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

pip install scikit-tools

Or from a local checkout:

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:

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:

import scikit_tools as st

st.pca()
st.numpy_stats()

3. Run from Command Line

Run any topic as a standalone module:

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).