Metadata-Version: 2.4
Name: scikit-tools
Version: 0.2.1
Summary: Thirteen runnable, straight-through scikit-learn and data-analysis examples
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: ipython
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: seaborn

# scikit-tools

`scikit-tools` provides 13 standalone, runnable Python examples for machine learning and data analysis. Each topic contains its own imports, dataset setup, and analysis code.

## Installation

```console
pip install scikit-tools
```

Or from a local checkout:

```console
python -m pip install -e .
```

## How to Use

### 1. Inspect the Raw Source Code Without Running
Importing `scikit_tools` does not execute any calculations or open plots. You can access the complete standalone source code of any topic using `inspect`:

```python
import inspect
import scikit_tools as st

# Prints the full raw code to the terminal
print(inspect.getsource(st.pca))
print(inspect.getsource(st.linear_regression))
```

### 2. Run Directly in Python
Call any topic function directly:

```python
import scikit_tools as st

st.pca()
st.numpy_stats()
```

### 3. Run from Command Line
Run any topic as a standalone module:

```console
python -m scikit_tools.pca
python -m scikit_tools.numpy_stats
```

## Topics Available

1. NumPy statistics (`st.numpy_stats`)
2. Inspecting the Iris dataset (`st.inspect_dataset`)
3. Handling missing values (`st.missing_values`)
4. Data transformation (`st.data_transformation`)
5. Histogram and scatter-plot EDA (`st.eda_plots`)
6. Box plot and IQR outlier detection (`st.outlier_detection`)
7. Descriptive statistics (`st.descriptive_stats`)
8. Correlation and covariance (`st.correlation_covariance`)
9. Independent-samples t-test (`st.hypothesis_testing`)
10. Linear regression (`st.linear_regression`)
11. Logistic-regression classification (`st.logistic_regression`)
12. K-means clustering (`st.kmeans_clustering`)
13. PCA and classification (`st.pca`)

All examples load the Iris dataset via `sklearn.datasets.load_iris(as_frame=True)` with standardized column names (`SepalLengthCm`, `SepalWidthCm`, `PetalLengthCm`, `PetalWidthCm`, `Species`).
