Use a linear regression model, preprocess data by normalizing and splitting into training and test sets.

Choose a decision tree model, preprocess data by encoding categorical variables and scaling numerical features.

Implement a time series forecasting model, preprocess data by handling missing values and creating lag features.

Select a neural network model, preprocess data by binning and reducing dimensionality.