Excited to share my latest Machine Learning project: a Soccer Tactical Map Generator! ⚽️🗺️ This tool uses Computer Vision to analyze a football match image, detect keypoints on the field (like penalty corners) and the referee, and then generate a 2D tactical map showing the players' positions. The core project was built to handle both video and static images, and as you can see in the demo, the deployed web app works perfectly with picture uploads. A special thanks to our supervising professor, Bram Heyns, for his guidance throughout this project. Tech Stack: Core: Python Deployment: Flask & Docker Want to check it out? You can see all the code and details on my GitHub: https://lnkd.in/gtGzu5Xg Or, you can run the app directly using Docker: Pull the image: docker pull jfgm299/tactical-map-app-final Run the container: docker run -p 5001:5001 jfgm299/tactical-map-app-final Open your browser to http://localhost:5001 This was a fantastic challenge, blending my interests in computer vision and giving me a real vision on how to work in bigger projects. Check out the quick demo video to see the clean UI and how it works! #MachineLearning #ComputerVision #Python #Flask #Docker #SportsAnalytics #Soccer #Homography #AI #TechProject

🚀 Day 4 of my 100 Days AI & Data Engineering Challenge! ☕ Today, I built a Streamlit app for a coffee shop and explored the power of layout and interactivity features in Streamlit. On this journey, I learned: ✅ How to use Sidebars for easy navigation ✅ Creating Columns and using with blocks for clean layouts ✅ Using Expanders and Tabs to organize content ✅ Encapsulating layouts in functions for reusable components ✅ Choosing the right layout elements to enhance user experience The result is a fully interactive coffee shop app that provides: 1.About Us section 2.FAQs section 3.Customer Feedback form This project helped me understand how thoughtful layouts can drastically improve user experience in Python web apps. 💻 Check out the project on GitHub: https://lnkd.in/gpzhdXvj Link of Deployed app in Streamlit Community : https://lnkd.in/gy4U27Ay #100DaysOfCode #AI #DataEngineering #Python #Streamlit #WebAppDevelopment #LearningJourney

🚀 Introducing Live Preview in Mercury! Building web apps from Jupyter Notebooks just became even smoother. With Live Preview, you can now create your app and see the results instantly — side by side. ✅ Edit your notebook ✅ Adjust widgets and settings ✅ See the live app update in real time ✅ No refreshing, no re-running, no friction This feature turns Mercury into a true app-building workspace, perfect for: data analysts, educators, domain experts, ML engineers. Your workflow stays simple. Your app stays visible. Your creativity stays in flow. Try it out in Mercury and experience how effortless notebook-to-app creation can be. 🚀 https://RunMercury.com 👉 https://lnkd.in/eV7UuMSS #Mercury #MLJAR #AutoML #Jupyter #WebApps #DataScience #Python #OpenSource #Productivity #Innovation #AI #AIAgent

🎬 Excited to share my Advanced Movie Comparison & Recommendation App! MovieFlix!!! Built with Streamlit, this tool offers: ✅ Personalized movie recommendations using content-based filtering ✅ Side-by-side movie comparison feature ✅ AI-powered chatbot for interactive movie queries ✅ Movie posters & detailed information via OMDB API ✅ Fetching movie trailer in the live app using YouTube API ✅ Intuitive search functionality ✅ Real-time similarity analysis This project helped me apply data science and machine learning concepts in a practical, user-friendly interface. Special thanks to Nitesh Dhar Badgayan (PhD) Sir, from KPMG, for the invaluable guidance and to Globsyn Business School for fostering an environment of innovation and learning. 🔗 Try it here: https://lnkd.in/g3_JpMPX #DataScience #MachineLearning #RecommendationSystem #Streamlit #Python #MovieRecommendation #AIChatbot

