What happens when a dataset has too many #variables? Here are few possible situations which you might come across:
• You find that most of the variables are correlated.
• You lose patience and decide to run a model on the whole data which returns poor accuracy
• You become indecisive about what to do
• You start thinking of some strategic method to find few important variables
But dealing with such situations isn’t as difficult as it sounds. Statistical techniques such as factor analysis and principal component analysis help to overcome such difficulties. Here's a detailed guide on Principal Component Analysis - a method to extract important variables from a large set of variables available in a #dataset. Read the full article here: https://lnkd.in/fbKgbrh
✴️ @AI_Python_EN
• You find that most of the variables are correlated.
• You lose patience and decide to run a model on the whole data which returns poor accuracy
• You become indecisive about what to do
• You start thinking of some strategic method to find few important variables
But dealing with such situations isn’t as difficult as it sounds. Statistical techniques such as factor analysis and principal component analysis help to overcome such difficulties. Here's a detailed guide on Principal Component Analysis - a method to extract important variables from a large set of variables available in a #dataset. Read the full article here: https://lnkd.in/fbKgbrh
✴️ @AI_Python_EN