#Python scraping Twitter

[actowizsolutions0]

News Extract: Unlocking the Power of Media Data Collection

In today's fast-paced digital world, staying updated with the latest news is crucial. Whether you're a journalist, researcher, or business owner, having access to real-time media data can give you an edge. This is where news extract solutions come into play, enabling efficient web scraping of news sources for insightful analysis.

Why Extracting News Data Matters

News scraping allows businesses and individuals to automate the collection of news articles, headlines, and updates from multiple sources. This information is essential for:

Market Research: Understanding trends and shifts in the industry.

Competitor Analysis: Monitoring competitors’ media presence.

Brand Reputation Management: Keeping track of mentions across news sites.

Sentiment Analysis: Analyzing public opinion on key topics.

By leveraging news extract techniques, businesses can access and process large volumes of news data in real-time.

How News Scraping Works

Web scraping involves using automated tools to gather and structure information from online sources. A reliable news extraction service ensures data accuracy and freshness by:

Extracting news articles, titles, and timestamps.

Categorizing content based on topics, keywords, and sentiment.

Providing real-time or scheduled updates for seamless integration into reports.

The Best Tools for News Extracting

Various scraping solutions can help extract news efficiently, including custom-built scrapers and APIs. For instance, businesses looking for tailored solutions can benefit from web scraping services India to fetch region-specific media data.

Expanding Your Data Collection Horizons

Beyond news extraction, companies often need data from other platforms. Here are some additional scraping solutions:

Python scraping Twitter: Extract real-time tweets based on location and keywords.

Amazon reviews scraping: Gather customer feedback for product insights.

Flipkart scraper: Automate data collection from India's leading eCommerce platform.

Conclusion

Staying ahead in today’s digital landscape requires timely access to media data. A robust news extract solution helps businesses and researchers make data-driven decisions effortlessly. If you're looking for reliable news scraping services, explore Actowiz Solutions for customized web scraping solutions that fit your needs.

[dykesbat]

pleaseee help me if youre familiar w python and webscraping.

im trying to webscrape this site w infinite scrolling (think like. twitter.) but parsehub only goes up to 200 pages in the free version. is there any possible way to start scraping the next 200 pages?

literally dont even know how to search this up bc i dont do this ever 😭😭😭😭😭😭😭

[darkexacademy]

Advanced Crypto Investment Analysis Strategies for 2025 – A Deep Dive by Academy Darkex

As we step into 2025, the landscape of digital assets continues to mature, demanding more sophisticated strategies from investors. Gone are the days when a simple buy-and-hold method could yield exponential gains. Today, success in the market hinges on deep, data-driven insights and strategic execution. At Academy Darkex, we believe that empowering investors with advanced crypto investment analysis tools and methodologies is the key to long-term success in this evolving space.

In this blog, we explore cutting-edge strategies in crypto investment analysis tailored for the high-stakes environment of 2025.

1. On-Chain Data Analysis – Decoding Blockchain Behavior

One of the most potent trends redefining crypto investment analysis is the use of on-chain metrics. In 2025, investors are leveraging tools that analyze wallet movements, miner behavior, staking trends, and whale activity to predict market sentiment. Platforms like Glassnode, Nansen, and IntoTheBlock are no longer optional—they're essential.

Academy Darkex Insight: Monitoring wallet outflows from centralized exchanges often precedes bullish price action. Our students learn how to track these flows in real-time for predictive trading decisions.

2. AI-Powered Predictive Models

Machine learning and AI have moved from buzzwords to foundational tools in crypto investment analysis. Predictive models analyze historical price movements, news sentiment, and macroeconomic factors to forecast market trends with impressive accuracy.

Strategy Highlight: Academy Darkex trains users on creating custom AI models using Python and TensorFlow to generate buy/sell signals based on dynamic datasets.

3. Cross-Market Correlation Analysis

Understanding how crypto interacts with traditional markets (stocks, commodities, interest rates) has become vital. In 2025, advanced investors apply correlation matrices and regression models to assess how events like Fed rate hikes or oil price swings affect crypto volatility.

Pro Tip: At Academy Darkex, we provide weekly macro-crypto correlation reports to help our members spot trends before they hit the mainstream.

4. DeFi Metrics & Protocol Health Indicators

With the explosion of DeFi, analyzing protocol fundamentals—such as total value locked (TVL), yield sustainability, and governance activity—has become integral to any robust crypto investment analysis framework.

Academy Darkex Masterclass: Learn how to identify protocol stress signals, such as liquidity drains or governance disputes, before they impact token value.

5. Sentiment and Narrative Tracking

In 2025, crypto is as much about psychology as it is about math. Advanced sentiment analysis tools now scrape Reddit, X (formerly Twitter), Discord, and news sites to detect emerging narratives and potential FOMO or FUD events.

Edge for Investors: Academy Darkex equips traders with dashboards that integrate sentiment scores directly into their investment models for early detection of trend reversals.

Final Thoughts

Crypto investment analysis is no longer a niche skill—it’s a necessity. At Academy Darkex, we stand at the forefront of crypto education, offering our community the strategies, tools, and training required to thrive in 2025’s complex digital asset market.

Whether you're a seasoned investor or an ambitious beginner, now is the time to level up your analytical game. Dive into the future of finance with Academy Darkex and make every data point work for your portfolio.

[educationtech]

Beyond the Books: Real-World Coding Projects for Aspiring Developers

One of the best colleges in Jaipur, which is Arya College of Engineering & I.T. They transitioning from theoretical learning to hands-on coding is a crucial step in a computer science education. Real-world projects bridge this gap, enabling students to apply classroom concepts, build portfolios, and develop industry-ready skills. Here are impactful project ideas across various domains that every computer science student should consider:

Web Development

Personal Portfolio Website: Design and deploy a website to showcase your skills, projects, and resume. This project teaches HTML, CSS, JavaScript, and optionally frameworks like React or Bootstrap, and helps you understand web hosting and deployment.

E-Commerce Platform: Build a basic online store with product listings, shopping carts, and payment integration. This project introduces backend development, database management, and user authentication.

Mobile App Development

Recipe Finder App: Develop a mobile app that lets users search for recipes based on ingredients they have. This project covers UI/UX design, API integration, and mobile programming languages like Java (Android) or Swift (iOS).

Personal Finance Tracker: Create an app to help users manage expenses, budgets, and savings, integrating features like OCR for receipt scanning.

Data Science and Analytics

Social Media Trends Analysis Tool: Analyze data from platforms like Twitter or Instagram to identify trends and visualize user behavior. This project involves data scraping, natural language processing, and data visualization.

