Welcome to the Iris Flower Classification Model project, a part of the Oasis InfoByte Summer Internship Program (OIBSIP). In this project, we build a machine learning model to classify iris flowers into different species based on their characteristics.
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Project Description: This Jupyter Notebook contains the code and documentation for our Iris Flower Classification Model. It is a part of our internship program at Oasis InfoByte, where we explore data science and machine learning techniques to solve real-world problems.
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Dataset: We use the famous Iris dataset, which contains samples of three species of iris flowers (Setosa, Versicolor, and Virginica). The dataset includes measurements of sepal length, sepal width, petal length, and petal width.
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Model: We implement a machine learning model to classify iris flowers based on their features. The project demonstrates data preprocessing, model training, evaluation, and prediction.
IRIS_FLOWER_CLASSIFICATION_MODEL.ipynb: The Jupyter Notebook containing the Python code and documentation for the Iris Flower Classification Model.
To run this project locally, follow these steps:
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Clone the repository to your local machine:
git clone https://github.com/CharanKocharla13/OIBSIP.git
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Navigate to the project directory:
cd OIBSIP -
Open the Jupyter Notebook
IRIS_FLOWER_CLASSIFICATION_MODEL.ipynbin your preferred Python environment. -
Follow the instructions and code in the notebook to explore the Iris Flower Classification Model.
The following Python libraries are used in this project:
- NumPy
- Pandas
- Scikit-Learn
- Matplotlib
- Seaborn
You can install these dependencies using pip:
pip install numpy pandas scikit-learn matplotlib seabornThis project is licensed under the MIT License - see the LICENSE file for details.
Feel free to explore, learn, and contribute to this project. If you have any questions or suggestions, please don't hesitate to reach out.
Happy coding!