AI-Enhanced Roommate Compatibility Matching

Hivenue aims to enhance its platform by integrating an AI-driven matching system to improve roommate compatibility. The current system relies on basic user inputs and manual matching, which can lead to suboptimal pairings and user dissatisfaction. The goal of this project is to develop an AI algorithm that can analyze user profiles, preferences, and behavioral data to suggest highly compatible roommate matches. This will not only improve user satisfaction but also reduce conflicts and turnover rates. The project will involve researching existing AI matching algorithms, designing a custom solution tailored to Hivenue's needs, and implementing a prototype for testing and evaluation. Key points: - Enhance Hivenue's platform with AI-driven roommate matching. - Improve user satisfaction and reduce conflicts. - Research existing AI algorithms and design a custom solution. - Implement and test a prototype. Required technical kills : AI, machine learning, LLM Python NojeJS MongoDB

Admin CRM / Inventory Management System

Hivenue seeks to enhance its platform's functionality to streamline the management of apartment listings, rooms, tenants, and reservations. The current system requires improvements to allow the admin to efficiently maintain and update apartment information, manage specific rooms within apartments, and handle tenant movements. Additionally, the platform needs to support seamless updates to tenant information and modifications to existing reservations. The goal is to create a more intuitive and efficient system that reduces administrative overhead and improves user experience. This project will provide learners with the opportunity to apply their knowledge of database management, user interface design, and software development to create a robust solution that meets these needs.

Interactive Map: Search & View

The main objective of the Interactive Location Explorer project is to create a user-friendly, visually intuitive map-based platform that allows users to: Search and Discover Locations : Enable users to explore locations based on specific search criteria, such as availability, type, or features. Display Key Information : Present detailed information about locations directly on the map via interactive markers or pop-ups. Improve Navigation : Allow seamless navigation across geographic areas with zooming and panning features. Enhance User Engagement : Provide a visually appealing and engaging interface to improve the overall user experience. Facilitate Decision-Making : Help users make informed decisions quickly by integrating search results with map visuals for easy comparison. This project aims to combine functionality with design to make searching for locations more intuitive and efficient.

Security Audit and Code Analysis for Hivenue Platform

Hivenue, a startup specializing in coliving rental platforms, has developed hivenue.ca using MongoDB, NodeJS, and Angular, with a Python module for machine learning to power their matching service. The company seeks to ensure the security and robustness of their platform by conducting a comprehensive audit of their architecture and source code. The goal is to identify and rectify any potential security vulnerabilities. This project will provide learners with the opportunity to apply their knowledge of web development, cybersecurity, and code analysis to a real-world application, ensuring the platform's integrity and reliability. Key tasks include: - Conducting a thorough security audit of the existing platform. - Analyzing the source code for potential vulnerabilities. - Recommending and implementing necessary security fixes. - Documenting findings and improvements made.