Design And Implementation Of House Rental System Based On Web

With the government's vigorous construction of Wuhan,the housing price in Wuhan has gradually increased.Although there is a gap compared with the first-tier cities such as Beijing,Shanghai,Guangzhou,and Shenzhen,it is still difficult for people from other places to come to Wuhan to work or study.In addition,Wuhan is known as "having millions of college students",and there are many people who choose to stay in Wuhan to work,so the rental market in Wuhan is also hot.With the rapid development of modern network technology,the housing rental industry is also developing in the direction of informationization.For those who have rental needs,online can reduce the loss of time and money.At present,there is still no uniform standard for housing rent,and most users rent houses through various websites,but they don't know much about Wuhan housing rent.Therefore,this system is based on web development and combined with data mining to analyze the influencing factors of housing rent,which is of great positive significance for users to choose suitable houses and promote the development of housing market.The development idea of the system is firstly to analyze the requirements,analyze the functions of different users and draw relevant use case diagrams to illustrate them,then design the relationship between tables of the database and consider the contents of the table structure.Then design the completion of the system as a whole,and analyze the interrelation between functional modules.In this system,SSM framework is used to develop background function modules,jsp technology is used for pages,data is stored in mysql,and modules related to crawler are developed in python.The users of this system are divided into tenants,owners and administrators.Tenants can browse the house information online.If they apply for a house to be rented,they can communicate with the owners further.If appropriate,you can sign a contract online as a voucher.Owners upload their own house information,agree or reject other people's applications for house viewing,and sign contracts with tenants.Administrators manage the details of users and houses.In order to provide a basis for users to discuss rent,the system additionally developed a rent prediction function,and the data came from online crawlers.Then test the system to check the completion of each business module.Considering the needs of different users,this system designs the corresponding functions,and realizes the rent prediction with the help of machine learning,which helps tenants better choose the houses with the right price,and at the same time,makes the owners choose the more suitable rental price.At present,the system has been tested,and the result of model fitting is good,which can help users to have a better experience in renting or renting a house.