In this project we use the dataset of Zameen.com which is also added in repository by the name of prop.zip before using must unzip this file
- Project Report
- Project ppt
- Project Source Code
Go to Server Folder and run cmd.exe then type "python server.py" and then open the HTML file on any browser to use this project
The goal is to forecast the most cost-effective property pricing for real estate buyers based on their budgets and goals. These are the Parameters on which we will evaluate ourselves:
- Create an effective price prediction model
- Validate the model’s prediction accuracy
- Identify the important home price attributes which feed the model’s predictive power.
There are many elements that might be added to improve the system's acceptance. One of the primary future goals is to expand the estate database to include other cities, allowing users to look at more properties and make more informed decisions.
In today's real estate environment, storing and extracting such large amounts of data for one's personal needs has become difficult. Furthermore, the information gathered should be useful. The Data Mining Algorithm is used to its full potential by the system. The system makes the most efficient use of such information. By raising the accuracy of estate selection and lowering the risk of investing in an estate, the Data mining algorithm helps to satisfy clients.
So, our Aim is achieved as we have successfully ticked all our parameters as mentioned in our Aim Column. It is seen that circle rate is the most effective attribute in predicting the house price and that the Decision Tree is the most effective model for our Dataset with RMSE score of 0.9025658262899986.
