本文介绍了Airbnb如何利用机器学习预测房源价值，重点讨论了Customer Lifetime Value (LTV)、特征工程、模型原型与训练、模型选择、生产部署以及关键学习点。Airbnb使用Zipline进行特征工程，通过sklearn和spark构建模型原型，并借助ML Automator将notebook转化为生产流程。文章强调了hive和spark的基础知识以及Bias-Variance tradeoff的重要性。
https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d
作者是：Robert Chang
1. Customer Lifetime Value (LTV)
客户生命周期价值模型， 应用场景：
At e-commerce companies like Spotify or Netflix, LTV is often used to make pricing decisions like setting subscription fees. At marketplace companies like Airbnb, knowing users’ LTVs enable us to allocate budget across different marketing channels more efficiently, calculate more precise bidding prices for online marketing based on keywords, and create better listing segments.
整个训练测试和部署的pipline，airbnb使用了很多amazing的工具，因此他们的data scientist不用关注太多data engineering的过程。pipline主要这四个步骤。
Feature Engineering: Define relevant features
Prototyping and Training: Tra
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