What should be done with a dataset before training a model?

Before training a model with a dataset, several important data preprocessing steps should be performed:

1. Data cleaning — removing or correcting missing, incorrect, or outlier values.
2. Normalization or standardization — bringing numerical features to a common scale to improve algorithm convergence.
3. Categorical feature encoding — transforming categories into numerical format (one-hot encoding, label encoding).
4. Splitting into training and testing sets — to evaluate model quality on new data.
5. Handling class imbalance — if classes are uneven, techniques like oversampling, undersampling, or using special metrics can be applied.
6. Feature selection — removing irrelevant or redundant features to enhance model efficiency.

These steps help improve the quality and stability of the model, as well as prevent overfitting.