Josue A. Bogran’s Post

Day 51 of #100DaysOfPython - Internet Speed Twitter Complaint Bot 🎯 Goals for Today: Create a Twitter bot that can monitor your internet speed and send a complaint tweet to your Internet Service Provider (ISP) if the speed is lower than expected. Here's a breakdown of what I aimed to achieve: ✅ Set Up Twitter Account: Create a Twitter account and prepare it for the bot to interact with. ✅ Create a Python Class: Write a class to handle the bot's core functions, like checking internet speed and sending complaints. ✅ Get Internet Speeds: Use Python and selenium to fetch the current internet speed. ✅ Building the Twitter Bot: Authenticate with Twitter's API, send tweets, and automate the complaint process. Step-by-Step Breakdown: Setup Twitter Account: Register the Twitter account where complaints will be sent. Create Python Class: Design a class that handles internet speed checking and Twitter posting. Get Internet Speeds: Fetch the download, upload, using selenium from speedtest.net. Send Tweets: Connect the bot with the Twitter API and tweet complaints based on the internet speed results. 💡 This project will help automate a useful task, allowing for real-time complaints to be made if the internet provider fails to meet the required speeds. Next step: Adding more features like scheduled checks, personalized complaints, and advanced error handling! #Python #TwitterBot #InternetSpeed #Automation #100DaysOfCode #DeveloperJourney

Day 8: Diving into the Twitter API Today, I began Chapter 3 of the Intermediate Importing Data in Python course, which focuses on interacting with the Twitter API. This chapter is all about streaming real-time Twitter data and analyzing it. Here’s what I covered: The Twitter API and Authentication: Setting up access to Twitter’s data. Streaming Tweets: Using Tweepy to stream live data. Exploring Twitter Data: Loading and analyzing tweets. DataFrame Manipulation: Converting Twitter data into a DataFrame for analysis. Twitter Text Analysis: Beginning to explore tweet content. However, I encountered a few challenges: Pandas DataFrame iterrows() Method: This was new to me, and I struggled to understand how to use it effectively. Python re Module: This module was introduced without explanation, leaving me a bit confused. These challenges highlighted the foundational concepts I still need to learn. But growth happens through doing, and I’m committed to mastering these skills. I discovered a highly recommended resource from the Data Engineering community on Reddit: Automate the Boring Stuff with Python by Al Sweigart. I believe it will help me fill in the gaps. The journey continues! #DataEngineering #TwitterAPI #Python #LearningJourney #GrowthMindset #90DaysOfLearning #DataCamp

Key takeaways from a small twitter-scrapping research: 1. Free Python libraries (selenium, twikit): not very reliable for an everyday scrapping. Many requests from the same non-masked IP will get your account/IP address banned for a particular website. 2. If you have some money to spear: use ScrapperAPI or Bright Data, though I couldn't find any good guides on programmatic integration of Bright Data. They offer Proxies, which will hide your requests. 3. If you plan on a enterprise production deploy and you are very rich - use Twitter API, though you'll have to comply with lots of policies they have in their License agreement and pay tons of money as well. 4. Best solution: try to find an API that is your data specific and has all the data scrapped already. The biggest benefit - you won't have to dig around data a lot, it will come ready from the box. Small note: Chat GPT gives a lot of good advises on this, don't hesitate to check everything it throws back at you. #twitter #x #scrapping #scrap #python

📊 Thrilled to announce the completion of my latest project: Twitter Sentiment Analysis! 🎉 This project involves analyzing tweet sentiments to gain insights into public opinions and trends using data analytics techniques. Key Highlights: ✨ Data Exploration & Cleaning: Understood dataset structure and ensured data quality. ✨ EDA & Visualization: Explored tweet patterns and sentiment distributions with visualizations. ✨Sentiment Prediction: Developed and evaluated a sentiment prediction model. ✨Word Frequency & Temporal Analysis: Analyzed word frequencies and sentiment variations over time. Explore the detailed project and findings here: [Twitter Sentiment Analysis on GitHub](https://lnkd.in/eab9Xbbk) #DataAnalysis #SentimentAnalysis #Twitter #MachineLearning #Python #DataVisualization #JupyterNotebook #GitHub

How this developer made $15K+ in 6 weeks with a side project …a simple Python script 🤯 Here’s the story: ───────────── People in my country were constantly searching for updates on the stock availability of a popular product: nicotine pouches. These pouches were in high demand, but supply was limited. When they dropped, they sold out within minutes. So, I wrote a Python script to check the stocks and alert me when they became available. I received notifications via Telegram, which allowed me to secure enough supply to meet my needs. But then it hit me: I wasn't the only one interested in this information. I created a Twitter bot that mirrored my bot notifications, letting us share the information with others. The account gained 5,000 followers in just two weeks. It got to the point where I had so many requests that I set up a paid Telegram channel. And it blew up. Final revenue: $15K+ in 6 weeks. Shared by Mjsal93 (Reddit) ───────────── That's like $0.78 per line of code. 100x ROI. Was there something special? Nope. Simple ideas executed well. The last reminder: Be Creative.

