🤖 AI Nexus - Multi-Purpose AI/ML Hub
AI Nexus is a central repository that brings together a collection of advanced AI and ML applications. These projects span a wide range of use cases, from image classification to predictive healthcare models.
🌐 Project Highlights
- 👗 StyleScan - Fashion MNIST Image Classification
- 🩺 GlycoTrack - Advanced Diabetes Prediction
- 🌸 IrisWise - Iris Species Classification
- 🎓 GradeCast - GPA Prediction Model
- 🧮 DigitSense - MNIST Handwritten Digit Classifier
- 🖼️ ObjexVision - CIFAR-10 Object Recognition
🎨 Project Image Previews
🧮 DigitSense - MNIST Handwritten Digit Classifier
🩺 GlycoTrack - Advanced Diabetes Prediction
🎓 GradeCast - GPA Prediction Model
🌸 IrisWise - Iris Species Classification
🖼️ ObjexVision - CIFAR-10 Object Recognition
👗 StyleScan - Fashion MNIST Image Classification
🚀 Quick Links to Live Demos
| Project |
Live Link |
| 👗 StyleScan - Fashion MNIST Image Classification |
 |
| 🩺 GlycoTrack - Advanced Diabetes Prediction |
|
| 🧮 DigitSense - MNIST Handwritten Digit Classifier |
|
| 🌸 IrisWise - Iris Species Classification |
|
| 🖼️ ObjexVision - CIFAR-10 Object Recognition |
|
| 🎓 GradeCast - GPA Prediction Model |
|
📂 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:
-
Clone the Repository:
git clone https://github.com/Hunterdii/AI-Nexus.git
-
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
-
Install Dependencies:
Install the required packages listed in the requirements.txt file:
pip install -r requirements.txt
-
Run the Application:
Start the Streamlit app by running:
streamlit run app.py
-
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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