Metadata-Version: 2.4
Name: ml-lab-prep
Version: 0.1.0
Summary: Practice and solved Jupyter notebooks for an ML lab exam: EDA, tuning, CV, PCA, significance tests.
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Education
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn>=1.2
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: xgboost

# ml-lab-prep

Practice and solved notebooks for a machine learning lab exam (classification, regression, K-Means, PCA, grid vs random search, 5-fold CV, Friedman/ANOVA and post-hoc tests), plus the datasets they use.

```bash
pip install ml-lab-prep
ml-lab-prep check            # verify scikit-learn / xgboost etc. work
ml-lab-prep init             # copies notebooks + data into ./ML_Lab_Prep
cd ML_Lab_Prep && jupyter notebook
```

Options: `ml-lab-prep init my_folder`, `ml-lab-prep init --practice-only`.
Start with `practice/01_Spambase_Classification.ipynb`; `solved/` has run outputs; read `EXAM_PLAYBOOK.md` last.

For study use. Follow your institution's rules on what material is allowed in an exam.
