🚀 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

🏏 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

Every journey begins with a single step — and here’s mine. I’ve built a Code Debugger App using Streamlit as part of my learning path in Data Science and Machine Learning. While it’s a simple project, it helped me understand how to turn logic into an interactive tool. 🔍 What I learned from this project: Building interactive apps with Python Structuring problem-solving logic Handling and analyzing code inputs Creating user-friendly interfaces 🌐 Live App: https://lnkd.in/gkKkyJtc 💡 My goal is to move toward more advanced projects like: Data analysis & visualization Machine learning model integration AI-powered tools This is just the beginning — more exciting projects coming soon! I’d really appreciate your feedback and suggestions 🙌 #DataScience #MachineLearning #Python #Streamlit #LearningJourney #CSE #AI #Projects

🚀 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

🚀 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

🚀 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

🚀 Just built my first Movie Recommendation System! As part of my journey learning Data Science, I created a Bollywood Movie Recommender Web App using Python. 💡 This project helped me understand how platforms suggest content based on your interests. 🛠️ Tech Stack: Pandas & NumPy (data handling) Scikit-learn (TF-IDF + Cosine Similarity) Streamlit (interactive UI) Matplotlib & Seaborn (visualizations) ✨ Features: Recommend movies based on similarity Filter by genre, actor, director, and year Trending & top-rated movie sections Interactive visual insights Movie comparison with similarity score 🧠 What I learned: How to clean and prepare real-world data Feature engineering using text data Building a content-based recommendation system Turning ML logic into a usable web app 🔗 GitHub Repository:https://lnkd.in/de3ZxfSH This is one of my first hands-on projects, and I’m excited to keep building and improving 🚀 I’d really appreciate your feedback or suggestions 🙌 #Python #DataScience #MachineLearning #Streamlit #Pandas #ScikitLearn #Projects #Learning #AI

🫀 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 one of my portfolio projects: an AI-Powered Medical Symptom Checker built with Python, scikit-learn, and Gradio. This app allows users to select symptoms and get: Top 3 likely disease predictions Confidence scores Basic precautions Safety disclaimer Clean professional UI with dark mode I built this project to strengthen my skills in machine learning, data preprocessing, model integration, and user-focused interface design. GitHub: https://lnkd.in/gCZkUFuj #Python #MachineLearning #AI #Gradio #ScikitLearn #HealthcareAI #GitHub #PortfolioProject

🚀 Excited to share my latest Machine Learning project — Cell Phone Price Prediction 📱🤖 In this project, I developed a machine learning model that predicts the price range of mobile phones based on different features and specifications. 🔍 Project Highlights: ✅ Data Cleaning & Preprocessing ✅ Exploratory Data Analysis (EDA) ✅ Feature Selection ✅ Model Training & Evaluation ✅ Accuracy Comparison of Multiple Algorithms ✅ Performance Visualization using Graphs & ROC Curve 🛠️ Technologies Used: • Python • Pandas & NumPy • Matplotlib & Seaborn • Scikit-learn • Jupyter Notebook 📊 This project helped me improve my understanding of: Machine Learning workflows, classification models, data preprocessing, and model evaluation techniques. 📌 GitHub Project Link: [ https://lnkd.in/gKXFjyh2 ] I’m continuously learning and building projects in Data Science & Artificial Intelligence. Feedback and suggestions are always welcome! #MachineLearning #DataScience #ArtificialIntelligence #Python #AI #StudentProject #ScikitLearn #DataAnalytics #JupyterNotebook #MLProject