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基于python的房屋信息可视化及价格预测系统设计与实现.docx
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基于python的房屋信息可视化及价格预测系统设计与实现
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本科毕业设计
Python 房屋信息可视化及价格预测系统
学 院
专 业 XXXXXXXXXXXXXXXXXXXX
学 号
姓 名 XXXXXXXXXXXXXXXXXXXX
指导教师姓名 XXXXXXXXXXXXXXXXXXXX
指导教师职称 XXXXXXXXXXXXXXXXXXXX
摘 要
进入二十一世纪以来,计算机技术蓬勃发展,人们的生活发生了许多变化。比如
说信息的传播和言论的发表变得越来越快了,当代大学生朋友可以通过网络平台快速
的了解当今社会的新闻及动态,除此之外还可以利用各个平台的评论功能发表自己的
意见或看法。由此可见,计算机技术对人们生活的改变不仅仅包含衣食住行等方面,
它在各种领域都对现代生活作出了贡献。在网络不发达的年代,人们如果想要购买新
房或者了解房价是只能通过宣传栏中的小广告或者通过中介获取信息的,但这种形式
费事费力,流程非常繁琐,并且无法保证购房者们获取到准确的信息,所以房产买卖
的咨询与房价预测的方式逐渐被网络化的电子系统替代了。在计算机刚开始发展的时
候就出现了许多的宣传自己楼盘的网页,但是因为技术的限制很多功能都无法实现,
再加上硬件设备的限制导致系统并不完美,有很多不符合购房者们使用习惯的瑕疵,
也有很多的功能缺陷。随着计算机编程语言的不断发展和移动设备的出现和各种算法
的发展,房屋价格的预测也越来越准确。
本系统使用Python语言和MySQL数据库开发,可以为各地准备购房的人群或者想
要了解房间的人群提供房价预测、房屋分析、用户管理等多种功能,让用户不需要再
繁琐的查看每个楼盘各时期的价格就可以进行对自己心仪的房屋进行价格的预测,避
免在房价最高时入场导致不必要的经济损失。
关键词:房价;预测;Python;MYSQL
Abstract
Since the beginning of the 21st century, computer technology has flourished,
and people's lives have undergone many changes. For example, the dissemination of
information and the publication of opinions are becoming faster and faster.
Contemporary college students can quickly understand the news and trends of
today's society through online platforms. In addition, they can also use the comment
function of various platforms to express their opinions or opinions. It can be seen
that computer technology has not only changed people's lives in areas such as
clothing, food, housing, and transportation, but also made contributions to modern
life in various fields. In the era of underdeveloped internet, if people want to buy a
new house or understand the housing price, they can only obtain information
through small advertisements in the bulletin board or through intermediaries.
However, this form is labor-intensive, the process is very cumbersome, and it cannot
guarantee that buyers can obtain accurate information. Therefore, the consultation
and housing price prediction methods for real estate sales have gradually been
replaced by networked electronic systems. At the beginning of the development of
computers, there were many web pages promoting their own properties. However,
due to technological limitations, many functions could not be implemented, and
hardware equipment limitations, the system was not perfect. There were many flaws
that did not meet the usage habits of homebuyers, as well as many functional
defects.
This system is developed using Python language and MySQL database. It can
provide various functions such as house price prediction, house analysis, and user
management for people who are preparing to purchase houses or want to know
about rooms in various regions. Users can predict the prices of their desired houses
without the need to check the prices of each property in different periods, avoiding
unnecessary economic losses caused by entering during the peak housing prices..
Keywords: House Price; Forecast; Python; MYSQL
目 录
1 绪论 .................................................................1
1.1 课题研究背景及意义 ..............................................2
1.2 国内外研究现状及发展趋势 ........................................2
1.3 本文的研究思路与结构 ............................................2
2 开发工具及技术 .......................................................2
2.1 B/S 结构的介绍...................................................2
2.2 Python 技术的介绍................................................2
2.3 HTML 技术的介绍..................................................2
2.4 MYSQL 数据库的介绍...............................................3
2.5 开发环境的介绍 ..................................................3
3 需求分析 .............................................................4
3.1 可行性分析 ......................................................4
3.2 功能需求分析 ....................................................4
3.3 非功能需求分析 ..................................................4
4 总体设计 .............................................................6
4.1 系统总体结构设计 ................................................6
4.2 系统的数据库设计 ................................................6
5 系统功能实现 .........................................................6
5.1 首页展示 ........................................................6
5.2 用户登录注册 ....................................................6
5.3 房价预测 ........................................................6
5.4 房屋管理 ........................................................6
5.5 房屋分析 ........................................................6
5.6 个人信息查看 ....................................................6
5.7 密码修改 ........................................................6
5.8 用户管理 ........................................................6
6 系统测试 .............................................................6
6.1 测试目的 ........................................................6
6.2 测试内容 ........................................................6
6.3 测试总结 ........................................................6
结语 ..................................................................16
参考文献 ..............................................................17
致谢 ..................................................................18
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