Add Claude Code and update dependencies

.github/CODEOWNERS

@@ -7,4 +7,4 @@
 # be identified in the format @org/team-name. Teams must have
 # explicit write access to the repository. In this example,
 # the octocats team in the octo-org organization owns all .txt files.
-* @opengood-aio/owners
+* @opengood-ai/owners

.github/workflows/build.yml

@@ -13,7 +13,7 @@ on:
 
 jobs:
   build:
-    uses: opengood-aio/central-workflows/.github/workflows/python-package-build.yml@main
+    uses: opengood-ai/central-workflows/.github/workflows/python-package-build.yml@main
     with:
       run-code-coverage: true
     secrets:

.github/workflows/cancel.yml

@@ -3,6 +3,6 @@ on: [push]
 
 jobs:
   cancel:
-    uses: opengood-aio/central-workflows/.github/workflows/workflow-cancel.yml@main
+    uses: opengood-ai/central-workflows/.github/workflows/workflow-cancel.yml@main
     secrets:
       workflow-token: ${{ secrets.WORKFLOW_TOKEN }}

.github/workflows/codeql.yml

@@ -15,4 +15,4 @@ on:
 
 jobs:
   build:
-    uses: opengood-aio/central-workflows/.github/workflows/python-codeql-analyze.yml@main
+    uses: opengood-ai/central-workflows/.github/workflows/python-codeql-analyze.yml@main

.github/workflows/coverage.yml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   coverage:
-    uses: opengood-aio/central-workflows/.github/workflows/python-code-coverage.yml@main
+    uses: opengood-ai/central-workflows/.github/workflows/python-code-coverage.yml@main
     with:
       run-code-coverage: true
     secrets:

.github/workflows/release.yml

@@ -15,7 +15,7 @@ on:
 
 jobs:
   release:
-    uses: opengood-aio/central-workflows/.github/workflows/python-py-pi-release.yml@main
+    uses: opengood-ai/central-workflows/.github/workflows/python-py-pi-release.yml@main
     with:
       git-email: ${{ vars.GIT_EMAIL }}
       git-user: ${{ vars.GIT_USER }}

.github/workflows/update-license-copyright.yml

@@ -7,6 +7,6 @@ on:
 
 jobs:
   update-license-copyright:
-    uses: opengood-aio/central-workflows/.github/workflows/workflow-update-license-copyright.yml@main
+    uses: opengood-ai/central-workflows/.github/workflows/workflow-update-license-copyright.yml@main
     secrets:
       workflow-token: ${{ secrets.WORKFLOW_TOKEN }}

CLAUDE.md

@@ -0,0 +1,129 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Python Machine Learning (ML) Plot is a library providing reusable functions for machine learning visualization plotting. The primary focus is on classification model visualization with decision boundaries and classified regions.
+
+## Architecture
+
+### Module Structure
+
+- `src/opengood/py_ml_plot/` - Main package implementing ML plotting utilities
+  - `classification_plot.py` - Contains `setup_classification_plot()` function for creating 2D classification visualizations
+  - `__init__.py` - Exports public API (`setup_classification_plot`)
+
+### Classification Plot Architecture
+
+The `setup_classification_plot()` function visualizes classification models through several key steps:
+
+1. **Meshgrid Generation**: Creates a grid of points across the feature space using configurable padding and step sizes for each axis
+2. **Feature Scaling** (optional): Applies inverse transformations via `feature_scale` lambda to handle models trained on scaled data
+3. **Prediction**: Uses the `predict` lambda to generate class predictions across the entire meshgrid
+4. **Visualization**: Combines filled contour plots (decision regions) with scatter plots (actual data points)
+
+The function is designed to work with any scikit-learn classifier by accepting prediction and feature scaling logic as lambda functions.
+
+## Development Commands
+
+### Environment Setup
+
+```bash
+# Create and activate virtual environment
+python3 -m venv .venv
+source .venv/bin/activate
+
+# Install dependencies
+python3 -m pip install matplotlib numpy pandas scikit-learn
+```
+
+### Testing
+
+```bash
+# Run all tests with coverage
+python -m pytest tests/
+
+# Run a specific test
+python -m pytest tests/py_ml_plot/test_classification_plot.py::TestClassificationPlot::test_logistic_regression_setup_classification_plot_with_shaded_regions
+
+# Run tests with verbose output
+python -m pytest tests/ -v
+
+# Run tests with coverage report
+python -m pytest tests/ --cov=src.opengood.py_ml_plot --cov-report=term-missing
+```
+
+**Note**: Some tests are marked with `@skip` decorator for long-running tests (e.g., K-NN visualization). These are intended for local verification only.
+
+### Test Configuration
+
+Test configuration is in `pyproject.toml` under `[tool.pytest.ini_options]`:
+- Coverage threshold: 70%
+- Test markers: `slow`, `smoke`, `unit`
+- Coverage report outputs to `coverage.xml`
+
+### Project Structure
+
+```
+src/opengood/py_ml_plot/
+  - classification_plot.py  # Core plotting function
+  - __init__.py             # Public API exports
+tests/
+  - py_ml_plot/
+    - test_classification_plot.py  # Tests for classification plotting
+  - resources/
+    - data.csv                     # Test dataset
+```
+
+## Key Implementation Details
+
+### Meshgrid Parameter Structure
+
+The `meshgrid` parameter controls the visualization bounds and resolution:
+
+```python
+meshgrid = {
+    0: {"min": 10, "max": 10, "step": 0.25},    # x-axis padding and step
+    1: {"min": 1000, "max": 1000, "step": 0.25}  # y-axis padding and step
+}
+```
+
+- Keys `0` and `1` represent x-axis and y-axis respectively
+- `min`/`max` define padding subtracted/added from data min/max values
+- `step` controls the density of the meshgrid (smaller = higher resolution but slower)
+
+### Lambda Function Patterns
+
+**Feature Scaling Lambda**:
+```python
+feature_scale = lambda x_set, y_set: (sc.inverse_transform(x_set), y_set)
+```
+- Inverts feature scaling applied during training to show original data ranges
+- Returns tuple of (transformed_x, original_y)
+
+**Prediction Lambda**:
+```python
+predict = lambda x1, x2: classifier.predict(
+    sc.transform(np.array([x1.ravel(), x2.ravel()]).T)
+).reshape(x1.shape)
+```
+- Takes meshgrid coordinates (x1, x2)
+- Applies feature scaling if the classifier was trained on scaled data
+- Uses `ravel()` to flatten, combines into 2D array, transforms, predicts, then reshapes
+
+## Package Management
+
+- Build system: setuptools
+- Python version: >=3.12
+- Package name: `opengood.py_ml_plot`
+- Current version tracked in `pyproject.toml`
+- Version bumping configured in `.bumpversion.toml`
+
+## Dependencies
+
+Core runtime dependencies:
+- matplotlib >= 3.10.3 (visualization)
+- numpy >= 2.3.0rc1 (numerical operations)
+- pandas >= 2.2.3 (data handling)
+- scikit-learn >= 1.7.0rc1 (ML models in examples/tests)

