Data Preparation in Scikit-learn

Scikit-learn provides tools to get your data ready for machine learning models. This often involves cleaning, transforming, and scaling your data. Cleaning might mean handling missing values using techniques like imputation. Transformation can involve converting categorical features into numerical ones using methods like one-hot encoding. Scaling ensures that all features contribute equally to the model by bringing them to a similar range, using techniques like standardization or normalization. These steps help improve the performance and accuracy of your machine learning models.

Visit the following resources to learn more:

• @official@Preprocessing data
• @video@Scikit-learn Crash Course - Machine Learning Library for Python