Stock Market Prediction Tool: Use historical stock data and machine learning algorithms to predict future trends, applying regression, classification, and data visualization techniques.

Artificial Intelligence and Machine Learning

Face Detection System: Implement a system that recognizes faces in images or video streams using OpenCV and Python. This project explores computer vision and deep learning.

Spam Filtering: Build a model to classify messages as spam or not using natural language processing and machine learning.

Cybersecurity

Virtual Private Network (VPN): Develop a simple VPN to understand network protocols and encryption. This project enhances your knowledge of cybersecurity fundamentals and system administration.

Intrusion Detection System (IDS): Create a tool to monitor network traffic and detect suspicious activities, requiring network programming and data analysis skills.

Collaborative and Cloud-Based Applications

Real-Time Collaborative Code Editor: Build a web-based editor where multiple users can code together in real time, using technologies like WebSocket, React, Node.js, and MongoDB. This project demonstrates real-time synchronization and operational transformation.

IoT and Automation

Smart Home Automation System: Design a system to control home devices (lights, thermostats, cameras) remotely, integrating hardware, software, and cloud services.

Attendance System with Facial Recognition: Automate attendance tracking using facial recognition and deploy it with hardware like Raspberry Pi.

Other Noteworthy Projects

Chatbots: Develop conversational agents for customer support or entertainment, leveraging natural language processing and AI.

Weather Forecasting App: Create a user-friendly app displaying real-time weather data and forecasts, using APIs and data visualization.

Game Development: Build a simple 2D or 3D game using Unity or Unreal Engine to combine programming with creativity.

Tips for Maximizing Project Impact

Align With Interests: Choose projects that resonate with your career goals or personal passions for sustained motivation.

Emphasize Teamwork: Collaborate with peers to enhance communication and project management skills.

Focus on Real-World Problems: Address genuine challenges to make your projects more relevant and impressive to employers.

Document and Present: Maintain clear documentation and present your work effectively to demonstrate professionalism and technical depth.

Conclusion

Engaging in real-world projects is the cornerstone of a robust computer science education. These experiences not only reinforce theoretical knowledge but also cultivate practical abilities, creativity, and confidence, preparing students for the demands of the tech industry.

[educationmore]

Unlock SEO & Automation with Python

In today’s fast-paced digital world, marketers are under constant pressure to deliver faster results, better insights, and smarter strategies. With automation becoming a cornerstone of digital marketing, Python has emerged as one of the most powerful tools for marketers who want to stay ahead of the curve.

Whether you’re tracking SEO performance, automating repetitive tasks, or analyzing large datasets, Python offers unmatched flexibility and speed. If you're still relying solely on traditional marketing platforms, it's time to step up — because Python isn't just for developers anymore.

Why Python Is a Game-Changer for Digital Marketers

Python’s growing popularity lies in its simplicity and versatility. It's easy to learn, open-source, and supports countless libraries that cater directly to marketing needs. From scraping websites for keyword data to automating Google Analytics reports, Python allows marketers to save time and make data-driven decisions faster than ever.

One key benefit is how Python handles SEO tasks. Imagine being able to monitor thousands of keywords, track competitors, and audit websites in minutes — all without manually clicking through endless tools. Libraries like BeautifulSoup, Scrapy, and Pandas allow marketers to extract, clean, and analyze SEO data at scale. This makes it easier to identify opportunities, fix issues, and outrank competitors efficiently.

Automating the Routine, Empowering the Creative

Repetitive tasks eat into a marketer's most valuable resource: time. Python helps eliminate the grunt work. Need to schedule social media posts, generate performance reports, or pull ad data across platforms? With just a few lines of code, Python can automate these tasks while you focus on creativity and strategy.

In Dehradun, a growing hub for tech and education, professionals are recognizing this trend. Enrolling in a Python Course in Dehradun not only boosts your marketing skill set but also opens up new career opportunities in analytics, SEO, and marketing automation. Local training programs often offer real-world marketing projects to ensure you gain hands-on experience with tools like Jupyter, APIs, and web scrapers — critical assets in the digital marketing toolkit.

Real-World Marketing Use Cases

Python's role in marketing isn’t just theoretical — it’s practical. Here are a few real-world scenarios where marketers are already using

Python to their advantage:

Content Optimization: Automate keyword research and content gap analysis to improve your blog and web copy.

Email Campaign Analysis: Analyze open rates, click-throughs, and conversions to fine-tune your email strategies.

Ad Spend Optimization: Pull and compare performance data from Facebook Ads, Google Ads, and LinkedIn to make smarter budget decisions.

Social Listening: Monitor brand mentions or trends across Twitter and Reddit to stay responsive and relevant.

With so many uses, Python is quickly becoming the Swiss army knife for marketers. You don’t need to become a software engineer — even a basic understanding can dramatically improve your workflow.

Getting Started with Python

Whether you're a fresh graduate or a seasoned marketer, investing in the right training can fast-track your career. A quality Python training in Dehradun will teach you how to automate marketing workflows, handle SEO analytics, and visualize campaign performance — all with practical, industry-relevant projects.

Look for courses that include modules on digital marketing integration, data handling, and tool-based assignments. These elements ensure you're not just learning syntax but applying it to real marketing scenarios. With Dehradun's increasing focus on tech education, it's a great place to gain this in-demand skill.

Python is no longer optional for forward-thinking marketers. As SEO becomes more data-driven and automation more essential, mastering Python gives you a clear edge. It simplifies complexity, drives efficiency, and helps you make smarter, faster decisions.

Now is the perfect time to upskill. Whether you're optimizing search rankings or building powerful marketing dashboards, Python is your key to unlocking smarter marketing in 2025 and beyond.

Python vs Ruby, What is the Difference? - Pros & Cons

[mysoulglitter]

How to Leverage Python Skills to Launch a Successful Freelance Career

The demand for Python developers continues to grow in 2025, opening exciting opportunities—not just in full-time employment, but in freelancing as well. Thanks to Python’s versatility, freelancers can offer services across multiple industries, from web development and data analysis to automation and AI.

Whether you're looking to supplement your income or transition into full-time freelancing, here's how you can use Python to build a thriving freelance career.

Master the Core Concepts

Before stepping into the freelance market, it's essential to build a solid foundation in Python. Make sure you're comfortable with:

Data types and structures (lists, dictionaries, sets)

Control flow (loops, conditionals)

Functions and modules

Object-oriented programming

File handling and error management

Once you’ve nailed the basics, move on to specialized areas based on your target niche.

