You will get production-ready churn prediction model with features and business insights
Project details
You will get a production-ready churn prediction model built on YOUR data — not a generic template. I use a rigorous, scientific approach with 7 feature selection methods to identify the real drivers of customer loss, then validate with 3 algorithms to ensure robust results.
What makes this different: I don't just hand you a model — I explain the business implications of every variable. You'll know exactly WHY customers leave and WHAT to do about it.
My approach reduces model complexity by ~50% while maintaining the same predictive power. Fewer variables means a simpler model that's easier to deploy, maintain, explain to stakeholders, and less prone to breaking in production.
Ideal for: SaaS, telecom, banking, e-commerce — any business with 1,000+ customer records and a churn/cancellation flag.
Tech: Python, scikit-learn, LightGBM, pandas. All code is documented and reproducible — run one command to retrain with new data.
Deliverables: Trained model (.pkl), executive report (PPTX), full source code, feature importance analysis, and optional drift monitoring setup.
What makes this different: I don't just hand you a model — I explain the business implications of every variable. You'll know exactly WHY customers leave and WHAT to do about it.
My approach reduces model complexity by ~50% while maintaining the same predictive power. Fewer variables means a simpler model that's easier to deploy, maintain, explain to stakeholders, and less prone to breaking in production.
Ideal for: SaaS, telecom, banking, e-commerce — any business with 1,000+ customer records and a churn/cancellation flag.
Tech: Python, scikit-learn, LightGBM, pandas. All code is documented and reproducible — run one command to retrain with new data.
Deliverables: Trained model (.pkl), executive report (PPTX), full source code, feature importance analysis, and optional drift monitoring setup.
Machine Learning Tools
Databricks Platform, MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, SQLWhat's included
| Service Tiers |
Starter
$350
|
Standard
$600
|
Advanced
$999
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 3 |
Number of Scenarios | 1 | 2 | 6 |
Number of Graphs/Charts | 3 | 10 | 16 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Additional Model Variation
(+ 3 Days)
+$75
Additional Scenario
(+ 2 Days)
+$50
Additional Graph/Chart
(+ 1 Day)
+$25
Model Documentation
(+ 2 Days)
+$50
Data Source Connectivity
(+ 10 Days)
+$100Frequently asked questions
About Michel
AI & Machine Learning | Data Analytics & Engineering
Dores de Campos, Brazil - 1:33 am local time
Steps for completing your project
After purchasing the project, send requirements so Michel can start the project.
Delivery time starts when Michel receives requirements from you.
Michel works on your project following the steps below.
Revisions may occur after the delivery date.
Data Assessment & Exploration
I review your dataset within 24h: check quality, identify missing values, understand distributions, and confirm the project scope. You receive a brief data health summary and EDA charts.
Feature Selection & Model Training
I apply 7 scientific methods to identify the top predictors of churn, then train and validate 3 ML algorithms using 5-fold cross-validation. You get a progress update with preliminary findings.