Building a Code Debugger App with Streamlit for Data Science Learning

Stop using Pandas for everything. I just published a full breakdown of 7 Python libraries that are redefining how developers build in 2026 with install commands + real code examples for each. Here's the quick cheat sheet: ⚡ Polars → 10x faster than Pandas for big data 📄 MarkItDown → Converts PDFs/Word docs into AI-ready Markdown 🤖 Smolagents → Build your first AI agent in 10 lines 🧑✈️ GPT Pilot → An AI that writes entire features, not just autocomplete 📱 Flet → Build web + mobile + desktop apps in pure Python 🛡️ Pyrefly → Catch bugs BEFORE you run your code (Meta-built) 🌐 Morphik-Core → AI that understands images and videos, not just text You don't need to learn all 7 today. Pick the one that solves YOUR problem right now. Working with data? → Polars Building an app? → Flet Curious about agents? → Smolagents The full blog (with code examples for every library) is linked in the comments 👇 Which of these are you already using? Drop it below 🔽 #Python #AI #MachineLearning #Programming #Developer #TechIn2026 #AITools #OpenSource #PythonDeveloper #CodingTips

🚀 Machine Learning With Python From Scratch Part 3! This one is about something every ML beginner struggles with — One Hot Encoding. Machine learning models only understand numbers. So what do you do when your data has text like "BMW X5" or "Audi A5"? You convert it. One Hot Encoding turns each category into its own column of 1s and 0s. Simple idea, but if you do it wrong your model breaks and most beginners don't even know why. There's also a trap that nobody warns you about, the Dummy Variable Trap. When you have 3 categories, you only need 2 columns. The third one is redundant and adds noise to your model. I cover exactly how to avoid it. In this notebook I cover two ways to do it: pd.get_dummies — quick and simple Sklearn's OneHotEncoder with ColumnTransformer — the proper production way Both approaches are used to predict car sell prices based on brand, mileage and age. 🔗 Full notebook + dataset + detailed explanation on GitHub: 👉 https://lnkd.in/dC5Pzygv Follow along, building this series one concept at a time, from scratch. #MachineLearning #Python #DataScience #OneHotEncoding #FeatureEngineering #GitHub #BeginnerML #100DaysOfCode

Skforecast Studio is an amazing tool that simplifies time series analysis and forecasting. This new interactive application helps you easily create forecasting workflows, while automatically generating reproducible Python code with the skforecast library. Here are some of the main features and functionality: 📈 Configure forecasting models through an intuitive interface. 🐍 Every step generates reproducible skforecast code ready for production. 🔍 Visualize time series, seasonality, and detect patterns before modeling. 📊 Evaluate model performance with built-in backtesting and metrics. 🚀 No installation needed, skforecast Studio Runs directly in your browser! Skforecast Studio can be significantly helpful to data scientists and researchers working with time series datasets. Domain experts can also benefit from the tool, by creating forecasting models without writing any code! Check the link below for more information and make sure to follow for regular data science content. 𝗦𝗸𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗦𝘁𝘂𝗱𝗶𝗼: https://lnkd.in/ga7b9vmQ 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #python #datascience #forecasting #AI

Learning Python feels a lot like climbing stairs… until you realize there’s a snake waiting halfway up 🐍 You start strong with: ✔️ print("Hello World") ✔️ Variables & Loops ✔️ Functions Confidence builds… “I’ve got this!” Then suddenly: ➡️ Data Structures ➡️ OOP ➡️ Libraries (NumPy, Pandas) ➡️ APIs / Automation ➡️ Machine Learning / AI And that’s when the sweat kicks in 😅 The truth? Every developer has stood on these same steps, wondering if they’re about to slip. The difference isn’t talent—it’s persistence. Keep climbing. One step at a time. Because eventually, that “scary staircase” becomes your daily routine… and the snake? Just part of the journey. #Python #LearningJourney #TechHumor #Programming #CareerGrowth #MachineLearning

🚀 Excited to share my latest project: Traitora, the Personality Predictor! 🧠✨ Ever wondered whether you're truly an introvert or an extrovert? This machine learning web app explores that by analyzing your everyday habits through fun, relatable inputs like: -> Time spent alone -> Stage fear -> Social event attendance -> Social media activity and more! Tech Stack: ✅ Python (Pandas, NumPy) for data handling & logic ✅ Scikit-learn for building the classification model ✅ Streamlit for a user-friendly interface ✅ Jupyter Notebook for data exploration and preprocessing The app processes your inputs, scales them using a pre-trained scaler, and predicts whether you lean more toward being an introvert or an extrovert instantly! 🔗 GitHub: https://lnkd.in/deJYiVBT 🔗 Website Link: https://lnkd.in/dxK3ktJd I’d love your feedback! 🙏 What features would you add or improve? Any suggestions to make the model or UI better? #MachineLearning #Python #Streamlit #DataScience #ScikitLearn #ArtificialIntelligence #Programming #coding #development

