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https://github.com/hunterdii/iriswise
IrisWise is a machine learning application for predicting Iris flower species. Built with Streamlit, this app provides a user-friendly interface to input flower measurements and receive predictions using various models, including K-Nearest Neighbors, (Random Forest, SVM, and Logistic Regression) **(Working On It...)**.
https://github.com/hunterdii/iriswise
classifier-model data-science flowers-recognition iris-dataset iris-recognition knn-classification machine-learning pickle python python3 streamlit streamlit-webapp
Last synced: 2 months ago
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IrisWise is a machine learning application for predicting Iris flower species. Built with Streamlit, this app provides a user-friendly interface to input flower measurements and receive predictions using various models, including K-Nearest Neighbors, (Random Forest, SVM, and Logistic Regression) **(Working On It...)**.
- Host: GitHub
- URL: https://github.com/hunterdii/iriswise
- Owner: Hunterdii
- Created: 2024-08-14T14:48:56.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-15T11:29:46.000Z (6 months ago)
- Last Synced: 2024-08-23T15:01:44.638Z (5 months ago)
- Topics: classifier-model, data-science, flowers-recognition, iris-dataset, iris-recognition, knn-classification, machine-learning, pickle, python, python3, streamlit, streamlit-webapp
- Language: Jupyter Notebook
- Homepage: https://iriswise.streamlit.app/
- Size: 920 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌼 IrisWise - Iris Species Prediction Model 🌺
Welcome to **IrisWise** – Welcome to the Iris Species Prediction app! Enter the details below to predict the species of an Iris flower based on its features. This application uses a K-Nearest Neighbors classification model to predict the species.
## Features
- **Interactive User Interface:** 🖥️ Enjoy a sleek, modern UI with custom styling, animations, and colorful themes, enhancing the overall user experience. The interface is designed to be intuitive and engaging.
- **Multiple Machine Learning Models:** 📊 Choose from a variety of models, including K-Nearest Neighbors, Random Forest, SVM, and Logistic Regression, each evaluated for performance to give you the best possible prediction. **(Working On It...)**
- **Real-time Predictions:** 🌸 Input the features of the Iris flower (sepal length, sepal width, petal length, petal width) and get an instant prediction of the species.
- **Dynamic Visualizations:** 📈 Explore decision boundaries and model performance graphs that update dynamically as you interact with the app. The visualizations help in understanding the decision process of each model.
- **Tooltips and Explanations:** 💡 Hover over options and checkboxes to get detailed tooltips, making the app more informative and easier to use.
- **Custom Animations:** 🎨 Experience unique animations and transitions, including Lottie and JavaScript animations, that enhance the visual appeal of the app. **(Working On It...)**
## Demo
Check out the live demo on [](https://iriswise.streamlit.app/)
## Preview
## Usage
Get started with IrisWise by following these simple steps:
1. Clone the repository to your local machine:
```bash
git clone https://github.com/Hunterdii/Iriswise.git
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit app:
```bash
streamlit run app.py
```
4. Open your browser and go to `http://localhost:8501` to start predicting Iris species.
## Customization
You can customize the app to your liking by modifying the CSS for styling, updating the machine learning models, or adding new features. The code is well-documented to help you navigate and tweak the application as needed.