介绍 (Introduction)

Airbnb provides an online platform for hosts to accommodate guests with short-term lodging. Guests can search for lodging using filters such as lodging type, dates, location, and price, and can search for specific types of homes, such as bed and breakfasts, unique homes, and vacation homes.

Airbnb为房东提供了一个在线平台，可以为短期住宿的客人提供住宿。 访客可以使用诸如住宿类型，日期，位置和价格之类的过滤器搜索住宿，还可以搜索特定类型的房屋，例如住宿加早餐旅馆，独特房屋和度假屋。

By reviewing the 2016 Seattle Airbnb Open Data, I will explore some interesting questions related to the lodging availability, pricing, and reviews. in addition I will try to predict the price of home listings based on the descriptive and non descriptive features.

通过回顾2016 Seattle Airbnb开放数据 ，我将探索一些与住宿可用性，价格和评论有关的有趣问题。 此外，我将尝试根据描述性和非描述性功能预测房屋清单的价格。

While analyzing the data I found that 63% of the listings are one-bedroom property, 42% accommodates 2 guests, 37% has a strict cancelation policy and 30% has a flexible cancelation policy. Capitol Hill and Ballard are the most popular neighborhoods in the listings.

在分析数据时，我发现63％的房源为一居室物业，42％的客房可容纳2位客人，37％的房屋实行严格的取消政策，30％的房屋实行灵活的取消政策。 国会山和巴拉德(Ballard)是清单中最受欢迎的街区。

一年中最繁忙的时间是西雅图？ 价格上涨多少？ (What are the busiest times of the year to visit Seattle? By how much do prices spike?)

Summer season is more expensive among the year, June July and August are showing the three highest average price per home listing than the other months. The price keeps going from January (122 average) and reached the peak on July (152 average), costing on average over 23.7% than January.

一年中的夏季价格更高，6月，7月和8月是每个房屋挂牌价格最高的三个月。 价格从1月份开始(平ASP格为122)，并在7月份达到峰值(平ASP格为152)，比1月份平ASP格高出23.7％。

When I observed the rate of change of average price of lodging listings for each month, I discovered that the biggest rate of change occurred in June and the lowest in September. The first 7 months of the year also experienced a positive percentage rate of change and then subsequently August, September, October and November experienced a negative rate of change and the rate of change becomes positive again in December. This shows that there is a significant dip for around 4 months in the fall until December.

当我观察到每个月房租平ASP格的变化率时，我发现最大的变化率发生在6月 ，而最低的变化发生在9月。 一年的前七个月也经历了正百分比变化率，然后随后的八月，九月，十月和十一月经历了负变化率，并且变化率在12月再次变为正。 这表明秋季直到12月的4个月左右都有明显的下降。

By analyzing the reviews data, I found that the number of home listings have been exponentially increased from 2009 to 2015 and were directly correlated with the number of visitors.

通过分析评论数据，我发现从2009年到2015年 ，房屋列表的数量呈指数增长，并且与访客数量直接相关。

西雅图最受欢迎的Airbnb房源是什么？ (What is the most popular Seattle neighborhood for Airbnb listings?)

By analyzing the listings data, I found that Capitol Hill and Ballard are the most popular neighborhoods in the Seattle listings, the below bar chart shows that Capitol Hill has 10.31 % Seattle listings, followed by Ballard with 6.26% of the listings.

通过分析清单数据，我发现Capitol Hill和Ballard是西雅图清单中最受欢迎的社区，下面的条形图显示Capitol Hill拥有10.31％西雅图清单，其次是Ballard，占6.26％。

我们可以预测西雅图Airbnb房源的价格吗？ 哪些方面与价格有很好的关联？ (Can we predict a price of Seattle Airbnb listings? What aspects correlate well to price?)

It could be possible to predict the price of Seattle Airbnb listings, however its not as straight forward as it seems to be. For modeling of price prediction, I tried three algorithms, ‘Linear Regression’, ‘Random Forest Regressor’, and ‘Gradient Boosting Regressor’.

可以预测西雅图Airbnb房源的价格，但是它并不像看起来那样简单。 为了对价格预测建模，我尝试了三种算法：“线性回归”，“随机森林回归”和“梯度提升回归”。

Compared to other two models, Linear Regression achieved the best result this time where it gave an accuracy of 56% on the training set and 58% on our test set. This is due to the lack of historical data and the data requiring a huge amount of transformation to be more accurate.

与其他两个模型相比，线性回归这次获得了最佳结果， 其训练集的准确性为56％，测试集的准确性为58％。 这是由于缺乏历史数据，并且数据需要大量转换才能更准确。

Further analysis, I manage to find some factors that cloud influence the price of a listing in order of importance are:

进一步分析后，我设法找到一些因素会影响重要性，这些因素会影响上市价格：

· Number of bedrooms

·卧室数量

· Number of accommodates

·容纳人数

· Number of Bathrooms

·浴室数量

· Room Type

· 房型

· Listing description

·清单说明

· Listing Neighborhood

·列出邻居

结论 (Conclusion)

In this article, I tried to analyze the 2016 Airbnb Seattle data in order to answer the below questions:

在本文中，我试图分析2016年Airbnb Seattle数据，以回答以下问题：

1. What are the busiest times of the year to visit Seattle? By how much do prices spike?

1.一年中最繁忙的时间是西雅图？ 价格上涨多少？

2. Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?

2.新的Airbnb房源和西雅图的Airbnb访客总数是否都有总体上升趋势？

3. What is the most populate Seattle neighborhood for Airbnb listings?

3. Airbnb房源在西雅图人口最多的地区是什么？

4. Can we predict a price of Seattle Airbnb listings? What aspects correlate well to price?

4.我们可以预测西雅图Airbnb房源的价格吗？ 哪些方面与价格有很好的关联？

To see more about this analysis, see the link to my Github available here

要了解有关此分析的更多信息，请参见此处的我的Github链接。