You will get Custom Predictive Model — End-to-End ML Pipeline
Project details
Do you need a machine learning model that actually works in production — not just in a notebook?
I build end-to-end predictive pipelines tailored to your business problem: churn prediction, fraud detection, credit risk scoring, demand forecasting, or any classification or regression task with structured data.
You provide the data. I deliver clean, documented, and reproducible notebooks your team can understand and use immediately — no black boxes.
Every project includes exploratory data analysis, a full feature engineering pipeline, model training with proper evaluation metrics, and a serialized model ready to generate predictions on new data.
Higher tiers add hyperparameter tuning, threshold optimization, SHAP-based explainability, and a production-ready FastAPI REST API with Docker — depending on how far you need to take it.
I build end-to-end predictive pipelines tailored to your business problem: churn prediction, fraud detection, credit risk scoring, demand forecasting, or any classification or regression task with structured data.
You provide the data. I deliver clean, documented, and reproducible notebooks your team can understand and use immediately — no black boxes.
Every project includes exploratory data analysis, a full feature engineering pipeline, model training with proper evaluation metrics, and a serialized model ready to generate predictions on new data.
Higher tiers add hyperparameter tuning, threshold optimization, SHAP-based explainability, and a production-ready FastAPI REST API with Docker — depending on how far you need to take it.
Machine Learning Tools
MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$350
|
Standard
$650
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75 - $200
Additional Revision
+$50Frequently asked questions
About Mickel
ML Engineer | Fraud Detection | Credit Risk | Predictive Modeling | ML
Santo Domingo Oeste, Dominican Republic - 10:56 pm local time
I've developed a fraud detection system on the IEEE-CIS dataset using LightGBM, with a full MLOps pipeline: feature engineering with ColumnTransformer, hyperparameter tuning via Optuna, experiment tracking in MLflow, threshold optimization, and SHAP-based explainability — all deployed via FastAPI. In parallel, I've worked on credit default risk modeling (Home Credit dataset), focused on maximizing AUC-PR under real-world class imbalance conditions.
I work in Python-first environments and deliver clean, reproducible, documented pipelines — not just notebooks.
Core stack: Python · LightGBM · XGBoost · Scikit-learn · FastAPI · MLflow · Optuna · SHAP · Pandas · SQL
If you need a model that actually works in production — let's talk."
Steps for completing your project
After purchasing the project, send requirements so Mickel can start the project.
Delivery time starts when Mickel receives requirements from you.
Mickel works on your project following the steps below.
Revisions may occur after the delivery date.
Exploratory Data Analysis
Review and analyze the dataset to understand distributions, missing values, and key patterns.
Feature Engineering Pipeline
Train and evaluate multiple models using proper metrics for your problem type.