I will data science and machine learning projects
Data Science, Python, Machine Learning
Über diesen Service
Hi! Im a Machine Learning Engineer dedicated to building smart, scalable systems that solve real-world problems. I bridge the gap between messy datasets and high-performing models that actually work in production.
What I Offer:
- Data Preprocessing: Expert cleaning and feature engineering to ensure your model has a solid foundation.
- Model Creation: Building accurate predictive models using algorithms like XGBoost and Random Forest.
- Testing & Fine-Tuning: Optimization through cross-validation and hyperparameter tuning for maximum reliability.
- Deployment & API Integration: Taking your model live with FastAPI or Streamlit and hosting it in the cloud (AWS).
Why Choose Me? I deliver clean, documented source code and prioritize transparent communication. My goal is to provide models that perform in the wild, not just on paper. With multiple revisions included, I ensure the final solution aligns perfectly with your goals.
Programmiersprache:
Python
•
Colab
•
MLflow
Frameworks:
scikit-learn
•
SimpleCV
•
Panda
Tools:
Jupyter-Notizbuch
•
Excel
•
MLflow
•
Colab
Mein Portfolio
FAQ
What information do I need to provide to get started?
Just share your dataset and a brief description of your goal. I'll take it from there.
What format should my dataset be in?
I work with CSV, Excel, and JSON files. If you have a different format, message me first.
Do you guarantee a specific model accuracy?
Accuracy depends on the quality and size of your data. I guarantee the best possible results with what you provide.
Will I receive the source code?
Source code is included in the Premium package only. It can also be added as an extra to other packages.
What if I don't understand the results?
I'll provide a clear summary of findings in plain language, no technical background needed.
How do I know which package is right for me?
Message me before ordering, I'll recommend the best package based on your needs.
Which cloud service will be used for deployment?
I primarily use AWS (EC2 & S3) for reliable hosting and storage. I also utilize Docker to ensure the environment is consistent and easily scalable.

