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I will build production ready machine learning pipelines in python xgboost scikit learn
Advanced Analytics and Statistical Modeling
I am a Senior Data Analytics/Project Management STEM major (GPA 3.983) and a proud member of Alpha Sigma Lambda honor society. My academic background is built on rigorous analysis, statistical modelin...
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Production-Grade Machine Learning Pipelines in Python
Stop relying on messy Jupyter Notebooks that break in production. I build scalable, enterprise-ready Machine Learning pipelines engineered for real-world deployment.
What I Build:
- Leak-Free Data Preprocessing: Custom Scikit-Learn transformers for scaling, encoding, and missing value imputation without data leakage.
- Production Models: Supervised learning using XGBoost, Random Forest, Scikit-Learn, and PyTorch.
- Model Optimization: Automated hyperparameter tuning (GridSearchCV/Optuna) and multi-model benchmarking.
- Serialization & Modular Code: .joblib/.pkl artifact export and clean, PEP-8 Python scripts.
- Documentation & Git Tracked: Comprehensive README.md, setup instructions, and Git version control integration.
Why Choose My Services?
- Production-first engineering approach.
- Clean, reusable, and modular codebases.
- Clear documentation for seamless handoff.
Please contact me before placing an order to discuss your dataset and project goals!
Lenguaje de programación:
Python
Marcos:
Scikit-learn
•
keras
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PyTorch
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Panda
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Otros
Herramientas:
Jupyter Notebook
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Excel
•
Colab
Mi porfolio
FAQ
What do I need to provide before we start the project?
You'll need to share your dataset (CSV, database access, or sample format), a brief description of your business objective, and any target performance metrics or constraints.
How do you prevent data leakage in the machine learning pipeline?
I strictly isolate training and validation splits prior to fitting any preprocessing transformers (encoders, scalers, imputers), using custom Scikit-Learn pipelines to ensure zero data leakage.
Will I receive modular code or just Jupyter Notebooks?
You will receive clean, modular .py Python scripts structured for production, along with a full README.md and serialized model artifacts (.joblib/.pkl). Notebooks are provided for EDA upon request.
