🏠 House Price Prediction App (Flask + Linear Regression) Built a web app that predicts house prices in real-time using a Linear Regression model trained on housing data. ⚙️ Tech: Python, Flask, scikit-learn ✨ Features: Simple UI, instant predictions, REST API (/api/predict) 📊 Input: area, bedrooms, bathrooms, age, garage Hands-on experience with ML model training + deployment. Feedback is welcome! #MachineLearning #LinearRegression #Flask #Python #AI #DataScience

🚀 I just built and deployed my first AI-powered Web Application! I wanted a faster way to extract value from long documents, so I built an AI Book Summarizer. You can drag and drop any PDF or text file into the app, and it instantly generates an Executive Summary, Key Themes, and Actionable Takeaways. I even added a chat feature so you can ask the document specific questions! Tech Stack: Python, Streamlit, and Google's new Gemini 2.5 Flash model. You can try it out live right here: [https://lnkd.in/gBC65w3T] Want to see how it works under the hood? Check out the code: [https://lnkd.in/guDPNYb7] I'd love to hear your feedback or see what documents you test it with! #Python #ArtificialIntelligence #GeminiAPI #Streamlit #SoftwareDevelopment #Portfolio

🚀 Introducing ALGO_TRACKER.AI – Bridging Machine Learning with Static Code Analysis for Python. As software systems scale, quantifying Technical Debt and maintainability becomes crucial. Traditional rules-based linters often miss the complex interplay of metrics that define genuine code risk. To address this, I built ALGO_TRACKER.AI, an intelligent auditor that moves beyond rigid rules. It leverages a trained XGBoost model to analyze static code metrics (LOC, Cyclomatic Complexity, Halstead Metrics) recursively fetched from any public Python repository via the GitHub API. The goal is simple: Provide developers and tech leads with a predictive, probability-based "Bullish" (Clean/Maintainable) or "Bearish" (High Technical Debt) rating for their codebase. Key Features: 🔹 Deep recursive scanning of Python (.py) files using GitHub’s /git/trees API. 🔹 Static Metric Extraction (Radon/Lizard) to quantify complexity. 🔹 Intelligent Risk Prediction using an optimized XGBoost classifier. Tech Stack (High Performance & Scalable): ⚛️ Frontend: React, Tailwind CSS (Deployed on Netlify) ⚡ Backend: FastAPI (Python), (Deployed on Railway) 🤖 Machine Learning: Scikit-learn & XGBoost Check out the working prototype here: https://lnkd.in/g2tVERcH #MachineLearning #SoftwareEngineering #Python #FastAPI #ReactJS #FullStack #ArtificialIntelligence #Innovation

Excited to Share My Latest Project! I’m proud to present SmartCodeFixer – AI-Based Code Error Detection & Fixing System 💻 This project is designed to help developers automatically detect coding errors and provide intelligent suggestions to fix them, improving efficiency and reducing debugging time. 🔹 Tech Stack: • Python • Machine Learning / AI • Flask / Backend Integration • HTML, CSS, JavaScript (Frontend) 🔹 Key Features: • Automatic code error detection • Smart suggestions for bug fixing • Clean and user-friendly interface • Faster debugging workflow 🔹 What I Learned: • Applying AI concepts to real-world problems • Building full-stack applications • Improving problem-solving and debugging skills 🔗 GitHub Repository: https://lnkd.in/gmjfqJ2v #ArtificialIntelligence #MachineLearning #Python #WebDevelopment #Innovation

Stop spending days reading code alone. 🚀 I built CodeBoard—an AI-Powered Repository Intelligence Platform that acts as your personal technical consultant. 🧠 Whether you are onboarding to a new project or auditing complex business logic, CodeBoard leverages Python AST parsing and Llama 3.3 (via Groq API) to give you instant insights. Key Highlights in this Demo: ✅ Real-time Logic Auditing: Identifying API handlers and function flows in seconds. ✅ Contextual Understanding: No more "hallucinations"—the AI understands the actual code structure. ✅ Onboarding Efficiency: Perfect for developers who want to skip the manual line-by-line review. Built with: Python, FastAPI, React, and Groq. 🛠️ Check out the demo below! 👇 #FullStack #AI #GenerativeAI #Python #WebDevelopment #OpenSource

