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

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

To preprocess data in C++, you can use libraries like OpenCV for image data or Armadillo for matrix operations. What type of data are you working with?

I'm working with numerical data from a CSV file.

You can use the C++ standard library to read CSV files. After loading the data, consider normalizing or standardizing it. Would you like code examples?

Yes, please provide a code example for normalizing data.

Here's a simple example:

#include <vector>
#include <algorithm>

void normalize(std::vector<double>& data) {
    double max_val = *std::max_element(data.begin(), data.end());
    for (auto& val : data) {
        val /= max_val;
    }
}

This function normalizes the data to a range of 0 to 1.

That looks great! How can I handle missing values?

You can handle missing values by either removing them or imputing them with the mean or median of the dataset. Would you like to see an example of imputation?