Introduction
Machine learning models are powerful, but their effectiveness hinges on the quality of their training data.
Without proper data preparation, even the most sophisticated algorithms will struggle to generate meaningful results.
Data preprocessing is a crucial step in the pipeline, transforming raw data into a clean and suitable format for model training.
This process typically involves:
- handling missing data,
- scaling numerical variables, and
- encoding categorical variables.
Though these preprocessing methods do not directly choose model algorithms, they can prepare the data in a way that makes it compatible with specific algorithms.
Data Preprocessing for Effective Machine Learning Models
