A learning collection of notebooks and small Python programs covering regression, classification, clustering, computer vision, agents, and engineering-oriented prediction.
- California housing and house-price regression
- Song clustering with k-means
- Spam, IMDB, cancer, and image classification
- Material-fatigue and tensile-strength experiments
- File-renaming automation and small game agents
Open notebooks in an isolated environment such as Jupyter or Colab. Dependencies differ between notebooks; inspect the imports and pin a dedicated environment before reproducing an experiment.
Results are educational and have not been validated for production or safety-critical decisions. Several notebooks depend on external datasets or hosted runtimes. Metrics shown in exploratory notebooks should not be treated as benchmarks.
Use public or sanitised datasets. Do not commit model-service tokens, private datasets, or personal information.
No repository-wide license has been selected.