Introduction to Preprocessing
Explore the fundamental preprocessing steps necessary for preparing raw data in machine learning. Understand feature extraction, scaling, encoding, discretizing, dimensionality reduction, and methods to handle missing values. This lesson helps you lay the groundwork to improve model quality, reliability, and performance using appropriate data transformation techniques in scikit-learn.
In the context of ML, preprocessing refers to the essential steps taken to prepare raw data for analysis and model training. It involves transforming and manipulating the data to address common issues and make it suitable for ML algorithms. The importance of preprocessing cannot be overstated, as it significantly impacts the quality, reliability, and effectiveness of ML models.
Common preprocessing steps
Let’s take a look at each of the steps of preprocessing.
Feature extraction
A crucial step in preprocessing is