Choose a Niche That Suits You

Python is used in many domains, but as a freelancer, it helps to specialize. Some profitable freelance niches include:

Web Development: Use frameworks like Django or Flask to build custom websites and web apps.

Data Analysis: Help clients make data-driven decisions using tools like Pandas and Matplotlib.

Automation Scripts: Streamline repetitive client tasks by developing efficient Python automation tools.

Web Scraping: Use tools such as BeautifulSoup or Scrapy to extract data from websites quickly and effectively.

Machine Learning: Offer insights, models, or prototypes using Scikit-learn or TensorFlow.

Choosing a niche allows you to brand yourself as an expert rather than a generalist, which can attract higher-paying clients.

Build a Portfolio

A portfolio is your online resume and a powerful trust builder. Create a personal website or use GitHub to showcase projects that demonstrate your expertise. Some project ideas include:

A simple blog built with Flask

A script that scrapes data and exports it to Excel

A dashboard that visualizes data from a CSV file

An automated email responder

The key is to show clients that you can solve real-world problems using Python.

Create Profiles on Freelance Platforms

Once your portfolio is ready, the next step is to start reaching out to potential clients. Create profiles on platforms like:

Upwork

Freelancer

Fiverr

Toptal

PeoplePerHour

When setting up your profile, write a compelling bio, list your skills, and upload samples from your portfolio. Use keywords clients might search for, like "Python automation," "Django developer," or "data analyst."

Start Small and Build Your Reputation

Landing your first few clients as a new freelancer can take some patience and persistence. Consider offering competitive rates or working on smaller projects initially to gain reviews and build credibility. Positive feedback and completed jobs on your profile will help you attract better clients over time. Deliver quality work, communicate clearly, and meet deadlines—these soft skills matter as much as your technical expertise.

Upskill with Online Resources

The tech landscape changes fast, and staying updated is crucial.Set aside time to explore new tools, frameworks, and libraries, ensuring you stay up-to-date and continuously grow your skill set. Many freelancers also benefit from taking structured courses that help them level up efficiently. If you're serious about freelancing as a Python developer, enrolling in a comprehensive python training course in Pune can help solidify your knowledge. A trusted python training institute in Pune will offer hands-on projects, expert mentorship, and practical experience that align with the demands of the freelance market.

Market Yourself Actively

Don’t rely solely on freelance platforms. Expand your reach by: Sharing coding tips or projects on LinkedIn and Twitter

Writing blog posts about your Python solutions

Networking in communities like Reddit, Stack Overflow, or Discord

Attend local freelancing or tech meetups in your area to network and connect with like-minded professionals. The more visible you are, the more likely clients will find you organically.

Set Your Rates Wisely

Pricing is a common challenge for freelancers. Begin by exploring the rates others in your field are offering to get a sense of standard pricing. Factor in your skill level, project complexity, and market demand. You can charge hourly, per project, or even offer retainer packages for ongoing work. As your skills and client list grow, don’t hesitate to increase your rates.

Stay Organized and Professional

Treat freelancing like a business.Utilize productivity tools to streamline time tracking, invoicing, and client communication.Apps like Trello, Notion, and Toggl can help you stay organized. Create professional invoices, use contracts, and maintain clear communication with clients to build long-term relationships.

Building a freelance career with Python is not only possible—it’s a smart move in today’s tech-driven world. With the right skills, mindset, and marketing strategy, you can carve out a successful career that offers flexibility, autonomy, and unlimited growth potential.

Start by mastering the language, building your portfolio, and gaining real-world experience. Whether you learn through self-study or a structured path like a python training institute in Pune, your efforts today can lead to a rewarding freelance future.

[souhaillaghchimdev]

Digital Marketing Application Programming

In today's tech-driven world, digital marketing is no longer just about catchy ads and engaging posts—it's about smart, automated, data-driven applications. Whether you're a developer building a marketing automation platform or a digital marketer looking to leverage tech, understanding how to program marketing applications is a game changer.

What Is Digital Marketing Application Programming?

Digital Marketing Application Programming refers to the development of tools, systems, and scripts that help automate, optimize, and analyze digital marketing efforts. These applications can handle tasks like SEO analysis, social media automation, email campaigns, customer segmentation, and performance tracking.

Key Areas of Digital Marketing Applications

Email Marketing Automation: Schedule and personalize email campaigns using tools like Mailchimp API or custom Python scripts.

SEO Tools: Build bots and crawlers to check page speed, backlinks, and keyword rankings.

Social Media Automation: Use APIs (e.g., Twitter, Instagram, Facebook) to schedule posts and analyze engagement.

Analytics and Reporting: Integrate with Google Analytics and other platforms to generate automated reports and dashboards.

Ad Campaign Management: Use Google Ads API or Meta Ads API to manage and analyze advertising campaigns.

Popular Technologies and APIs

Python: Great for automation, scraping, and data analysis.

JavaScript/Node.js: Excellent for real-time applications, chatbots, and front-end dashboards.

Google APIs: For accessing Google Ads, Google Analytics, and Google Search Console data.

Facebook Graph API: For managing posts, ads, and analytics across Facebook and Instagram.

Zapier/IFTTT Integration: No-code platforms for connecting various marketing tools together.

Example: Sending an Automated Email with Python

import smtplib from email.mime.text import MIMEText def send_email(subject, body, to_email): msg = MIMEText(body) msg['Subject'] = subject msg['From'] = 'you@example.com' msg['To'] = to_email with smtplib.SMTP('smtp.example.com', 587) as server: server.starttls() server.login('you@example.com', 'yourpassword') server.send_message(msg) send_email("Hello!", "This is an automated message.", "client@example.com")

Best Practices

Use APIs responsibly and within rate limits.

Ensure user privacy and comply with GDPR/CCPA regulations.

Log all automated actions for transparency and debugging.

Design with scalability in mind—marketing data grows fast.

Secure API keys and sensitive user data using environment variables.

Real-World Use Cases

Marketing dashboards pulling real-time analytics from multiple platforms.

Automated tools that segment leads based on behavior.

Chatbots that qualify sales prospects and guide users.

Email drip campaigns triggered by user activity.

Dynamic landing pages generated based on campaign source.

Conclusion

Digital marketing is being transformed by smart programming. Developers and marketers working together can create systems that reduce manual labor, improve targeting, and increase ROI. Whether you're automating emails, analyzing SEO, or building AI chatbots—coding skills are a superpower in digital marketing.

