https://github.com/priyanshulathi/iris-flower-species-prediction-model

Support Vector Machine (SVM) model for classifying the Iris dataset to predict iris species based on sepal and petal dimensions.
https://github.com/priyanshulathi/iris-flower-species-prediction-model

Last synced: 5 days ago
JSON representation

Support Vector Machine (SVM) model for classifying the Iris dataset to predict iris species based on sepal and petal dimensions.

• Host: GitHub
• URL: https://github.com/priyanshulathi/iris-flower-species-prediction-model
• Owner: PriyanshuLathi
• License: mit
• Created: 2024-09-01T11:55:49.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-06T16:41:41.000Z (4 months ago)
• Last Synced: 2024-11-09T14:07:03.801Z (2 months ago)
• Language: Jupyter Notebook
• Size: 13.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Iris Flower Species Prediction Model

This project focuses on building a Support Vector Machine (SVM) model to classify the Iris dataset. The Iris dataset is a classic dataset in machine learning that contains data on three different species of iris flowers: Setosa, Versicolor, and Virginica. The goal is to predict the species of an iris flower based on the features provided.

## Dataset Description

The dataset used in this project was obtained from Scikit-learn's built-in datasets module. It is a well-known dataset frequently used for benchmarking classification algorithms.

The dataset consists of 150 samples, with each sample containing four features:

- **Sepal Length**

- **Sepal Width**

- **Petal Length**

- **Petal Width**

Each sample is labeled with one of three target classes representing the species of the iris flower.

  


## Methodology

The following steps were taken to develop the SVM model:

1. **Data Loading and Exploration**: The Iris dataset was loaded using Scikit-learn's built-in dataset library. Basic data exploration was conducted to understand the feature distributions and target labels.

2. **Data Preprocessing**: 

   - The dataset was split into training (70%) and testing (30%) sets.

   - No additional feature scaling was applied since SVM models are generally robust to feature scaling due to kernel functions.

3. **Model Training**: 

   - An SVM model was trained using the default hyperparameters.

   - The model was fit using the training data, learning to classify the iris species based on the features.

4. **Model Evaluation**: 

   - Predictions were made on the test set.

   - The model was evaluated using a confusion matrix and a classification report, which includes precision, recall, and F1-score for each class.

## Accuracy and Model Performance

**Accuracy**: The model achieved an overall accuracy of **98%**, correctly classifying 98% of the test samples.

  


## Future Scope

There are several avenues for enhancing and expanding this project:

- **Hyperparameter Tuning**: The SVM model could benefit from fine-tuning hyperparameters such as the kernel type, C value, and gamma to potentially improve classification performance.

- **Feature Scaling**: Although SVMs are generally robust, scaling features using techniques like StandardScaler or MinMaxScaler could further optimize model performance.

- **Comparison with Other Models**: Comparing the SVM model's performance against other classification algorithms like Decision Trees, Random Forests, or Neural Networks would provide insights into the strengths and weaknesses of different approaches.

- **Data Augmentation**: Techniques like synthetic data generation or bootstrapping could be used to enhance the training data, potentially improving the model’s generalizability.

- **Real-Time Deployment**: Implementing the model as part of an interactive web application or API would showcase its practical use in classifying iris species from new input data.

## Dependencies

To run this project, you'll need the following libraries:

- **Pandas**

- **NumPy**

- **Matplotlib**

- **Seaborn**

- **Scikit-learn**

## Installation

1. Clone this repository to your local machine:

    ```bash

    git clone 

    ```

2. Install the required Python dependencies:

    ```bash

    pip install pandas numpy matplotlib seaborn scikit-learn

    ```

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/PriyanshuLathi/Iris-Flower-Species-Prediction-Model/blob/main/LICENSE) file for details.

## Contact

For questions or feedback, feel free to reach out:

- LinkedIn: [Priyanshu Lathi](https://www.linkedin.com/in/priyanshu-lathi)

- GitHub: [Priyanshu Lathi](https://github.com/PriyanshuLathi)

## Authors

- Priyanshu Lathi