Property tracker

Project Title

Property tracker

Project Area of Specialization Artificial IntelligenceProject Summary

We are hoping to create a property website and mobile app, where buyers can visit and browse property from around the city but one issue buyers face when purchasing property is to track the price of the property, usually buyers have to go to an estate agent to get the price of the property but we can solve this problem with the help of our website where a buyer can go to a location where a property is set up for purchase buyer can take one picture of the property and upload it to the website where the different searching algorithms will search the database for the location and it give an estimated price of the house to the buyer, it is also possible for the buyer to input information along with the image of the property like the location and address if possible this will be better for searching and will provide better output to the buyer once the property is searched up through image buyer can view its further details such number of rooms in the house, size of the land, address of the house, estimated price of the house. It will be necessary for buyers to register on the website by creating an account and needing to login to view house details.

Project Objectives

Our objective for this project is to create a website that will make it easier for people to find houses and know how much they are going to cost if purchased and what would be their estimated price with the ability of image searching.

Project Implementation Method

The website and mobile app are connected to the backend by using Framework called python flask. The flask provides a local IP address or API through which the website is connected to. When user enter details about property on website, the IP address or API provided by flask is used to pass data to flask. And the website is built on react while the mobile is created with capacitor.js

Benefits of the Project

one issue buyers face when purchasing property is to track the current and future price of the property. Usually buyers have to go to an estate agent to get the current price of the property without an estimated price of the future but we can solve this problem with our project.

Technical Details of Final Deliverable

In python, the model is trained by using decision linear regression by the help of this library numpy, matplotlib, pandas, sklearn. The output is given as a predicted price while for image search we use vgg16 architecture of CNN and trained it with imagenet dataset and used it to find the features of our images from our dataset ojce that is done we used euclidean distance to find the images similar to the one uploaded by user

Final Deliverable of the Project Software SystemCore Industry OthersOther Industries IT , Finance Core Technology Artificial Intelligence(AI)Other Technologies OthersSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	40000
upgradation of laptop(SSD)	Equipment	1	10000	10000
Software license	Equipment	1	10000	10000
upgradation of laptop(RAM))	Equipment	2	10000	20000