Datasets:
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.