Telecom Customer Churn Prediction Dataset

This dataset contains structured customer-level telecom records including subscription details, call usage patterns, international plan and voicemail plan information, service complaint metrics, and churn outcome labels. The dataset has been processed through a structured exploratory data analysis workflow including duplicate removal, missing value treatment, categorical normalization, and numeric range validation. Outlier detection was applied to identify unrealistic call usage values. The final dataset is exported in a clean CSV format optimized for classification modeling, churn prediction, and customer analytics workflows.

Data Quality

The dataset is organized in a normalized tabular structure with consistent column naming and validated numeric data types. Duplicate customer records have been removed to maintain integrity. Numeric attributes such as call minutes and charges have undergone statistical validation to ensure realistic usage values. Categorical attributes such as international plan and voicemail plan have been standardized. The dataset is fully analytics-ready and supports classification modeling, clustering, and behavioral segmentation workflows.

Use Cases

This dataset can be used for telecom churn prediction modeling, customer retention strategy research, and behavioral segmentation analysis. Data scientists can build classification models to predict churn probability based on usage patterns. Telecom businesses can use it for retention strategy benchmarking and customer lifetime value modeling. Researchers can explore feature importance for churn behavior. The dataset is also widely used in machine learning education for binary classification tasks.

Limitations

The dataset is community-sourced and reflects synthetic or sample-level telecom records rather than live operator data. It may not represent real-time churn behavior or operator-specific market conditions. Geographic and demographic attributes are limited. The dataset is intended for educational and analytical purposes and should not be used for direct commercial customer profiling.

Methodology

The dataset was prepared using a structured validation and normalization framework including schema standardization, duplicate removal, missing value assessment, numeric range validation of call usage attributes, and categorical normalization. Statistical anomaly detection was applied to identify unrealistic usage patterns. The final dataset was exported in UTF-8 encoded CSV format to ensure compatibility with machine learning libraries, statistical modeling tools, and business intelligence platforms.