How can I preprocess data for machine learning in C++?

How can I preprocess data for machine learning in C++?

You can use libraries like Dlib or Armadillo for data preprocessing in C++. What specific preprocessing steps are you interested in?

I'm looking to handle missing values and normalize my data.

For missing values, you can use techniques like mean imputation or remove rows with missing data. For normalization, you can scale your features to a range of [0, 1] or standardize them to have a mean of 0 and a standard deviation of 1.

Can you show me an example of normalization in C++?

Sure! Here's a simple example of min-max normalization:

#include <vector>
#include <algorithm>

void normalize(std::vector<double>& data) {
    double min = *std::min_element(data.begin(), data.end());
    double max = *std::max_element(data.begin(), data.end());
    for (auto& value : data) {
        value = (value - min) / (max - min);
    }
}