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Python Feature Engineering Cookbook Over 70 recipes for creatiing and transforming features to build machine learning models 1st edition by Soledad Galli ISBN 1789807824 9781789807820

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ISBN 10: 1789807824
ISBN 13: 9781789807820
Author: Soledad Galli

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python’s pandas, scikit-learn, SciPy and NumPy libraries Book Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems. What you will learn Simplify your feature engineering pipelines with powerful Python packages Get to grips with imputing missing values Encode categorical variables with a wide set of techniques Extract insights from text quickly and effortlessly Develop features from transactional data and time series data Derive new features by combining existing variables Understand how to transform, discretize, and scale your variables Create informative variables from date and time Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.

Python Feature Engineering Cookbook Over 70 recipes for creatiing and transforming features to build machine learning models 1st Table of contents:

  1. Packt is searching for authors like you
  2. Preface
  3. Who this book is for
  4. What this book covers
  5. To get the most out of this book
  6. Download the example code files
  7. Download the color images
  8. Conventions used
  9. Sections
  10. Getting ready
  11. How to do it…
  12. How it works…
  13. There’s more…
  14. See also
  15. Get in touch
  16. Reviews
  17. Foreseeing Variable Problems When Building ML Models
  18. Technical requirements
  19. Identifying numerical and categorical variables
  20. Getting ready
  21. How to do it…
  22. How it works…
  23. There’s more…
  24. See also
  25. Quantifying missing data
  26. Getting ready
  27. How to do it…
  28. How it works…
  29. Determining cardinality in categorical variables
  30. Getting ready
  31. How to do it…
  32. How it works…
  33. There’s more…
  34. Pinpointing rare categories in categorical variables
  35. Getting ready
  36. How to do it…
  37. How it works…
  38. Identifying a linear relationship
  39. How to do it…
  40. How it works…
  41. There’s more…
  42. See also
  43. Identifying a normal distribution
  44. How to do it…
  45. How it works…
  46. There’s more…
  47. See also
  48. Distinguishing variable distribution
  49. Getting ready
  50. How to do it…
  51. How it works…
  52. See also
  53. Highlighting outliers
  54. Getting ready
  55. How to do it…
  56. How it works…
  57. Comparing feature magnitude
  58. Getting ready
  59. How to do it…
  60. How it works…
  61. Imputing Missing Data
  62. Technical requirements
  63. Removing observations with missing data
  64. How to do it…
  65. How it works…
  66. See also
  67. Performing mean or median imputation
  68. How to do it…
  69. How it works…
  70. There’s more…
  71. See also
  72. Implementing mode or frequent category imputation
  73. How to do it…
  74. How it works…
  75. See also
  76. Replacing missing values with an arbitrary number
  77. How to do it…
  78. How it works…
  79. There’s more…
  80. See also
  81. Capturing missing values in a bespoke category
  82. How to do it…
  83. How it works…
  84. See also
  85. Replacing missing values with a value at the end of the distribution
  86. How to do it…
  87. How it works…
  88. See also
  89. Implementing random sample imputation
  90. How to do it…
  91. How it works…
  92. See also
  93. Adding a missing value indicator variable
  94. Getting ready
  95. How to do it…
  96. How it works…
  97. There’s more…
  98. See also
  99. Performing multivariate imputation by chained equations
  100. Getting ready
  101. How to do it…
  102. How it works…
  103. There’s more…
  104. Assembling an imputation pipeline with scikit-learn
  105. How to do it…
  106. How it works…
  107. See also
  108. Assembling an imputation pipeline with Feature-engine
  109. How to do it…
  110. How it works…
  111. See also
  112. Encoding Categorical Variables
  113. Technical requirements
  114. Creating binary variables through one-hot encoding
  115. Getting ready
  116. How to do it…
  117. How it works…
  118. There’s more…
  119. See also
  120. Performing one-hot encoding of frequent categories
  121. Getting ready
  122. How to do it…
  123. How it works…
  124. There’s more…
  125. Replacing categories with ordinal numbers
  126. How to do it…
  127. How it works…
  128. There’s more…
  129. See also
  130. Replacing categories with counts or frequency of observations
  131. How to do it…
  132. How it works…
  133. There’s more…
  134. Encoding with integers in an ordered manner
  135. How to do it…
  136. How it works…
  137. See also
  138. Encoding with the mean of the target
  139. How to do it…
  140. How it works…
  141. See also
  142. Encoding with the Weight of Evidence
  143. How to do it…
  144. How it works…
  145. See also
  146. Grouping rare or infrequent categories
  147. How to do it…
  148. How it works…
  149. See also
  150. Performing binary encoding
  151. Getting ready
  152. How to do it…
  153. How it works…
  154. See also
  155. Performing feature hashing
  156. Getting ready
  157. How to do it…
  158. How it works…
  159. See also
  160. Transforming Numerical Variables
  161. Technical requirements
  162. Transforming variables with the logarithm
  163. How to do it…
  164. How it works…
  165. See also
  166. Transforming variables with the reciprocal function
  167. How to do it…
  168. How it works…
  169. See also
  170. Using square and cube root to transform variables
  171. How to do it…
  172. How it works…
  173. There’s more…
