Building a Scholarship Matcher: Scraping 500+ Award Databases

Creating a scholarship matcher using web scraping and natural language processing (NLP) is indeed an effective way to help students discover potential funding opportunities that might otherwise go unnoticed. Here’s a detailed breakdown of how you can implement this system, including the necessary steps for data collection, preprocessing, matching, and maintaining the freshness of your database.

Step 1: Data Collection via Web Scraping

Identify Scholarship Sources

First, identify major scholarship databases to scrape from:

python
 
1SCHOLARSHIP_SOURCES = [
2    "https://scholarships.com",
3    "https://fastweb.com/scholarships",
4    # Add more sources here
5]

Scrape Scholarships Data

Use libraries like requests, BeautifulSoup, or Scrapy to scrape data from these websites. Here’s an example of scraping a single source:

python
 
1import requests
2from bs4 import BeautifulSoup
3
4def scrape_source(url):
5    response = requests.get(url)
6    soup = BeautifulSoup(response.text, 'html.parser')
7    
8    scholarships = []
9    for scholarship in soup.find_all('div', class_='scholarship-item'):
10        name = scholarship.find('h2').text.strip()
11        description = scholarship.find('
12
13[Read the full article at DEV Community](https://dev.to/agenthustler/building-a-scholarship-matcher-scraping-500-award-databases-4e13)
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