30 Web Scraping Project Ideas for All Skill Levels
building real projects is the fastest way to learn web scraping. tutorials are fine for understanding the basics, but projects force you to deal with pagination, anti-bot protection, data storage, error handling, and all the messy reality of production scraping.
this list covers 30 project ideas organized by difficulty. each one includes what you’ll learn, a code starter, and notes on the challenges you’ll encounter.
Beginner Projects (1-10)
these projects use simple HTTP requests and basic HTML parsing. no JavaScript rendering or anti-bot bypass needed.
1. Book Price Tracker
scrape book prices from multiple online bookstores and track price changes over time.
what you’ll learn: basic requests, HTML parsing, CSV storage, scheduled scraping
import requests
from bs4 import BeautifulSoup
import csv
from datetime import datetime
def scrape_book_price(isbn):
"""scrape book price from a public book listing site"""
url = f"https://openlibrary.org/isbn/{isbn}"
response = requests.get(url, headers={
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
})
soup = BeautifulSoup(response.text, "html.parser")
title = soup.select_one("h1.work-title")
title_text = title.get_text(strip=True) if title else "unknown"
return {
"isbn": isbn,
"title": title_text,
"scraped_at": datetime.now().isoformat(),
}
def save_to_csv(data, filename="book_prices.csv"):
with open(filename, "a", newline="") as f:
writer = csv.DictWriter(f, fieldnames=data.keys())
if f.tell() == 0:
writer.writeheader()
writer.writerow(data)
# track a list of ISBNs
isbns = ["9781491985571", "9781593279509", "9781098145125"]
for isbn in isbns:
result = scrape_book_price(isbn)
save_to_csv(result)
print(f"tracked: {result['title']}")
2. Weather Data Collector
scrape weather data from multiple cities and build a historical dataset.
what you’ll learn: API-like endpoints, JSON parsing, data aggregation
import requests
import json
from datetime import datetime
def get_weather(city):
"""get weather data from wttr.in (public, no auth needed)"""
url = f"https://wttr.in/{city}?format=j1"
response = requests.get(url)
data = response.json()
current = data["current_condition"][0]
return {
"city": city,
"temp_c": current["temp_C"],
"humidity": current["humidity"],
"description": current["weatherDesc"][0]["value"],
"wind_speed_kmph": current["windspeedKmph"],
"timestamp": datetime.now().isoformat(),
}
cities = ["Singapore", "London", "New+York", "Tokyo", "Sydney"]
weather_data = [get_weather(city) for city in cities]
for w in weather_data:
print(f"{w['city']}: {w['temp_c']}C, {w['description']}")
3. Job Listing Aggregator
scrape job listings from public job boards and compile them into a searchable database.
what you’ll learn: pagination handling, data deduplication, structured data extraction
4. Wikipedia Data Extractor
extract structured data from Wikipedia tables (population data, country statistics, historical events).
what you’ll learn: table parsing, data cleaning, handling inconsistent HTML structures
import requests
from bs4 import BeautifulSoup
import pandas as pd
def scrape_wikipedia_table(url, table_index=0):
"""extract a table from a Wikipedia page"""
response = requests.get(url, headers={
"User-Agent": "Mozilla/5.0 (compatible; DataCollector/1.0)"
})
soup = BeautifulSoup(response.text, "html.parser")
tables = soup.select("table.wikitable")
if table_index >= len(tables):
return None
table = tables[table_index]
headers = [th.get_text(strip=True) for th in table.select("tr th")]
rows = []
for tr in table.select("tr")[1:]:
cells = [td.get_text(strip=True) for td in tr.select("td")]
if cells:
rows.append(cells)
df = pd.DataFrame(rows, columns=headers[:len(rows[0])] if headers else None)
return df
# example: scrape a Wikipedia data table
df = scrape_wikipedia_table(
"https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)"
)
if df is not None:
print(df.head(10))
5. Recipe Scraper
collect recipes from public recipe websites, extracting ingredients, instructions, and nutritional info.
what you’ll learn: structured data (JSON-LD), nested HTML parsing, data normalization
6. GitHub Repository Tracker
monitor public GitHub repositories for star counts, fork counts, and release information.
what you’ll learn: API consumption, rate limiting, data visualization
import requests
import time
def track_github_repos(repos):
"""track stats for a list of GitHub repositories"""
results = []
for repo in repos:
response = requests.get(
f"https://api.github.com/repos/{repo}",
headers={"Accept": "application/vnd.github.v3+json"}
)
if response.status_code == 200:
data = response.json()
results.append({
"repo": repo,
"stars": data["stargazers_count"],
"forks": data["forks_count"],
"open_issues": data["open_issues_count"],
"language": data["language"],
})
time.sleep(1) # respect rate limits
return results
repos = [
"playwright-community/playwright-python",
"scrapy/scrapy",
"psf/requests",
]
stats = track_github_repos(repos)
for s in stats:
print(f"{s['repo']}: {s['stars']} stars, {s['forks']} forks")
7. News Headline Monitor
scrape news headlines from multiple sources and track trending topics.
what you’ll learn: RSS parsing, text analysis, scheduling
8. Sports Scores Dashboard
scrape live sports scores and build a dashboard that updates automatically.
what you’ll learn: real-time data, WebSocket alternatives, caching
9. Academic Paper Metadata Collector
scrape paper titles, abstracts, and citation counts from Google Scholar or Semantic Scholar.
what you’ll learn: handling anti-bot (Scholar is aggressive), proxy rotation basics
10. Public Dataset Downloader
build a tool that discovers and downloads public datasets from government open data portals.
what you’ll learn: file downloads, metadata extraction, data catalog navigation
Intermediate Projects (11-20)
these projects require JavaScript rendering, proxy usage, or more complex data processing.
