https://github.com/shaadclt/feature-engineering-techniques
This project involves the implementation of different feature engineering techniques in Jupyter Notebook for practice. Feature engineering is a crucial step in machine learning that involves transforming raw data into meaningful features to improve model performance. Through this project, we aim to practice various feature engineering techniques.
https://github.com/shaadclt/feature-engineering-techniques
Last synced: about 2 months ago
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This project involves the implementation of different feature engineering techniques in Jupyter Notebook for practice. Feature engineering is a crucial step in machine learning that involves transforming raw data into meaningful features to improve model performance. Through this project, we aim to practice various feature engineering techniques.
- Host: GitHub
- URL: https://github.com/shaadclt/feature-engineering-techniques
- Owner: shaadclt
- Created: 2022-10-19T06:00:13.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T09:47:30.000Z (over 2 years ago)
- Last Synced: 2025-06-30T10:46:19.865Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 1020 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Feature Engineering Techniques in Machine Learning - Practice
This project involves the implementation of different feature engineering techniques in Jupyter Notebook for practice. Feature engineering is a crucial step in machine learning that involves transforming raw data into meaningful features to improve model performance. Through this project, we aim to explore and practice various feature engineering techniques to enhance our understanding and skills.
## Getting Started
To get started with the project, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Feature-Engineering-Techniques.git
```
2. Change into the project directory:
```bash
cd Feature-Engineering-Techniques
```
3. Install the required dependencies:
4. Run Jupyter Notebook:
```bash
jupyter notebook
```
5. Open the `Feature Engineering Techniques.ipynb` notebook in Jupyter.
6. Follow the instructions in the notebook to implement and explore different feature engineering techniques.
## Project Overview
The notebook provides practice exercises for different feature engineering techniques. The exercises include the following techniques:
1. 3 Standard Deviation
2. Imputation
3. Binning
4. Log Transform
5. One Hot Encoding
6. Feature Splitting
Each exercise includes code snippets, and sample datasets to practice and gain hands-on experience with feature engineering techniques.
## Results and Insights
As this project is for practice, the emphasis is on implementing and understanding different feature engineering techniques rather than providing specific results or insights. Each exercise will provide you with the opportunity to observe the impact of feature engineering 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 engineering 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 engineering 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 engineering 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.