I’ve just completed the Analyzing Social Media Data in Python Course on DataCamp! Learning a new skill was quick, interactive, and fun! Plus, everything you need is entirely in-browser. Discover which of the 500+ data and AI courses can help you build a stronger career.

🚀 Task 4 Completed 🚀 I've recently completed a Twitter Sentiment Analysis task assigned by Prodigy InfoTech where I worked on understanding public sentiment towards various topics and brands on Twitter using a real-world dataset. 🔍 Key Highlights: 👉 Analyzed and visualized the sentiment patterns of over thousands of tweets related to popular topics like Google, Microsoft, and more. 👉 Generated insightful visualizations like pie charts, bar plots, and word clouds to help uncover public opinion. 👉 Key findings include a majority of negative sentiments, but balanced sentiment for major tech companies. 💡 Tech Stack: 📌 Python (Pandas, Matplotlib, Seaborn) 📌 Data visualization and basic text analysis (WordCloud) 📌 Jupyter Notebook It was a rewarding experience working through the project, which allowed me to further hone my data analysis and visualization skills. I’m excited to keep exploring machine learning models in future projects. 🔗 Check out the full project here: https://lnkd.in/gSbGciHz #DataScience #SentimentAnalysis #Python #Twitter #Visualization #MachineLearning #ProdigyInfotech #LinkedIn #GitHub #DataAnalytics

Top 10 Must-Know Data Structures Every Developer Should Master: Understanding data structures is crucial for any software developer. They are the foundation of efficient algorithms and system designs, playing a vital role in solving complex problems. Here are the top 10 data structures you must know and their everyday applications: List: Essential for managing ordered collections, such as your Twitter feeds. Stack: Supports functionalities like undo/redo in word editors. Queue: Crucial for handling tasks like printer jobs and in-game user actions. Hash Table: Integral to caching systems for fast data retrieval. Array: Fundamental for performing efficient math operations. Heap: Used in algorithms for task scheduling. Tree: Organizes data hierarchically, like HTML documents or AI decision trees. Suffix Tree: Optimizes searching for substrings within large texts. Graph: Represents networks, such as friendships on social media or paths in navigation systems. R-Tree: Enables efficient spatial queries, like finding the nearest neighbor. Vertex Buffer: Essential for sending data to the GPU for rendering graphics. Mastering these data structures will enhance your problem-solving skills and prepare you for real-world challenges. Keep learning and coding! 🚀 Follow me for more insight on programming Naveen chandrawanshi ✅ #DataStructures #Coding #Programming #SoftwareDevelopment #TechInsights #CareerGrowth #MustKnowTech #dsa #interview

Want to scrape Twitter data? Try PyTweetToolkit! 📣 Despite the recent challenges in accessing X (Twitter) data, I've managed to scrape a substantial dataset of 4705 tweets from 6 popular Indonesian menfess accounts. Each tweet contains 12 rich metadata, including ID, username, text, and engagement metrics like likes, retweets, and views. This data collection opens up numerous possibilities for analyzing topics, trends, and user behaviors in Indonesian social media. 📈📈📈 #datascience #python #twitter #datamining #indonesia #scraping #data #pytweettoolkit #webscraping #x

Latest project update: Building a Twitter(x) Trends Analysis MCP Server for Claude! I've developed a custom Model Control Protocol (MCP) server that enables Claude to access real-time Twitter trends data. This integration bridges the gap between Claude's capabilities and Twitter's dynamic social landscape. 🔧 Technical Highlights: -Built using Python with Tweepy for Twitter API integration -Implements the Model Control Protocol (MCP) for seamless communication with Claude -Features real-time trends fetching and analysis capabilities -Robust error handling and logging system -Modular architecture for easy extensibility 🎯 Key Features: -Real-time Twitter trends monitoring -Custom trend analysis tools -Resource-based architecture -Asynchronous operation -Secure API handling ⚡ Current Implementation: Currently utilizing Twitter's (X) free API tier, which means the data access is limited. However, the architecture is designed to easily scale up when needed, making it future-proof for expanded capabilities with premium API access. This project demonstrates how we can extend Claude's capabilities by providing structured access to external data sources. It's a step forward in making AI assistants more connected and capable. For detailed steps follow this link https://lnkd.in/gbH9aP_x #AI #Programming #Claude #TwitterAPI #Python #SoftwareEngineering #AIIntegration #OpenSource