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Project
Datathon
Fullstack DeveloperJanuary 2026 – January 2026
Short-form data science project covering exploratory analysis, feature engineering, and model evaluation in a reproducible notebook pipeline.
Datathon
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Highlights
- Objective: (What problem did the datathon solve? e.g., forecasting, classification, anomaly detection.)
- Dataset: (source, size, main features, any cleaning required)
- Approach: (feature engineering, modeling, validation strategy)
- Models / Methods: (e.g., Random Forest, XGBoost, neural nets, ensembling)
- Evaluation: (metrics used and final results — e.g., RMSE=..., AUC=..., rank in competition)
- Pipeline & Reproducibility: (notebooks, scripts, Docker, CI for training/evaluation)
- Deliverables: (notebooks, reproducible code, slides, report, demo/API)
- Role & Contributions: (your responsibilities — data cleaning, modeling, visualization, deployment)
- Impact / Outcome: (improvement vs baseline, competition placement, business outcome)
- Link: https://github.com/gitOfKuKue/datathon
What it includes
- Complete EDA and data-cleaning notebooks
- Feature engineering with automated pipelines
- Model training and evaluation scripts
- Notebook summarizing insights and recommendations
- Optional: simple web demo or API for predictions
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Pythonpandasnumpyscikit-learnXGBoostJupyterDockerGitHub Actions