This repository is dedicated to exploring various techniques used in Scikit-learn, a popular machine learning library in Python. The focus is on understanding and implementing different stages of a machine learning pipeline, from data preparation to model evaluation.

1. Scalers: Understanding different scaling techniques and their impact on model performance.
2. Preprocessing: Techniques for transforming raw data into a format suitable for machine learning.
3. PCA (Principal Component Analysis): Implementing PCA for dimensionality reduction.
4. Classifiers: Exploring different classification algorithms provided by Scikit-learn.

Each topic includes code examples and explanations to help understand the concepts better.

This project serves as a practical guide to using Scikit-learn for machine learning. It is mainly for self-learning purposes and is not intended to be a comprehensive guide to Scikit-learn. I hope you find it useful.

Have a bug or a feature request? Please first read and search for existing and closed issues. If your problem or idea is not addressed yet, please open a new issue.

• @Torbidos7

Thank you for coming 😝

This project is licensed under the MIT License.