Datasets:
Tasks:
Tabular Classification
Formats:
csv
Languages:
English
Size:
1K - 10K
File size: 7,235 Bytes
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---
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. |