I will build classification and regression models for your dataset
Data Scientist
About this Gig
Are you sitting on a dataset with no idea what to do with it? I'll build you a clean, well-documented machine learning model from raw data to final predictions.
I specialize in supervised machine learning classification and regression problems. Whether you need to predict customer behavior, detect fraud, or diagnose medical conditions, I'll deliver a high-performance model tailored to your dataset.
Why me: I achieved 0.947 ROC-AUC on a Kaggle competition using XGBoost and LightGBM, and have built predictive models across healthcare, finance, and sports domains. Every delivery includes clean, commented code you can understand and reuse.
My workflow:
- Understand your data and business goal
- Clean and preprocess the data
- Engineer meaningful features
- Train and compare multiple models
- Tune for best performance
- Deliver with full documentation
What you'll always get:
- Clean Jupyter notebook with full source code
- EDA visualizations
- Model evaluation metrics (ROC-AUC, F1, accuracy)
- Plain-English summary of results
Tools: Python, Scikit-learn, XGBoost, LightGBM, Pandas, NumPy, Matplotlib, Seaborn
Message me before ordering - I'll make sure your project is a perfect fit.
Programming language:
Python
•
SQL
Frameworks:
Scikit-learn
•
Panda
•
Other
APIs:
Other
Tools:
Jupyter Notebook
•
Colab
FAQ
What file formats do you accept?
CSV or Excel (.xlsx). Dataset should ideally be under 50MB.
What will I receive?
A clean Jupyter notebook with full code, EDA visualizations, model evaluation metrics, and a plain-English summary of results.
Can you handle imbalanced datasets?
Yes — I apply resampling techniques and use ROC-AUC and F1-score to evaluate properly on skewed data.
Do I need to know machine learning to work with you?
Not at all. Just share your dataset and tell me what you want to predict — I'll handle everything and explain the results in plain English.
Can you guarantee a specific accuracy or ROC-AUC score?
No — model performance depends on data quality and size. What I guarantee is a clean, well-optimized model with honest evaluation metrics and full transparency on results.

