当前位置:网站首页>Using tensorflow to forecast the rental price of airbnb in New York City
Using tensorflow to forecast the rental price of airbnb in New York City
2020-11-06 01:14:00 【Artificial intelligence meets pioneer】
author |TIMOTHY102 compile |VK source |Analytics Vidhya
Introduce
Airbnb It's an online market , Allow people to rent their property or spare rooms to guests . Per booking 3 Guests , collect 12% and 6% Commission .
The company started from 2009 Since its establishment in , Has been helped from every year 2.1 Ten thousand guests found accommodation , To help every year 600 Ten thousand people on holiday , Currently in 90 From different countries 34000 Cities list amazing 80 Ten thousand houses .
In this paper , I will use Kaggle-newyorkcityairbnb Open data set , Try to use TensorFlow Build a neural network model to predict .
The goal is to build a suitable machine learning model , Be able to predict the price of future accommodation data .
In this paper , I'm going to show you what I've created Jupyter Notebook. You can GitHub Find it on the :https://github.com/Timothy102/Tensorflow-for-Airbnb-Prices
Load data
First , Let's see how to load data . We use it wget Directly from Kaggle Get data on the website . Be careful -o The flag indicates the file name .
The dataset should look like this . share 48895 That's ok 16 Column .
Data analysis and preprocessing
Seaborn There's a very simple API, You can draw all kinds of graphs for all kinds of data . If you are not familiar with grammar , Check out this article :https://www.analyticsvidhya.com/blog/2019/09/comprehensive-data-visualization-guide-seaborn-python/
stay pandas Use on data frame corr after , We pass it on to a heatmap function . give the result as follows :
Since we have longitude and longitude and neighborhood data , Let's create a scatter plot :
Besides , I've deleted duplicate items and some unnecessary Columns , And filled in “reviews_per_month”, Because it has too many missing values . The data looks like this . It has 10 Column , There is no zero value :
very good , Right ?
First , Computers do numbers . That's why we need to convert a sort column into a one-hot Encoded vector . This is the use of pandas Of factorize Method . You can use a lot of other tools :
In order to keep the loss function in a stable range , Let's normalize some data , Let the average be 0, The standard deviation is 1.
Feature crossover
We have to make a change , This is an essential change . To correlate longitude and latitude with model output , We have to create a feature crossover . The following links should provide you with sufficient background knowledge , So that you can feel the cross of features correctly :
- https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
- https://www.kaggle.com/vikramtiwari/feature-crosses-tensorflow-mlcc
Our goal is to introduce latitude longitude crossing , This is one of the oldest techniques in the book . If we just put these two columns in the model as values , It will assume that these values are gradually related to the output .
contrary , We're going to use feature crossover , That means we're going to put longitude * The longitude map is divided into a grid . Fortunately, ,TensorFlow Make it easy .
I go through iteration (max-min)/100, So as to generate a frame grid with uniform distribution .
I use it 100×100 grid :
Essentially , What we're doing here , Is to define a bucked Columns and the boundaries defined earlier , And create a DenseFeatures layer , Then pass it to Sequential API.
If you're not familiar with it Tensorflow grammar , Please check the documentation :https://www.tensorflow.org/api_docs/python/tf/feature_column/
Now? , finally , We are ready for model training . Apart from splitting the data part , in other words .
obviously , We have to create two datasets , One contains all the data , The other contains the predicted score . Due to data size mismatch , This may cause problems for our model , So I decided to truncate data that was too long .
Creating models
Last , Established Keras Sequence model .
We use Adam Optimizer 、 Mean square error loss and two metrics to compile the model .
Besides , We use two callbacks :
-
Stop early , This is self-evident
-
Reduce the learning rate at high altitude .
after 50 individual epoch Training for ,batch The size is 64, Our model is quite successful .
ending
We use New York City AirBnB The data builds a fully connected neural network to predict future prices .Pandas and seaborn It makes it very easy to visualize and examine data . We introduce the idea of latitude longitude crossing as a feature in the model . And thanks to that Kaggle Open data set of , We have a fully operational machine learning model .
Link to the original text :https://www.analyticsvidhya.com/blog/2020/10/predicting-nyc-airbnb-rental-prices-tensorflow/
Welcome to join us AI Blog station : http://panchuang.net/
sklearn Machine learning Chinese official documents : http://sklearn123.com/
Welcome to pay attention to pan Chuang blog resource summary station : http://docs.panchuang.net/
版权声明
本文为[Artificial intelligence meets pioneer]所创,转载请带上原文链接,感谢
边栏推荐
- 一时技痒,撸了个动态线程池,源码放Github了
- Vue.js移动端左滑删除组件
- Microservices: how to solve the problem of link tracing
- 解決pl/sql developer中資料庫插入資料亂碼問題
- 基于深度学习的推荐系统
- Details of dapr implementing distributed stateful service
- mac 安装hanlp,以及win下安装与使用
- 技術總監,送給剛畢業的程式設計師們一句話——做好小事,才能成就大事
- 嘗試從零開始構建我的商城 (二) :使用JWT保護我們的資訊保安,完善Swagger配置
- 免费的专利下载教程(知网、espacenet强强联合)
猜你喜欢
随机推荐
Pattern matching: The gestalt approach一种序列的文本相似度方法
Electron应用使用electron-builder配合electron-updater实现自动更新
有关PDF417条码码制的结构介绍
50 + open source projects are officially assembled, and millions of developers are voting
基于深度学习的推荐系统
谁说Cat不能做链路跟踪的,给我站出来
快快使用ModelArts,零基礎小白也能玩轉AI!
中国提出的AI方法影响越来越大,天大等从大量文献中挖掘AI发展规律
不吹不黑,跨平臺框架AspNetCore開發實踐雜談
hadoop 命令总结
How do the general bottom buried points do?
【效能優化】納尼?記憶體又溢位了?!是時候總結一波了!!
[C#] (原創)一步一步教你自定義控制元件——04,ProgressBar(進度條)
制造和新的自动化技术是什么?
Polkadot series (2) -- detailed explanation of mixed consensus
你的财务报告该换个高级的套路了——财务分析驾驶舱
我们编写 React 组件的最佳实践
python 保存list数据
Vue 3 responsive Foundation
Jmeter——ForEach Controller&Loop Controller