Challenge Framing
Churn modeling often starts in notebooks and becomes difficult to reproduce, compare, or operationalize once preprocessing and training logic spread across experiments.
Telco Customer Churn Prediction Pipeline
This project emphasizes engineering discipline as much as model quality. The core work was not just fitting a classifier, but structuring the pipeline so preprocessing, evaluation, and inference all remain inspectable.
Case Study
Overview
Project Overview
What I built, the problem, and the solution
Reworked exploratory notebook experimentation into a cleaner pipeline architecture that separates data preparation, model training, inference, and artifact tracking while preserving reproducibility and reviewability.
Solution Strategy
I centralized configuration, separated data and training stages, used PySpark for scalable preparation, and logged experiments plus artifacts with MLflow.
Project Highlights
PySpark-first preprocessing flow with reusable train/test artifacts.MLflow tracking for metrics, parameters, artifacts, and model versions.Streaming-style inference telemetry to surface latency and batch behavior.Config-driven execution that replaces one-off notebook assumptions.
Gallery
Product screens and workflow snapshots
Selected screens show the visible product experience and the operational surfaces behind each project. Projects without captured assets keep a structured placeholder until real screenshots are available.
Module 02Frame 01
Telco Customer Churn Prediction Pipeline
PythonPySpark
Pipeline decomposition
The repository is split into preparation, training, inference, and utility layers for reproducible execution.
Module 02Frame 02
Telco Customer Churn Prediction Pipeline
PythonPySpark
MLflow experiment surface
Metrics, models, and processed outputs are logged as first-class artifacts rather than buried in notebook outputs.
Module 02Frame 03
Telco Customer Churn Prediction Pipeline
PythonPySpark
Inference telemetry
Batch-level prediction summaries keep latency and output behavior visible after training ends.
Tech Stack
Built with tools chosen for reliability and iteration speed
ML pipeline engineering, traceability, artifact lineage, experiment management, and model evaluation for imbalanced classification.
AI / ML
- Python
- PySpark
- scikit-learn
- XGBoost
- pandas
- NumPy
DevOps
- MLflow
Key Features
Reusable preprocessing
Cleaning, encoding, scaling, and split generation are orchestrated as pipeline code rather than notebook cells.
Model experimentation
Multiple classical baselines stay comparable through consistent evaluation and shared artifact outputs.
Experiment visibility
MLflow captures model parameters, metrics, processed datasets, and serialized artifacts.
Inference observability
Prediction latency and batch-level analytics are surfaced through a streaming-style inference wrapper.
Architecture
System architecture designed as a readable engineering story
Each layer stays explicit so reviewers can quickly understand where interface, orchestration, persistence, and service responsibilities live.
01
Preparation Layer
Raw telco data is transformed into consistent train/test artifacts with PySpark-backed cleaning and feature prep.
PySparkpandasconfig.yaml
02
Training Layer
Classifiers are trained, evaluated, and versioned with a shared experiment workflow.
scikit-learnXGBoostMLflow
03
Inference Layer
Saved models and preprocessing assets are reused for prediction and telemetry logging.
PythonMLflowserialized artifacts
System Flow
Key stages broken down as a readable execution path
The pipeline section keeps the most important engineering steps visible without collapsing them into generic bullet lists.
01
Ingest
Load the telco churn dataset and normalize schema-level issues such as incomplete numeric values.
CSVPySpark
02
Prepare
Handle missing values, remove outliers, encode categoricals, and scale key numeric features.
PySparkconfig.yaml
03
Train
Benchmark classical ML baselines and log metrics, parameters, and artifacts into MLflow.
scikit-learnXGBoostMLflow
04
Infer
Load serialized assets for downstream prediction while recording latency and batch-level telemetry.
PythonMLflow
Timeline
A case-study flow that explains how the system took shape
This timeline keeps the implementation story concise: what was framed first, what was hardened next, and what ultimately made the project production-ready.
01
Phase 01
Notebook consolidation
Moved scattered preparation and evaluation logic into a coherent repository structure.
02
Phase 02
Pipeline hardening
Created modular data, training, and inference stages with shared configuration.
03
Phase 03
Experiment tracking
Added MLflow-based lineage so metrics and model artifacts remain reviewable across runs.
Challenges
Technical constraints, decisions, and the reasoning behind them
Each challenge is tied to a concrete design choice and a specific outcome.
Solution
Centralized logic under scriptable modules and orchestrated pipelines with shared config.
Outcome
Reduced manual repetition and improved auditability.
Solution
Used PySpark for deterministic transformation, then converted to pandas for estimator compatibility.
Outcome
Kept the system scalable without sacrificing model tooling.
Solution
Elevated F1, precision, and recall beside raw accuracy during evaluation and reporting.
Outcome
Made the model evaluation more transparent and technically defensible.
Results
Metrics and outcomes presented for quick technical review
The emphasis here is signal, not decoration: key numbers, verifiable outcomes, and the context needed to interpret them responsibly.
0.845
Best CV F1
Random Forest baseline during notebook benchmarking.
0.735
Holdout Recall
Kept churn detection visible instead of hiding behind accuracy.
80/20
Train/Test Split
Consistent split strategy for reproducible evaluation.
4stages
Pipeline Scope
Data prep, training, evaluation, and inference telemetry.
Key Results
- Established a reproducible churn workflow from raw CSV to tracked model artifact.
- Benchmarked Random Forest as the strongest baseline at roughly 0.845 cross-validated F1.
- Surfaced class-aware metrics so churn detection quality stays visible.
- Created a stronger foundation for future API serving and scheduled retraining.
Business Impact
Business value
Supports retention-risk analysis with a pipeline that can evolve into batch scoring or service-based predictions.
Engineering value
Shows ML systems maturity through configuration, artifacts, reproducibility, and observability rather than just raw metrics.
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