Scholarship Spy is a web-based platform designed to help users find scholarships that match their profiles. It provides personalized recommendations based on the user's information and preferences, offering a centralized repository of scholarships from various sources.

• Objective: To create a single repository for scholarships and provide personalized scholarship recommendations.
• Languages/Technologies Used:
  • HTML, CSS, JavaScript
  • Python (Django)
  • SQLite
• Operating System: Windows 10

• Personalized Recommendations: Scholarships are recommended based on user profiles and personal statement.

  

• Search Functionality: Users can search for scholarships by country, field of study, and other criteria.

  

• Profile Management: Users can update their profiles and view their scholarship history.

  

• User Feedback: Users can submit feedback on their experience.

  

• Admin Panel: Admins can manage users and scholarship data.

  

The recommendation system in Scholarship Spy employs a content-based filtering approach to provide personalized scholarship recommendations based on users' personal statements. This process involves several key steps to ensure accurate and relevant suggestions.

1. Text Cleaning:

   • The input personal statement undergoes preprocessing to enhance data quality. This involves:
     • Converting all text to lowercase to maintain uniformity.
     • Removing non-alphanumeric characters and punctuation to focus on the content.
     • Tokenization, which breaks the text into individual words for analysis.
     • Filtering out common stopwords (e.g., "and," "the," "is") using the NLTK library to reduce noise in the data.

2. Word Embeddings:

   • The system utilizes pre-trained GloVe (Global Vectors for Word Representation) embeddings (glove.6B.50d.txt), which convert words into numerical vector representations. This allows the model to capture semantic meanings and relationships between words.
   • Each word in the user's cleaned personal statement is converted into a vector, and the average of these vectors creates a single vector representation for the entire statement.

3. Clustering:

   • The dataset is processed to form clusters of scholarships based on their textual features. The centroids of these clusters are stored in a file named cluster_centers.npy.
   • Each centroid represents a distinct group of scholarships, allowing the model to categorize scholarships based on similarities in their descriptions and titles.

4. Recommendation:

   • When a user inputs their personal statement, the system generates a vector representation of the statement using the techniques mentioned above.
   • It calculates the Euclidean distance between the user's statement vector and the centroids of the scholarship clusters.
   • The system identifies the n closest centroids (scholarships) to the user's vector and retrieves the corresponding scholarship titles, universities, and links from the dataset.

The code outputs the following information for each recommended scholarship:

• University: The name of the university offering the scholarship.
• Scholarship: The title of the scholarship.
• Link: A URL linking directly to the scholarship application page.

This recommendation system enhances the user experience by providing tailored scholarship opportunities that align closely with individual aspirations and qualifications.

1. Clone the repository:

   git clone https://github.com/Sherryyy00/Scholarship-Spy.git
   cd Scholarship-Spy
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up the database:

   python manage.py makemigrations
   python manage.py migrate
   

4. Run the development server:

   python manage.py runserver
   

• User: Register or log in to the platform to view and apply for scholarships.
• Admin: Log in to manage scholarships and initiate the recommendation crawler.

• Adding an online career counseling feature.
• Expanding the database to include more scholarship opportunities from various platforms.