Machine learning for optimizing university campus room stock #

Value Proposition #

Traditional processes for assigning students to dorm rooms often lead to mismatched roommates, causing dissatisfaction and conflicts. The lack of consideration for personal preferences and interests in room assignments results in a stressful living environment, which can negatively impact students’ academic performance and overall well-being. Additionally, manual assignment methods are time-consuming and resource-intensive for campus administrators.

We utilize advanced machine learning algorithms and an intuitive web platform to optimize the allocation of dorm rooms based on students’ preferences and mutual interests. The system gathers data through questionnaires and processes it to provide personalized roommate recommendations.

Benefits to Users #

1. Student: Enhanced living experience with compatible roommates, leading to a more positive social environment and improved academic performance.
2. Administrator: Streamlined and automated room assignment process reduces administrative burden, saving time and resources.
3. Institution: Higher student satisfaction rates contribute to a better reputation and increased enrollment rates.

Differentiation #

We stand out by creating machine learning that offers personalized roommate recommendations. This personalized and data-driven approach significantly improves the accuracy and satisfaction of room assignments.

User Impact #

Students experience less stress and conflict with their roommates, leading to a more harmonious living environment. Administrators benefit from a reduced workload and more efficient room assignment processes. Overall, the institution enjoys higher student satisfaction and retention rates.

Use Cases #

• Scenario: A university’s housing department needs to assign rooms to incoming freshmen.

  Steps:

  1. Students fill out detailed questionnaires about their preferences and interests.
  2. System processes the data using machine learning algorithms.
  3. The system generates personalized roommate recommendations.
  4. Administrators review and finalize assignments.
  5. Students receive their roommate information and begin their university experience with compatible roommates.

  Benefit: Improved roommate compatibility, reduced administrative workload, and higher student satisfaction.

• Scenario: A summer camp for teenagers wants to ensure participants have compatible cabin mates.

  Steps:

  1. Campers fill out questionnaires prior to arrival.
  2. System analyzes the data to match campers with similar interests.
  3. The camp’s administration uses the system to organize cabin assignments.
  4. Campers are informed of their cabin mates before camp starts.
  5. Staff can monitor and adjust assignments as needed during the camp.

  Benefit: Enhanced camper experience and easier management for staff.

Lean Questionnaire #

1. What problem or need does your software project address?

   The system addresses the inefficiencies and dissatisfaction in traditional room assignment processes for dormitories, summer camps, conferences, and other shared living environments. It solves the problem of mismatched roommates by using data-driven methods to ensure compatibility based on preferences and interests, thus improving the overall living experience and reducing administrative burdens.

2. Who are your target users or customers?

   Our target customers are:

   • University administrators and housing departments
   • Organizers of summer camps and youth programs
   • Conference organizers
   • Managers of corporate training programs and other mass accommodation events

3. How will you validate and test your assumptions about the project?

   We will validate our assumptions through:

   • Pilot tests in real-world settings, such as universities and summer camps
   • Iterative testing and refining of our algorithms and user interfaces

4. What metrics will you use to measure the success of your project?

   • User satisfaction: Surveys and feedback from students and administrators
   • Efficiency improvements: Reduction in time and resources spent on room assignments
   • Compatibility rates: Decrease in roommate conflict reports
   • Adoption rates: Number of institutions and events adopting the system
   • Retention rates: Continued use of the system by institutions over time

5. How do you plan to iterate and pivot if necessary based on user feedback?

   • Continuous feedback loop: Regularly collect and analyze user feedback to identify areas for improvement
   • Scrum development: Implement changes and new features in short development cycles
   • User testing sessions: Conduct usability testing with real users to validate changes
   • Flexibility in features: Be open to adding or modifying features based on evolving user needs and feedback
   • Scalability considerations: Ensure the system can adapt to various scales and types of institutions or events

Leveraging AI, Open-Source, and Experts #

We plan to leverage machine learning algorithms to analyze student preferences and interests, providing personalized roommate recommendations. By utilizing open-source libraries, frameworks and AI, we can accelerate the development process and ensure the reliability and scalability of our system.

Defining the Vision for Your Project #

Overview #

Our project aims to revolutionize the room assignment process for shared living environments by leveraging advanced machine learning algorithms to match individuals based on their preferences and interests. The system will provide personalized roommate recommendations to enhance the living experience for students, campers, and participants in various shared accommodation settings.

Schematic Drawings #

Tech Stack #

Python, Pandas, NumPy, PyTorch, Faiss.