🚬 Can artificial intelligence detect smoking habits from health data? In this video, I demonstrate a Machine Learning web application that predicts whether a person is a Smoker or Non-Smoker using biosignal features. The model is trained, evaluated, and deployed as an interactive app for real-time predictions. ⚙️ Tech Stack: Python | Scikit-learn | Streamlit 📊 Model Accuracy: ~80% 👉 Try the live application below and explore the predictions yourself 🔗 GitHub: https://lnkd.in/gdu5DYyc 🚀 Try Live App: https://lnkd.in/gJ2GqCDa 💬 I’d love to hear your feedback! #MachineLearning #DataScience #Python #AI #Projects #OpenToWork

🚀 Excited to share my latest Machine Learning project! ❤️ Heart Stroke Prediction Web App I built a web-based application using Machine Learning (KNN) and Streamlit that predicts the risk of heart disease based on user health parameters in real-time. 🔍 Key Features: • Data preprocessing (Feature Scaling & One-Hot Encoding) • KNN classification model • Interactive and user-friendly UI • Real-time prediction system 💡 Through this project, I gained hands-on experience in building ML pipelines, data preprocessing, and deploying models using Streamlit. 🛠️ Tech Stack: Python | Pandas | Scikit-learn | Streamlit | Joblib 🔗 GitHub Repository: https://lnkd.in/giwA6PET I’d love to hear your feedback and suggestions! #MachineLearning #Python #DataScience #AI #Streamlit #Healthcare #StudentProject

🚀 Turning Data into Decisions: My IPL Prediction Project is Live! I’m excited to share a project I’ve been working on — an IPL Match Prediction Web App powered by Machine Learning 🏏📊 This app analyzes match conditions and predicts outcomes in real-time through a clean and interactive interface. 🔧 Tech Stack: Python Scikit-learn Streamlit ✨ What makes it interesting? Real-time match prediction User-friendly interface End-to-end ML project (from model to deployment) Fully deployed and accessible online 🌐 Live Demo: https://lnkd.in/gREa9CHi This project helped me strengthen my understanding of ML deployment, model integration, and building interactive data apps. I’d really appreciate your feedback 🙌 Let’s connect and grow together! #MachineLearning #DataScience #Python #Streamlit #AI #WebDevelopment #IPL #Projects #LearningByDoing

I recently cleared a 5-round gauntlet at #Lyft (Python, SQL, System Design, Data Modeling, and Behavioral). While I’ve decided our paths don’t align for now, the process was an incredible masterclass in scale. Here are my top 3 takeaways: 1️⃣ Start before you’re "Ready" – Don't wait to master every tool. Start even if you don't know the stack fully. Pressure is the best teacher. 2️⃣ AI is your Tutor – I used AI as a mock examiner to grill me on edge cases. It's the fastest way to bridge a knowledge gap. 3️⃣ Context is King – For Architecture and System Design, don't just memorize theory. Link every learning to real-world use cases and trade-offs. Forever thankful for the insights and the challenge. Onward! 📈 #SoftwareEngineering #DataEngineering #SystemDesign #CareerGrowth #TechInterview #Lyft #EngineeringExcellence #AI #LearningMindset #ViralTech #CareerGoals #TorontoTech #PrincipalEngineer

🚀 Just Built an IPL Match Winner Prediction App! 🏏 IPL predictions aren’t just guesses anymore — I built a data-driven ML application to predict match outcomes. 💡 What it does: • Predicts match winner based on teams, toss & venue • Shows win probability for both teams • Interactive UI built using Streamlit ⚙️ Tech Stack: • Python • Pandas & NumPy • Scikit-learn • Streamlit 🧠 Model Used: • Random Forest Classifier 📊 Highlights: • End-to-end ML pipeline (Data → Model → Deployment) • Feature engineering for better predictions • Real IPL match dataset 🔗 GitHub Repo: https://lnkd.in/gzBRgVE6 This project helped me understand how to take an idea from raw data to a working ML product. #MachineLearning #Python #DataScience #IPL #Streamlit #Projects #AI #OpenToWork

