Avoid ‘account limitation’: Bookmakers, in general, dislike people who are good at gambling (no matter how they win); that’s why some people who earn money in betting sites get limitations. This means that you’d only bet a maximum amount of money per event set by the bookmaker — $5, $10, etc. If you start getting money with surebets, you may be seen as a ‘good bettor.’ To appear like an average person under bookmakers’ radars, experience bettors do this:Use many bookmakers: Create accounts in different bookmakers and spread your bets around them. It’ll be harder to identify you as a smart player in this way.

Round your stake: Although in the example I gave, I used decimal numbers; you shouldn’t do this just because most people don’t bet like that. Avoid decimal numbers at any cost and do your best to round your stake to the nearest number of five. If the formula gives you $47, then bet either $45 or $50 instead.

Do not make unnecessary withdrawals from a bookmaker: After you get some money don’t try to cash out right away or withdraw big amounts at once, this may arouse suspicion.

Avoid betting on smaller markets: Not many people bet on less popular sports like table tennis or water polo, so making money here would be suspicious. Mix up small and large markets.

Remember that limited accounts can still withdraw money. Hopefully, with the tips above, you’ll avoid limitations for a good time.

Finally, these are some markets where surebets happen often:Hand to Hand (Win or lose sports like tennis, baseball, etc.)

Double chance

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Both teams to score

Asian Handicaps

Over / Under

Ecommerce:

Speaking generally, web scraping is the act of programatically gathering information from websites.

Lets say you want to find the price of an item on an eCommerce website. Normally, you will visit the website, search for the item and then scroll until you find the item.

But now let’s say you want to do this for thousands of items, perhaps across multiple websites. Maybe you are starting your own business and you want to keep track of the going prices for a variety of items. Manually checking prices on all of them is going to be very time consuming. To help you do this work faster, you can write a web scraper.

So how does this work?

When you visit a website with your browser, a server sends you some files, and the browser then renders them into pages that look nice and are easy for a human to use (hopefully). But you don’t need a browser to ask for those files. You can also write a computer program that requests those files. A web scraper (usually) will not render those files into pretty, usable pages, but instead load them into a format that makes them easy for a machine to read extremely quickly.

At that point, you can scan all of the files for all of the prices, and do whatever you like with them. You could average them and output a number. Or output the minimum and maximum prices. Or output the prices of the highest rated listings for whatever product you are curious about. Or feed the numbers to a graphing library that visualizes the data. Or put them into an Excel sheet. The possibilities are endless!

Some websites are hostile to this practice, however, and make you jump through hoops to prove that you are a real user and not a computer program. This makes sense, because too many webscrapers crawling all over your website can slow your site down or crash it. It’s also a way for competitors to get real time data about you, and you may want to make it more difficult for them to do so.

Build Your Own Dataset With Beautiful Soup

How to download Images from Google using Python?

Stock Market Screening and Analysis: Using Web Scraping, Neural Networks, and Regression Analysis

Stock markets tend to react very quickly to a variety of factors such as news, earnings reports, etc. While it may be prudent to develop trading strategies based on fundamental data, the rapid changes in the stock market are incredibly hard to predict and may not conform to the goals of more short term traders. This study aims to use data science as a means to both identify high potential stocks, as well as attempt to forecast future prices/price movement in an attempt to maximize an investor’s chances of success. Read more…

How to Generate Leads with Web Scraping in Python

Lead Generation is crucial for any business, without new leads to fill your sales funnel it’s impossible to acquire your customers and grow your company. Some businesses garner a lot of inbound interest so PPC or social media ads may be enough to generate leads. But what if your product or service is something that most people don’t specifically search for? This might be a new technology, a niche product or B2B services where very few people might use a search engine to find you. Read more ….

Responsible Web Scraping: Gathering Data Ethically and Legally

Find Relevant Top Hashtags Using Python

The good thing about this code is that you do not need to log into any Instagram account. Anyone can access publicly available posts on Instagram using the hashtag. For example if you want to see the posts for the hashtag #newyork, you can do so by using the following URL:

    https://www.instagram.com/explore/tags/newyork
    Read more ...

If You are Web Scraping Don’t Do These Things

• Don’t Hard Code Session Cookies:

So what should you do instead? Code your program to login and use the sessions to ensure your cookies get sent with every request!

s = requests.Session()
s.post("https://fakewebsite.com/login", login_data)for url in url_list:
    response = s.get(url)

It takes just a little extra work but it will save you time from having to constantly update the code.

• Don’t DOS Websites: Not that type of DOS. I mean Denial Of Service. If you don’t think you are doing this you should read this section because I’m about to blow your mind. Writing a for loop to access a website is a DOS.
• Don’t Copy and Paste Reusable Code
• Don’t Write Single Threaded Scrapers: Note that more threads doesn’t always mean better performance. This is because all these threads live on the same core. Confusing I know but this is something you will likely come across in testing.
• Don’t Use the Same Pattern for Scraping: Many websites will ban you if you do the same thing over and over again. There are some strategies you can use to circumvent this.

