🚀 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

🏏 IPL Win Probability Predictor - Real-Time ML Project An interactive machine learning project where I explored predicting IPL chase outcomes using real match context. Built this in my free time as a self-initiated project to understand how data-driven decision-making works in sports and how machine learning models can be applied beyond theory. Try it here : https://lnkd.in/gZZuS4TQ This project is part of my portfolio and learning journey, where I worked with historical IPL data, built an ensemble machine learning model using Logistic Regression, Random Forest, and Extra Trees, and engineered features like run rate and match pressure to improve predictions. I used Python with scikit-learn for model development and Flask for backend integration, connecting it with a JavaScript-based frontend to create a complete working system. I’m currently continuing backend improvements and refining the model, so this is not the final version. The goal is to keep enhancing accuracy, performance, and overall system design as I continue learning and iterating. 💻 GitHub : https://lnkd.in/gcXCFRZF #MachineLearning #DataScience #WebDevelopment #IPL #Cricket #Python #Vercel

🫀 Built and deployed my first ML project — a Heart Disease Risk Predictor! Here's what I built: 🔹 A binary classification model using Python & Scikit-learn 🔹 Trained on real-world health parameters (age, cholesterol, blood pressure, etc.) 🔹 A clean, interactive UI built with Streamlit 🔹 Fully deployed and accessible online What I learned along the way: ✅ Data preprocessing & feature selection ✅ Model evaluation (accuracy, precision, recall) ✅ Turning a model into a usable product with Streamlit Live App - https://lnkd.in/gD_WmvZd GitHub Repo - https://lnkd.in/gviMh5Dm This project taught me that ML isn't just about building models — it's about building solutions. Would love your feedback! 🙌 #MachineLearning #Python #ScikitLearn #Streamlit #DataScience #HealthcareAI #MLProject #StudentDeveloper #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

🚀 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

Built an End-to-End ML Pipeline to Predict IPL 2026 Winner - Using ball-by-ball IPL data (2008–2025, 1100+ matches), I created a complete pipeline with a Streamlit dashboard to simulate the 2026 season and predict outcomes. Highlights: • Win probabilities for all 10 teams using historical + Bayesian strength • Match-by-match fixture simulation • Interactive dashboard for insights and comparisons Tech Stack: Python, Pandas, Plotly, SQLite, Streamlit Models: Stacking Ensemble (Random Forest, XGBoost, LightGBM, ExtraTrees, Neural Networks) Result: RCB has the highest win probability (11.43%), followed by RR. Is 2026 again the year for RCB? DM me for GitHub repo! Streamlit Royal Challengers Bengaluru (RCB) Drishti Bagla #RoyalChallengersBengaluru #MLPipeline #Python #Prediction

Built a Machine Learning Project using #RandomForest + #GridSearchCV I developed a model to predict whether an esports player should be offered a contract based on performance and behavioral data. - Key Highlights: • Worked with both numerical and categorical features • Applied feature engineering to improve model learning • Used One-Hot Encoding for categorical data • Performed hyperparameter tuning using GridSearchCV • Achieved: Accuracy = 86 Precision = 89 Recall = 84 F1-score = 90 Key Learning: Improving data quality and feature relationships had a much bigger impact than just tuning the model. Once the data became more structured and meaningful, the model performance improved significantly. Tech Stack: Python | Pandas | NumPy | Scikit-learn | Random Forest | GridSearchCV - Grateful for the guidance from Abhishek Jivrakh Sir during this project. 🔗 Check out the project: [https://lnkd.in/g8qw8NMF] #MachineLearning #DataScience #AI #Python #RandomForest #GridSearchCV #Projects #LearningByDoing #Bagging #Boosting

💕 What started as a simple idea is now evolving into something much bigger. Today, I added some exciting new features to make relationships more interactive, meaningful, and fun: ✨ AI Couple Photo Generator (Prompt-based) 🎨 Couple Avatar Generator (Anime-style) 🎁 AI Surprise Generator (Smart romantic ideas) 📅 Daily Tasks System with Love Score 🧠 Memory Wall to store special moments The goal? Not just another app… but a digital space where emotions, memories, and AI come together ❤️ 🔗 Try it here: https://lnkd.in/gSeXBVvG Built using Python & Streamlit — turning ideas into real-world products, one feature at a time. This is just the beginning… more coming soon 👀 #AI #StartupJourney #AppDevelopment #Streamlit #Python #Innovation #CoupleConnect #BuildInPublic

Excited to share one of my computer vision projects: Eye Detection / Drowsiness Monitoring System 👁️💻 Built a real-time system that detects eye activity using a webcam feed to monitor blink patterns and eye closure behavior. 🔧 Tech Stack: • Python • OpenCV • MediaPipe • Pygame (for alert system) 🚀 Key Features: • Real-time eye landmark detection • Eye open/closed state tracking • Drowsiness alert triggering • Low-latency live performance This project uses EAR (Eye aspect ratio) calculation, takes the input measurements from the webcam via the eye landmarks, calculates them compares it to the given threshold value and gives the output. Always interesting to see how simple vision signals can turn into impactful applications. GitHub: https://lnkd.in/dKG6CyhW #Python #OpenCV #ComputerVision #AI #MachineLearning #MediaPipe #Automation #TechProjects #Developer #GitHub

Cricket is a game of uncertainties, but can data predict the future? 🏏🤖 With IPL fever at its peak, I decided to put my Machine Learning skills to the test. I built an ML model in Google Colab, trained on a decade of historical IPL match data (2015 to 2025), to predict the ultimate champion of IPL 2026! 🏆 Check out the video below to see the data processing in action and find out which franchise the algorithm favors. The result might surprise you! 👀 🛠️ Tech Stack: Python, Google Colab, Scikit-Learn The model has made its choice, but I want to hear from you. Do you agree with the AI, or is your favorite team taking the cup this year? Drop your predictions in the comments! 👇 #IPL2026 #MachineLearning #DataScience #Python #GoogleColab #CricketAnalytics #TechProjects #AI