House Price Prediction Web App with ML

Book Recommender System | Python, Flask, Machine Learning, Pandas • Redeveloped a machine learning–based Book Recommender System and deployed it as a web application using Flask • Integrated trained ML model and similarity matrix to provide real-time book recommendations • Converted the standalone ML project into a full-stack web application with interactive frontend using HTML, CSS, Bootstrap, and JavaScript • Displayed book details such as title, author, ratings, votes, and cover images dynamically • Used Pandas and Pickle for data processing and model integration

From ML Model to Live Web App 📈 I just deployed my Gold Price Predictor, a full-stack project that moves beyond the notebook and into production. The Stack: ML: Random Forest Regressor (Scikit-learn) for price forecasting. Backend: FastAPI (Python) for high-performance, async API serving. Frontend: React & Tailwind CSS for a modern user dashboard. Deployment: Fully hosted on Render. Key Takeaway: Building this taught me how to bridge the gap between Python data science and a production-ready web UI, specifically handling CORS and model serialization. Check it out: Live: https://lnkd.in/dTKdfkP5 GitHub: https://lnkd.in/dfxnez7A #MachineLearning #FastAPI #ReactJS #Python #FullStack #BuildInPublic

𝘼 𝟐𝙂𝘽 𝘿𝙤𝙘𝙠𝙚𝙧 𝙞𝙢𝙖𝙜𝙚 𝙞𝙨 𝙖 𝙙𝙚𝙥𝙡𝙤𝙮𝙢𝙚𝙣𝙩 𝙗𝙤𝙩𝙩𝙡𝙚𝙣𝙚𝙘𝙠. I was building a GenAI API and the image size was massive. Every deploy took forever. Then I switched to multi-stage builds. Here is the exact snippet that cut the size by 70%: # 𝘚𝘵𝘢𝘨𝘦 1: 𝘉𝘶𝘪𝘭𝘥 𝘍𝘙𝘖𝘔 𝘱𝘺𝘵𝘩𝘰𝘯:3.10-𝘴𝘭𝘪𝘮 𝘈𝘚 𝘣𝘶𝘪𝘭𝘥𝘦𝘳 𝘞𝘖𝘙𝘒𝘋𝘐𝘙 /𝘢𝘱𝘱 𝘊𝘖𝘗𝘠 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘮𝘦𝘯𝘵𝘴.𝘵𝘹𝘵 . 𝘙𝘜𝘕 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 --𝘵𝘢𝘳𝘨𝘦𝘵=/𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 -𝘳 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘮𝘦𝘯𝘵𝘴.𝘵𝘹𝘵 # 𝘚𝘵𝘢𝘨𝘦 2: 𝘙𝘶𝘯 𝘍𝘙𝘖𝘔 𝘱𝘺𝘵𝘩𝘰𝘯:3.10-𝘢𝘭𝘱𝘪𝘯𝘦 𝘞𝘖𝘙𝘒𝘋𝘐𝘙 /𝘢𝘱𝘱 𝘊𝘖𝘗𝘠 --𝘧𝘳𝘰𝘮=𝘣𝘶𝘪𝘭𝘥𝘦𝘳 /𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 /𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 𝘊𝘖𝘗𝘠 . . 𝘌𝘕𝘝 𝘗𝘠𝘛𝘏𝘖𝘕𝘗𝘈𝘛𝘏=/𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 𝘊𝘔𝘋 ["𝘱𝘺𝘵𝘩𝘰𝘯", "𝘢𝘱𝘱.𝘱𝘺"] The logic is simple: • 𝙎𝙩𝙖𝙜𝙚 𝟏 installs dependencies in a full environment. • 𝙎𝙩𝙖𝙜𝙚 𝟐 copies only the artifacts needed to run. No build tools. No cache. Just the app. Smaller images mean faster scaling and cheaper storage. 𝘼𝙧𝙚 𝙮𝙤𝙪 𝙨𝙩𝙞𝙡𝙡 𝙪𝙨𝙞𝙣𝙜 𝙨𝙞𝙣𝙜𝙡𝙚-𝙨𝙩𝙖𝙜𝙚 𝙗𝙪𝙞𝙡𝙙𝙨 𝙛𝙤𝙧 𝙝𝙚𝙖𝙫𝙮 𝙖𝙥𝙥𝙨? #Docker #DevOps #Python #PlatformEngineering #ShreyasTech

Meta built a Python type checker in Rust that makes mypy feel like dial-up. 🔥 Pyrefly already has 5.4k stars, and it's not hard to see why. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗲𝗮𝗹: Python's type checking story has always been slow and clunky. Pyrefly rewrites the whole thing from scratch in Rust (92% of the codebase). Giving you a lightning-fast type checker AND a full language server in one tool. Install it with pip, point it at your code, done. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 👉🏽 Three-step pipeline: resolve module exports, convert modules to bindings, solve bindings across your entire project 👉🏽 Uses Type::Var placeholders for recursive and unknown types that resolve later 👉🏽 Module-centric design prioritizing raw speed over fine-grained identifier solving 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁 👉🏽 Type inference that auto-detects variables and return types without annotations 👉🏽 Flow-sensitive types that track control flow (if/else, isinstance checks) 👉🏽 Incremental checking and parallel processing for large codebases 👉🏽 IDE support for VSCode, Neovim, and Zed out of the box 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 👉🏽 Modular Rust crate architecture (pyrefly_util, pyrefly_types, pyrefly_config) 👉🏽 MIT licensed, 153 contributors, 11k+ commits 👉🏽 Browser sandbox so you can try it without installing anything 👉🏽 Biweekly office hours and an active Discord community Python type checking has been an afterthought for too long. A Rust-powered checker that doubles as your language server could finally make it a first-class experience. 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘴𝘵𝘪𝘭𝘭 𝘶𝘴𝘪𝘯𝘨 𝘮𝘺𝘱𝘺, 𝘰𝘳 𝘩𝘢𝘷𝘦 𝘺𝘰𝘶 𝘴𝘸𝘪𝘵𝘤𝘩𝘦𝘥 𝘵𝘰 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘧𝘢𝘴𝘵𝘦𝘳? 🔗 Link in comments #python #rust #opensource #meta