[alcoanalysis]

Intro to Web Scraping

Chances are, if you have access to the internet, you have heard of Data Science. Aside from the buzz generated by the title ‘Data Scientist’, only a few in relevant fields can claim to understand what data science is. The majority of people think, if at all, that a data scientist is a mad scientist type able to manipulate statistics and computers to magically generate crazy visuals and insights seemingly out of thin air.

Looking at the plethora of definitions to be found in numerous books and across the internet of what data science is, the layman’s image of a data scientist may not be that far off.

While the exact definition of ‘data science’ is still a work in progress, most in the know would agree that the data science universe encompasses fields such as:

Big Data

Analytics

Machine Learning

Data Mining

Visualization

Deep Learning

Business Intelligence

Predictive Modeling

Statistics

Data Source: Top keywords

Image Source – Michael Barber

Further exploration of the skillset that goes into what makes a data scientist, consensus begins to emerge around the following:

Statistical Analysis

Programming/Coding Skills: - R Programming; Python Coding

Structured Data (SQL)

Unstructured Data (3-5 top NoSQL DBs)

Machine Learning/Data Mining Skills

Data Visualization

Big Data Processing Platforms: Hadoop, Spark, Flink, etc.

Structured vs unstructured data

Structured data refers to information with a high degree of organization, such that inclusion in a relational database is seamless and readily searchable by simple, straightforward search engine algorithms or other search operation

Examples of structured data include numbers, dates, and groups of words and numbers called strings.

Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.

Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web pages, or word-processor document. Source: Unstructured data - Wikipedia

Implied within the definition of unstructured data is the fact that it is very difficult to search. In addition, the vast amount of data in the world is unstructured. A key skill when it comes to mining insights out of the seeming trash that is unstructured data is web scraping.

What is web scraping?

Everyone has done this: you go to a web site, see an interesting table and try to copy it over to Excel so you can add some numbers up or store it for later. Yet this often does not really work, or the information you want is spread across a large number of web sites. Copying by hand can quickly become very tedious.

You’ve tried everything else, and you haven’t managed to get your hands on the data you want. You’ve found the data on the web, but, alas — no download options are available and copy-paste has failed you. Fear not, there may still be a way to get the data out. Source: Data Journalism Handbook

As a data scientist, the more data you collect, the better your models, but what if the data you want resides on a website? This is the problem of social media analysis when the data comes from users posting content online and can be extremely unstructured. While there are some websites who support data collection from their web pages and have even exposed packages and APIs (such as Twitter), most of the web pages lack the capability and infrastructure for this. If you are a data scientist who wants to capture data from such web pages then you wouldn’t want to be the one to open all these pages manually and scrape the web pages one by one. Source: Perceptive Analytics

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis. Source: Wikipedia

Web Scraping is a method to convert the data from websites, whether structured or unstructured, from HTML into a form on which analysis can be performed.

The advantage of scraping is that you can do it with virtually any web site — from weather forecasts to government spending, even if that site does not have an API for raw data access. While this method is very powerful and can be used in many places, it requires a bit of understanding about how the web works.

There are a variety of ways to scrape a website to extract information for reuse. In its simplest form, this can be achieved by copying and pasting snippets from a web page, but this can be unpractical if there is a large amount of data to be extracted, or if it spread over a large number of pages. Instead, specialized tools and techniques can be used to automate this process, by defining what sites to visit, what information to look for, and whether data extraction should stop once the end of a page has been reached, or whether to follow hyperlinks and repeat the process recursively. Automating web scraping also allows to define whether the process should be run at regular intervals and capture changes in the data.

https://librarycarpentry.github.io/lc-webscraping/

 Web Scraping with R

Atop any data scientist’s toolkit lie Python and R. While python is a general purpose coding language used in a variety of situations; R was built from the ground up to mold statistics and data. From data extraction, to clean up, to visualization to publishing; R is in use. Unlike packages such as tableau, Stata or Matlab which are skewed either towards data manipulation or visualization, R is a general purpose statistical language with functionality cutting across all data management operations. R is also free and open source which contributes to making it even more popular.

To extend the boundaries limiting data scientists from accessing data from web pages, there are packages based on ‘Web scraping’ available in R. Let us look into web scraping technique using R.

Harvesting Data with RVEST

R. Hadley Wickham authored the rvest package for web scraping using R which will be demonstrated in this tutorial. Although web scraping with R is a fairly advanced topic it is possible to dive in with a few lines of code within a few steps and appreciate its utility, versatility and power.

We shall use 2 examples inspired by Julia Silge in her series cool things you can do with R in a tweet:

Scraping the list of districts of Uganda

Getting the list of MPs of the Republic of Rwanda

[gloriousfestgentlemen02]

Sure, here is the article formatted according to your specifications:

Cryptocurrency data scraping TG@yuantou2048

In the rapidly evolving world of cryptocurrency, staying informed about market trends and price movements is crucial for investors and enthusiasts alike. One effective way to gather this information is through cryptocurrency data scraping. This method involves extracting data from various sources on the internet, such as exchanges, forums, and news sites, to compile a comprehensive dataset that can be used for analysis and decision-making.

What is Cryptocurrency Data Scraping?

Cryptocurrency data scraping refers to the process of automatically collecting and organizing data related to cryptocurrencies from online platforms. This data can include real-time prices, trading volumes, news updates, and social media sentiment. By automating the collection of this data, users can gain valuable insights into the cryptocurrency market, enabling them to make more informed decisions. Here’s how it works and why it’s important.

Why Scrape Cryptocurrency Data?

1. Real-Time Insights: Scraping allows you to access up-to-date information about different cryptocurrencies, ensuring that you have the latest details at your fingertips.

2. Market Analysis: With the vast amount of information available online, manual tracking becomes impractical. Automated scraping tools can help you stay ahead by providing timely and accurate information.

3. Tools and Techniques:

Web Scrapers: These are software tools designed to extract specific types of data from websites. They can gather data points like current prices, historical price trends, and community sentiment, which are essential for making informed investment decisions.

2. Automation: Instead of manually checking multiple platforms, automated scrapers can continuously monitor and collect data, saving time and effort.

3. Customization: You can tailor your scraper to focus on specific metrics or platforms, allowing for personalized data collection tailored to your needs.

4. Competitive Advantage: Having access to real-time data gives you an edge in understanding market dynamics and identifying potential opportunities or risks.

5. Legal Considerations: It's important to ensure that the data collected complies with legal guidelines and respects terms of service agreements of the websites being scraped. Always check the legality and ethical considerations before implementing any scraping projects.

6. Use Cases:

Price Tracking: Track the value of different cryptocurrencies across multiple exchanges.

Sentiment Analysis: Analyze social media and news feeds to gauge public opinion and predict market movements.

