Welcome to the site!

So, the outcome which you get from PCA explain the most of your original dataset. You need to name them based on your business understanding(Assuming that you know about data, as you mentioned that you wanted to apply, Linear Regression) else you might need some Subject Matter Experts expertise.

Of-course, the Features won't be same with the original data or else what is the point in performing PCA(I know that you understood this part). To decide on the number of features, you need to look at Scree Plot.

PCA is a Dimensionality Reduction algorithm which helps you to derive new features based on the existing ones. PCA is an Unsupervised Learning Method, used when the has many features, when you don't understand anything about the data, no data dictionary etc.For better understanding on PCA you can go through this link-1,link-2.

Now before performing Linear Regression, you need to check if these new features are explaining the Target Variable by applying Predictor Importance test(PI Test). Based on the outcome of PI Test you can go ahead and use those important feature for modeling and discarding the features which are not explaining the target variable well.

Finally, you can achieve the results which you are looking for.

Let me know if you are stuck somewhere.

Welcome to the site!

So, the outcome which you get from PCA explain the most of your original dataset. You need to name them based on your business understanding(Assuming that you know about data, as you mentioned that you wanted to apply, Linear Regression) else you might need some Subject Matter Experts expertise.

Of-course, the Features won't be same with the original data or else what is the point in performing PCA(I know that you understood this part). To decide on the number of features, you need to look at Scree Plot.

PCA is a Dimensionality Reduction algorithm which helps you to derive new features based on the existing ones. PCA is an Unsupervised Learning Method, used when the has many features, when you don't understand anything about the data, no data dictionary etc.For better understanding on PCA you can go through this link-1,link-2.

Now before performing Linear Regression, you need to check if these new features are explaining the Target Variable by applying Predictor Importance test(PI Test), you can go through the Feature Selection test in the python,R. 

Based on the outcome of PI Test you can go ahead and use those important feature for modeling and discarding the features which are not explaining the target variable well.

Finally, you can achieve the results which you are looking for.

Let me know if you are stuck somewhere.

Welcome to the site!

So, the outcome which you get from PCA explain the most of your original dataset. You need to name them based on your business understanding or else you might need some Subject Matter Experts help.

Of-course, the Features won't be same with the original data or else what is the point in performing PCA(I know that you understood this part).

PCA is a Dimensionality Reduction algorithm which helps you to derive new features based on the existing ones. PCA is an Unsupervised Learning Method, used when the has many features, when you don't understand anything about the data, no data dictionary etc.

Now before performing Linear Regression, you need to check if these new features are explaining the Target Variable by applying Predictor Importance test(PI Test). Based on the outcome of PI Test you can go ahead and use those important feature for modeling and discarding the features which are not explaining the target variable well.

Finally, you can achieve the results which you are looking for.

Let me know if you are stuck somewhere.

Welcome to the site!

So, the outcome which you get from PCA explain the most of your original dataset. You need to name them based on your business understanding(Assuming that you know about data, as you mentioned that you wanted to apply, Linear Regression) else you might need some Subject Matter Experts expertise.

Of-course, the Features won't be same with the original data or else what is the point in performing PCA(I know that you understood this part). To decide on the number of features, you need to look at Scree Plot.

PCA is a Dimensionality Reduction algorithm which helps you to derive new features based on the existing ones. PCA is an Unsupervised Learning Method, used when the has many features, when you don't understand anything about the data, no data dictionary etc.For better understanding on PCA you can go through this link-1,link-2.

Now before performing Linear Regression, you need to check if these new features are explaining the Target Variable by applying Predictor Importance test(PI Test). Based on the outcome of PI Test you can go ahead and use those important feature for modeling and discarding the features which are not explaining the target variable well.

Finally, you can achieve the results which you are looking for.

Let me know if you are stuck somewhere.