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An Iris Classification project built with comparision of four different Machine Learning models
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Table of Contents
The Iris Classification Machine Learning Project is a thorough investigation of multi-modal machine learning methods used to classify iris blossoms into several species according to their morphological traits. This project includes the collection of data, data preprocessing, feature scaling, model training, model assessment, and finally the creation and implementation of an intuitive interface using Streamlit.
The project follows a structured workflow:
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Data Gathering: Collecting the iris dataset, which includes measurements of sepal length, sepal width, petal length, petal width, and corresponding species labels.
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Data Preprocessing: Cleaning and preparing the data for training, including handling missing values, encoding categorical variables, and splitting into training and testing sets.
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Feature Scaling: Scaling the features to ensure that they have a consistent influence on the machine learning model.
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Model Training: Choosing a machine learning algorithm and training the model using the preprocessed data.
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Model Evaluation: Assessing the model's performance using various metrics such as accuracy, precision, recall, and F1-score to gauge its effectiveness in classifying iris species.
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Model Building and Deployment: Developing a user-friendly Streamlit application to interact with the trained model. Users can input iris measurements and receive predictions on the species of the flower.
Using this as an example, you may describe how to set up your project locally. Follow these easy sample steps to set up and operate a local copy.
You must have Python installed on your machine in order to use this project. Python may be downloaded from this page if you don't already have it installed.
- Clone the repository to your local machine
git clone https://github.com/Ruban2205/Iris_Classification.git
- Change directory into the repository
cd Iris_Classification
- Explore the notebooks in the repository using a Jupyter Notebook or JupyterLab environment. You can launch the environment by running the following command:
jupyter notebook
or
jupyter lab
- Run the Streamlit application with the given command:
streamlit run streamlitapi.py
- Access the application in your web browser, input iris flower measurements, and receive predictions on the species.
Contributions to this repository are welcome! If you have any improvements, additional examples, or new topics you would like to add, please follow these steps:
- Fork the repository in GitHub.
- Create a new branch with a descriptive name for your changes.
- Make your modifications, additions, or improvements.
- Commit and push your changes to your forked repository.
- Submit a pull request to the original repository.
Please ensure your contributions adhere to the coding style and guidelines used in the repository.
This repository is licensed under the MIT LICENSE. You are free to use, modify, and distribute the code and content within this repository for personal or commercial purposes. However, please provide attribution to the original repository by linking back to it.
I want to express my appreciation to the people who created the Iris dataset and the larger machine learning and data science community for their insightful contributions.
You may learn more about the principles of machine learning, the use of models, and the actual applications of AI in the categorization of issues by investigating and participating in my Iris categorization Machine Learning Project.
For any questions or inquiries, please feel free to approach me through the following channels:
- Ruban info@rubangino.in
Feel free to report any issues or suggest improvements by creating an issue in the GitHub repository.
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