Feature Engineering

• Feature engineering is a machine learning technique that leverages data to create new variables that aren’t in the training set.

• It can produce new features for both supervised and unsupervised learning, with the goal of simplifying and speeding up data transformations while also enhancing model accuracy.

• Feature engineering is required when working with machine learning models.

• Regardless of the data or architecture, a terrible feature will have a direct impact on your model.

Importance of Feature Engineering

• Feature Engineering is a very important step in machine learning.

• Feature engineering refers to the process of designing artificial features into an algorithm.

• These artificial features are then used by that algorithm in order to improve its performance.

Notebook List

Here is a list of Jupyter Notebooks included in this repository :

• Dummy Variable

• Inter Quartile Range

• Z-Score

• Modified Z-Score

• Data Standardization

• Handling Imbalance Dataset

Getting Started

• You can clone this repository using the following command:

  git clone https://github.com/TheMrityunjayPathak/FeatureEngineering.git
  

• Install the Jupyter Notebook :

  pip install notebook

• Launch the Jupyter Notebook :

  jupyter notebook

• Open the desired notebook from the repository in your Jupyter environment and start learning!

Contributing

• Contributions are Welcome!

• If you'd like to contribute to this repository, feel free to submit a pull request.

License

• This repository is licensed under the MIT License.

• You are free to use, modify and distribute the code in this repository.

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