https://github.com/lovnishverma/helloworldirismachinelearning
This is a simple Flask-based machine learning web application that predicts the species of an Iris flower based on user inputs. The model is trained using Logistic Regression and deployed on Glitch.com.
https://github.com/lovnishverma/helloworldirismachinelearning
iris-classification iris-dataset machinelearning-python
Last synced: 7 months ago
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This is a simple Flask-based machine learning web application that predicts the species of an Iris flower based on user inputs. The model is trained using Logistic Regression and deployed on Glitch.com.
- Host: GitHub
- URL: https://github.com/lovnishverma/helloworldirismachinelearning
- Owner: lovnishverma
- License: mit
- Created: 2025-03-06T06:41:24.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-07-11T14:52:08.000Z (11 months ago)
- Last Synced: 2025-10-06T15:39:51.881Z (8 months ago)
- Topics: iris-classification, iris-dataset, machinelearning-python
- Language: HTML
- Homepage: https://helloworldirismachinelearning.onrender.com/
- Size: 146 KB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# Iris Machine Learning Web App
This is a simple **Flask-based machine learning web application** that predicts the **species of an Iris flower** based on user inputs. The model is trained using **Logistic Regression** and deployed on **Glitch.com**.
## 🎥 Youtube Tutorial (Hindi)
[Click here view youtube video!](https://youtu.be/DlCdQ1MZXsc?si=ACw7zrwmCLGsYNVp)
## 🌐 Live Demo
[Click here to try the app!](https://helloworldirismachinelearning.onrender.com/)
## 📂 Project Links
- **GitHub Repository:** [GitHub](https://github.com/lovnishverma/helloworldirismachinelearning)
### **Additional Documentation**
- [Logistic Regression vs. Linear Regression](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/Logistic%20Regression%20vs.%20Linear%20Regression.md)
- [Encoding](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/encoding.md)
- [NumPy](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/numpy.md)
- [Pandas](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/pandas.md)
- [Scaling](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/scaling.md)
- [Slicing](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/slicing.md)
- [Readme](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/readme.md)
- [DataCleaningGuide](https://github.com/lovnishverma/helloworldirismachinelearning/blob/master/DataCleaningGuide.md)
## 🛠️ Technologies Used
- **Flask** (Web Framework)
- **Scikit-Learn** (Machine Learning Model)
- **Pandas & NumPy** (Data Processing)
- **HTML & Jinja2** (Frontend Template Engine)
- **Glitch.com** (Free Deployment Platform)
## 📌 Features
✅ Train and deploy a **Logistic Regression** model for Iris species classification.
✅ Simple web interface to input flower measurements.
✅ Real-time predictions displayed on the web page.
✅ Deployed on **Glitch** for easy access.
## 📦 Installation & Setup
To run this project locally, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/lovnishverma/helloworldirismachinelearning.git
cd helloworldirismachinelearning
```
2. **Create a virtual environment (optional but recommended):**
```bash
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
4. **Run the Flask app:**
```bash
python app.py
```
5. **Access the web app locally:**
Open a browser and go to `http://127.0.0.1:5000/`
## 🚀 Deployment on Glitch
To deploy the app on Glitch:
1. Go to [Glitch.com](https://glitch.com/)
2. Click **New Project** → **Import from GitHub**
3. Paste your repository URL: `https://github.com/lovnishverma/helloworldirismachinelearning`
4. Wait for the installation to complete.
5. Click **Preview - Open in a new window** to access your app!
## 📷 Screenshot
## 📜 License
This project is licensed under the **MIT License**.
## 🤝 Contributing
Feel free to contribute to this project! You can fork the repo, create a new branch, and submit a pull request.
## ✨ Acknowledgments
- **Scikit-Learn** for the ML model.
- **Flask** for making web deployment easy.
- **Glitch** for providing free hosting.
# *Future Enhancements for This Project 🎯*
This Flask-based *Iris Flower Classification* project can be improved further with the following enhancements:
✅ *Use train-test split for better accuracy* – Implement `train_test_split` from `sklearn.model_selection` to split data into *80% training and 20% testing* for better generalization.
Currently, the model is trained on the entire dataset, which is *not a good practice* because:
- It might memorize the dataset instead of learning general patterns (overfitting).
- We cannot evaluate how well it performs on unseen data.
✅ *Feature Scaling for Better Predictions* (optional but recommended)– Use `StandardScaler` from `sklearn.preprocessing` to normalize feature values, ensuring better model performance.
✅ *Train Once, Save Model for Future Use* – Use `joblib` or `pickle` to *save the trained model* and load it later instead of retraining it every time the app runs.
✅ *Load Trained Model in Flask* – Instead of training on every restart, modify the code to *load a pre-trained model* for faster predictions.
✅ *Enhance UI with Bootstrap* – Improve the *frontend* using *Bootstrap* for a more professional, responsive, and visually appealing UI.
✅ *Error Handling & Input Validation* – Implement input validation and exception handling to *prevent crashes* due to invalid user inputs.
✅ *Logging & Debugging* – Add `logging` to capture errors and debug issues more efficiently in a production environment.
✅ *Deploy as a REST API* – Convert the app into an *API* using `Flask-RESTful` for better integration with *mobile apps or other web services*.
✅ *Host Model and Project on Hugging Face* – Deploy the trained model and project on *Hugging Face*, which provides 24x7 accessibility.
- Hugging Face is *better than Glitch* for ML projects as it supports *Flask, Streamlit, and Gradio*, while Glitch only supports Flask.
✅ *Use a Database for Storing Predictions* – Store *user inputs and predictions* in a database like *SQLite or PostgreSQL* for better data analysis and tracking.
💡 These improvements will make your ML model more efficient, user-friendly, and scalable!
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👉 Made with ❤️ by [Lovnish Verma](https://github.com/lovnishverma/) 🚀