7. Challenges:

Dynamic Content: Websites often use JavaScript to load content dynamically, which requires advanced techniques to capture this data accurately.

Scraping Tools: Popular tools include Python libraries like BeautifulSoup and Selenium, which can parse HTML and interact with web pages to extract relevant information efficiently.

8. Best Practices:

Respect Terms of Service: Ensure that your scraping activities comply with the terms of service of the websites you’re scraping from. Some popular platforms like CoinMarketCap, Coingecko, and Twitter for sentiment analysis.

9. Ethical and Legal Scrutiny: Be mindful of the ethical implications and ensure compliance with website policies.

10. Data Quality: The quality of the data is crucial. Use robust frameworks and APIs provided by exchanges directly when possible to avoid overloading servers and ensure reliability.

11. Conclusion: Cryptocurrency data scraping is a powerful tool for anyone interested in the crypto space. However, always respect the terms of service of the platforms you scrape from.

12. Future Trends: As the landscape evolves, staying updated with the latest technologies and best practices is key. Always respect the terms of service of the platforms you're scraping from.

13. Conclusion: Cryptocurrency data scraping offers a wealth of information but requires careful implementation to avoid violating terms of service or facing legal issues.

14. Final Thoughts: While scraping can provide significant advantages, it’s vital to use these tools responsibly and ethically.

This structured approach ensures that you adhere to ethical standards while leveraging the power of automation to stay informed without infringing on copyright laws and privacy policies.

Feel free to adjust the length and tone as needed.

加飞机@yuantou2048

EPP Machine

蜘蛛池出租

[gts6465]

Building the Perfect Dataset for AI Training: A Step-by-Step Guide

Introduction

As artificial intelligence progressively transforms various sectors, the significance of high-quality datasets in the training of AI systems is paramount. A meticulously curated dataset serves as the foundation for any AI model, impacting its precision, dependability, and overall effectiveness. This guide will outline the crucial steps necessary to create an optimal Dataset for AI Training.

Step 1: Define the Objective

Prior to initiating data collection, it is essential to explicitly outline the objective of your AI model. Consider the following questions:

What specific issue am I aiming to address?

What types of predictions or results do I anticipate?

Which metrics will be used to evaluate success?

Establishing a clear objective guarantees that the dataset is in harmony with the model’s intended purpose, thereby preventing superfluous data collection and processing.

Step 2: Identify Data Sources

To achieve your objective, it is essential to determine the most pertinent data sources. These may encompass:

Open Data Repositories: Websites such as Kaggle, the UCI Machine Learning Repository, and Data.gov provide access to free datasets.

Proprietary Data: Data that is gathered internally by your organization.

Web Scraping: The process of extracting data from websites utilizing tools such as Beautiful Soup or Scrapy.

APIs: Numerous platforms offer APIs for data retrieval, including Twitter, Google Maps, and OpenWeather.

It is crucial to verify that your data sources adhere to legal and ethical guidelines.

Step 3: Collect and Aggregate Data

Upon identifying the sources, initiate the process of data collection. This phase entails the accumulation of raw data and its consolidation into a coherent format.

Utilize tools such as Python scripts, SQL queries, or data integration platforms.

Ensure comprehensive documentation of data sources to monitor quality and adherence to compliance standards.

Step 4: Clean the Data

Raw data frequently includes noise, absent values, and discrepancies. The process of data cleaning encompasses:

Eliminating Duplicates: Remove redundant entries.

Addressing Missing Data: Employ methods such as imputation, interpolation, or removal.

Standardizing Formats: Maintain uniformity in units, date formats, and naming conventions.

Detecting Outliers: Recognize and manage anomalies through statistical techniques or visual representation.

Step 5: Annotate the Data

Data annotation is essential for supervised learning models. This process entails labeling the dataset to establish a ground truth for the training phase.

Utilize tools such as Label Studio, Amazon SageMaker Ground Truth, or dedicated annotation services.

To maintain accuracy and consistency in annotations, it is important to offer clear instructions to the annotators.

Step 6: Split the Dataset

Segment your dataset into three distinct subsets:

Training Set: Generally comprising 70-80% of the total data, this subset is utilized for training the model.

Validation Set: Constituting approximately 10-15% of the data, this subset is employed for hyperparameter tuning and to mitigate the risk of overfitting.

Test Set: The final 10-15% of the data, this subset is reserved for assessing the model’s performance on data that it has not encountered before.

Step 7: Ensure Dataset Diversity

AI models achieve optimal performance when they are trained on varied datasets that encompass a broad spectrum of scenarios. This includes:

Demographic Diversity: Ensuring representation across multiple age groups, ethnic backgrounds, and geographical areas.

Contextual Diversity: Incorporating a variety of conditions, settings, or applications.

Temporal Diversity: Utilizing data gathered from different timeframes.

Step 8: Test and Validate

Prior to the completion of the dataset, it is essential to perform a preliminary assessment to ensure its quality. This assessment should include the following checks:

Equitable distribution of classes.

Lack of bias.

Pertinence to the specific issue being addressed.

Subsequently, refine the dataset in accordance with the findings from the assessment.

Step 9: Document the Dataset

Develop thorough documentation that encompasses the following elements:

Description and objectives of the dataset.

Sources of data and methods of collection.

Steps for preprocessing and data cleaning.

Guidelines for annotation and the tools utilized.

Identified limitations and possible biases.

Step 10: Maintain and Update the Dataset

AI models necessitate regular updates to maintain their efficacy. It is essential to implement procedures for:

Regular data collection and enhancement.

Ongoing assessment of relevance and precision.

Version management to document modifications.

Conclusion

Creating an ideal dataset for AI training is a careful endeavor that requires precision, specialized knowledge, and ethical awareness. By adhering to this comprehensive guide, you can develop datasets that enable your AI models to perform at their best and produce trustworthy outcomes.

For additional information on AI training and resources, please visit Globose Technology Solutions.AI.

[coconutsplit]

Creating a tool that helps manage digital mental space while sifting through content and media is a valuable and challenging project. Here’s a high-level breakdown of how you might approach this:

1. Define the Scope and Features

Digital Mental Space Management:

Focus Mode: Create a feature that blocks or filters out distracting content while focusing on specific tasks.

Break Reminders: Set up reminders for taking regular breaks to avoid burnout.

Content Categorization: Allow users to categorize content into different sections (e.g., work, personal, leisure) to manage their mental space better.

Content Sifting and Filtering:

Keyword Filtering: Implement a keyword-based filtering system to highlight or exclude content based on user preferences.

