Create README.md

README.md

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+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
+
+---
+## 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.