gepbook
Healthcare ML lab experiments (Exp 1-12) bundled as ready-to-run scripts.
Install
pip install gepbook # just the scripts, no heavy dependencies
pip install "gepbook[ml]" # numpy, pandas, scikit-learn, matplotlib, scipy
pip install "gepbook[all]" # everything incl. tensorflow, spacy, shap, lime
Extras: ml, dl (tensorflow, kagglehub), nlp (nltk, spacy), xai (shap, lime), all.
Usage
import gepbook
gepbook.list() # table of experiments
gepbook.show(1) # print the source of Experiment 1
gepbook.copy(1) # copy Exp1 into the current folder
gepbook.copy_all() # copy every experiment into ./gepbook_experiments
gepbook.run(1) # execute Exp1 (its CSV must be in the current folder)
Command line:
gepbook list
gepbook show 3
gepbook copy 4 .
gepbook run 2
Experiments
| # | File | Topic | Dataset |
|---|---|---|---|
| 1 | Exp1_Regression.py | Regression analysis | insurance.csv |
| 2 | Exp2_DTree_Risk.py | Decision tree risk prediction | heart.csv |
| 3 | Exp3_KNN_Risk.py | KNN risk classification | diabetes.csv |
| 4 | Exp4_Hypothesis_Tests.py / .xlsx | ANOVA and Chi-square | generated in script |
| 5 | Exp5_Data_Prep.py | Data transformation and preparation | healthcare-dataset-stroke-data.csv |
| 6 | Exp6_EDA_R.R | Exploratory analysis in R | healthcare-dataset-stroke-data.csv |
| 7 | Exp7_Heart_NN.py | Keras neural network | heart.csv |
| 10 | Exp10_Clinical_NLP.py | NLTK / spaCy extraction | generated in script |
| 11 | Exp11_CNN_TL_GradCAM.py | CNN vs transfer learning, Grad-CAM | chest X-ray (Kaggle) |
| 12 | Exp12_XAI_Risk.py | SHAP and LIME | diabetes.csv |
Datasets are not bundled; each script's header links to its Kaggle source.
1 maintainer
Metadata
1 maintainer
Release files for gepbook 0.1.0
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