https://github.com/shaadclt/feature-selection-techniques
This project involves the implementation of different feature selection techniques in Jupyter Notebook for practice. Feature selection is an important step in machine learning that aims to select the most relevant features from a given dataset. Through this project, we aim to explore and understand various feature selection techniques.
https://github.com/shaadclt/feature-selection-techniques
Last synced: 6 months ago
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This project involves the implementation of different feature selection techniques in Jupyter Notebook for practice. Feature selection is an important step in machine learning that aims to select the most relevant features from a given dataset. Through this project, we aim to explore and understand various feature selection techniques.
- Host: GitHub
- URL: https://github.com/shaadclt/feature-selection-techniques
- Owner: shaadclt
- Created: 2022-10-19T05:54:22.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T09:36:51.000Z (about 2 years ago)
- Last Synced: 2025-02-02T09:41:25.406Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 156 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Feature Selection Techniques in Machine Learning - Practice
This project involves the implementation of different feature selection techniques in Jupyter Notebook for practice. Feature selection is an important step in machine learning that aims to select the most relevant features from a given dataset. Through this project, we aim to explore and understand various feature selection techniques and their impact on model performance.
## Getting Started
To get started with the project, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Feature-Selection-Techniques.git
```
2. Change into the project directory:
```bash
cd Feature-Selection-Techniques
```
3. Install the required dependencies:
4. Run Jupyter Notebook:
```bash
jupyter notebook
```
5. Open the `Feature Selection Techniques.ipynb` notebook in Jupyter.
6. Follow the instructions in the notebook to implement and explore different feature selection techniques.
## Project Overview
The notebook provides practice exercises for different feature selection techniques. The exercises include the following techniques:
1. Filter Methods: Implementing statistical tests such as correlation coefficient, Variance Threshold, Chi-Square, Information Gain and to rank features.
2. Wrapper Methods: Implementing techniques like Forward Selection, Backward Elimination and Exhaustive Feature Selection to select features based on model performance.
3. Embedded Methods: Implementing techniques like Regularization to select features during the model training process.
Each exercise includes code snippets, and sample datasets to practice and gain hands-on experience with feature selection techniques.
## Results and Insights
As this project is for practice, the emphasis is on implementing and understanding different feature selection techniques rather than providing specific results or insights. Each exercise will provide you with the opportunity to observe the impact of feature selection on the dataset and model performance. Feel free to experiment with different techniques, datasets, and models to explore their effects and gain insights.
## Customization
You can customize the project by adding your own datasets, trying different feature selection techniques, or expanding the exercises with additional techniques or challenges. This project serves as a starting point for you to practice and enhance your understanding of feature selection in machine learning.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
## Acknowledgments
- This project is created for the purpose of practicing and exploring feature selection techniques in machine learning.
## Contributing
Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more exercises, please open an issue or submit a pull request.