JuliaCon 2025’s Post

🏆 Announcing the Winners of Genie's First App Competition! A few months ago, we challenged the Julia community to build innovative, real-world web apps with Genie Builder, our VS code extension designed to streamline the development of Julia web apps. With generous support from our friends at JuliaHub, these apps are taking home cash prizes and hosting credits: 🥇 1st Place: ClapeyronGUI by Pierre Walker Revolutionizing thermodynamic modeling with user-friendly calculations 🥈 2nd Place: LPGenie by Joe Tan Transforming production planning with data-driven optimization 🥉 3rd Place: StrayStat by Victor F. Advancing veterinary research with diagnostic dashboards ✨ Honorable Mentions: - OPTI-Flex by Kai Gen Tay: an aggregate production planning app for optimizing workforce and inventory levels. - Endonuclease Digestion by Jean Braulio Ramírez: a DNA digestion simulator using popular restriction enzymes. - Nami by Katharine Medetgul-Ernarm, founder of Guardiome: a genetic variant explorer for personal DNA data. 📖 Read the full blog post for details and links to the winning apps, and stay tuned for an upcoming webinar where these creators will demo their work and share their experiences. 🔗 https://lnkd.in/eedaWdHj #Genie #JuliaLang #LowCode #NoCode #DataScience #DataApp

🌟 Day 179: 🎬 Movie Recommendation App with Streamlit 🎬 Today, I built a Movie Recommendation App using Streamlit, a powerful framework for creating interactive web applications. This project allowed me to dive deep into web development, data manipulation, and recommendation systems. Here’s what I worked on: - Setting Up the Environment: Installed Streamlit and Pandas to handle data operations. - Loading Data: Utilized a movie dataset from Kaggle, which contains millions of movies along with their genres, ratings, and other details. - Genre-Based Recommendations: Implemented a feature where users can select a genre and receive personalized movie recommendations. The app filters movies based on the selected genre and displays details like title, release year, rating, and even the movie poster. - Caching for Performance: Applied caching to improve app performance when loading large datasets. - Interactive User Interface: Created an intuitive layout, allowing users to easily navigate through the dataset and discover movies that match their interests. - Additional Features: Added a sidebar that provides information about the app, as well as statistics like the top 5 highest-rated movies in the dataset. Check out the full project on GitHub: https://lnkd.in/eW4q5QkV #Streamlit #WebDevelopment #RecommendationSystem #DataScience #CodingProjects #Python #LearningJourney #TechInnovation #MovieRecommendations Excited to keep building and learning new technologies! 🚀

Hello everyone! I'm thrilled to share that we're embarking on an exciting journey into the latest advancements in web technology. Recently, I completed my first FastAPI lecture as part of our Web3.0 and Metaverse course, and it's been a game-changer! FastAPI has completely revolutionized my approach to website building. It's incredibly user-friendly yet powerful, allowing me to effortlessly define data structures, interact with databases, and set up endpoints for accessing and updating information. And the best part? FastAPI automatically generates API documentation, making life a whole lot easier! Check out more in the video! In addition, I've added Poetry and Unicorn to our development toolkit. Poetry, the Python dependency manager and build tool, has transformed how I handle project dependencies. Its intuitive interface simplifies dependency declaration, virtual environment management, and package distribution—all in one convenient package. With its lock file feature, reproducible builds are a piece of cake. And let's not forget Unicorn—the lightning-fast ASGI server powering FastAPI applications with unmatched speed and performance. With Unicorn, handling thousands of requests per second is effortless, making it perfect for crafting high-performance APIs and web apps. I'm beyond excited to delve deeper into Python development and Web3.0 tech. Let's embark on this journey together and explore new horizons! #FastAPI #PythonDevelopment #Web3 #Metaverse #Poetry #Unicorn #ExcitingJourney #TechInnovation #CodeDevelopment #PythonProgramming #LearningAndDevelopment #SoftwareEngineering

🔍 Exploring Machine Learning with Supervised Learning! I’m excited to share that I’ve started my journey into Machine Learning, beginning with Supervised Learning! 🎓 My first project, House Price Prediction, applies a regression model to predict housing prices based on features like location, size, and year built. 🏡💻 This project has allowed me to: Get hands-on experience with data preprocessing, feature engineering, and model training. Deploy a Flask app to serve predictions via an API. This is just the beginning, and I’m looking forward to diving deeper into more advanced models and real-world applications! 🚀 https://lnkd.in/gAVsSAnW #MachineLearning #SupervisedLearning #DataScience #HousePricePrediction #Flask #Python #LearningJourney

