feat: Implement Principal Component Analysis (PCA) #12595
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- Added a Python implementation of PCA using NumPy and scikit-learn - Standardizes the dataset before applying PCA for better performance - Computes principal components and explained variance ratio - Uses the Iris dataset as a sample for demonstration - Provides a modular structure for easy extension and dataset modification
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…11/Python into principle-component-analysis
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| from sklearn.datasets import load_iris | ||
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| def collect_dataset(): |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/principle_component_analysis.py, please provide doctest for the function collect_dataset
Please provide return type hint for the function: collect_dataset. If the function does not return a value, please provide the type hint as: def function() -> None:
| return np.array(data.data), np.array(data.target) | ||
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| def apply_pca(data_x, n_components): |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/principle_component_analysis.py, please provide doctest for the function apply_pca
Please provide return type hint for the function: apply_pca. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: data_x
Please provide type hint for the parameter: n_components
| return principal_components, explained_variance | ||
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| def main(): |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/principle_component_analysis.py, please provide doctest for the function main
Please provide return type hint for the function: main. If the function does not return a value, please provide the type hint as: def function() -> None:
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