Week 2 of My AI/ML Journey Completed This week has been an exciting step forward in my journey into Artificial Intelligence and Machine Learning with Python. Here’s a quick reflection on what I explored and learned: Object-Oriented Programming (OOP): Strengthened my understanding of core concepts like classes, objects, inheritance, encapsulation, and polymorphism, building a strong foundation for scalable code. Streamlit Basics: Learned how to turn Python scripts into interactive web apps. From user inputs to displaying outputs, Streamlit made development fast and intuitive. Working with APIs (Gemini API): Integrated AI capabilities into applications using APIs. Also faced real-world challenges like rate limits and quota issues, which helped me understand deployment constraints. Hands-on Practice Days: Applied concepts through practice sessions, reinforcing learning by building small projects and experimenting with code. Project Development Built and deployed an AI-powered app using Streamlit and APIs. Live App: https://lnkd.in/giasawZs GitHub Repo: https://lnkd.in/giKqqH3W Note: Due to limited API request quotas Or Inactivity the app may sometimes show errors. . Looking forward to diving deeper into machine learning models in the coming weeks. #AI #MachineLearning #Python #Streamlit #LearningJourney #AIProjects

🚀 Day 19 of My Generative & Agentic AI Journey! Today’s focus was on exploring different types of functions in Python and how they are used in real-world programming. Here’s what I learned: ⚙️ Pure vs Impure Functions: • Pure Functions → Always return the same output for the same input and don’t modify external data 👉 More predictable and easier to test • Impure Functions → Depend on or modify external variables 👉 Less predictable, generally avoided in clean code 🔁 Recursive Functions: • A function that calls itself to solve a problem step by step 👉 Example use case: Breaking a problem into smaller parts (like factorial, countdown, etc.) ⚡ Lambda (Anonymous) Functions: • Small, one-line functions without a name • Useful for short operations where defining a full function is unnecessary 👉 Example use case: Quick calculations or transformations 💡 Key takeaway: Understanding different types of functions helps in writing cleaner, efficient, and more maintainable code. Slowly moving towards writing optimized and professional-level Python 🚀 #Day19 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

📊 Project Showcase: Student Performance Predictor Developed a machine learning model to predict student academic performance using features like study time, absences, and parental support. 🔧 Implementation: • KNN Algorithm • Data preprocessing & scaling • Model deployment using Flask • Frontend integration with React This project demonstrates end-to-end ML workflow from data to deployment. 🔗 GitHub Repository: https://lnkd.in/dkwmXV-n #DataScience #MachineLearning #AI #Python #ProjectShowcase

🚀 From Jupyter Notebook to Live ML Web App! I recently completed a project predicting the age of abalones using the UCI Abalone dataset. While the data analysis was done in Python, I wanted to make the insights accessible through a live interactive interface. 🛠️ The Technical Side: Performed deep EDA using Seaborn and Matplotlib to understand physical feature correlations. Trained and optimized a Random Forest Regressor to achieve the best prediction accuracy. Deployed the front-end visualization using Lovable to create an interactive "Viz Studio." 🔗 Check out the live app here: https://lnkd.in/dqgSp7hP 📂 Github Code: https://lnkd.in/dwUPKrSe This project helped me bridge the gap between backend machine learning models and frontend user experience. Feedback is always welcome! #DataScience #MachineLearning #Python #DataAnalytics #PortfolioProject #RandomForest

💻 Excited to share my latest project! I’ve built a Streamlit-based Scientific Calculator using Python that performs both basic and advanced mathematical operations. 🧮 Features: ●Addition, Subtraction, Multiplication, Division ●Square Root Calculation ●Power (Exponent) Function ●Logarithmic Operations Clean and interactive web UI using Streamlit 🌐 Tech Stack: Python | Streamlit | Math Library 🚀 I also deployed this project on Hugging Face Spaces, making it accessible as a live web application. This project helped me strengthen my understanding of: ✔ Python functions ✔ UI development with Streamlit ✔ Deployment of web apps ✔ Problem-solving logic 🔗 GitHub Repo: https://lnkd.in/d4n946w7 🌐 Live Demo: https://lnkd.in/dMti6kJX ✨ Always learning, building, and improving one project at a time! #Python #Streamlit #MachineLearning #WebDevelopment #Coding #StudentDeveloper #AI #Projects