Data Preprocessing

Raw datasets often require preprocessing before they can be used for machine learning.

Data preprocessing improves data quality and prepares the dataset for training.

Typical preprocessing steps include:

• Checking missing values
• Removing duplicate records
• Verifying data types
• Separating input and target variables
• Scaling numerical features

Proper preprocessing helps improve model performance and ensures consistent results.