🚀 Excited to share my latest project — **Dr. AI Healthcare**! An AI-powered web application that predicts diseases based on user symptoms and provides smart health insights using Machine Learning. 💡 Built with: Python | Flask | Machine Learning | HTML | CSS | JavaScript 🔗 Check it out here: https://lnkd.in/gPwyWQmw Would love your feedback and suggestions 🙌 #AI #MachineLearning #Healthcare #Python #WebDevelopment #StudentDeveloper #Innovation #TechProjects

🚀 Step-by-Step Roadmap to Learn Machine Learning in 3 Months Machine Learning may look complex at first, but with the right roadmap, anyone can start from zero and build real skills in just 90 days. 📌 Month 1: Build the Foundation Learn Python, NumPy, Pandas, basic math & data handling. 📌 Month 2: Understand Core ML Concepts Supervised vs Unsupervised Learning, key algorithms, and how models actually work. 📌 Month 3: Practice & Build Projects Work on real datasets, train models, evaluate results, and build end-to-end projects. 💡 The secret is not speed — it’s consistency. Even 1–2 hours of daily focused practice can completely change your direction in tech. Start today. Your future self will thank you. 💪 #MachineLearning #DataScience #AI #Roadmap #LearningJourney #TechCareers #Python

Tired of manual attendance sheets? Say hello to FaceTrack. 🛡️📸 I’m excited to share a project I’ve been working on: FaceTrack, an AI-powered attendance system designed to make classroom management seamless and secure. By leveraging facial recognition, I've eliminated the need for manual logs, reducing errors and saving valuable time for both professors and students. The Tech Stack: Backend: Python & Django Computer Vision: OpenCV Frontend: Tailwind CSS Database: SQLite I’m focusing on refining the AI accuracy and expanding the analytics dashboard next. I'd love to hear your thoughts or feedback in the comments! 👇 #Python #Django #OpenCV #WebDevelopment #AI #MachineLearning #SoftwareEngineering #PortfolioProject #DHRM

I built a Python event recommender to stop manually scrolling through hundreds of irrelevant events to find the good Data / AI / Tech stuff happening in Chicago. It pulls from 4 sources (Northwestern, UChicago, UIC, 1871), scores every event with a custom ranking model, and spits out a clean dashboard with the top 20 most relevant events after every run. The fun part was tuning the scoring — figuring out why a computer vision talk called "Fun with Fashion" was ranking correctly, why a generic upcoming event shouldn't float up just because it's soon, and how to stop one source from flooding the whole list. Still a work in progress but happy with where it landed 👇 🔗 https://lnkd.in/grk6yWMW #Python #DataEngineering #Analytics #AI #Chicago #OpenToWork

🚀 Built my first AI-based project using Python I created an **AI Fitness Recommendation System** that takes user inputs like age, weight, height, and diet preference to generate: ✔ Daily calorie requirements ✔ Protein intake suggestions ✔ Basic health analysis using BMI & BMR Instead of directly jumping into machine learning, I focused on understanding how systems make decisions using **logic and mathematical models**. ⚙️ Tech Stack: • Python – core logic and calculations • Flask – backend framework • HTML/CSS – basic frontend interface Through this project, I learned: • How to break down real-world problems into input → processing → output • How rule-based decision systems work • Basics of building backend using Flask • Structuring logic to generate personalized outputs This is just the beginning. Next, I plan to apply these concepts in **cybersecurity projects**. 🔗 GitHub Repository: https://lnkd.in/gzqqf5q4 #BuildInPublic #LearningInPublic #TechJourney #StudentInTech  #FutureDeveloper #SelfTaughtTech #DevJourney #ProjectShowcase  #CareerInTech #BreakingIntoTech #ConsistencyPays  #CodeEveryday #PracticalLearning #HandsOnProjects  #CyberSecurityJourney #AIProjects #PythonDeveloperLife  #AdityaBuilds #AdityaLearns