name: Married dtype: bool
    • name: Dependents dtype: bool
    • name: Number of Dependents dtype: int32
    • name: Country dtype: string
    • name: State dtype: string
    • name: City dtype: string
    • name: Zip Code dtype: int32
    • name: Lat Long dtype: string
    • name: Latitude dtype: float32
    • name: Longitude dtype: float32
    • name: Population dtype: int32
    • name: Quarter dtype: string
    • name: Referred a Friend dtype: bool
    • name: Number of Referrals dtype: int32
    • name: Tenure in Months dtype: int32
    • name: Offer dtype: string
    • name: Phone Service dtype: bool
    • name: Avg Monthly Long Distance Charges dtype: float32
    • name: Multiple Lines dtype: bool
    • name: Internet Service dtype: bool
    • name: Internet Type dtype: string
    • name: Avg Monthly GB Download dtype: float32
    • name: Online Security dtype: bool
    • name: Online Backup dtype: bool
    • name: Device Protection Plan dtype: bool
    • name: Premium Tech Support dtype: bool
    • name: Streaming TV dtype: bool
    • name: Streaming Movies dtype: bool
    • name: Streaming Music dtype: bool
    • name: Unlimited Data dtype: bool
    • name: Contract dtype: string
    • name: Paperless Billing dtype: bool
    • name: Payment Method dtype: string
    • name: Monthly Charge dtype: float32
    • name: Total Charges dtype: float32
    • name: Total Refunds dtype: float32
    • name: Total Extra Data Charges dtype: float32
    • name: Total Long Distance Charges dtype: float32
    • name: Total Revenue dtype: float32
    • name: Satisfaction Score dtype: int32
    • name: Customer Status dtype: string
    • name: Churn Label dtype: string
    • name: Churn Value dtype: bool
    • name: Churn Score dtype: int32
    • name: CLTV dtype: float32
    • name: Churn Category dtype: string
    • name: Churn Reason dtype: string
    • name: Partner dtype: bool config_name: default splits:
    • name: train num_bytes: 121430 # Replace with actual value num_examples: 7043 download_size: 121430 # Replace with actual value dataset_size: 121430 # Replace with actual value train-eval-index:
  • config: default task: tabular-classification task_id: multi_class_classification splits: train_split: train eval_split: validation # Update if you have a validation split col_mapping: label: Churn Label metrics:
    • type: accuracy name: Accuracy

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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.