11. Price Comparison Engine
scrape product prices from multiple ecommerce sites and compare them in real-time.
what you’ll learn: multi-site scraping, price normalization, proxy rotation
from curl_cffi import requests
from bs4 import BeautifulSoup
import json
class PriceComparer:
def __init__(self, proxy=None):
self.session = requests.Session(impersonate="chrome124")
self.proxy = {"http": proxy, "https": proxy} if proxy else None
def scrape_site(self, url, price_selector, name_selector):
"""generic scraper for any product page"""
response = self.session.get(
url,
headers={
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
},
proxies=self.proxy,
)
soup = BeautifulSoup(response.text, "html.parser")
price_el = soup.select_one(price_selector)
name_el = soup.select_one(name_selector)
return {
"name": name_el.get_text(strip=True) if name_el else "unknown",
"price": price_el.get_text(strip=True) if price_el else "N/A",
"url": url,
}
12. Real Estate Data Pipeline
scrape property listings, extract structured data, and analyze market trends.
what you’ll learn: large-scale scraping, database storage (SQLite/PostgreSQL), data analysis
13. Social Media Profile Analyzer
collect public profile information and analyze posting patterns, follower growth, and content types.
what you’ll learn: rate limiting, ethical scraping boundaries, data anonymization
14. Review Sentiment Analyzer
scrape product reviews, run sentiment analysis, and identify common complaints and praises.
what you’ll learn: NLP integration, text preprocessing, visualization
from curl_cffi import requests
from bs4 import BeautifulSoup
from textblob import TextBlob
def analyze_reviews(reviews):
"""run sentiment analysis on scraped reviews"""
analyzed = []
for review in reviews:
blob = TextBlob(review["text"])
analyzed.append({
"text": review["text"][:100],
"sentiment": blob.sentiment.polarity,
"subjectivity": blob.sentiment.subjectivity,
"label": "positive" if blob.sentiment.polarity > 0 else
"negative" if blob.sentiment.polarity < 0 else "neutral",
})
return analyzed
# example output processing
sample_reviews = [
{"text": "this proxy service is incredibly fast and reliable"},
{"text": "terrible support, waited 3 days for a response"},
{"text": "average service, nothing special but works fine"},
]
results = analyze_reviews(sample_reviews)
for r in results:
print(f"[{r['label']}] ({r['sentiment']:.2f}) {r['text']}")
15. Flight Price Predictor
scrape historical flight prices and build a prediction model for optimal booking times.
what you’ll learn: time series data, ML integration, anti-bot bypass (airline sites are heavily protected)
16. SEO Rank Tracker
monitor keyword rankings across different search engines and locations.
what you’ll learn: SERP scraping, geo-targeted proxies, data dashboards
this is a great project to pair with the Google Search URL parameters reference to construct precise search URLs. you can also use different proxy locations to check rankings from various countries.
17. Cryptocurrency Market Monitor
scrape crypto prices, trading volumes, and social sentiment from multiple sources.
what you’ll learn: real-time data, API integration, multi-source aggregation
18. Patent Search Tool
scrape patent databases to track new filings in specific technology areas.
what you’ll learn: complex search interfaces, PDF parsing, classification systems
19. Supply Chain Price Tracker
monitor raw material prices, shipping rates, and supplier pricing across markets.
what you’ll learn: B2B data collection, behind-login scraping, data normalization
20. Competitive Intelligence Dashboard
track competitor websites for changes in pricing, product offerings, and marketing messages.
what you’ll learn: change detection, diff algorithms, alerting systems
Advanced Projects (21-30)
these projects involve serious infrastructure, anti-bot bypass, or large-scale data processing.
21. Distributed Scraping Framework
build a multi-node scraping system that distributes work across servers.
what you’ll learn: message queues (Redis/RabbitMQ), task distribution, fault tolerance
import redis
import json
from curl_cffi import requests
import time
class DistributedScraper:
def __init__(self, redis_url="redis://localhost:6379"):
self.redis = redis.from_url(redis_url)
self.session = requests.Session(impersonate="chrome124")
def add_urls(self, urls, queue="scrape_queue"):
"""add URLs to the work queue"""
for url in urls:
self.redis.rpush(queue, json.dumps({"url": url, "retries": 0}))
print(f"added {len(urls)} URLs to queue")
def worker(self, proxy, queue="scrape_queue", results_queue="results"):
"""worker process that pulls URLs and scrapes them"""
proxy_dict = {"http": proxy, "https": proxy}
while True:
task_json = self.redis.lpop(queue)
if not task_json:
time.sleep(1)
continue
task = json.loads(task_json)
url = task["url"]
try:
response = self.session.get(
url,
proxies=proxy_dict,
timeout=30,
headers={
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
}
)
result = {
"url": url,
"status": response.status_code,
"content_length": len(response.text),
}
self.redis.rpush(results_queue, json.dumps(result))
except Exception as e:
if task["retries"] < 3:
task["retries"] += 1
self.redis.rpush(queue, json.dumps(task))
time.sleep(2)
22. Anti-Bot Bypass Testing Suite
build a tool that tests different scraping configurations against various anti-bot solutions.
