RSK World - Customer Churn Dataset | CSV | Churn Prediction | RSK World

Churn Analysis Retention EDA Feature Engineering ML Ready CSV

This project features a meticulously curated Customer Churn dataset designed for both learning and real-world prototyping. The data covers anonymized customer demographics, service/usage patterns, account tenure, payment details, and annotated churn outcomes. You get everything needed to build, validate, and explain churn models: Python scripts for data cleaning, exploratory analysis, and feature engineering (with Pandas/NumPy), as well as a Jupyter notebook for EDA, visualization, and baseline model training with scikit-learn. The package includes flexible YAML configs, utility helpers for metrics and reporting, containerization with Docker, and a clear README to get started fast. Whether for teaching, bootcamps, or quick experimentation, this resource accelerates the full churn prediction workflow from raw CSVs to actionable insights.

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Dataset Overview

Demographics, account details, usage patterns, billing history, and churn labels in one clean CSV.

CustomerID, demographics, tenure, account type
Usage metrics and billing amounts
Payment method and contract length
Support interactions and last login
Churn flag with timestamp
Geography, partner, and dependents indicators
Add-on services (streaming, security, backups, tech support)
Billing preferences (paperless, autopay, payment method)
Contract status, renewal windows, and churn reason hints

Data Quality & Prep

Ready-to-run preprocessing pipeline with sensible defaults for churn modeling.

Header comments skipped automatically
Missing value checks and imputation hooks
Categorical encoding and scaling ready
Class imbalance handling with SMOTE
Saves preprocessed CSV output
Schema validation and basic drift flags
Leakage guardrails for target columns
Profiling summary before/after prep
Verbose logging for reproducibility

Exploratory Analysis

Notebook and scripts to explore churn distribution and feature patterns.

Churn rate and class balance
Feature distributions and correlations
Retention and churn segmentation
Quick visualizations for key metrics
Ready Matplotlib/Seaborn plots
Cohort and tenure evolution charts
Heatmaps for cross-feature signals
Top drivers surfaced via simple scores
Reusable plotting templates

Feature Engineering

Practical helpers to create ML-friendly inputs for churn models.

Engagement scores and tenure buckets
Login recency and usage ratios
Billing vs usage derived metrics
Outlier handling hooks
Train/test split helpers
One-hot and target encoding toggles
Revenue normalization and ARPU rollups
Session/window aggregation helpers
Feature store-ready export option

Model Starting Points

Baseline churn modeling options to accelerate experimentation.

Logistic regression baseline
Tree-based models ready to plug
Cross-validation scaffolding
Metric tracking for AUC/Accuracy
Easily extensible training script
Hyperparameter grids you can extend
Class weights and sampling knobs
Early-stopping ready training loop
SHAP/feature importance hooks

Evaluation & Reporting

Consistent evaluation patterns for churn use cases.

Confusion matrix utilities
Precision/Recall/F1 tracking
AUC-ROC support
Class imbalance notes
Exportable summaries
Lift/decile and KS diagnostics
Calibration and threshold sweep
Cost-sensitive scoring examples
Cohort-wise and tenure-slice reporting

What You Get

Everything packaged for quick starts and teaching.

CSV dataset in data/
Preprocessing & training scripts
Analysis notebook
Output folder for results
README and setup guides
Sample queries and EDA prompts
Configurable YAML for pipeline runs
Lightweight Dockerfile to containerize
Makefile shortcuts for common tasks

Deployment & Serving

Starter scaffolding to push churn models into production flows.

Dockerfile and compose snippets for batch/online use
RESTful prediction endpoint example (FastAPI-style)
Batch scoring CLI for CSV inputs
Scheduled retraining hooks for fresh data
Inference logging pattern for audits

Monitoring & Drift

Keep tabs on model health and data quality over time.

Drift checks on inputs and predictions
Alerts for churn-rate swings and KPI drops
PSI/JS metrics templates
Data freshness and volume monitors
Simple dashboard layout suggestion

Governance & Security

Guidance to operate responsibly with customer data.

PII handling checklist and masking hints
Role-based access and secrets guidance
Audit log stubs for scoring activity
Data retention and deletion notes
Consent/opt-out flag propagation

Credits & Acknowledgments

This dataset is provided for educational use. Core tools and inspirations are credited below.

Python - PSF License
Pandas - BSD License
NumPy - BSD License
Scikit-learn - BSD License
Matplotlib - PSF License
RSK World - Dataset provider and documentation
GitHub Repository - Source and releases
License compatibility notes for reuse
Attribution guidance for publications
Open-source contributions welcome

Support & Contact

For paid applications, please contact us for integration help or feedback.

Support Email: help@rskworld.in
Contact Number: +91 9330539277
Website: RSKWORLD.in
GitHub Project
Join Our Discord
Slack Support Channel
Customer Churn Dataset Documentation
Issue tracker & SLAs
Feature request form

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Get the complete dataset bundle. You can view the files or download the dataset directly.

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CSV Dataset

Pandas

NumPy

Scikit-learn

Jupyter Notebook

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Customer Churn Dataset Ready To Use

Dataset Overview

Data Quality & Prep

Exploratory Analysis

Feature Engineering

Model Starting Points

Evaluation & Reporting

What You Get

Deployment & Serving

Monitoring & Drift

Governance & Security

Credits & Acknowledgments

Support & Contact

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Additional Sponsored Content

Download Free Source Code

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