基于Python的济南市房价数据分析展示系统的设计(论文15000字)
摘 要
现在有越来越多的房产中介，各家房产中介到处推广，人们想看房也是得到处奔波，考察、问价，非常的繁琐，在人力、精力、经济上都消耗比较多。发展到现在，数据方面也越来越完善，网络上的中介公司也越来越多，各家公司都掌握着各自的数据。对于目前的济南市房地产市场上，还是存在大量的房产中介，大多数人还是要通过中介来了解房屋信息。过程比较麻烦，既要进行预约，还要抽出时间去和中介沟通，这种模式对于一些上班族来说过于繁琐，对时间方面的要求也比较高，还要支付一定的中介服务费。对于购房用户来说找到一套自己觉得合适的房子，还是要不停的翻阅、搜索资料、找中介才可以，想要找到自己乐于接受的房子还是麻烦一点的。因此，本人决定设计一个关于济南市房价数据分析的展示系统。
本系统旨在利用所学习的知识和技术构建一个系统，通过网络爬虫技术实现济南房价数据的爬取与分析，对济南市在售楼盘数据进行爬取，再对其进行可视化操作以图表的形式呈现给用户，将爬取的真实房价数据与分析结果用直观的方式展示在网页上方便用户进行购房参考。济南市房价数据分析展示系统的开发基于Python语言，采用B/S结构进行前后端交互，通过这些完成数据的存储工作数据库使用MySQL数据库，MySQL在使用起来也是非常安全稳定的。为本系统的数据安全也做了保障。
本论文总结了数据爬取，数据清洗，数据分析，可视化展示的工作流程，同时包含业务需求的分析与设计构思。系统的后台管理通过Django框架为用户提供的简洁而强大的后台管理功能，可以轻松实现前后端的交互操作和后台的数据管理和维护。经过多次的测试，也证明本系统是一个操作简便快捷且功能实用的房价展示系统。
关键词：Python；网络爬虫；数据分析；Django；房价
ABSTRACT
Now there are more and more real estate agents, the various real estate agents everywhere to promote, people want to see the house is also to travel around, inspection, asking prices, very cumbersome, in manpower, energy, economic consumption is more. Up to now, the data aspect is also more and more perfect, the intermediary company on the network is also more and more, each company has its own data. For the current Jinan real estate market, there are still a large number of real estate agents, most people still need to understand housing information through intermediaries. The process is more cumbersome, both to make appointments, but also to take the time to communicate with the intermediary, this model for some office workers too much cumbersome, the time requirements are also relatively high, but also pay a certain intermediary service charge. For the purchase of users to find a house they feel suitable, or to keep looking through, search information, find intermediary can, want to find their own willing to accept the house or a trouble. Therefore, I decided to design a display system about the analysis of house price data in Jinan.
This system aims to use the knowledge and technology to build a system, through the web crawler technology to achieve Jinan house price data crawling and analysis, Jinan in the sale of real estate data crawling, and then visual operation in the form of charts presented to users, crawling real house price data and analysis results in an intuitive way to display on the web page for users to purchase reference. The development of Jinan house price data analysis and display system is based on the Python language, using the B/S structure to carry on the front-end interaction, through these complete data storage work database uses the MySQL database, MySQL starts to use come is also very safe and stable. For our data security.
This paper summarizes the work flow of data crawling, data cleaning, data analysis, visual display, including the analysis of business requirements and design ideas. Background management of the system through the Django framework to provide users with simple and powerful background management functions, can easily achieve the front and rear end of the interactive operation and background data management and maintenance. After many tests, it is also proved that the system is a simple, fast and functional house price display system.
Keywords: Python,web crawler, data analysis, Django, house price
目录
第1章 前言 - 1 -
1.1 研究背景 - 1 -
1.2 国内外研究现状 - 1 -
1.3 研究目的和意义 - 2 -
1.3.1 研究目的 - 2 -
1.3.2 研究意义 - 3 -
1.4 全文组织结构 - 3 -
第2章 相关技术介绍 - 4 -
2.1 Python语言简介 - 4 -
2.2 网络爬虫 - 4 -
2.3 Jupyter数据分析 - 4 -
2.4 jieba分词 - 4 -
第3章 系统需求分析 - 6 -
3.1 系统需求分析 - 6 -
3.2 可行性分析 - 6 -
3.2.1 技术可行性 - 6 -
3.2.2 经济可行性 - 6 -
3.2.3 法律可行性 - 7 -
3.3 系统安全性 - 7 -
第4章 系统设计 - 8 -
4.1 爬虫组件设计 - 8 -
4.1.1 爬虫模块 - 8 -
4.1.2 数据分析及可视化 - 8 -
4.2 网站搭建 - 9 -
4.3 系统数据库设计 - 9 -
第5章 系统实现 - 11 -
5.1 数据采集计划分析 - 11 -
5.1.1 数据来源 - 11 -
5.1.2 网页结构分析 - 11 -
5.2 数据获取 - 12 -
5.2.1 爬虫脚本实现 - 12 -
5.2.2 数据清洗 - 13 -
5.2.3 数据存储 - 15 -
5.3 后台管理模块 - 16 -
5.3.1 后台用户实现 - 16 -
5.3.2 房屋管理实现 - 19 -
5.4 用户主界面实现 - 22 -
5.5数据可视化展示 - 23 -
5.5.1 房屋用途可视化 - 23 -
5.5.2 整体房源信息可视化展示 - 23 -
5.5.3 部分房源信息可视化展示 - 25 -
第6章 系统测试 - 29 -
6.1 测试目的 - 29 -
6.2 测试方法 - 29 -
6.3 测试用例 - 29 -
6.4 测试结论 - 30 -
第7章 结论 - 31 -
附录 - 32 -
参考文献 - 34 -
致谢 - 35 - |
