Kernel-Principle Componenet Analysis
Implementing Machine Learning Algorithm : Kernel Principle Component Analysis on the data-set of Social Network Ads
Kernel Principal Component Analysis(Kernel PCA): Principal component analysis (PCA) is a popular tool for dimensionality reduction and feature extraction for a linearly separable dataset. But if the dataset is not linearly separable, we need to apply the Kernel PCA algorithm. It is similar to PCA except that it uses one of the kernel tricks to first map the non-linear features to a higher dimension, then it extracts the principal components as same as PCA.
Project Objective
In this project, we are going to implement the Kernel PCA alongside with a Logistic Regression algorithm on a nonlinear dataset. For this task, we will use the "Social_Network_Ads.csv" dataset. In the dataset, the features have a non-linear correlation with the dependent variable. So, we have to apply Kernel PCA to extract the independent variables.