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 OpenGood
+Copyright (c) 2025-2026 OpenGood
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,13 +1,13 @@
 # Python Machine Learning (ML) Plot
 
-[![Build](https://github.com/opengood-aio/py-ml-plot/workflows/build/badge.svg)](https://github.com/opengood-aio/py-ml-plot/actions?query=workflow%3Abuild)
-[![Release](https://github.com/opengood-aio/py-ml-plot/workflows/release/badge.svg)](https://github.com/opengood-aio/py-ml-plot/actions?query=workflow%3Arelease)
-[![CodeQL](https://github.com/opengood-aio/py-ml-plot/actions/workflows/codeql.yml/badge.svg)](https://github.com/opengood-aio/py-ml-plot/actions/workflows/codeql.yml)
-[![Codecov](https://codecov.io/gh/opengood-aio/py-ml-plot/graph/badge.svg?token=WX6Er5S6Vj)](https://codecov.io/gh/opengood-aio/py-ml-plot)
-[![Release Version](https://img.shields.io/github/release/opengood-aio/py-ml-plot.svg)](https://github.com/opengood-aio/py-ml-plot/releases/latest)
+[![Build](https://github.com/opengood-ai/py-ml-plot/workflows/build/badge.svg)](https://github.com/opengood-ai/py-ml-plot/actions?query=workflow%3Abuild)
+[![Release](https://github.com/opengood-ai/py-ml-plot/workflows/release/badge.svg)](https://github.com/opengood-ai/py-ml-plot/actions?query=workflow%3Arelease)
+[![CodeQL](https://github.com/opengood-ai/py-ml-plot/actions/workflows/codeql.yml/badge.svg)](https://github.com/opengood-ai/py-ml-plot/actions/workflows/codeql.yml)
+[![Codecov](https://codecov.io/gh/opengood-ai/py-ml-plot/graph/badge.svg?token=WX6Er5S6Vj)](https://codecov.io/gh/opengood-ai/py-ml-plot)
+[![Release Version](https://img.shields.io/github/release/opengood-ai/py-ml-plot.svg)](https://github.com/opengood-ai/py-ml-plot/releases/latest)
 [![PyPI](https://img.shields.io/pypi/v/opengood.py-ml-plot)](https://pypi.org/project/opengood.py-ml-plot/)
 ![Python](https://img.shields.io/pypi/pyversions/opengood.py-ml-plot)
-[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/opengood-aio/py-ml-plot/master/LICENSE)
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/opengood-ai/py-ml-plot/master/LICENSE)
 
 Modules containing reusable functions for machine learning visualization
 plotting
@@ -98,7 +98,7 @@ plt.show()
 