🚀 Excited to share my latest project! I built a Streamlit-based Image Compressor Web App using Python that allows users to easily compress images and reduce file size while maintaining good quality. 🔧 What this tool does: • Upload an image directly in the browser • Compress the image efficiently • Reduce file size for faster sharing and storage • Download the optimized image instantly 💡 Why I built this: Large image files can slow down websites and take up unnecessary storage. I wanted to create a simple and fast tool that anyone can use without installing software. 🛠 Tech Stack: • Python • Streamlit • Image processing libraries 🌐 Try it here: Website link:- https://lnkd.in/gFMFCapa #Python #Streamlit #WebDevelopment #AI #MachineLearning #Projects #Developer #BuildInPublic

I built a Python automation engine that can hit 11ms reaction times. ⚡ To put that in perspective, the average human reaction time is about 250ms. Macro Studio is responding to screen changes before a human brain could even register that something happened. If you've ever tried building a high-speed desktop bot in Python, you know the struggle. Standard libraries are great for simple tasks, but the moment you try to scale up, time.sleep() bottlenecks your logic, and managing OS threads usually ends up freezing your UI thanks to the Global Interpreter Lock (GIL). I wanted an engine that actually respected Python's capabilities while providing ultimate control, so I built it myself. I’m excited to share Macro Studio. It’s a free, open-source automation framework I developed that bridges the gap between simple macro recorders and complex software development. Instead of heavy OS threads, it uses a cooperative multitasking yield architecture and direct GDI polling. The result? High-frequency pixel retrieval that easily averages superhuman reaction times on the Human Benchmark, while keeping the UI and subsequent tasks completely responsive. Because it runs pure Python, the possibilities are infinite. You can hook in OpenCV, ping external APIs, or integrate LLMs directly into your automation loop. Check out the full showcase below demonstrating how it flawlessly navigates complex game environments (like Roblox). Building this architecture from the ground up has been an incredible month-long side project. I'm keeping it 100% free and open-source under GPLv3 for anyone who wants to build high-performance macros without the paywalls. I've dropped a link to the YouTube showcase, the GitHub repository, and the documentation in the comments below. 👇 Drop a star, build something ridiculous, and let me know what you think of the architecture! #Python #SoftwareDevelopment #OpenSource #Automation #ComputerScience

Created a Machine Learning-based Heart Disease Prediction Web App using Python, Flask, React.js, HTML, CSS, and JavaScript. Integrated a trained Scikit-learn model through REST APIs to predict heart disease based on user inputs

𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝗻𝘁𝗼 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝘄𝗶𝘁𝗵 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 From raw Python code to beautiful, interactive dashboards — that’s the power of Streamlit. 🔹 Python handles the logic, data processing, and intelligence 🔹 Streamlit transforms it into a clean, interactive web interface 🔹 Users interact with dashboards, forms, and real-time analytics effortlessly No complex frontend frameworks. No heavy web development setup. Just Python + Streamlit = Rapid deployment. Whether you're building: 📊Data dashboards 🤖 ML model demos 📈 Business analytics tools 🧠 AI-powered apps Streamlit makes it simple, fast, and production-ready. As someone deeply interested in building practical tech solutions, I find tools like Streamlit incredibly powerful for converting ideas into usable products quickly. #SNSInstitutions #SNSDesignThinking #DesignThinkers #Python #Streamlit #WebDevelopment #TechInnovation

Switching to an AI-First model has really shown me where Python backends shine versus Node.js for complex logic. When integrating LLM orchestration or heavy data processing alongside our Next.js frontend, I find Django's structure and FastAPI’s performance just handle the state management and asynchronous tasks more cleanly. It’s less about raw speed and more about maintainable, predictable code for complex operations. For instance, setting up reliable long-running background workers? Python ecosystems feel much more mature for that heavy lifting than wrestling with Node's event loop in those scenarios. Am I alone in finding Python/Django/FastAPI a better fit for serious backend logic these days? #BackendDevelopment #Python #FastAPI #Django

If you work in computer vision and spend time wrangling datasets - curating, labeling, annotating, or doing QA - LightlyStudio was built for you. 🎬 It's a fully integrated computer vision platform that runs locally as a Python package. Getting started is literally one command: pip install lightly_studio. 😎 Under the hood, it's powered by Python (backend), Svelte (frontend), with performance-critical parts written in Rust and DuckDB handling database operations - making it fast even on large-scale datasets. Watch the full intro video to see how it all fits together and get up and running in minutes. See the video below 👇