How to handle missing data and categorical features in support vector methods and neural networks?

When working with Support Vector Machine (SVM) methods and neural networks, it is important to properly handle missing values and categorical features:

• Missing values:

  • Usually, missing values are filled (imputed) before training. For numerical features, the mean, median, or more complex methods (e.g., KNN imputation) can be used.
  • In some cases, special missing indicators can be used.

• Categorical features:

  • SVM and neural network methods require numerical inputs, so categorical features are encoded.
  • One-hot encoding is often used — creating binary features for each unique value.
  • For large categories, embedding representations (especially in neural networks) can be applied, which are trained together with the model.

Example of one-hot encoding in Python with pandas:

import pandas as pd

df = pd.DataFrame({'color': ['red', 'blue', 'green', None]})
df['color'].fillna('missing', inplace=True)  # Filling missing values
one_hot = pd.get_dummies(df['color'])

Thus, data preprocessing is a key step for the successful application of SVMs and neural networks.