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https://github.com/sharmas1ddharth/iris-classification
A Machine Learning Model that can classify the species of the Iris flower whether its Iris-Setosa, Iris-Virsicolour, Iris-Virginica
https://github.com/sharmas1ddharth/iris-classification
data-science data-science-projects iris-classification iris-dataset machine-learning machine-learning-projects project python
Last synced: about 2 months ago
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A Machine Learning Model that can classify the species of the Iris flower whether its Iris-Setosa, Iris-Virsicolour, Iris-Virginica
- Host: GitHub
- URL: https://github.com/sharmas1ddharth/iris-classification
- Owner: sharmas1ddharth
- License: mit
- Created: 2021-10-16T17:27:41.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-11T16:03:10.000Z (over 2 years ago)
- Last Synced: 2023-03-04T00:43:43.592Z (almost 2 years ago)
- Topics: data-science, data-science-projects, iris-classification, iris-dataset, machine-learning, machine-learning-projects, project, python
- Language: HTML
- Homepage:
- Size: 1.27 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
[![MIT License][license-shield]][license-url]
[![LinkedIn][linkedin-shield]][linkedin-url]
Iris Classification
A Machine Learning Model that can classify the species of the Iris flower whether its Iris-Setosa, Iris-Virsicolour, Iris-Virginica
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Table of Contents
## About The Project
[![Product Name Screen Shot][product-screenshot]](https://example.com)
## Take a look at the notebook
### Built With
* [Python](https://www.python.org/)
* [Pandas](https://pandas.pydata.org/)
* [Scikit-learn](https://scikit-learn.org/)
* [Numpy](https://numpy.org/)
* [Matplotlib](https://matplotlib.org/)
* [Seaborn](https://seaborn.pydata.org/)
Project Organization
------------
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
## Getting Started
To run this project locally you need to have Python3 installed in your system.
To get a local copy up and running follow these simple example steps.
### Prerequisites
* [Python3](https://www.python.org/)
* [Jupyter Noteboook](https://jupyter.org/)
* [Pandas](https://pandas.pydata.org/)
* [Scikit-learn](https://scikit-learn.org)
* [Matplotlib](https://matplotlib.org/)
* [Numpy](https://numpy.org/)
* [Seaborn](https://seaborn.pydata.org/)
Install the above requirements by follow the steps below:
### Installation
1. Clone the repo
```sh
git clone https://github.com/sharmas1ddharth/Iris-classification.git
```
2. Install `requirements.txt`
```sh
pip install -r requirements.txt
```
After installing all the requirements type the following in **terminal**(Linux), **cmd/powershell**(Windows) to open project notebook in the jupyter-lab
```sh
jupyter-lab
```
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
Distributed under the MIT License. See `LICENSE.txt` for more information.
## Contact
Siddharth Sharma- [@sharmas1ddharth](https://twitter.com/sharmas1ddharth) - [email protected]
Project Link: [https://github.com/sharmas1ddharth/Iris-classification](https://github.com/sharmas1ddharth/Iris-classification)
[contributors-shield]: https://img.shields.io/github/contributors/sharmas1ddharth/Iris-classification.svg?style=for-the-badge
[contributors-url]: https://github.com/sharmas1ddharth/Iris-classification/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/sharmas1ddharth/Iris-classification.svg?style=for-the-badge
[forks-url]: https://github.com/sharmas1ddharth/Iris-classification/network/members
[stars-shield]: https://img.shields.io/github/stars/sharmas1ddharth/Iris-classification.svg?style=for-the-badge
[stars-url]: https://github.com/sharmas1ddharth/Iris-classification/stargazers
[issues-shield]: https://img.shields.io/github/issues/sharmas1ddharth/Iris-classification.svg?style=for-the-badge
[issues-url]: https://github.com/sharmas1ddharth/Iris-classification/issues
[license-shield]: https://img.shields.io/github/license/sharmas1ddharth/Iris-classification.svg?style=for-the-badge
[license-url]: https://github.com/sharmas1ddharth/Iris-classification/blob/master/LICENSE.txt
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
[linkedin-url]: https://linkedin.com/in/sharmas1ddharth
[product-screenshot]: other/screenshot.png