🚀 𝗘𝘅𝗰𝗶𝘁𝗲𝗱 𝘁𝗼 𝗦𝗵𝗮𝗿𝗲 𝗠𝘆 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗟𝗼𝗮𝗻 𝗔𝗽𝗽𝗿𝗼𝘃𝗮𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺 I’m happy to present my latest project – a Loan Approval Prediction System built using Machine Learning and Flask API. 💡 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗱𝗼𝗲𝘀: This system predicts whether a loan application will be Approved ✅ or Rejected ❌ based on key factors like: CIBIL Score Annual Income Number of Dependents Other financial details ⚙️ 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: Real-time prediction using a trained ML model Simple and user-friendly interface Backend powered by Flask API Clear decision output with probability insights 🧠 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗨𝘀𝗲𝗱: Python Machine Learning (Model Training) Flask (API Development) HTML/CSS (Frontend UI) 📊 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: End-to-end ML project development Model training and evaluation API integration with frontend Handling real-world financial datasets This project gave me hands-on experience in building a complete 𝗠𝗟-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝘄𝗲𝗯 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻. 🎯 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗳𝘂𝗿𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀! #MachineLearning #Python #Flask #DataScience #AI #WebDevelopment #Projects #LearningJourney

From building systems to uncovering insights. 🚀 As a Software Engineer (Laravel/PHP), my focus has always been on the architecture- the "how" of data. Now, I’m mastering the "why" by diving deep into Statistics and Python. I’m not starting over; I’m upgrading my stack. The newest tool in my kit: Streamlit 🛠️ For a Backend Developer, discovering Streamlit feels like a superpower. It’s the perfect bridge for my transition into Data Science because: ✅ Zero Frontend Overhead: I can turn Python logic into interactive apps in minutes. ✅ Pure Logic: I’m focusing 100% on the statistical story, not UI boilerplate. ✅ Rapid Prototyping: It allows me to build as I learn. The Goal: To become an engineer who doesn't just build the engine but understands the data driving it. 📈 Currently building a project to bring these statistical concepts to life. The results are coming soon! Any Streamlit pros in my network? What’s one tip you wish you knew when you started? 👇 #DataScience #Python #Streamlit #SoftwareEngineering #BackendDeveloper #Statistics #BuildInPublic #CareerGrowth

🚀 Built a Simple AI Code Debugger I recently created a small project using Streamlit where users can upload screenshots of their code and get help to debug it. 🔍 Features: * Upload up to 3 code screenshots * Get step-by-step hints (without code) * Or get full solution with corrected code * Clean and simple UI ⚙️ Tech Used: * Python * Streamlit * Google Gemini API 💡 Idea: Sometimes beginners struggle to understand errors. This tool helps them by giving hints or solutions directly from their code screenshots. 🌐 Live Demo: https://lnkd.in/gwedCaYA 🔗 GitHub: https://lnkd.in/g-FFETRh I am still improving this project and planning to add more features soon. Feedback is always welcome 🙌 #Python #Streamlit #AI #MachineLearning #WebApp #BeginnerProject #Coding #Developer

I got tired of manually clicking through my app after every change. You build a feature… and then spend the next 10–15 minutes just verifying it works. No proper tests. Just clicking around. So I built something that does it for me. Introducing RunSight. npx runsight ./my-project It: • Detects your project (Node.js, Python, or even static HTML) • Starts your dev server automatically • Launches a browser and explores your UI • Uses AI vision to decide what to click • Captures screenshots at every step • Records a full demo video • Outputs a structured report.json All in seconds. The part I’m most excited about: It works as a skill for Kiro, Cursor, and Claude - so your AI coding assistant can run it and understand your app without you explaining anything. This is v1 - just shipped, already usable, and I’m actively improving it based on feedback. I’m exploring a future where your project can demo itself. Would love feedback from builders here 👇 📦 npm: https://lnkd.in/gYhJj9YH ⭐ GitHub: https://lnkd.in/gEFV3_Mi #buildinpublic #opensource #devtools #ai #nodejs

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