Telecom Customer Churn Analysis
Analyzed 7,043 telecom customer records and used explainable classification models to identify churn-risk segments and propose targeted retention actions.
Period · Summer 2025
Role · Individual project · KW-Corporation university virtual-company program
01 · Business / research question
The question
Which customers are most likely to leave, why are they at risk, and which retention response fits each segment?
02 · Evidence
What the analysis used
03 · Analysis
How the work progressed
- 01
Prepare the evidence
Converted blank TotalCharges entries, encoded categorical variables, and created analysis-ready training and test sets.
- 02
Find meaningful segments
Examined churn patterns across contract type, tenure, monthly charges, payment method, and service adoption.
- 03
Compare and interpret models
Compared seven classifiers, selected Gradient Boosting based on the strongest reported ROC-AUC, and used SHAP and dependence analysis to explain key drivers.
- 04
Translate analysis into action
Connected risk drivers to proposed contract, onboarding, and service-support interventions.
04 · Interpretation
Main insight
Month-to-month contracts were the clearest churn-risk signal.
Shorter tenure and higher monthly charges were associated with greater risk.
Non-use of selected security and technical-support services provided additional risk signals.
Gradient Boosting produced a reported ROC-AUC of approximately 0.842; a reproducible final metrics table is still needed.
05 · Practical decision
Decision value
The analysis turns a binary prediction task into a prioritization framework and proposes retention strategies targeting a 5.0 percentage-point reduction in churn; the target has not been achieved or validated.
06 · Validation
Limitations and next checks
- •The dataset is public and does not include campaign exposure, complaint history, intervention cost, or observed retention outcomes.
- •The final model pipeline, hyperparameters, random seed, and metrics table should be reproduced before treating the exact score as verified.
- •Proposed interventions require experimental validation and explicit retention KPIs.
07 · Visual evidence
Evidence, with boundaries
Churn rate by contract type
Verified sourceCalculated directly from the source workbook. The bars show a descriptive association between contract type and churn; they do not show that an intervention reduced churn.
Source · WA_Fn-UseC_-Telco-Customer-Churn.xlsx · 7,043 rows
Churn rate by tenure group
Verified sourceTenure was grouped into four intervals directly from the workbook. The chart describes higher churn among newer customers; it does not identify individual causation or validate a proposed retention action.
Source · WA_Fn-UseC_-Telco-Customer-Churn.xlsx · bins computed from tenure
Model metrics and SHAP evidence
Pending verificationMetrics table pending
Add ROC-AUC, precision, recall, and F1 only after rerunning the final pipeline.
SHAP export pending
Add only a SHAP export confirmed to match the final Gradient Boosting model.
ROC and SHAP images exist in the report, but they are not yet tied to one reproducible final pipeline, split, and model export. The approximately 0.842 ROC-AUC remains a reported value rather than a verified chart.