How to preprocess data for machine learning models

Short answer

Preprocessing includes handling missing values, encoding categorical variables, scaling numerical features, and splitting data into training and test sets. For evaluation after preprocessing, see how to evaluate machine learning model performance. For understanding learning paradigms, see what is the difference between supervised and unsupervised learning.

Steps

1. Load data and inspect missing values
2. Impute or drop missing values
3. Encode categories: one-hot encoding or label encoding
4. Scale features: standardization or normalization
5. Split into train and test sets

Tips

• Fit scalers and encoders only on training data to avoid data leakage
• Use pipelines to bundle preprocessing with model training
• For handling skewed classes, see how to handle imbalanced datasets in classification