Cleaning the data: Handling missing values or correcting errors.
Scaling features: Adjusting the values so they fall within a similar range, which helps models perform better.
Encoding categorical variables: Converting text labels into numbers so models can understand them.
Splitting the data: Dividing your data into training and testing sets to evaluate model performance.
Column Transformer
Making Pipelines Making pipelines in order to reduce the redunduncy in code and preparing the model for production purpose.