 ##### Output
 
-![Setup Classification Plot Visualization](https://raw.githubusercontent.com/opengood-aio/py-ml-plot/main/docs/images/setup-classification-plot-visualization.png)
+![Setup Classification Plot Visualization](https://raw.githubusercontent.com/opengood-ai/py-ml-plot/main/docs/images/setup-classification-plot-visualization.png)
 
 ---
 
@@ -109,8 +109,8 @@ plt.show()
 `feature_scale` lambda implementation logic for function
 `setup_classification_plot` is as follows:
 
-* Inverse feature scaling is invoked via a featuring scaling object, such as
-  the `StandardScalar` object `sc` created earlier for feature scaling
+* Inverse feature scaling is invoked via a feature scaling object, such as
+  the `StandardScaler` object `sc` created earlier for feature scaling
 * `x_set` and `y_set` are assigned non-feature scaled values of the matrix of
   features and the dependent variable
   * `x_set` values are inverted from their feature-scaled values in `x`
@@ -150,7 +150,7 @@ as follows:
 * If the `feature_scale` lambda is defined, `x_set` and `y_set` are
   assigned non-feature scaled values of the matrix of features and the dependent
   variable from the sets using a feature scaling object, such as the
-  `StandardScalar` object created earlier for feature scaling
+  `StandardScaler` object created earlier for feature scaling
     * `x_set` values are inverted from their feature-scaled values in `x`
     * `y_set` values are not inverted and taken directly from `y`
 * `meshgrid` function from the NumPy library returns a tuple of coordinate
@@ -187,7 +187,7 @@ as follows:
             * `step` parameter
                 * Spacing between values
                 * Value of `0.25` is added for spacing
-* The prediction logic implemented in the `preodict` lambda is executed, and
+* The prediction logic implemented in the `predict` lambda is executed, and
   the result is assigned to `y_pred`, containing the predictions
 * `contourf` function from the Matplotlib library is used for creating filled
   contour plots
@@ -264,32 +264,54 @@ as follows:
 
 ## Python Virtual Environment
 
-Create Python virtual environment:
+Create and activate Python virtual environment:
 
 ```bash
-cd ~/workspace/opengood-aio/py-ml-plot/.venv
-python3 -m venv ~/workspace/opengood-aio/py-ml-plot/.venv
+python3 -m venv .venv
 source .venv/bin/activate
 ```
 
-## Install Packages
+## Install Dependencies
+
+Install the package in editable mode with all dependencies:
+
+```bash
+python3 -m pip install -e .
+```
+
+Install with development dependencies (includes `pip-tools`, `pytest`, `pytest-cov`):
 
 ```bash
-python3 -m pip install matplotlib
-python3 -m pip install numpy
-python3 -m pip install pandas
-python3 -m pip install scikit-learn
+python3 -m pip install -e ".[dev]"
 ```
 
-## Create Requirements File
+## Generate Requirements File
+
+Generate a pinned `requirements.txt` file from pyproject.toml:
 
 ```bash
-pip freeze > requirements.txt
+pip-compile pyproject.toml -o requirements.txt
 ```
 
+This creates a `requirements.txt` with exact versions of all dependencies and their transitive dependencies.
+
 ## Run Tests
 
+Run all tests with coverage:
+
 ```bash
 python -m pytest tests/
 ```
 
+Run tests with verbose output and coverage report:
+
+```bash
+python -m pytest tests/ -v --cov=src.opengood.py_ml_plot --cov-report=term-missing
+```
+
+Run a specific test:
+
+```bash
+python -m pytest tests/py_ml_plot/test_classification_plot.py::TestClassificationPlot::test_logistic_regression_setup_classification_plot_with_shaded_regions
+```
+

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools >= 77.0.3", "wheel"]
+requires = ["setuptools >= 82.0.1", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -19,18 +19,26 @@ readme = "README.md"
 license = { file = "LICENSE" }
 
 dependencies = [
-    "matplotlib >= 3.10.3",
-    "numpy >= 2.3.0rc1",
-    "pandas >= 2.2.3",
-    "scikit-learn >= 1.7.0rc1",
+    "matplotlib >= 3.10.8",
+    "numpy >= 2.4.3",
+    "pandas >= 3.0.1",
+    "scikit-learn >= 1.8.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pip-tools >= 7.5.3",
+    "pytest >= 9.0.2",
+    "pytest-cov >= 7.0.0",
 ]
 
 [project.urls]
-Homepage = "https://github.com/opengood-aio/py-ml-plot"
-Documentation = "https://github.com/opengood-aio/py-ml-plot"
+Homepage = "https://github.com/opengood-ai/py-ml-plot"
+Documentation = "https://github.com/opengood-ai/py-ml-plot"
 
 [tool.pytest.ini_options]
-addopts = "tests/ -v --cov=src.opengood.py_ml_plot --cov-report=xml --cov-fail-under=70"
+addopts = "-v --cov=src.opengood.py_ml_plot --cov-report=xml --cov-fail-under=70"
+testpaths = ["tests"]
 python_files = "test_*.py"
 python_functions = "test_*"
 markers = [