文件名称：Python-Feature-Engineering-Cookbook:Packt发行的《 Python Feature Engineering Cookbook》
文件大小：5.24MB
文件格式：ZIP
更新时间：2024-06-15 10:34:25
JupyterNotebook
Python功能工程手册 这是Packt发行的的代码存储库。 超过70种配方，用于创建，工程设计和转换功能以构建机器学习模型 这本书是关于什么的？ 特征工程对于开发和丰富您的机器学习模型非常有用。 在本书中，您将使用最好的Python工具来简化功能工程流水线，功能工程技术并简化和提高代码质量。 本书涵盖以下激动人心的功能： 强大的Python软件包简化了功能工程流水线 掌握估算缺失值的方法 使用多种技术对分类变量进行编码 快速轻松地从文本中提取见解 利用交易数据和时间序列数据开发功能 通过结合现有变量派生新功能 了解如何变换，离散化和缩放变量 从日期和时间创建信息性变量 如果您觉得这本书适合您，请立即获取！ 说明和导航 所有代码都组织在文件夹中。 该代码将如下所示： def get_first_cabin(row): try: return row.split()[0]
【文件预览】：
Python-Feature-Engineering-Cookbook-master
----requirements.txt(380B)
----Chapter09()
--------Recipe5-PCA.ipynb(25KB)
--------Recipe1-Add-Multiply-Features.ipynb(264KB)
--------Recipe4-Combining-features-with-trees.ipynb(67KB)
--------Recipe2-Substraction-Quotient-Features.ipynb(64KB)
--------Recipe3-PolynomialExpansion.ipynb(148KB)
----Chapter10()
--------R1-Aggregating-transactional-data-with-math-operations.ipynb(20KB)
--------energydata_complete.csv(11.41MB)
--------R5-Creating-features-with-featuretools.ipynb(71KB)
--------AirQualityUCI.csv(757KB)
--------R4-Calculating-distance-between-events.ipynb(184KB)
--------R2--aggregate-transactional-data-in-time-windows.ipynb(143KB)
--------R3-Identifying-and-counting-local-maxima-and-minima.ipynb(483KB)
----LICENSE(1KB)
----Chapter11()
--------Recipe3-bag-of-words.ipynb(21KB)
--------Recipe1-Capturing-text-complexity-in-features.ipynb(29KB)
--------Recipe2-Sentence-tokenization.ipynb(17KB)
--------Recipe5-cleaning-text.ipynb(19KB)
--------Recipe4-TFIDF.ipynb(22KB)
----Chapter06()
--------Recipe2-Winsorisation.ipynb(264KB)
--------Recipe3-Capping.ipynb(13KB)
--------Recipe1-Outlier-Trimming.ipynb(24KB)
--------Recipe4-Zero-coding.ipynb(30KB)
----Chapter08()
--------Recipe1-Standardization.ipynb(83KB)
--------Recipe2-Mean-normalization.ipynb(81KB)
--------Recipe4-Maximum-Absolute-Scaling.ipynb(121KB)
--------Recipe3-MinMaxScaling.ipynb(82KB)
--------Recipe5-Robust-Scaling.ipynb(78KB)
--------Recipe6-Scaling-to-unit-length.ipynb(16KB)
----README.md(5KB)
----Chapter03()
--------Recipe-6-target-mean-encoding.ipynb(65KB)
--------Recipe-7-weight-of-evidence.ipynb(57KB)
--------Recipe-9-Binary-Encoding.ipynb(16KB)
--------Recipe-3-Replacing-categories-by-ordinal-numbers.ipynb(27KB)
--------CreditApprovalUCI_dataPrep.ipynb(19KB)
--------Recipe-5-ordered-ordinal-encoding.ipynb(63KB)
--------Recipe-2-One-hot-encoding-top-categories.ipynb(25KB)
--------Recipe-4-replacing-categories-by-counts-frequency.ipynb(24KB)
--------Recipe-10--Feature-Hashing.ipynb(15KB)
--------Recipe-1-One-hot-encoding.ipynb(39KB)
--------Recipe-8-grouping-rare-categories.ipynb(22KB)
----Chapter05()
--------tree_model.png(149KB)
