You will get a churn prediction model in Python with XGBoost
Project details
Most churn models get built once and forgotten. I build churn prediction systems that are production-ready, interpretable, and tied to revenue outcomes not just accuracy scores.
I've applied this exact approach on a 250,000-user platform, improving model AUC from 0.74 to 0.79 and projecting ~$180K in annual revenue preservation through automated retention triggers. Every deliverable I produce includes SHAP-based feature importance so your team understands why users are flagged, not just which ones.
What makes this project different:
— Revenue-first framing: I calibrate thresholds to maximise retention ROI, not just model metrics
— Production-ready code: clean Python scripts and a documented Jupyter notebook your team can maintain
— Business impact report: churn probability scores, top risk segments, and a revenue projection
— Honest evaluation: I compare multiple algorithms and tell you which one actually wins on your data
I hold an IBM Machine Learning Professional Certificate and have built churn systems across fintech, telecom, media, and retail. If your users are leaving and you want to know why — and who to save first — this is the project.
I've applied this exact approach on a 250,000-user platform, improving model AUC from 0.74 to 0.79 and projecting ~$180K in annual revenue preservation through automated retention triggers. Every deliverable I produce includes SHAP-based feature importance so your team understands why users are flagged, not just which ones.
What makes this project different:
— Revenue-first framing: I calibrate thresholds to maximise retention ROI, not just model metrics
— Production-ready code: clean Python scripts and a documented Jupyter notebook your team can maintain
— Business impact report: churn probability scores, top risk segments, and a revenue projection
— Honest evaluation: I compare multiple algorithms and tell you which one actually wins on your data
I hold an IBM Machine Learning Professional Certificate and have built churn systems across fintech, telecom, media, and retail. If your users are leaving and you want to know why — and who to save first — this is the project.
Machine Learning Tools
Databricks Platform, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, Tableau, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$250
|
Standard
$500
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 3 | 7 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
About Imeobong
Data Scientist | Causal ML , MLOps , LLM Pipelines , Power BI
Uyo, Nigeria - 1:02 am local time
Over 3+ years, I've delivered 30+ end-to-end data solutions for clients in fintech, telecom, media, healthcare, and real estate. My work has projected $516K+ in combined revenue outcomes:
→ Churn prediction model (AUC 0.74 → 0.79) across 250K users — ~$180K annual revenue preserved
→ A/B experimentation platform across 120K users — ~$336K annualised revenue lift
→ PSI-based ML drift detection on 500K+ monthly predictions — ~$40K/month revenue-at-risk mitigated
→ ETL pipeline automation — 60% faster runtime, 70% faster client reporting
WHAT I BUILD:
🔹 Machine Learning & AI
Churn prediction, causal uplift modelling (S/T/X-Learner meta-algorithms, IPTW), A/B testing, NLP pipelines, LLM orchestration, real-time signal detection. Evaluated with rigorous metrics (AUC, Qini, AUUC).
🔹 Data Engineering
Apache Airflow, Apache Kafka, ETL pipelines, dbt, BigQuery, PostgreSQL, MySQL. I build pipelines that don't fail — 99.9% ingestion success on 80K–150K daily records.
🔹 MLOps & Deployment
MLflow experiment tracking, Docker containerisation, FastAPI microservice APIs, PSI drift monitoring with automated retraining triggers. Models don't just train — they ship and stay accurate.
🔹 Business Intelligence
Power BI dashboards with DAX, Looker Studio, Streamlit — translating raw data into decisions executives can act on.
TECH STACK:
Python · SQL · scikit-learn · XGBoost · LightGBM · MLflow · Apache Airflow · Apache Kafka · PostgreSQL · BigQuery · FastAPI · Docker · Streamlit · Power BI · AWS · GCP · dbt
I hold an IBM Machine Learning Professional Certificate (IBM/Coursera, 2026) and a Certified Data Scientist credential (DataCamp, 2026), with a B.Sc. in Statistics.
Available for full-time, part-time, and contract remote engagements. Let's talk about what your data can do.
Steps for completing your project
After purchasing the project, send requirements so Imeobong can start the project.
Delivery time starts when Imeobong receives requirements from you.
Imeobong works on your project following the steps below.
Revisions may occur after the delivery date.
Data review and scoping
I review your dataset, confirm the churn definition, check data quality, and flag any missing values or feature engineering opportunities before modelling begins.
Feature engineering and EDA
I build predictive features from your raw data — recency, frequency, engagement signals — and run exploratory analysis to confirm they carry signal for churn prediction.