https://github.com/keneandita/iris-intel
Iris Flower Classifier is a simple web app built with Streamlit that predicts the species of an Iris flower based on user-input flower features. It uses pre-trained machine learning models including Logistic Regression, K-Nearest Neighbors, SVM, and Decision Tree to make real-time predictions.
https://github.com/keneandita/iris-intel
iris-classification jupyter-notebook machine-learning python scikit-learn streamlit
Last synced: 7 months ago
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Iris Flower Classifier is a simple web app built with Streamlit that predicts the species of an Iris flower based on user-input flower features. It uses pre-trained machine learning models including Logistic Regression, K-Nearest Neighbors, SVM, and Decision Tree to make real-time predictions.
- Host: GitHub
- URL: https://github.com/keneandita/iris-intel
- Owner: KeneanDita
- Created: 2025-05-16T19:37:59.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2025-08-17T17:47:15.000Z (7 months ago)
- Last Synced: 2025-08-17T19:24:09.242Z (7 months ago)
- Topics: iris-classification, jupyter-notebook, machine-learning, python, scikit-learn, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 1.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌸 Iris Intel
An interactive **Streamlit web app** for predicting the type of Iris flower based on its features.
This project uses multiple machine learning models trained on the classic [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris).
## Features
* Interactive **web interface** powered by Streamlit.
* Supports multiple ML models:
* Logistic Regression
* K-Nearest Neighbors (KNN)
* Support Vector Machine (SVM)
* Decision Tree
* User-friendly sliders to input flower measurements.
* Displays the **predicted class** along with **confidence scores**.
* Clean and responsive UI.
## Models
All models are pre-trained and stored as `.joblib` files inside the `Exported Models/` folder:
* `Logistic_regression.joblib`
* `KNN.joblib`
* `svm_model.joblib`
* `Decision_Tree.joblib`
## 🛠Installation
1. **Clone the repository**
```bash
git clone https://github.com/keneandita/iris-intel.git
cd iris-intel
```
2. **Create a virtual environment (recommended)**
```bash
python -m venv venv
source venv/bin/activate # On Linux/Mac
.\venv\Scripts\activate # On Windows
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Run the Streamlit app**
```bash
streamlit run Stream.py
```
---
## 📂 Project Structure
```project-structure
Iris-Intel/
│── Exported Models/
│ ├── Logistic_regression.joblib
│ ├── KNN.joblib
│ ├── svm_model.joblib
│ ├── Decision_Tree.joblib
│
│── Iris_Classification.ipynb # ML training notebook
│── Stream.py # Main Streamlit app
│── requirements.txt # Dependencies
│── README.md # Project documentation
```
## Demo
**Input example**:
* Sepal Length: `5.1 cm`
* Sepal Width: `3.5 cm`
* Petal Length: `1.4 cm`
* Petal Width: `0.2 cm`
**Output**:
```sample_output
The predicted class is Setosa using Logistic Regression!
```
Confidence Scores:
* Setosa: 97.3%
* Versicolor: 2.6%
* Virginica: 0.1%
Author: [Kenean Dita](https://github.com/keneandita/)# 🌸 Iris Flower Classifier