Sentiment Analysis: Integrate a sentiment analysis tool that can categorize content as positive, negative, or neutral, helping users choose what to engage with.

Source Verification: Develop a feature that cross-references content with reliable sources to flag potential misinformation.

2. Technical Components

Front-End:

UI/UX Design: Design a clean, minimalistic interface focusing on ease of use and reducing cognitive load.

Web Framework: Use frameworks like React or Vue.js for responsive and interactive user interfaces.

Content Display: Implement a dashboard that displays categorized and filtered content in an organized way.

Back-End:

API Integration: Use APIs for content aggregation (e.g., news APIs, social media APIs) and filtering.

Data Storage: Choose a database (e.g., MongoDB, PostgreSQL) to store user preferences, filtered content, and settings.

Authentication: Implement a secure authentication system to manage user accounts and personalized settings.

Content Filtering and Analysis:

Text Processing: Use Python with libraries like NLTK or SpaCy for keyword extraction and sentiment analysis.

Machine Learning: If advanced filtering is needed, train a machine learning model using a dataset of user preferences.

Web Scraping: For content aggregation, you might need web scraping tools like BeautifulSoup or Scrapy (ensure compliance with legal and ethical standards).

3. Development Plan

Phase 1: Core Functionality

Develop a basic UI.

Implement user authentication.

Set up content aggregation and display.

Integrate keyword filtering.

Phase 2: Advanced Features

Add sentiment analysis.

Implement break reminders and focus mode.

Add source verification functionality.

Phase 3: Testing and Iteration

Conduct user testing to gather feedback.

Iterate on the design and features based on user feedback.

Optimize performance and security.

4. Tools and Libraries

Front-End: React, Redux, TailwindCSS/Material-UI

Back-End: Node.js/Express, Django/Flask, MongoDB/PostgreSQL

Content Analysis: Python (NLTK, SpaCy), TensorFlow/PyTorch for ML models

APIs: News API, Twitter API, Facebook Graph API

Deployment: Docker, AWS/GCP/Azure for cloud deployment

5. Considerations for User Well-being

Privacy: Ensure user data is protected and handled with care, possibly offering anonymous or minimal data modes.

Customization: Allow users to customize what types of content they want to filter, what kind of breaks they want, etc.

Transparency: Make the filtering and analysis process transparent, so users understand how their content is being sifted and managed.

This is a comprehensive project that will require careful planning and iteration. Starting small and building up the tool's features over time can help manage the complexity.

[professor00786]

Title: Mastering Python Development: A Comprehensive Guide

In today's tech-driven world, Python stands out as one of the most versatile and widely-used programming languages. From web development to data science, Python's simplicity and power make it a top choice for both beginners and seasoned developers alike. If you're looking to embark on a journey into Python development, you're in the right place. In this comprehensive guide, we'll walk you through the steps to master Python development.

Understanding the Basics

Before diving into Python development, it's essential to grasp the fundamentals of the language. Start with the basics such as data types, variables, loops, and functions. Online platforms like Codecademy, Coursera, and Udemy offer excellent introductory courses for beginners. Additionally, Python's official documentation and interactive tutorials like "Learn Python" by Codecademy provide hands-on learning experiences.

Build Projects

The best way to solidify your understanding of Python is by building projects. Start with small projects like a simple calculator or a to-do list app. As you gain confidence, challenge yourself with more complex projects such as web development using frameworks like Flask or Django, data analysis with libraries like Pandas and NumPy, or machine learning projects using TensorFlow or PyTorch. GitHub is a fantastic platform to showcase your projects and collaborate with other developers.

Dive Deeper into Python Ecosystem

Python boasts a rich ecosystem of libraries and frameworks that cater to various domains. Explore different areas such as web development, data science, machine learning, and automation. Familiarize yourself with popular libraries like requests for making HTTP requests, BeautifulSoup for web scraping, Matplotlib and Seaborn for data visualization, and scikit-learn for machine learning tasks. Understanding the strengths and applications of these libraries will broaden your Python development skills.

Learn from Others

Engage with the Python community to accelerate your learning journey. Participate in online forums like Stack Overflow, Reddit's r/learnpython, and Python-related Discord servers. Follow Python developers and experts on social media platforms like Twitter and LinkedIn. Attending local meetups, workshops, and conferences can also provide valuable networking opportunities and insights into the latest trends in Python development.

Practice Regularly

Consistency is key to mastering Python development. Dedicate time each day to practice coding, whether it's solving coding challenges on platforms like LeetCode and HackerRank or contributing to open-source projects on GitHub. Set achievable goals and track your progress over time. Remember, proficiency in Python, like any skill, comes with practice and dedication.

Stay Updated

The field of technology is constantly evolving, and Python is no exception. Stay updated with the latest advancements, updates, and best practices in Python development. Follow blogs, newsletters, and podcasts dedicated to Python programming. Attend webinars and online courses to learn about emerging trends and technologies. Continuous learning is essential to stay relevant and competitive in the ever-changing tech industry.

Conclusion

Learning Python development is an exciting journey filled with endless possibilities. Whether you're a beginner or an experienced developer, mastering Python can open doors to a wide range of career opportunities and creative projects. By understanding the basics, building projects, exploring the Python ecosystem, learning from others, practicing regularly, and staying updated, you can embark on a rewarding path towards becoming a proficient Python developer. So, what are you waiting for? Start your Python journey today!

[probabilityofyouandme]

Progress on 2 personal projects

Project # 1 - Trade Triggerer Phase 2

What was Trade Triggerer Phase 1?

In my previous posts, I've shared some snippets of the analysis, development, and deployment of my app Trade Triggerer. It started as a NodeJS project that accomplished the end-to-end data scraping, data management, conditional checking for trades, and email sendout. The deployment was done in Heroku (just like how I deployed my twitter bot previously). I scrapped all that due to the technical upkeep and the increasing difficulty to analyze data in NodeJS.

Sometime around 2017-18, I've started learning Python from online courses for fun, and eventually re-wrote all the NodeJS functionality in a significantly shorter and simpler code. Thus, Trade Trigger-PY was born and deployed circa Aug 2019. Some technologies I've used for this project are simply: Heroku, GMail API, GSheets API. I originally only was monitoring PH Stock Market, but it was easy to add US stocks and Crypto. Hence, I was receiving these emails every day.

I followed the trading instructions strictly, and I became profitable. However, I stopped for the following reasons:

I was beginning to lose money. It seems I had beginner's luck around 2020-2022 since the market was going up on average as a bounce back to the pandemic.

