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I will audit your machine learning model for data leakage and overfitting
ML Engineer, Transforming Data into Intelligent Solutions
I'm a professional ML/DL engineer specializing in building intelligent systems that drive business value. With expertise in Python, TensorFlow, PyTorch, and scikit-learn, I've delivered 20+ successful...
About this Gig
Is your model's 99% accuracy too good to be true? Often it is. Data leakage, wrong validation splits and uncalibrated probabilities make models shine in a notebook and fail in production.
I audit your machine learning pipeline, show exactly what is inflating your results, and help you fix it.
I check:
- Target, temporal and group data leakage
- Train, validation and test split design
- Overfitting and metric choice for your problem
- Probability calibration and class imbalance
- Robustness with placebo and out-of-time tests
I'm an AI engineer and first author of multiple peer-reviewed IEEE paper. I use these checks as standard in my own work, for example an ICU mortality model validated on a separate hospital dataset.
You get a clear written report ranked by impact, and on higher packages a corrected notebook with before and after results.
Programming language:
Python
•
R
•
SQL
•
Colab
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
Excel
•
MLflow
•
Colab
•
Other
Technology:
Python
•
R
•
TensorFlow
•
PyTorch
•
scikit-learn
•
Pandas
My Portfolio
FAQ
1. What do I need to send?
Your notebook or scripts, the dataset or a representative sample, and a short note on what the model predicts and how it will be used.
1. What if your audit finds my accuracy drops?
That's the point of the audit: a lower, honest number now is far cheaper than a model that fails after launch. I explain each cause and how to fix it.
1. Which models do you cover?
Tabular ML (scikit-learn, XGBoost, LightGBM), time series, and deep learning in PyTorch, for classification, regression and forecasting.
1. Can you sign an NDA or keep my data private?
Your files are used only for your order and deleted on request. Fiverr's terms cover confidentiality; I can also follow your own data-handling rules.
1. Do you work on large datasets?
Yes. For very large data, a representative sample is usually enough for the audit.
