Real Estate Management System

Today I talk about another Database Research Project. Here goes:

Real Estate Management System can be an ERP(enterprise resource planning) software which contains management and accounting of a company. This software is a web based software which can run on internet and also intranet. The management part of the software can contain all the information about the main features of a real estate company. It contains five main parts of a real estate. They are purchase, engineering, sales and marketing and h-r(human resource management). Another part contains all the information of accounting. The latter part contains all the account information and all the costs and revenues of the company. An administrator account is also added to this company which can be accessed only by the administrator. Only admin can have access to the administrator account and create multiple user roles. To build this software initially all the requirement activities of the company should be sorted out. First  an ER (entity relationship) diagram based on entities and relationships can be designed. Then this can be mapped to relational models for both accounting and management and the design in SQL Server can be developed. Next the front end interface of the software using software tools such as, Asp.net framework 3.5 using C#, HTML, Ajax control toolkit and CSS (Cascading Style Sheet) can be implemented. Try to make the interface as versatile and user friendly as possible. Additionally, it can be further developed for future scope adding more features according to further needs and requirements of a real estate company and which can hence provide much better and more convenient service.

Electronic Human Resources Management (E-HRM) System Project

I have been discussing Data Mining Theses for several weeks now. Now let’s go through an interesting Database Project.

Electronic Human Resources Management System (E-HRM System) is a web-based solution that takes advantage of the latest web application technology to deliver an online real-time human resource management solution. It is comprehensive but easy to use, feature-rich yet flexible enough to be tailored to one’s specific needs.  The software should contain many modules-Admin module, Performance module, Timesheet module, Department module, Benefit module, Training module, Leave module, Recruitment module and Overtime module. The system should have a centralized admin who can access all information of all departments. And the department-wise admin can only access all information of his department. Here centralized admin can add, delete, and edit any information of any module, and the departmemt-wise admin can do so for his own department only. An employee can view his information about performance, salary, benefit, overtime etc. This is a large scale project to maintain the human resources of a company efficiently. A company may need new employees or job applicants. Here the software can provide a great facility that a job seeker can upload his/her resume directly from the company’s website. The updated resume goes straight to the admin and he can check the resume. The software should be a user-friendly one and it can be updated or modified according to company requirements.

Data Mining by Classification and Prediction

A bank loans officer needs analysis of her data in order to learn which loan applicants are "safe" and which are "risky" for the bank. A marketing manager at a company that needs data analysis to help guess whether a customer with a given profile will buy a new computer. A medical researcher wants to analyze breast cancer data in order to predict which one of three specific treatments a patient should receive. In each of these examples, the data analysis task is classification, where a model or classifier is constructed to predict categorical labels, such as "safe" or "risky" for the loan application data; "yes" or "no" for the marketing data; or "treatment A," "treatment B," or "treatment C" for the medical data. These categories can be represented by discrete values, where the ordering among values has no meaning. For example, the values 1, 2, and 3 may be used to represent treatments A, B, and C, where there is no ordering implied among this group of treatment regimes.


Suppose that the marketing manager would like to predict how much a given customer will spend during a sale. This data analysis task is an example of numeric prediction, where the model constructed predicts a continuous-valued function, or ordered value, as opposed to a categorical label. This model is a predictor.

Classification- a two step process

Model construction: describing a set of predetermined classes

·         Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute.

·        The set of tuples used for model construction is a training set.

·         The model is represented as classification rules, decision tress, or mathematical formulae.

Model Prediction: for classifying future or unknown objects

• Estimate Accuracy of the model
  
  
  ·         The unknown label of test sample is compared with the classified result from the model.
  ·         Accuracy rate is the percentage of test set samples that are correctly classified by the model.
  ·         Test set is independent of training set, otherwise over-fitting will occur.
  
  
  If the accuracy is acceptable, use the model to classify data tuples whose class labels are not known.
  
  
  Comparison of Data Mining Algorithms Based on Classification and Prediction
  
  
  Classification and Prediction can be applied as explained above to data mining algorithms like Decision Tree Induction. And its accuracy can be compared to a classification algorithm like Naive Bayesian Classification and an analysis between the two may be chalked out.