I'm starting to learn about data science. I have sample data including different features run over several people, for each person n times. these features represent the fluency of the participants in some languages. they are including the number of correct words per sentence, the average number of the correct words in total, the average of speaking duration, the average of pauses in each turn, etc. So for features such as the number of correct words, the higher the score the better, and there is a feature such as average pauses which the lower the score the better.
I want todecide at the end which features are more significant and worth keeping and which features can be ignored.
I read about the weighted composite scores, and as far as I understood the main step is figuring out the coefficients (weights). but I didn't understand how these coefficients should be calculated. some cases are based on the analysis intuition, as I saw in some examples, but it can be so subjective.