预处理criteo数据集以预测广告的点击率

weixin_26729375

于 2020-10-11 18:03:45 发布

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文章标签：机器学习

原文链接：https://medium.com/@amany.m.abdelhalim/preprocessing-criteo-dataset-for-prediction-of-click-through-rate-on-ads-7dee096a2dd9

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本文介绍了如何使用PySpark在DataBricks上预处理Criteo数据集，以预测广告点击率。数据集包含40个属性，包括13个整数列和26个类别列。由于数据不平衡，作者通过创建新的平衡数据集进行处理。此外，处理了空值、异常值和高维分类列，应用了布尔转换、空值替换、离群值处理、z-score标准化、存储桶化、编码和向量汇编等技术。最后，训练了逻辑回归、随机森林和线性SVM模型，通过调参实现了约70%至78%的预测准确性。

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Amany Abdelhalim 阿曼尼·阿卜杜勒哈林

Sep 18 九月18

Preprocessing Criteo Dataset for Prediction of Click Through Rate on Ads

预处理Criteo数据集以预测广告的点击率

In this post, I will be taking you through the steps that I performed to preprocess the Criteo Data set.

在这篇文章中，我将指导您完成预处理Criteo数据集的步骤。

Image for post — **Some Aspects to Consider when Preprocessing the Data** **预处理数据时应考虑的一些方面**

Criteo data set is an online advertising dataset released by Criteo Labs. It contains feature values and click feedback for millions of display Ads, the data serves as a benchmark for clickthrough rate (CTR) prediction. Every Ad has features that describe the data. The data set has 40 attributes, the first attribute is the label where a value 1 represents that the Ad has been clicked on and a 0 represents it wasn’t clicked on. The attributes consist of 13 integer columns and 26 categorical columns.

Criteo数据集是Criteo Labs发布的在线广告数据集。它包含数百万个展示广告的功能值和点击反馈