  174. Using power transformations on numerical variables
  175. How to do it…
  176. How it works…
  177. There’s more…
  178. See also
  179. Performing Box-Cox transformation on numerical variables
  180. How to do it…
  181. How it works…
  182. See also
  183. Performing Yeo-Johnson transformation on numerical variables
  184. How to do it…
  185. How it works…
  186. See also
  187. Performing Variable Discretization
  188. Technical requirements
  189. Dividing the variable into intervals of equal width
  190. How to do it…
  191. How it works…
  192. See also
  193. Sorting the variable values in intervals of equal frequency
  194. How to do it…
  195. How it works…
  196. Performing discretization followed by categorical encoding
  197. How to do it…
  198. How it works…
  199. See also
  200. Allocating the variable values in arbitrary intervals
  201. How to do it…
  202. How it works…
  203. Performing discretization with k-means clustering
  204. How to do it…
  205. How it works…
  206. Using decision trees for discretization
  207. Getting ready
  208. How to do it…
  209. How it works…
  210. There’s more…
  211. See also
  212. Working with Outliers
  213. Technical requirements
  214. Trimming outliers from the dataset
  215. How to do it…
  216. How it works…
  217. There’s more…
  218. Performing winsorization
  219. How to do it…
  220. How it works…
  221. There’s more…
  222. See also
  223. Capping the variable at arbitrary maximum and minimum values
  224. How to do it…
  225. How it works…
  226. There’s more…
  227. See also
  228. Performing zero-coding – capping the variable at zero
  229. How to do it…
  230. How it works…
  231. There’s more…
  232. See also
  233. Deriving Features from Dates and Time Variables
  234. Technical requirements
  235. Extracting date and time parts from a datetime variable
  236. How to do it…
  237. How it works…
  238. See also
  239. Deriving representations of the year and month
  240. How to do it…
  241. How it works…
  242. See also
  243. Creating representations of day and week
  244. How to do it…
  245. How it works…
  246. See also
  247. Extracting time parts from a time variable
  248. How to do it…
  249. How it works…
  250. Capturing the elapsed time between datetime variables
  251. How to do it…
  252. How it works…
  253. See also
  254. Working with time in different time zones
  255. How to do it…
  256. How it works…
  257. See also
  258. Performing Feature Scaling
  259. Technical requirements
  260. Standardizing the features
  261. How to do it…
  262. How it works…
  263. See also
  264. Performing mean normalization
  265. How to do it…
  266. How it works…
  267. There’s more…
  268. See also
  269. Scaling to the maximum and minimum values
  270. How to do it…
  271. How it works…
  272. See also
  273. Implementing maximum absolute scaling
  274. How to do it…
  275. How it works…
  276. There’s more…
  277. See also
  278. Scaling with the median and quantiles
  279. How to do it…
  280. How it works…
  281. See also
  282. Scaling to vector unit length
  283. How to do it…
  284. How it works…
  285. See also
  286. Applying Mathematical Computations to Features
  287. Technical requirements
  288. Combining multiple features with statistical operations
  289. Getting ready
  290. How to do it…
  291. How it works…
  292. There’s more…
  293. See also
  294. Combining pairs of features with mathematical functions
  295. Getting ready
  296. How to do it…
  297. How it works…
  298. There’s more…
  299. See also
  300. Performing polynomial expansion
  301. Getting ready
  302. How to do it…
  303. How it works…
  304. There’s more…
  305. See also
  306. Deriving new features with decision trees
  307. Getting ready
  308. How to do it…
  309. How it works…
  310. There’s more…
  311. Carrying out PCA
  312. Getting ready
  313. How to do it…
  314. How it works…
  315. See also
  316. Creating Features with Transactional and Time Series Data
  317. Technical requirements
  318. Aggregating transactions with mathematical operations
  319. Getting ready
  320. How to do it…
  321. How it works…
  322. There’s more…
  323. See also
  324. Aggregating transactions in a time window
  325. Getting ready
  326. How to do it…
  327. How it works…
  328. There’s more…
  329. See also
  330. Determining the number of local maxima and minima
  331. Getting ready
  332. How to do it…
  333. How it works…
  334. There’s more…
  335. See also
  336. Deriving time elapsed between time-stamped events
  337. How to do it…
  338. How it works…
  339. There’s more…
  340. See also
  341. Creating features from transactions with Featuretools
  342. How to do it…
  343. How it works…
  344. There’s more…
  345. See also
  346. Extracting Features from Text Variables
  347. Technical requirements
  348. Counting characters, words, and vocabulary
  349. Getting ready
  350. How to do it…
  351. How it works…
  352. There’s more…
  353. See also
  354. Estimating text complexity by counting sentences
  355. Getting ready
  356. How to do it…
  357. How it works…
  358. There’s more…
  359. Creating features with bag-of-words and n-grams
  360. Getting ready
  361. How to do it…
  362. How it works…
  363. See also
  364. Implementing term frequency-inverse document frequency
  365. Getting ready
  366. How to do it…
  367. How it works…
  368. See also
  369. Cleaning and stemming text variables
  370. Getting ready
  371. How to do it…
  372. How it works…
  373. Other Books You May Enjoy
  374. Leave a review – let other readers know what you think

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Tags: Soledad Galli, Python Feature, transforming features, learning models