🚀 New Project Deployed! — Skin Disorder Classification App Thrilled to share my latest Machine Learning project: a fully functional Streamlit web app that predicts different types of skin disorders based on dermatological parameters. 💻 The app features: • CSV-first bulk prediction interface • Auto header correction & scaling • Random Forest & Logistic Regression models • 98.61% accuracy achieved • Live deployment on Streamlit Cloud 🌐 Try it here 👉 Skin Disorder App 🔗 GitHub Repo 👉 View Source Code Built using: Python, Scikit-learn, Pandas, NumPy, Streamlit, Joblib #DataScience #MachineLearning #Streamlit #Python #AI #MLProjects #Portfolio #WomenInTech #DataScientist

Thrilled to share my new project — Visual Product Matcher It’s a Streamlit web app that uses CLIP (ViT-B/32) embeddings to find visually similar products! 🛍️ You can upload an image or paste an image URL, and the app searches through a catalog of 1000+ products to show the most visually similar matches — complete with thumbnails, metadata, and similarity scores. 💡 Highlights: Image upload & live preview Visual similarity search using CLIP Adjustable filters (Top-K & similarity threshold) Cached responses for speed Clean, mobile-responsive Streamlit interface Tech Stack: Python | Streamlit | Sentence-Transformers | CLIP | JSON API This project combines AI + Computer Vision + Interactive Dashboards — all running seamlessly on free tiers! #AI #MachineLearning #DeepLearning #ComputerVision #Streamlit #CLIP #Python #DataScience #Project #Innovation

🚀 EMI Predictor: Transforming Financial Decisions with Machine Learning Proud to showcase my latest project—a full-cycle EMI Eligibility Prediction system, blending the power of XGBoost and an interactive Streamlit interface to solve real-world financial classification challenges. What’s inside the project? • Developed a robust XGBoost model for accurate EMI eligibility prediction • Built a responsive Streamlit app for real-time, user-friendly predictions • Streamlined end-to-end ML pipeline: data preprocessing ➡️ model training ➡️ deployment • Used Git LFS for efficient model versioning and smooth collaboration Tech Stack: Python | XGBoost | Streamlit | scikit-learn | pandas Project Link: https://lnkd.in/dRveE3MM This journey covered data exploration, model optimization, app prototyping, and best engineering practices. Excited to apply these learnings to future ML and data-driven product roles! #MachineLearning #Python #FinTech #DataScience #XGBoost #Streamlit

🚀 Excited to share my latest project: Healthcare Disease Predictor! I built a Streamlit-based web application that predicts the likelihood of diabetes using a machine learning model. This project combines AI/ML with interactive UI design to make health insights more accessible. Features 🩺 Predict diabetes probability using a trained Random Forest Classifier ⚙️ Auto BMI calculation from height and weight 🌓 Light/Dark mode toggle for better UI experience 📊 Interactive charts and user-friendly design 💻 Built using Python, Pandas, Scikit-learn, Streamlit, and Matplotlib Tech Stack : Python | Streamlit | Scikit-learn | Pandas | Matplotlib Try it out 👉 GitHub repo: https://lnkd.in/gBSYjAW2

Project Showcase: Diabetes Prediction Web App 🚀 I recently built a Diabetes Prediction Web App using Python (Flask) and Machine Learning! 🩺💻 It predicts whether a person is diabetic or not based on key health features such as glucose level, BMI, insulin, and age. This project helped me understand how to: ✅ Integrate ML models into Flask ✅ Handle user input and web forms ✅ Deploy predictive models with a clean UI Tech Stack: 🔹 Python 🔹 Flask 🔹 Scikit-learn 🔹 HTML, CSS 🔗 Video demo below! Would love your feedback and suggestions 💬 #MachineLearning #Python #Flask #DataScience #WebDevelopment #WomenInTech #AI #MLProjects #FlaskApp

🚀 AI-Powered Loan Approval Prediction System | Machine Learning + Flask Deployment I built an AI-powered web app that predicts loan approval based on applicant data like income, credit history, and loan amount. The model is trained using Python (scikit-learn) and deployed with Flask on Render, giving real-time predictions through a clean, user-friendly interface. It helps businesses and finance professionals automate decisions, save time, and reduce manual effort. If you’re looking to integrate machine learning solutions into your business — let’s connect. 🔗 Live Project Link : https://lnkd.in/gjPKFhjq