K means visualisation after reducing dimensionality with PCA In clustering ($K$ means, for example) when I have $N$ features and after creating the model (with this $N$ features) to visualize this model I need to reduce this $N$ dimensions into $2$ or $3$ dimensions, let's  say I will use (PCA) fro example.

My question is how I can analyze the results (grape with principal component)?

this is simple example :

Data without reducing :
AGE,GENDER,SPENT,SALARY,CAR,.....

Data after reducing dimension: 

Principal Component $1$, Principal Component $2$, Principal Component 3

what do  PC1,PC2,PC3 means
K-means minimizes sum of squared errors, and PCA finds the projection with maximum sum of squares. So they are a quite natural fit.

Just run k-means and project it to 2d for visualization with PCA. They you are largely seeing the data the same way as k-means (if you only use the rotation, not the scaling!)

What I'd be more concerned about is the input data, as it is not particularly well suited for neither k-means nor PCA. So I wouldn't be surprised if the results are barely interpretable. Both methods make most sense if the input variables are continuous and of the same scale.