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GlycoTrack is an advanced diabetes prediction app built with Streamlit. It leverages multiple machine learning algorithms, including K-Nearest Neighbors, Random Forest, SVM, and XGBoost, to provide accurate predictions and detailed performance insights like F1-Score and AUC-ROC.

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Here's a combined README for your AI Nexus repository, featuring all the apps you've developed so far. This README covers each project, outlines their features, and provides installation and usage instructions:


🤖 AI Nexus - Multi-Purpose AI/ML Hub

AI Nexus is a central repository that hosts multiple AI/ML projects under one roof. From image classification to healthcare predictions, explore a diverse range of applications powered by advanced machine learning algorithms.

Projects Included:

  1. 👗 StyleScan - Fashion MNIST Image Classification
  2. 🩺 GlycoTrack - Advanced Diabetes Prediction
  3. 🌸 IrisWise - Iris Species Classification
  4. 🎓 GradeCast - GPA Prediction Model
  5. 🧮 DigitSense - MNIST Handwritten Digit Classifier
  6. 🖼️ ObjexVision - CIFAR-10 Object Recognition

🚀 Quick Links to Live Demos

  • 👗 StyleScan - Fashion MNIST Image Classification : Open in Streamlit
  • 🩺 GlycoTrack - Advanced Diabetes Prediction : Open in Streamlit
  • 🧮 DigitSense - MNIST Handwritten Digit Classifier : Open in Streamlit
  • 🌸 IrisWise - Iris Species Prediction : Open in Streamlit
  • 🖼️ ObjexVision - CIFAR-10 Object Recognition : Open in Streamlit
  • 🎓 GradeCast - GPA Prediction Model : Open in Streamlit

📂 Projects Overview

1. 🩺 GlycoTrack - Advanced Diabetes Prediction

An intuitive app for predicting diabetes based on health metrics like Glucose, Blood Pressure, BMI, etc. It uses various machine learning models (KNN, Random Forest, SVM, etc.) to provide predictions and performance insights.

Features:

  • Real-time diabetes prediction
  • Interactive user interface with animations
  • Supports multiple machine learning models

2. 🌸 IrisWise - Iris Species Classification

Predict the species of Iris flowers based on input features (sepal and petal dimensions) using K-Nearest Neighbors and other machine learning models.

Features:

  • Real-time iris species prediction
  • Dynamic visualizations and tooltips for enhanced user experience

3. 🎓 GradeCast - GPA Prediction Model

Estimate GPA/CGPA based on student performance data, providing an accurate prediction of academic success. Built using regression models.

Features:

  • Input academic scores to predict GPA
  • Simple and user-friendly interface

4. 🧮 DigitSense - MNIST Handwritten Digit Classifier

Identify handwritten digits (0-9) with this accurate, real-time classifier powered by a CNN model.

Features:

  • Recognizes digits from 0-9
  • Instant results with confidence scores

5. 👗 StyleScan - Fashion MNIST Image Classification

Predict the clothing category from grayscale images of fashion items (shirts, shoes, dresses, etc.) using deep learning models.

Features:

  • Classifies 10 categories of fashion items
  • Accurate predictions using CNN architecture

6. 🖼️ ObjexVision - CIFAR-10 Object Recognition

Recognizes 10 types of objects including airplanes, birds, and automobiles using CNNs.

Features:

  • Real-time object recognition
  • 10 object categories with instant prediction feedback

🛠️ Installation & Setup

To set up any of the projects, follow the steps below:

  1. Clone the Repository:

    git clone https://github.com/Hunterdii/AI-Nexus.git
  2. Navigate to the Desired Project Directory: For example, for StyleScan:

    cd AI-Nexus/StyleScan

    For example, for GlycoTrack:

    cd AI-Nexus/GlycoTrack

    For example, for GradeCast:

    cd AI-Nexus/GradeCast

    For example, for ObjexVision:

    cd AI-Nexus/ObjexVision

    For example, for Iriswise:

    cd AI-Nexus/Iriswise

    For example, for DigitSense:

    cd AI-Nexus/DigitSense
  3. Install Dependencies: Install the required packages listed in the requirements.txt file:

    pip install -r requirements.txt
  4. Run the Application: Start the Streamlit app by running:

    streamlit run app.py
  5. Access the App in Browser: Open your browser and navigate to http://localhost:8501 to view and interact with the application.

📈 Future Enhancements

  • Adding more AI/ML models for healthcare and image recognition.
  • Deploying all apps for broader accessibility and public demos.
  • Introducing more advanced animations and dynamic visualizations.

💡 Customization & Contributions

Feel free to fork this repository, customize the UI, or add new machine learning models. Contributions are welcome! Make sure to submit a pull request with your proposed changes.


Happy exploring!

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GlycoTrack is an advanced diabetes prediction app built with Streamlit. It leverages multiple machine learning algorithms, including K-Nearest Neighbors, Random Forest, SVM, and XGBoost, to provide accurate predictions and detailed performance insights like F1-Score and AUC-ROC.

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