Rentify

Rentify

Efficiency at Your Fingertips: Rentify Made It Easy.

Created on 6th August 2023

Rentify

Rentify

Efficiency at Your Fingertips: Rentify Made It Easy.

The problem Rentify solves

The renting process can often be a cumbersome and challenging task for both tenants and landlords. However, the implementation of a robust rent management system can significantly streamline and simplify the entire process. Our software aims to explore the benefits and features of a rent management system and how it can effectively solve common renting problems. By automating tasks, enhancing communication, and ensuring transparency, a rent management system can revolutionize the rental experience for both parties involved.

Our website is currently for landlords to effectively store tenant's information and their rental details.Landlord can view due month and can notify the tenant through email if the tenant has not paid the rent. He can also update tenant details.[Future Scope]: The tenant's can also access the website to view their due date and payment details and they can also pay through our server.

Every tenant can not acess online services and prefer payment through offline mode but lanlord may not be present to receive so we will be using face detection through machine learning to detect the tenet face ,the amount of money paid and automatically update our server.[Future Scope]:Building a proper hardware mechanism to detect face, count and store money , like cash deposit machine but with face detection

Challenges we ran into

In the course of our project, we encountered several challenges regarding UI/UX, database management, and face detection. Initially unfamiliar with UI/UX principles, we dedicated time to learn and successfully implemented user-friendly interfaces. However, we couldn't host our database in the cloud, but AWS proved to be a valuable solution, ensuring efficient database management. The primary obstacle was face detection under varying light conditions. To address this, we trained our model with diverse data, incorporating impurities such as different contrasts, brightness levels, shapes, and positions. This comprehensive approach significantly improved the model's accuracy and robustness in real-world scenarios. By overcoming these challenges through learning and innovation, we successfully delivered a robust and user-friendly solution.

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Replit

Our project is deployable in Replit
Replit

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