Web scraping doesn’t have to be hard. The best thing you can do for yourself is build good tools that you can reuse and your web scraping life will be much easier. If you need assistance with a web scraping project feel free to reach out to me on twitter as I do consulting.

How to Web Scrape in 8 Minutes

Worldometers

Wordometers is a website that provides data on live world statistics, and is the website we are going to scrape. Specifically we are going to scrape world population data that is in a table (seen below). Scraping data from a table is one of the most used forms of web scraping because most often then not the data we need in tables are not downloadable. So instead of getting the data manually we let a computer do it in mere seconds.

Beautiful Soup

Beautiful Soup is one of the most powerful web scraping libraries and in my opinion the easiest to learn which is why were going to use it.

Web scraping is a very powerful tool, where used in the right settings can make your life a whole lot easier

How to scrape an image from a website?

You can first extract images URLs (where the image is stored on the website) using Octoparse (a coding-free visual web scraping tool), and then download the images using image downloaders.

from collections import deque
from html.parser import HTMLParser
from threading import Lock
from urllib.error import HTTPError
from urllib.parse import urljoin
from urllib.request import urlopen
from concurrent.futures import ThreadPoolExecutor

NUMBER_OF_THREADS = 10
MAX_DEPTH = 3
TARGET_URL = r"https://www.reddit.com/r/Python/comments/v89fm9/is_it_possible_to_do_web_scraping_without_using/"


class MyHTMLParser(HTMLParser):
    def __init__(self, url=None):
        super().__init__()
        self.links = []
        self.url = url

    def handle_starttag(self, tag, attrs):
        if tag == "a":
            if "href" not in dict(attrs):
                return

            href = dict(attrs)["href"]

            # Convert relative links to absolute links
            if self.url:
                href = urljoin(self.url, href)

            self.links.append(href)


def get_html(url):
    """
    Get the content of a URL.
    """
    try:
        return urlopen(url).read().decode("utf-8")
    except HTTPError as e:
        return e.read().decode("utf-8")



def parse_html(html, url=None):
    """
    Parse the HTML of a web page.
    """
    parser = MyHTMLParser(url)
    parser.feed(html)
    return parser


def handle(url, depth, callback, lock):
    """
    Handle a web page.
    """
    html = get_html(url)
    links = parse_html(html, url).links

    # Lock when printing to the terminal to avoid two threads printing at the same time
    with lock:
        print(depth, url)

    for link in links:
        # Lock when adding to the queue to avoid two threads adding to the queue at the same time
        with lock:
            callback((depth + 1, link))


def crawl(url, max_depth):
    """
    Crawl a web page.
    """
    seen = set()
    crawling = deque([(0, url)])
    lock = Lock()
    with ThreadPoolExecutor(max_workers=NUMBER_OF_THREADS) as executor:
        tasks = []
        while crawling:
            depth, url = crawling.popleft()

            # If the depth is equal to the maximum depth, skip the URL (remember depth starts at 0)
            if depth == max_depth:
                continue

            # If the URL has already been seen, skip it
            if url in seen:
                continue
            seen.add(url)

            # Submit the task and add the task to the list of tasks
            tasks.append(executor.submit(handle, url, depth, crawling.append, lock))

            # If the queue is empty and we still have tasks, wait for them one by one until we have something to do
            while tasks and not crawling:
                tasks.pop().result()


if __name__ == "__main__":
    crawl(TARGET_URL, max_depth=MAX_DEPTH)

How to Make Money From Web Scraping – To conclude:

Web scraping can be a great way to make money online. There are a few different ways to go about it, but one of the most popular is to scrap web pages for sport arbitrage. This involves looking for discrepancies in odds between different bookmakers and then placing bets accordingly. Another way to make money from web scraping is to create a dataset with Beautiful Soup, a Python-based tool for extracting data from HTML and XML documents. This can be used to create a database of products for an ecommerce site, or to generate leads for a sales team. Finally, it’s also possible to scrape images from websites. This can be useful for creating memes or for other creative purposes. However, it’s important to follow the etiquette of web scraping and only scrape data that is publicly available. Otherwise, you could face legal action.

Web scraping can also be used to supplement your main income. In order to make money from web scraping, you will need to find a reliable source of data. One of the best places to find data for web scraping is Worldometers. This website provides a wealth of information on a variety of topics, and it is constantly updated with new data. Another great place to find data for web scraping is Beautiful Soup. Python is one of the best programming languages for web scraping, and it is relatively easy to learn. Once you have learned how to use Python for web scraping, you can start generating leads or collecting data for research purposes. Web scraping can be an extremely lucrative business, and it is a great way to make money online.

How to Make Money From Web Scraping? – Python Breaking News

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