AI-Powered Medical Symptom Checker with Python and Scikit-Learn

✨ A New Beginning in My AI/ML Journey As part of the Industry Immersion Program by MeetMux, Day 3 marked my transition from setup to execution. 🔹 What I tackled today: Built a basic data pipeline using Python, NumPy, and Pandas — focusing on how data is processed, structured, and analyzed. 🔹 What I learned : The concept of vectorization in NumPy — instead of using loops, operations can be applied to entire datasets at once, making computations significantly faster. This is a core technique used in real-world AI systems. 🔹 My goal: To continue building a strong foundation in data handling and move towards implementing real-world machine learning models by the end of this week. 🔗 My Work (GitHub): https://lnkd.in/gQNYJ8ce #AI #MachineLearning #Python #NumPy #Pandas #IndustryImmersion #LearningInPublic #MeetMux

🚀 Excited to share my latest project: Student Performance Prediction System I built a Machine Learning web application that predicts student performance based on various academic and demographic factors. 🔍 Key Highlights: • End-to-end ML pipeline (data preprocessing → training → prediction) • Built using Flask for deployment • Clean and interactive UI • Model serialization using dill 🌐 Live Demo: https://lnkd.in/gGBekFvt 💻 Tech Stack: Python, Scikit-learn, Pandas, NumPy, Flask This project helped me strengthen my understanding of real-world ML deployment and pipeline design. I would love your feedback and suggestions! 🙌 #MachineLearning #DataScience #Python #Flask #AI #StudentProject #MLProjects

Most beginner datasets are either too simple… or too unrealistic. So I decided to create one. I built a dataset around student life — study habits, sleep patterns, social media usage, and exam performance. 👉 https://lnkd.in/ejXkATfr The idea was simple: make something beginner-friendly, but still useful for real analysis. You can: • Predict exam scores • Explore behavior patterns • Build and test ML models No complex setup. Just clean, usable data. I’ve also added a small challenge — build something with it and share your results. Curious to see what you create. #DataScience #MachineLearning #Kaggle #Python #DataProjects #LearningJourney #BeginnerFriendly

📊 Project Showcase: Student Performance Predictor Developed a machine learning model to predict student academic performance using features like study time, absences, and parental support. 🔧 Implementation: • KNN Algorithm • Data preprocessing & scaling • Model deployment using Flask • Frontend integration with React This project demonstrates end-to-end ML workflow from data to deployment. 🔗 GitHub Repository: https://lnkd.in/dkwmXV-n #DataScience #MachineLearning #AI #Python #ProjectShowcase

I’m excited to share the latest version of NTQR. A Python package for the logic of unsupervised evaluation of classifiers. Want to get started in under 60 seconds? Here is the "Quick Start" flow to get the documentation and interactive notebooks running locally: 1️⃣ Install the package: pip install ntqr 2️⃣ Set up your workspace: Navigate to the folder where you want your tutorial notebooks to live: cd path/to/your/working/directory 3️⃣ Fetch the docs: Run the built-in helper to copy all tutorial notebooks into your current folder: ntqr-docs 4️⃣ Launch & Learn: Open the environment and dive into the examples: jupyter notebook You can see the notebooks at readthedocs.io (NTQR doc page: https://lnkd.in/eugreNDd). The notebooks walk you through the math and the code, making it easy to apply these techniques to your own AI evaluations of classifiers. Give it a spin and let me know what you think! 👇 #Python #DataScience #MachineLearning #AI #OpenSource #NTQR #FormalVerification #AIEvaluation #UnsupervisedEvaluation

🚀 Day 3 – Industry Immersion Program (AI/ML Track) Today’s focus was shifting from “just coding” to data handling and processing. ✅ Revised Python fundamentals (loops, functions, data containers) ✅ Explored NumPy for matrix operations and vectorization ✅ Used Pandas to load and analyze datasets ✅ Completed proper project structure and GitHub documentation 💡 Key Learning: Vectorization helped me understand how large datasets can be processed efficiently without using loops. 🎯 Goal for this week: Build a strong foundation in data handling and move towards machine learning models. GitHub - https://lnkd.in/d2WNQcQs #IndustryImmersion #AI #MachineLearning #Python #NumPy #Pandas #LearningInPublic 😊

I’m excited to share my first-ever deployed machine learning project: a Heart Disease Prediction App. It’s been a rewarding challenge to move beyond notebooks and build a functional tool that is fun to use. The Tech Stack: Model: K-Nearest Neighbors (KNN) built with Scikit-Learn. Interface: Streamlit for the front-end and deployment. Data: End-to-end pipeline including cleaning and feature scaling. Try the app here: https://lnkd.in/gW8qFtmN #MachineLearning #DataScience #Python #Streamlit #FirstProject

Built a Machine Learning API using FastAPI I developed a machine learning-based API that predicts salary based on user input level. My all project and machine learning model based API on github. GitHub : https://lnkd.in/gR_qsxwM 🔹 Implemented Machine Learning algorithms and integrated them with FastAPI 🔹 Enabled real-time prediction using API based on user input 🔹 Designed RESTful endpoints for seamless interaction 🔹 Stored and retrieved prediction data dynamically 💡 This project demonstrates how ML models can be deployed and used through APIs in real-world applications. Tech Stack: Python, FastAPI, scikit-learn #MachineLearning #FastAPI #Python #DataScience #AI #BackendDevelopment #MLProjects

🚀 Excited to share my latest work on Machine Learning & AI Practicals! I’ve created a collection of hands-on Jupyter Notebooks covering core ML concepts and algorithms as part of my academic learning journey. This project helped me strengthen my understanding by implementing models from scratch and analyzing real datasets. 📂 Key topics covered: 🔹 DataFrame Operations 🔹 Correlation Matrix 🔹 Normal Distribution 🔹 Simple Linear Regression 🔹 Logistic Regression 🔹 Decision Trees (ID3 Algorithm) 🔹 Confusion Matrix 🔹 Decision Tree Pruning 🛠️ Tools & Technologies: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Jupyter Notebook 💡 Through this project, I gained practical experience in: ✔️ Data preprocessing ✔️ Model building & evaluation ✔️ Data visualization ✔️ Understanding ML algorithms in depth 🔗 Check out my GitHub repository: https://lnkd.in/gSbCu_Aq I’m continuously learning and exploring more in the field of AI & ML. Open to feedback and suggestions! #MachineLearning #ArtificialIntelligence #DataScience #Python #LearningJourney #GitHub #Students #AI #ML

🚀 Excited to share my project: AI-Based Clinical Decision Support System (CDSS) Built a simple ML-based system that predicts diseases from symptoms to support faster decision-making in healthcare. 🔹 Uses Random Forest 🔹 Built with Python & Flask 🔹 Clean web interface with real-time results 🔗 GitHub: https://lnkd.in/dnFTqB3c Would love your feedback! 😊 #AI #MachineLearning #Healthcare #Python