├── .github └── FUNDING.yml ├── .gitignore ├── Datasets ├── AirQualityUCI_ready.csv ├── Readme.md ├── bag_of_words.csv ├── creditApprovalUCI.csv ├── loan.csv ├── sample_s2.csv └── titanic.csv ├── LICENSE ├── README.md ├── Section-03-Types-of-Variables ├── 1-Numerical-Variables.ipynb ├── 2-Categorical-Variables.ipynb ├── 3-Date-Time-Variables.ipynb └── 4-Mixed-Variables.ipynb ├── Section-04-Variable-Characteristics ├── 1-Missing-Data.ipynb ├── 2-Cardinality.ipynb ├── 3-Rare-Labels.ipynb ├── 5-Outliers.ipynb ├── 6-Linear-Model-Assumptions.ipynb └── 7-Variable-magnitude.ipynb ├── Section-05-Missing-Data-Imputation ├── 01-introduction │ ├── 01-Mean-Median-Imputation.ipynb │ ├── 02-Arbitrary-Value-Imputation.ipynb │ ├── 03-Frequent-Category-Imputation.ipynb │ ├── 04-Missing-Category-Imputation.ipynb │ └── 05-Missing-Indicator.ipynb ├── 02-pandas │ ├── 01-Mean-Median-Imputation.ipynb │ ├── 02-Arbitrary-Value-Imputation.ipynb │ ├── 03-Frequent-Category-Imputation.ipynb │ ├── 04-Missing-Category-Imputation.ipynb │ └── 05-Missing-indicator.ipynb ├── 03-sklearn │ ├── 01-Mean-Median-Imputation.ipynb │ ├── 02-Arbitrary-Value-Imputation.ipynb │ ├── 03-Frequent-Category-Imputation.ipynb │ ├── 04-Missing-Category-Imputation.ipynb │ ├── 05-MissingIndicator-Sklearn.ipynb │ └── 06-Grid-search.ipynb └── 04-feature-engine │ ├── 01-Mean-Median-Imputation.ipynb │ ├── 02-Arbitrary-Value-Imputation.ipynb │ ├── 03-Frequent-Category-Imputation.ipynb │ ├── 04-Missing-Category-Imputation.ipynb │ └── 05-Missing-Indicator.ipynb ├── Section-06-Imputation-Alternative ├── 01-introduction │ ├── 1-Complete-Case-Analysis.ipynb │ ├── 2-End-Distribution-Imputation.ipynb │ └── 3-Random-Sample-Imputation.ipynb ├── 02-pandas │ ├── 01-cca.ipynb │ ├── 02-End-Tail-Imputation.ipynb │ ├── 03-random-sample-imputation.ipynb │ └── 04-Mean-Median-per-group.ipynb └── 03-feature-engine │ ├── 01-cca.ipynb │ ├── 02-End-Tail-Imputation.ipynb │ └── 03--Random-Sample-Imputation.ipynb ├── Section-07-Multivariate-Imputation ├── 05.01-KNN-imputation.ipynb └── 05.02-MICE.ipynb ├── Section-08-Categorical-Encoding-Basic ├── 01-pandas │ ├── 01-One-hot-encoding.ipynb │ ├── 02-Ordinal-encoding.ipynb │ └── 03_Count-frequency-encoding.ipynb ├── 02-sklearn │ ├── 01-One-hot-encoding.ipynb │ └── 02-Ordinal-encoding.ipynb ├── 03-feature-engine │ ├── 01-One-hot-encoding.ipynb │ ├── 02-Ordinal-encoding.ipynb │ └── 03_Count-frequency-encoding.ipynb └── 04-category-encoders │ ├── 01-One-hot-encoding.ipynb │ ├── 02-Ordinal-encoding.ipynb │ └── 03_Count-frequency-encoding.ipynb ├── Section-09-Categorical-Encoding-Monotonic ├── 01-pandas │ ├── 01-Ordered-Ordinal-Encoding.ipynb │ ├── 02-Mean-Encoding.ipynb │ └── 03-Weight-of-Evidence.ipynb ├── 02-feature-engine │ ├── 01-Ordered-Ordinal-Encoding.ipynb │ ├── 02-Mean-Encoding.ipynb │ └── 03-Weight-of-Evidence.ipynb ├── 03-category-encoders │ ├── 02-Mean-Encoding.ipynb │ └── 03-Weight-of-Evidence.ipynb └── Comparison-categorical-encoding-techniques.ipynb ├── Section-10-Categorical-Encoding-Rare-Labels ├── 01-pandas │ ├── 01-ohe-frequent-categories.ipynb │ └── 02-Grouping-rare-categories.ipynb ├── 02-feature-engine │ ├── 01-ohe-frequent-categories.ipynb │ └── 02-Engineering-Rare-Categories.ipynb └── 03-sklearn │ └── 01-ohe-frequent-categories.ipynb ├── Section-11-Variable-Transformation ├── 01-numpy-scipy │ ├── 01-logarithmic-transformation.ipynb │ ├── 02-reciprocal-transformation.ipynb │ ├── 03-square-root-transformation.ipynb │ ├── 04-power-transformation.ipynb │ ├── 05-Box-Cox-transformation.ipynb │ ├── 06-Yeo-Johnson-transformation.ipynb │ └── 07-arcsin-transformation.ipynb ├── 02-sklearn │ ├── 01-logarithmic-transformation.ipynb │ ├── 02-reciprocal-transformation.ipynb │ ├── 03-square-root-transformation.ipynb │ ├── 04-power-transformation.ipynb │ ├── 05-Box-Cox-transformation.ipynb │ ├── 06-Yeo-Johnson-transformation.ipynb │ └── 07-arcsin-transformation.ipynb └── 03-feature-engine │ ├── 01-logarithmic-transformation.ipynb │ ├── 02-reciprocal-transformation.ipynb │ ├── 03-square-root-transformation.ipynb │ ├── 04-power-transformation.ipynb │ ├── 05-Box-Cox-transformation.ipynb │ ├── 06-Yeo-Johnson-transformation.ipynb │ └── 07-arcsin-transformation.ipynb ├── Section-12-Discretization-Basic ├── 01-pandas │ ├── 01-Equal-width-discretization.ipynb │ ├── 02-Equal-frequency-discretization.ipynb │ └── 03-Arbitrary-discretization.ipynb ├── 02-sklearn │ ├── 01-Equal-width-discretization.ipynb │ └── 02-Equal-frequency-discretization.ipynb └── 03-feature-engine │ ├── 01-Equal-width-discretization.ipynb │ ├── 02-Equal-frequency-discretization.ipynb │ ├── 03-Arbitrary-discretization.ipynb │ └── 04-Discretization-plus-encoding.ipynb ├── Section-13-Discretization-Other ├── 01-sklearn │ ├── 01-Discretization-with-kmeans.ipynb │ ├── 02-Discretization-using-decision-trees.ipynb │ └── 03-Binarization.ipynb └── 02-feature-engine │ └── 02-Discretization-using-decision-trees.ipynb ├── Section-14-Outlier-Engineering ├── 01-pandas │ ├── 01-Removing-outliers.ipynb │ └── 02-Capping-outliers.ipynb └── 02-feature-engine │ ├── 01-Removing-outliers.ipynb │ ├── 02-Capping-outliers.ipynb │ └── 03-Arbitrary-Capping.ipynb ├── Section-15-Date-Time-Features ├── 01-pandas │ ├── 01-Extracting-date-related-features.ipynb │ ├── 02-Extracting-time-related-features.ipynb │ └── 03-periodic-features.ipynb └── 02-feature-engine │ ├── 01-datetime-with-Feature-engine.ipynb │ └── 02-periodic-features.ipynb ├── Section-16-Mixed-Variables └── 01-mixed-variables.ipynb ├── Section-17-Feature-creation ├── 01-sklearn │ ├── 01-Combine-features-with-functions.ipynb │ ├── 02-Comparing-features-to-reference-variable.ipynb │ ├── 03-PolynomialExpansion.ipynb │ ├── 04-Combining-features-with-trees.ipynb │ └── 05-Spline-features.ipynb └── 02-feature-engine │ ├── 01-Combine-features-with-functions.ipynb │ └── 02-Comparing-features-to-reference-variable.ipynb ├── Section-18-Feature-Scaling ├── 01-Standardisation.ipynb ├── 02-MinMaxScaling.ipynb ├── 03-Mean-normalisation.ipynb ├── 04-Maximum-Absolute-Scaling.ipynb ├── 05-Robust-Scaling.ipynb └── 06-Scaling-to-unit-length.ipynb ├── Section-19-Putting-it-altogether ├── 01-Classification-titanic.ipynb ├── 02-Regression-house-prices.ipynb └── 03-Pipeline-with-crossvalidation.ipynb ├── course-banner.png ├── exercises ├── 03-variable-types │ ├── assignment.ipynb │ └── solution.ipynb ├── 04-variable-characteristics │ ├── assignment.ipynb │ └── solution.ipynb ├── 05-imputation-basic │ ├── assignment.ipynb │ └── solution.ipynb ├── 06-imputation-alternative │ ├── assignment.ipynb │ └── solution.ipynb ├── 07-multivariate-imputation │ ├── assignment.ipynb │ └── solution.ipynb ├── bank-marketing.csv ├── datasets │ └── taiwan-bankrupcy.ipynb ├── obesity_nan.csv └── taiwan_na.csv ├── feml_logo.png ├── old-prepare-datasets ├── bag-of-words.ipynb ├── bank-marketing.ipynb ├── credit-approval.ipynb ├── obesity.ipynb ├── pollutants.ipynb └── 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