README.md

metadata

language:
  - en
tags:
  - tabular-classification
  - churn-prediction
  - telecom
  - customer-retention
  - demographics
  - customer-service
pretty_name: Telco Customer Churn
dataset_info:
  - config_name: default
    features:
      - name: Customer ID
        dtype: string
      - name: Gender
        dtype: ClassLabel
      - name: Age
        dtype: int64
      - name: Under 30
        dtype: bool
      - name: Senior Citizen
        dtype: bool
      - name: Married
        dtype: bool
      - name: Dependents
        dtype: bool
      - name: Number of Dependents
        dtype: int64
      - name: Country
        dtype: ClassLabel
      - name: State
        dtype: ClassLabel
      - name: City
        dtype: ClassLabel
      - name: Zip Code
        dtype: ClassLabel
      - name: Lat Long
        dtype: string
      - name: Latitude
        dtype: float64
      - name: Longitude
        dtype: float64
      - name: Population
        dtype: int64
      - name: Quarter
        dtype: ClassLabel
      - name: Referred a Friend
        dtype: bool
      - name: Number of Referrals
        dtype: int64
      - name: Tenure in Months
        dtype: int64
      - name: Offer
        dtype: ClassLabel
      - name: Phone Service
        dtype: bool
      - name: Avg Monthly Long Distance Charges
        dtype: float64
      - name: Multiple Lines
        dtype: ClassLabel
      - name: Internet Service
        dtype: bool
      - name: Internet Type
        dtype: ClassLabel
      - name: Avg Monthly GB Download
        dtype: float64
      - 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: ClassLabel
      - name: Paperless Billing
        dtype: bool
      - name: Payment Method
        dtype: ClassLabel
      - name: Monthly Charge
        dtype: float64
      - name: Total Charges
        dtype: float64
      - name: Total Refunds
        dtype: float64
      - name: Total Extra Data Charges
        dtype: float64
      - name: Total Long Distance Charges
        dtype: float64
      - name: Total Revenue
        dtype: float64
      - name: Satisfaction Score
        dtype: int64
      - name: Customer Status
        dtype: ClassLabel
      - name: Churn Label
        dtype: ClassLabel
      - name: Churn Value
        dtype: int64
      - name: Churn Score
        dtype: int64
      - name: CLTV
        dtype: float64
      - name: Churn Category
        dtype: ClassLabel
      - name: Churn Reason
        dtype: ClassLabel
      - name: Partner
        dtype: bool
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: test
        path: test.csv
train-eval-index:
  - config: default
    task: text-classification
    task_id: multi_label_classification
    col_mapping:
      Customer ID: id
      Gender: Gender
      Age: Age
      Under 30: Under 30
      Senior Citizen: Senior Citizen
      Married: Married
      Dependents: Dependents
      Number of Dependents: Number of Dependents
      Country: Country
      State: State
      City: City
      Zip Code: Zip Code
      Lat Long: Lat Long
      Latitude: Latitude
      Longitude: Longitude
      Population: Population
      Quarter: Quarter
      Referred a Friend: Referred a Friend
      Number of Referrals: Number of Referrals
      Tenure in Months: Tenure in Months
      Offer: Offer
      Phone Service: Phone Service
      Avg Monthly Long Distance Charges: Avg Monthly Long Distance Charges
      Multiple Lines: Multiple Lines
      Internet Service: Internet Service
      Internet Type: Internet Type
      Avg Monthly GB Download: Avg Monthly GB Download
      Online Security: Online Security
      Online Backup: Online Backup
      Device Protection Plan: Device Protection Plan
      Premium Tech Support: Premium Tech Support
      Streaming TV: Streaming TV
      Streaming Movies: Streaming Movies
      Streaming Music: Streaming Music
      Unlimited Data: Unlimited Data
      Contract: Contract
      Paperless Billing: Paperless Billing
      Payment Method: Payment Method
      Monthly Charge: Monthly Charge
      Total Charges: Total Charges
      Total Refunds: Total Refunds
      Total Extra Data Charges: Total Extra Data Charges
      Total Long Distance Charges: Total Long Distance Charges
      Total Revenue: Total Revenue
      Satisfaction Score: Satisfaction Score
      Customer Status: Customer Status
      Churn Label: label
      Churn Value: Churn Value
      Churn Score: Churn Score
      CLTV: CLTV
      Churn Category: Churn Category
      Churn Reason: Churn Reason
      Partner: Partner
    metrics:
      - type: accuracy
        name: Accuracy
      - type: precision
        name: Precision
      - type: recall
        name: Recall
      - type: f1
        name: F1 Score

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.