Equalify

By prioritizing DEI, we revolutionize access to educational funding and connect underrepresented communities with financial opportunities they might otherwise miss.

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Equalify: Empowering Underrepresented Communities with Scholarship Opportunities

Inspiration

We were inspired by the challenges faced by underrepresented students in accessing scholarship information. While existing portals like ASU's scholarship database provide valuable resources, we identified opportunities to enhance the search experience and make it more inclusive. Our goal was to create a platform that not only simplifies the scholarship search process but also actively promotes diversity, equity, and inclusion in higher education.

What it does

Equalify is a sophisticated search engine designed specifically for underrepresented communities. Key features include:

  1. Personalized Filtering: Users can fine-tune their search based on various criteria such as ethnicity, gender, major, LGBTQ+ status, and specific underrepresented groups (e.g., women in STEM, students with disabilities, first-generation college students).

  2. Interactive Dashboard: Our platform offers an intuitive interface for applying to scholarships, saving favorites, and tracking application progress.

  3. Data Visualization: We've implemented an advanced clustering algorithm that graphically represents scholarship categories. This visualization helps applicants identify closely related opportunities and uncover patterns in the scholarship landscape.

  4. Comprehensive Database: Our system aggregates scholarships from multiple sources, with a focus on Arizona and ASU opportunities, providing a centralized hub for diverse funding options.

How we built it

Equalify leverages a modern tech stack to deliver a robust and scalable solution:

  1. Data Collection: We employed BeautifulSoup for web scraping, overcoming challenges like DUO authentication and complex website structures.

  2. Database Management: MongoDB serves as our backend, offering flexibility for storing and querying diverse scholarship data.

  3. Data Augmentation: We utilized generative AI techniques to enhance and standardize our dataset, ensuring consistency and enriching scholarship descriptions.

  4. Frontend Development: Streamlit powers our user-friendly interface, enabling rapid development and deployment.

  5. Data Analysis and Visualization: We implemented clustering algorithms using scikit-learn, numpy, and pandas, with seaborn for creating insightful visualizations.

Challenges we overcame

  1. Advanced Web Scraping: We developed sophisticated algorithms to navigate DUO authentication and traverse complex website structures, including implementing depth-first search for hierarchical data extraction.

  2. Database Integration: We resolved intermittent connectivity issues between our MongoDB backend and Streamlit frontend, ensuring seamless data flow.

  3. Data Standardization: Unifying scholarship information from diverse sources required careful data cleaning and augmentation techniques.

  4. Ethical AI Implementation: We thoughtfully integrated AI to enhance our dataset while maintaining data integrity and avoiding bias.

Accomplishments that we're proud of

  1. Inclusive Design: We've created a platform that actively promotes diversity in scholarship access, aligning with DEI principles.

  2. Technical Innovation: Our implementation of clustering algorithms for scholarship visualization sets us apart from traditional search engines.

  3. Scalable Architecture: Our solution is built to handle a growing database and user base, with potential for expansion beyond Arizona.

  4. Data-Driven Insights: By leveraging MongoDB and advanced analytics, we're providing valuable insights into scholarship trends and opportunities.

What we learned

  1. Full-Stack Development: We gained hands-on experience in end-to-end application development, from data collection to user interface design.

  2. AI Integration: We explored practical applications of generative AI for data augmentation and gained insights into its potential and limitations.

  3. Advanced Data Analysis: Implementing clustering algorithms deepened our understanding of machine learning techniques and their real-world applications.

What's next for Equalify

  1. Expanded Dataset: We aim to refine our web scraping algorithms to increase our database even more entries, providing even more opportunities for students.

  2. Geographic Expansion: While currently focused on Arizona schools, we plan to extend our coverage to institutions nationwide.

  3. Personalized Recommendations: Implement a machine learning-based recommendation system to suggest scholarships based on user profiles and application history.

  4. Community Features: Introduce forums and mentorship connections to foster a supportive community for underrepresented students.

  5. Mobile App Development: Create a mobile application to increase accessibility and provide real-time notifications for new opportunities.

By focusing on data-driven insights, ethical AI implementation, and a strong commitment to DEI principles, Equalify is an innovative solution in the edtech space, addressing critical needs in higher education accessibility.

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