Metadata-Version: 2.4
Name: gepbook
Version: 0.1.0
Summary: Healthcare ML lab experiments (Exp 1-12) bundled as ready-to-run scripts
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: ml
Requires-Dist: numpy; extra == "ml"
Requires-Dist: pandas; extra == "ml"
Requires-Dist: scikit-learn; extra == "ml"
Requires-Dist: matplotlib; extra == "ml"
Requires-Dist: scipy; extra == "ml"
Provides-Extra: dl
Requires-Dist: tensorflow; extra == "dl"
Requires-Dist: kagglehub; extra == "dl"
Provides-Extra: nlp
Requires-Dist: nltk; extra == "nlp"
Requires-Dist: spacy; extra == "nlp"
Provides-Extra: xai
Requires-Dist: shap; extra == "xai"
Requires-Dist: lime; extra == "xai"
Provides-Extra: all
Requires-Dist: numpy; extra == "all"
Requires-Dist: pandas; extra == "all"
Requires-Dist: scikit-learn; extra == "all"
Requires-Dist: matplotlib; extra == "all"
Requires-Dist: scipy; extra == "all"
Requires-Dist: tensorflow; extra == "all"
Requires-Dist: kagglehub; extra == "all"
Requires-Dist: nltk; extra == "all"
Requires-Dist: spacy; extra == "all"
Requires-Dist: shap; extra == "all"
Requires-Dist: lime; extra == "all"

# gepbook

Healthcare ML lab experiments (Exp 1-12) bundled as ready-to-run scripts.

## Install

```bash
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

```python
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:

```bash
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.
