auto-sklearn

This package will help you perform ML on ANY tabular data. The result of modelling will be a weighted network describing which features were associated with which modelling method. This will help you understand:

• Feature Bias
• Useless Features
• Co-dependency of Features
• PCA efficiency

Apart from the above valuable information, this package requires minimal requirements.txt. The odds are you probably have these already installed.

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Installation

To install the package, you can use pip with the URL of the GitHub repository.

Clone the Repository

git clone https://github.com/your-username/auto-sklearn.git
cd auto-sklearn

Create a Virtual Environment (OPTIONAL|RECOMMENDED)

You can set the environment's name as you wish by replacing auto-env.

python -m venv auto-env
source auto-env/bin/activate  # On Windows: auto-env\Scripts\activate

Installing the Package

Note that the -e flag is important.

pip install -e .

Verify the Install

import auto-sklearn

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Why?

To learn intricacies of ML. ML is not a statistical method when dealing with data. I see it as a means to get some result through methodical data morphology. Results and my inference is what makes a good data analysis. To ensure that I am able to see through ML well enough that I can focus in honing inferential skills.

I am developing this project to blaze through basic modelling when I want to:

• Test new datasets.
• Experiment with modelling methods.
• Compare modelling with Deep Learning etc.

Finally, this package will help me write a Data Science blog in a very short time by reducing preliminary data testing time. This inturn helps me focus on novelty and creative aspects of Data Science. I also get to learn new areas of DS in a weekly basis.

You can find my blogs here: bhargavkantheti.com.

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Usage

TBA