Transformation of mix normal and skew features I have a weather dataset containing four features that are continuous values. Temperature is almost normal, but precipitation is highly negatively skewed. In addition, wind speed and humidity are positively skewed!
Performing log transformation on all features can somehow improve the skewness of the precipitation but no other features.
Can I perform a transformation on each feature separately? For example, implementing log transformation on temperature, and cube root on precipitation.
It should be noted that features are independent and not correlated.

First, note that there is no normality assumption about the features, so you might be transforming for nothing.
However, if you choose to transform, it is fine to use different transformations of different variables. The following is still a linear regression.
$$
\hat y=\hat\beta_0 
+
\hat\beta_1 \log(x_1)
+
\hat\beta_2 \sqrt[3]{x_2}
$$
If you have theoretical reasons to care about the logarithm and cube root, or if those transformations improve your performance, perhaps those transformed variables and the features to input into your regression.
Jeffrey Miller’s  “mathematicalmonk” channel on YouTube has a good video about this topic. He takes it a step further and allows for transformations involving multiple variables, such as using $\sqrt{x_1x_2}$ as a feature.
