You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model. What should you do?

A. Standardize the data by transforming it with a logarithmic function. B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature. C. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number. D. Normalize the data by scaling it to have values between 0 and 1.