https://github.com/ajaykumar095/seed_classification_ml_model

The Seed Classification ML Model aims to classify different types of wheat seeds into three categories: Kama, Rosa, and Canadian. The model uses various features related to the geometric properties of the seeds to accurately distinguish between these types.
https://github.com/ajaykumar095/seed_classification_ml_model

bootstrap flask html-css-javascript jinja2 machine-learning pandas svc-model

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The Seed Classification ML Model aims to classify different types of wheat seeds into three categories: Kama, Rosa, and Canadian. The model uses various features related to the geometric properties of the seeds to accurately distinguish between these types.

• Host: GitHub
• URL: https://github.com/ajaykumar095/seed_classification_ml_model
• Owner: AjayKumar095
• License: mit
• Created: 2024-07-06T14:49:51.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-09T11:05:33.000Z (almost 2 years ago)
• Last Synced: 2024-12-29T17:55:42.244Z (over 1 year ago)
• Topics: bootstrap, flask, html-css-javascript, jinja2, machine-learning, pandas, svc-model
• Language: Jupyter Notebook
• Homepage:
• Size: 482 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Wheat Classifier: Predict Wheat Type

The Seed Classification ML Model aims to classify different types of wheat seeds into three categories: Kama, Rosa, and Canadian. The model uses various features related to the geometric properties of the seeds to accurately distinguish between these types.

## Understanding the Dataset

- **Area**: The area of the seed.

- **Perimeter**: The perimeter of the seed.

- **Compactness**: Calculated as square of perimeter divided by area.

- **Length of Kernel**:  The length of the kernel.

- **Width of Kernel**: The width of the kernel.

- **Asymmetry Coefficient**: A measure of the asymmetry of the seed.

- **Length of Kernel Groove**: The length of the kernel groove.

These features provide a comprehensive set of attributes that help in differentiating between the three types of wheat seeds. The model uses these features to learn patterns and make predictions about the seed type based on new input data.

## Applications

- **Predictive Modeling**: Machine learning algorithms can be applied to classify wheat seed type based on its input features.

- **Exploratory Data Analysis (EDA)**: Techniques such as data visualization and correlation analysis help uncover patterns and relationships within the data.

- **Challenges and Considerations**: Addressing missing values, handling imbalanced datasets, selecting appropriate evaluation metrics, and ensuring model interpretability are crucial considerations.

## Getting Started

1. **Clone Repository**: Clone this repository to your local machine.

git clone https://github.com/AjayKumar095/SEED_CLASSIFICATION_ML_MODEL.git

2. **Install Dependencies**: Install required Python packages.

3. **Create .venv environment**: I use python 3.11.9

4. **Explore the Data**: Use Jupyter Notebook or any other preferred tool to explore the dataset and run analysis scripts.

## Tools and Software required

1. **VS Code IDE**: Download the IDE from https://code.visualstudio.com/download

2. **GitHub Account**: Create a GitHub account on github: https://github.com/

## Conclusion

The Seed Classification ML Model effectively differentiates between three types of wheat seeds: Kama, Rosa, and Canadian, using geometric properties of the seeds. By leveraging features such as area, perimeter, compactness, kernel length, kernel width, asymmetry coefficient, and kernel groove length, the model can accurately classify seed types with high precision and recall.

Through comprehensive evaluation using cross-validation and test sets, the model demonstrates robust performance across various metrics. Logistic Regression, Random Forest, and SVM models were tested, with each showing strong accuracy and balanced precision and recall. The model's detailed analysis through confusion matrices further highlights its strengths in correctly identifying seed types while minimizing misclassifications.

In summary, the Seed Classification ML Model provides a reliable and efficient tool for categorizing wheat seeds, aiding in agricultural research and quality control processes.

For detailed documentation and code implementation, refer to the provided Jupyter Notebook files.

## Contributors

- [Ajay kumar](https://github.com/AjayKumar095)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.