这里面包含了有关机器学习的特征工程中的几个常用数据集,例如aisles.csv, factor_returns.csv order_products__prior.csv orders.csv products.csv

人工智能特征工程介绍---

本次代码是2020数字中国创新大赛—算法赛：智慧海洋建设比赛复赛程序，复赛A榜18，B榜26。 程序使用了3个模型，分别是词嵌入+textcnn、lightgbm、randomforest，对最终的预测取得了不错的效果。

aipython.pdf

《数据准备和特征工程》

特征工程和xgboost调优案例，面向初学xgboost以及想要进阶学习xgboost调参的程序猿

使用集成学习模型xgboost、lightGBM、Catboost以及深度学习模型LSTM预测目标变量（因子），通过丰富的EDA、数据预处理、特征工程、特征选择、参数调优、模型对比分析、可视化分析（箱图、密度图、pair plot、）等步骤来进行整体的合理评估和分析。

kaggle平台上波士顿房价预测的数据集以及代码实现过程， 主要涉及遇到一个新问题是如何分析数据，对数据进行处理，做特征工程，到最后的预测

Sanet.st.Feature Engineering Made Easy - Sinan Ozdemir.epub(正式版) 让特征工程变得简单正式版 This book will cover the topic of feature engineering. A huge part of the data science and machine learning pipeline, feature engineering includes the ability to identify, clean, construct, and discover new characteristics of data for the purpose of interpretation and predictive analysis. In this book, we will be covering the entire process of feature engineering, from inspection to visualization, transformation, and beyond. We will be using both basic and advanced mathematical measures to transform our data into a form that's much more digestible by machines and machine learning pipelines. By discovering and transforming, we, as data scientists, will be able to gain a whole new perspective on our data, enhancing not only our algorithms but also our insights.

Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists Alice Zheng and Amanda Casari