Why is data preprocessing important in machine learning?

Why is data preprocessing important in machine learning?

Data preprocessing is crucial because it cleans and transforms raw data into a format suitable for analysis, improving model accuracy.

What are some common data preprocessing techniques?

Common techniques include normalization, handling missing values, encoding categorical variables, and feature scaling.

How does data preprocessing affect model performance?

Proper preprocessing can significantly enhance model performance by reducing noise and ensuring the data is in the right format.

Can you give an example of a preprocessing step?

Sure! One example is removing duplicates from the dataset, which helps prevent bias in the model training.