--------tree_model_files()
--------Recipe-4-Arbitrary-interval-discretisation.ipynb(16KB)
--------Recipe-5-Discretisation-Kmeans.ipynb(46KB)
--------tree_model.txt(1KB)
--------graphiz_browser.png(71KB)
--------Recipe-3-Discretisation-plus-categorical-encoding.ipynb(40KB)
--------Recipe-1-Equal-width-discretisation.ipynb(138KB)
--------Recipe-2-Equal-frequency-discretisation.ipynb(123KB)
--------Recipe-6-Discretisation-with-decision-trees.ipynb(380KB)
----Chapter02()
--------Recipe-01-Removing-observations-with-missing-data.ipynb(8KB)
--------Recipe-02-Performing-mean-or-median-imputation.ipynb(18KB)
--------Recipe-07-Implementing-random-sample-imputation.ipynb(19KB)
--------CreditApprovalUCI_dataPrep.ipynb(19KB)
--------Recipe-05-Capturing-missing-values-in-a-bespoke-category.ipynb(17KB)
--------Recipe-08-Adding-a-missing-value-indicator-variable.ipynb(31KB)
--------Recipe-03-Implementing-mode-or-frequent-category-imputation.ipynb(17KB)
--------Recipe-06-Replacing-missing-values-by-a-value-at-the-end-of-the-distribution.ipynb(11KB)
--------Recipe-09-Performing-multivariate-imputation-by-chained-equations-MICE.ipynb(39KB)
--------Recipe-10-Assembling-an-imputation-pipeline-with-Scikit-learn.ipynb(15KB)
--------Recipe-11-Assembling-an-imputation-pipeline-with-Feature-Engine.ipynb(12KB)
--------Recipe-04-Replacing-missing-values-by-an-arbitrary-number.ipynb(18KB)
----Chapter01()
--------Recipe-4-Pinpointing-rare-categories.ipynb(23KB)
--------Recipe-5-Identifying-a-linear-relationship.ipynb(193KB)
--------DataPrep_Titanic.ipynb(1KB)
--------Recipe-9-Comparing-feature-magnitude.ipynb(15KB)
--------Recipe-8-Highlighting-outliers.ipynb(23KB)
--------Recipe-7-Distinguishing-variable-distribution.ipynb(48KB)
--------Recipe-1-indetifying-variables-types.ipynb(31KB)
--------Recipe-6-Identifying-a-normal-distribution.ipynb(103KB)
--------Recipe-3-Determining-cardinality.ipynb(22KB)
--------Recipe-2-Quantifying-missing-data.ipynb(28KB)
----Chapter04()
--------Recipe-4-power-transformation.ipynb(150KB)
--------Recipe-3-square-cube-root.ipynb(170KB)
--------Recipe-1-logarithmic-transformation.ipynb(166KB)
--------Recipe-5-Box-Cox-transformation.ipynb(145KB)
--------Recipe-6-Yeo-Johnson-transformation.ipynb(147KB)
--------Recipe-2-reciprocal-transformation.ipynb(150KB)
----Chapter07()
--------Recipe3-Creating-representations-of-week-day.ipynb(16KB)
--------TechReqs-Dataset-creation.ipynb(4KB)
--------Recipe4-Extracting-time-parts.ipynb(12KB)
--------Recipe5-Capturing-elapsed-time-between-2-variables.ipynb(16KB)
--------Recipe2-Deriving-year-month-semester-quarter.ipynb(12KB)
--------Recipe1-Extracting-date-and-time-part.ipynb(11KB)
--------Recipe6--different-time-zones.ipynb(14KB)