PH market is not as liquid as my experience in crypto trading.

The US stocks I'm monitoring are very limited, as I focused more on PH.

On April 2023, DLSU deleted alumni emails, where my stocks data are being stored.

On November 2023, Heroku stopped offering free deployments.

Today, I am highly motivated to revive Trade Triggerer for only 1 reason, I don't want money to be a problem for the lifestyle that I want. Learning from my past mistakes, Trade Triggerer Phase 2 will be implemented using the following:

Increased technical analysis - Use of statistical models to analyze historical and current stock data

Start fundamental analysis - Review of historical events that changed market behaviour + Review of balance sheets, starting with banks

Focus on strong International Stocks (US, JPN, EUR, CHN)

Deploy on a local Raspberry Pi

I am still at the beginning. I've only been able to train models using historical data and found promising results. There's a long way to go but I believe I can do the MVP on or before my Birthday :)

Project # 2 - Web scrape properties for sale

For personal use lol. Can't deploy in Heroku anymore; and I dont want to depend on other online alternatives, too. I'll start playing around with raspberry pi for this project.

[actowiz1]

Scrape Tweets Data by Location Using Python and snscrap

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location. We will also delve into why the wrapper may not always perform as expected. Let's dive in

snscrape is a remarkable Python library that enables users to scrape tweets from Twitter without the need for personal API keys. With its lightning-fast performance, it can retrieve thousands of tweets within seconds. Moreover, snscrape offers powerful search capabilities, allowing for highly customizable queries. While the documentation for scraping tweets by location is currently limited, this blog aims to comprehensively introduce this topic. Let's delve into the details:

Introduction to Snscrape: Snscrape is a feature-rich Python library that simplifies scraping tweets from Twitter. Unlike traditional methods that require API keys, snscrape bypasses this requirement, making it accessible to users without prior authorization. Its speed and efficiency make it an ideal choice for various applications, from research and analysis to data collection.

The Power of Location-Based Tweet Scraping: Location-based tweet scraping allows users to filter tweets based on geographical coordinates or place names. This functionality is handy for conducting location-specific analyses, monitoring regional trends, or extracting data relevant to specific areas. By leveraging Snscrape's capabilities, users can gain valuable insights from tweets originating in their desired locations.

Exploring Snscrape's Location-Based Search Tools: Snscrape provides several powerful tools for conducting location-based tweet searches. Users can effectively narrow their search results to tweets from a particular location by utilizing specific parameters and syntax. This includes defining the search query, specifying the geographical coordinates or place names, setting search limits, and configuring the desired output format. Understanding and correctly using these tools is crucial for successful location-based tweet scraping.

Overcoming Documentation Gaps: While snscrape is a powerful library, its documentation on scraping tweets by location is currently limited. This article will provide a comprehensive introduction to the topic to bridge this gap, covering the necessary syntax, parameters, and strategies for effective location-based searches. Following the step-by-step guidelines, users can overcome the lack of documentation and successfully utilize snscrape for their location-specific scraping needs.

Best Practices and Tips: Alongside exploring Snscrape's location-based scraping capabilities, this article will also offer best practices and tips for maximizing the efficiency and reliability of your scraping tasks. This includes handling rate limits, implementing error-handling mechanisms, ensuring data consistency, and staying updated with any changes or updates in Snscrape's functionality.

Introduction of snscrape Using Python

In this blog, we’ll use tahe development version of snscrape that can be installed withpip install git+https://github.com/JustAnotherArchivist/snscrape.git

Note: this needs Python 3.8 or latest

Some familiarity of the Pandas module is needed.

We encourage you to explore and experiment with the various features of snscrape to better understand its capabilities. Additionally, you can refer to the mentioned article for more in-depth information on the subject. Later in this blog, we will delve deeper into the user field and its significance in tweet scraping. By gaining a deeper understanding of these concepts, you can harness the full potential of snscrape for your scraping tasks.

Advanced Search Features

In this code snippet, we define the search query as "pizza near:Los Angeles within:10km", which specifies that we want to search for tweets containing the word "pizza" near Los Angeles within a radius of 10 km. The TwitterSearchScraper object is created with the search query, and then we iterate over the retrieved tweets and print their content.

Feel free to adjust the search query and radius per your specific requirements.

For comparing results, we can utilize an inner merging on two DataFrames:common_rows = df_coord.merge(df_city, how='inner')

That returns 50 , for example, they both have the same rows.

What precisely is this place or location?

When determining the location of tweets on Twitter, there are two primary sources: the geo-tag associated with a specific tweet and the user's location mentioned in their profile. However, it's important to note that only a small percentage of tweets (approximately 1-2%) are geo-tagged, making it an unreliable metric for location-based searches. On the other hand, many users include a location in their profile, but it's worth noting that these locations can be arbitrary and inaccurate. Some users provide helpful information like "London, England," while others might use humorous or irrelevant descriptions like "My Parents' Basement."

Despite the limited availability and potential inaccuracies of geo-tagged tweets and user profile locations, Twitter employs algorithms as part of its advanced search functionality to interpret a user's location based on their profile. This means that when you look for tweets through coordinates or city names, the search results will include tweets geotagged from the location and tweets posted by users who have that location (or a location nearby) mentioned in their profile.

To illustrate the usage of location-based searching on Twitter, let's consider an example. Suppose we perform a search for tweets near "London." Here are two examples of tweets that were found using different methods:

The first tweet is geo-tagged, which means it contains specific geographic coordinates indicating its location. In this case, the tweet was found because of its geo-tag, regardless of whether the user has a location mentioned in their profile or not.

The following tweet isn’t geo-tagged, which means that it doesn't have explicit geographic coordinates associated with it. However, it was still included in the search results because a user has given a location in the profile that matches or is closely associated with London.

When performing a location-based search on Twitter, you can come across tweets that are either geo-tagged or have users with matching or relevant locations mentioned in their profiles. This allows for a more comprehensive search, capturing tweets from specific geographic locations and users who have declared their association with those locations.

Get Location From Scraped Tweets

If you're using snscrape to scrape tweets and want to extract the user's location from the scraped data, you can do so by following these steps. In the example below, we scrape 50 tweets within a 10km radius of Los Angeles, store the data in a DataFrame, and then create a new column to capture the user's location.

If It Doesn’t Work According to Your Expectations

The use of the near: and geocode: tags in Twitter's advanced search can sometimes yield inconsistent results, especially when searching for specific towns, villages, or countries. For instance, while searching for tweets nearby Lewisham, the results may show tweets from a completely different location, such as Hobart, Australia, which is over 17,000 km away.