New retrospective post 🔎 This time it's about a Streamlit wallpaper generation app and how I had to rebuild the entire dataset from a new data source :) Streamlit #webapp #imagegeneration #retrospective #personalprojects

Might be useful for others as well

🚀 Excited to share my latest project! 🚀 I have developed a Diabetes Prediction Model using a Support Vector Machine (SVM) classifier Algorithm (SVC) to help identify individuals at risk of diabetes based on various health metrics. This project represents a significant step in combining machine learning with web development to create a practical, user-friendly tool for health monitoring. Key Features: 💡 Machine Learning: Implemented an SVM (SVC) model for accurate diabetes prediction. 🔍📊 🌐 Frontend: Crafted a responsive and interactive user interface using HTML, CSS, and JavaScript to enhance user experience. 🖥️✨ 🔧 Backend: Built the web application using Flask, ensuring seamless interaction between the frontend and the prediction model. 🔄🛠️ Why This Matters: Early detection of diabetes can significantly improve management and treatment outcomes. 🏥 This web app aims to provide a simple yet effective tool for individuals to assess their risk and take proactive steps toward their health. ❤️ Learnings and Challenges: Throughout this project, I honed my skills in machine learning model development, web design, and backend integration. 🎓 One of the key challenges was ensuring that the prediction model not only performed well but also integrated seamlessly with the web app to provide real-time results to users. ⏱️💻 Check it out and let me know your thoughts! 🌟 #MachineLearning #WebDevelopment #Flask #DiabetesPrediction #DataScience #SVM #HTML #CSS #JavaScript #HealthCare #Python #AI

I HAVE COMPLETED MY TASK #1 GIVEN BY CODTECH IT SOLUTIONS 🚀 Excited to Share My Latest Project: Housing Price Prediction Web App! 🏠 I recently developed a Housing Price Prediction web application that combines machine learning and web development to deliver a seamless user experience. Here's what the project entails: ✅ User-Friendly Interface: Built with HTML and CSS, the app has a clean, responsive design for inputting housing details like square footage, bedrooms, and location. ✅ Powerful Back-End: Using Flask (Python), the app processes user inputs and connects them to a pre-trained Linear Regression model. ✅ Machine Learning Integration: Trained on the Boston Housing Dataset, the model accurately predicts housing prices, leveraging scaled and preprocessed features. 🔗 How It Works: 1️⃣ Users input housing details into the web form. 2️⃣ The Flask back-end processes the inputs and scales the data. 3️⃣ The trained model predicts the price, which is then displayed on the webpage. 💡 Skills Highlighted: This project showcases my ability to integrate front-end design, back-end development, and machine learning models into a cohesive application. I'm thrilled to keep learning and creating practical solutions like this! Feedback and suggestions are always welcome. 😊 #WebDevelopment #MachineLearning #Flask #LinearRegression #HTML #CSS #Python #HousingPricePrediction

💻 Proud to announce the completion of my 5th and final project at CodexCue Software Solutions !💻 Introducing the Laptop Price Prediction App, a tool designed to help users estimate the price of a laptop based on specific parameters. Users can select various features, and the app will not only predict the price but also display the full specifications of the chosen laptop. 🔍 How it works: 1. Select Laptop Features:Choose the specifications that matter to you. 2. Predict:The app calculates the estimated price based on the selected parameters. 3. Review Specifications: View detailed specifications of the selected laptop along with the predicted price. This project was a fantastic journey, and it’s been a pleasure to develop innovative solutions at CodexCue Software Solutions. 💻 Check out the project on GitHub: https://lnkd.in/dwx7APd8 #MachineLearning #LaptopPricePrediction #DataScience #Python #CodexCue #SoftwareDevelopment #AI #TechInnovation #UIUX

"A web application for predicting the risk of stroke using machine learning models based on user-provided health data." "This project is a heart stroke prediction web application built with Django. It utilizes a pre-trained Random Forest model to assess the likelihood of a stroke based on various health parameters entered by the user. The app aims to raise awareness and provide early insights into stroke risks." "Heart Stroke Prediction App using Django and Machine Learning. This application allows users to input various health metrics, such as age, glucose levels, and lifestyle factors, to predict the likelihood of stroke occurrence. Features include data validation, responsive design, and user-friendly interface." #Machinelearning #Django #HTML #CSS #Randomforestmodel #classification #prediction