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https://github.com/md-emon-hasan/6-classification-iris-ml-apps

A ML project on the classification of the Iris dataset, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.
https://github.com/md-emon-hasan/6-classification-iris-ml-apps

classification data-science iris-classification iris-dataset iris-flower-classification predictive-modeling scikit-learn

Last synced: about 2 months ago
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A ML project on the classification of the Iris dataset, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.

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README 

        
# Machine Learning Project: Classification Iris ML Apps



Welcome to the **Classification Iris ML Apps** machine learning project repository! This project focuses on classifying iris flowers into three species using machine learning techniques and providing a simple web-based application for users to interact with streamlit.



![6](https://github.com/user-attachments/assets/17eeb822-c15f-4b9d-b2b6-af6e76e6ec12)



## 📋 Contents



- [Introduction](#introduction)

- [Why This Project](#why-this-project)

- [Dataset](#dataset)

- [Features](#features)

- [Setup and Installation](#setup-and-installation)

- [Demo](#demo)

- [Contributing](#contributing)

- [Challenges Faced](#challenges-faced)

- [Lessons Learned](#lessons-learned)

- [License](#license)

- [Contact](#contact)



---



## 📖 Introduction



This repository contains a machine learning project focused on classifying iris flowers into three species using various machine learning algorithms and providing a user-friendly web application for predictions and insights.



---



## 🎯 Why This Project



The primary motivation behind creating this project is to demonstrate the process of building a machine-learning model for classification tasks and to provide an educational tool for those interested in learning about machine learning and web application development.



---



## 📊 Dataset



The dataset used for this project is the famous Iris dataset, which contains 150 samples of iris flowers with four features: sepal length, sepal width, petal length, and petal width. Each sample is classified into one of three species: Setosa, Versicolor, and Virginica.



---



## 🌟 Features



- **Data Preprocessing:** Cleaning and transforming the dataset for model compatibility.

- **Model Development:** Training and evaluating multiple machine learning models for classification.

- **Deployment:** Developing a simple web-based application for users to input flower measurements and obtain species predictions.



---



## 🚀 Setup and Installation



To run this project locally, follow these steps:



1. Clone the repository:



   ```bash

   git clone https://github.com/Md-Emon-Hasan/6-Classification-Iris-ML-Apps.git

   ```



2. Navigate to the project directory:



   ```bash

   cd 6-Classification-Iris-ML-Apps

   ```



3. Install the required dependencies:



   ```bash

   pip install -r requirements.txt

   ```



4. Run the web application:



   ```bash

   python app.py

   ```



5. Open your web browser and go to `http://localhost:5000` to interact with the app.



---



## 🌐 Demo



Explore the live demo of the project [here](https://six-classification-iris-ml-apps.onrender.com/).



---



## 🤝 Contributing



Contributions to enhance or expand the project are welcome! Here's how you can contribute:



1. **Fork the repository.**

2. **Create a new branch:**



   ```bash

   git checkout -b feature/new-feature

   ```



3. **Make your changes:**



   - Implement new features, improve model performance, or enhance user interface.



4. **Commit your changes:**



   ```bash

   git commit -am 'Add a new feature or update'

   ```



5. **Push to the branch:**



   ```bash

   git push origin feature/new-feature

   ```



6. **Submit a pull request.**



---



## 🛠️ Challenges Faced



During the development of this project, the following challenges were encountered:



- Handling data preprocessing and feature engineering.

- Selecting the most appropriate machine learning algorithms for classification.

- Developing an intuitive and responsive web application interface.



---



## 📚 Lessons Learned



Key lessons learned from this project include:



- Practical application of classification algorithms in machine learning.

- Importance of feature selection and engineering in classification tasks.

- Deployment and usability considerations for interactive web applications.



---



## 📄 License



This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more details.



---



## 📬 Contact



- **Email:** [[email protected]](mailto:[email protected])

- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)

- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)

- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)

- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)



Feel free to reach out for any questions or feedback regarding the project!



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