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📊 A practical toolkit for feature engineering and selection in Python. Explore EDA, handle missing data and outliers, scale/encode/transform features, discretize, and select via filter, wrapper, embedded, shuffling, and hybrid methods. Comes with datasets, clean notebooks, reusable utils, and clear visuals for fast, reproducible ML. Workflows.
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103
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Update README.md to enhance project documentation and structure
86694b6View on GitHubAdd seaborn to requirements.txt for enhanced data visualization capabilities
d11b8edView on GitHubAdd requirements.txt file with necessary dependencies for the project
91161e5View on GitHubUpdate Barplot_Pclass_Survived.png with new visualizations
12d8522View on GitHubEnhance data exploration functions with detailed docstrings and improve output file naming conventions
c4b80a9View on GitHubUpdate execution counts and IDs in Jupyter notebook for recursive feature addition and elimination examples
540a4aaView on GitHubAdd recursive feature elimination and addition functions using Random Forest for feature selection
d21969bView on GitHubImplement code changes to enhance functionality and improve performance
1bf8ee9View on GitHubEnhance documentation for feature_shuffle_rf function with detailed descriptions and examples
1221bc0View on GitHubAdd feature shuffle function using Random Forest for feature importance analysis
ad857e4View on GitHubAdd feature selection shuffling notebook for breast cancer dataset analysis
86223edView on GitHubAdd Random Forest and Gradient Boosting feature importance functions
948a879View on GitHubImplement feature X to enhance user experience and optimize performance
a282119View on GitHub