To ensure more accurate results when scraping tweets by locations using snscrape, it is recommended to use the geocode tag having longitude & latitude coordinates, along with a specified radius, to narrow down the search area. This approach will provide more reliable and precise results based on the available data and features.

Conclusion

In conclusion, the snscrape Python module is a valuable tool for conducting specific and powerful searches on Twitter. Twitter has made significant efforts to convert user input locations into real places, enabling easy searching by name or coordinates. By leveraging its capabilities, users can extract relevant information from tweets based on various criteria.

For research, analysis, or other purposes, snscrape empowers users to extract valuable insights from Twitter data. Tweets serve as a valuable source of information. When combined with the capabilities of snscrape, even individuals with limited experience in Data Science or subject knowledge can undertake exciting projects.

Happy scrapping!

For more details, you can contact Actowiz Solutions anytime! Call us for all your mobile app scraping and web scraping services requirements.

know more https://www.actowizsolutions.com/scrape-tweets-data-by-location-python-snscrape.php

[iwebscrapingblogs]

How to Scrape Travel Trips Data with Travel Trends and Travel APIs

Introduction:

The world of travel is constantly evolving, and staying updated with the latest travel trends can be a game-changer for both travelers and travel industry professionals. With the abundance of data available on the internet, scraping travel trip data using travel trends and travel APIs has become an invaluable skill. In this blog post, we'll explore how to scrape travel trip data by leveraging travel trends and travel APIs, opening up a world of possibilities for travel enthusiasts and businesses alike.

1. Understanding Travel Trends:

Before diving into data scraping, it's crucial to grasp the concept of travel trends. Travel trends are patterns in travel behavior, preferences, and destinations that evolve over time. These trends can be influenced by various factors, including global events, technological advancements, and changing consumer preferences. To effectively scrape travel trip data, you need to keep an eye on these trends as they can provide valuable insights into what data to target.

2. Choose the Right Data Sources:

There are numerous websites and platforms where you can find valuable travel trip data. Some popular sources include travel blogs, review websites like TripAdvisor, travel forums, and social media platforms like Instagram and Twitter. Additionally, government tourism websites and databases can provide statistical data on tourism trends. Identifying the right data sources is crucial to ensure the accuracy and relevance of the scraped data.

3. Web Scraping Basics:

Web scraping is the process of extracting data from websites. To scrape travel trip data, you can use programming languages like Python along with libraries like BeautifulSoup and Scrapy. These tools enable you to navigate websites, locate specific elements (such as reviews, ratings, and comments), and extract the desired information.

4. Use Travel APIs:

While web scraping is a powerful technique, it may not always be the most efficient or reliable method for accessing travel trip data. Many travel-related platforms offer APIs (Application Programming Interfaces) that allow developers to access data directly in a structured format. For example, platforms like Google Maps, Airbnb, and TripAdvisor provide APIs that grant access to valuable travel data. Using APIs can simplify the process and provide real-time data updates.

5. Data Cleaning and Structuring:

After scraping or retrieving data from travel websites or APIs, the collected information may be unstructured and messy. It's essential to clean and structure the data to make it usable. This involves removing duplicates, handling missing values, and organizing the data into a structured format such as CSV or a database.

6. Analyzing Travel Data:

Once you have scraped and structured the travel trip data, you can perform various analyses to gain valuable insights. You can identify popular destinations, trending travel itineraries, average travel expenses, and customer reviews. These insights can be used for market research, competitive analysis, or to create personalized travel recommendations.

7. Creating Customized Travel Solutions:

For businesses in the travel industry, scraped travel trip data can be a goldmine for creating customized solutions. Travel agencies can use this data to offer tailored vacation packages, hotels can enhance their services based on customer feedback, and airlines can optimize routes and pricing strategies. Personalized travel recommendations can also be offered through mobile apps or websites.

8. Ethical Considerations:

While web scraping can provide valuable data, it's essential to be aware of the ethical and legal considerations. Always respect the website's terms of service and robots.txt file to ensure you are scraping data responsibly and legally. Additionally, be cautious about scraping personal information or violating users' privacy.

Conclusion:

Scraping travel trip data with travel trends and travel APIs is a powerful tool for both travelers and businesses in the travel industry. By staying informed about travel trends, choosing the right data sources, and utilizing web scraping techniques or APIs, you can access valuable information that can drive decision-making, enhance customer experiences, and create innovative travel solutions. Just remember to approach data scraping responsibly and ethically to maintain the trust of users and website owners. With the right approach, the world of travel data is at your fingertips, waiting to be explored and leveraged for your benefit.

[greywyrd]

What's Grey been up to?

Looking to get away from retail work , so I began to self-learn coding. Then scraped together funds to go on a course for a bit and got myself a Python for beginners certificate.

The course was fun, although - I was left waiting since last year and then the first start date ended up getting cancelled so I didn’t get to do course till march . Got to make some basic things like a currency converter , a calculator. Then got handed the main project which was to take what I had learned and make something of my own choosing , so I made a text adventure game that is played in the terminal and then with a bit of self-research I began messing around with GUI with the Tkinter module and got the basics of a paint program down.

One lesson we tried doing some things with a Raspberry Pi 3 and using the GPIO hat , but the LED light strip was dead. So the lesson turned into a brief crash course in C++ to demonstrate differences in coding languages , what’s a compiler , compiled v. interpretated language , high v. low level ect.

With the foundations now under my belt I think I’m going to spend the following year just jumping in and building up my skills , get to really master Python and branch out into another language. I don’t want to try for a full course at a higher level immediately.

In the meantime I’ve also been quietly been putting my twitter into quarantine. I’m extremely burned out on social media sites. I nuked my FB a long time ago due to my old pa getting into bad things online , that I have to keep dragging him out of – getting conned out of money and cyber-skimmed and warning him not to take whatever pops up in the feed as fact and to not touch the marketplace . Now watching Twitter get X-terminated is that last straw for me - can’t safely do commission work , can’t link to my other spots in case the boss has a hissy fit , can’t shield from bad actors and trolls because the boss wants to hose pipe everyone. So my Twitter is just sitting dormant now. For now I'm trying to get my various art spots back up and running, dusting off the DA , NG and Lospec with the intent to catch up .

Hoping to re-start the webcomic I was running on DA - The Twisted Adventures of Von Slayer, get that going with streaming drawing the pages again. I had to stop previously due to old computer setup dying ( Motherboard was on it's last legs.) It's been all upgraded and fixed since then except for GPU - hoping to upgrade that in the new year.

So there, that's what I've been up to in 2023 :P