what you’ll learn: anti-bot detection mechanisms, fingerprinting, TLS analysis
you can use the Browser Fingerprint Tester on dataresearchtools.com as a baseline to verify your scraper’s fingerprint before testing against live targets.
23. Web Archive Builder
create your own web archive that periodically snapshots websites and stores historical versions.
what you’ll learn: content deduplication, storage optimization, incremental crawling
24. Product Data Enrichment API
build an API that takes a product URL and returns enriched data (price history, reviews summary, competitor pricing).
what you’ll learn: API development (FastAPI), caching, concurrent scraping
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from curl_cffi import requests as curl_requests
import asyncio
app = FastAPI()
class ProductRequest(BaseModel):
url: str
include_reviews: bool = False
class ProductResponse(BaseModel):
title: str
price: str
currency: str
availability: str
reviews_count: int = 0
@app.post("/api/v1/enrich", response_model=ProductResponse)
async def enrich_product(req: ProductRequest):
"""scrape and enrich product data from a URL"""
session = curl_requests.Session(impersonate="chrome124")
response = session.get(req.url, timeout=30)
if response.status_code != 200:
raise HTTPException(status_code=502, detail="failed to fetch product page")
# parse product data (simplified)
from bs4 import BeautifulSoup
soup = BeautifulSoup(response.text, "html.parser")
return ProductResponse(
title=soup.title.get_text() if soup.title else "unknown",
price="0.00",
currency="USD",
availability="unknown",
)
25. SERP Feature Extractor
scrape Google SERPs and extract all SERP features (featured snippets, People Also Ask, knowledge panels, local packs).
what you’ll learn: complex HTML parsing, SERP analysis, geo-targeted scraping
26. Multi-Language Content Scraper
scrape content across different languages and regions, handling character encoding, locale differences, and translation.
what you’ll learn: internationalization, encoding handling, geo-proxies
27. Dark Web Monitor (Legal)
monitor .onion sites for public threat intelligence data (leaked credentials, brand mentions).
what you’ll learn: Tor integration, security considerations, ethical boundaries
28. AI Training Data Pipeline
build a pipeline that scrapes, cleans, and formats web data for training machine learning models.
what you’ll learn: data quality, deduplication, format conversion, large-scale processing
29. Browser Automation Testing Platform
create a platform that tests how different browser configurations perform against anti-bot systems.
what you’ll learn: browser fingerprinting, automation detection, stealth techniques
30. Full-Stack Price Intelligence Platform
combine scraping, storage, analysis, and visualization into a complete price intelligence product.
what you’ll learn: end-to-end system design, scalability, production deployment
# architecture overview for a price intelligence platform
"""
components:
1. scraper workers (distributed across regions using proxies)
2. message queue (Redis or RabbitMQ for task distribution)
3. database (PostgreSQL for structured data, S3 for raw HTML)
4. API layer (FastAPI for serving data)
5. dashboard (Streamlit or React for visualization)
6. scheduler (APScheduler or cron for periodic scraping)
7. alerting (email/Slack notifications for price changes)
"""
# simplified scheduler example
from apscheduler.schedulers.blocking import BlockingScheduler
scheduler = BlockingScheduler()
@scheduler.scheduled_job("interval", hours=6)
def run_price_scrape():
"""scrape prices every 6 hours"""
# load target URLs from database
# distribute across worker pool
# store results
# check for price alerts
pass
# scheduler.start()
Tips for Choosing Your Project
- start with something you actually need – scraping projects you use yourself stay motivated
- pick a site without heavy anti-bot for your first project – build skills before fighting DataDome
- add proxies early – even beginner projects benefit from proxy rotation to avoid rate limits. use the Proxy Cost Calculator to find affordable options for learning
- store raw HTML – always save the raw response alongside your parsed data. when your parser breaks (and it will), you can re-parse without re-scraping
- build incrementally – start with a single page scraper, then add pagination, then error handling, then proxy rotation
Summary
the 30 projects above cover the full spectrum of web scraping skills. beginners should start with projects 1-10 using simple HTTP requests and BeautifulSoup. intermediate developers can tackle projects 11-20 which introduce proxy rotation, JavaScript rendering, and database storage. advanced developers should challenge themselves with projects 21-30 which require distributed systems, anti-bot bypass, and production-grade architecture.
the best web scraping portfolio demonstrates not just that you can extract data, but that you can build reliable, maintainable systems that handle the messy reality of the web.