Usage of Principle Component Analysis for Continous and categorical Variables mixed dataset

Hello,

Can any one tell me how to use the Principle Component Analysis technique for mixed dataset i.e for categorical and continuous variables. If it cannot be used then which are the other techniques that can be used for the feature reduction specially in R language.
Thanks.

Change the categorical variable into continuous one making some assumption of its function. Like https://www.analyticsvidhya.com/blog/2015/11/8-ways-deal-continuous-variables-predictive-modeling/

and then use PCA

Thanks for your input, but I have aquery like we have to create the dummy variables for categorical variables and If we convert them to dummy variables, then can we standardize the dummy variables also to make PCA work for categorical variables.

The simplest way is to standardize the variables and take a judgement based on your final business metric. Is it improving by this change? Strictly speaking there are other ways to deal with categorical variables as well like multiple correspondence analysis, categorical PCA, Generalized low rank models etc. But it all depends on how much time you have to try these techniques vs the perceived gain you think they will give.

Things that are simple work in practice