<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Title
Step1:  Create Linearly Inseparable Data
Step2:  Conduct Kernel PCA
Step3:  View Results
<ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles
Explanation: Title: Dimensionality Reduction With Kernel PCA
Slug: dimensionality_reduction_with_kernel_pca
Summary: How to reduce the dimensions of the feature matrix using kernels for machine learning in Python. 
Date: 2017-09-09 12:00
Category: Machine Learning
Tags: Feature Engineering
Authors: Chris Albon
<a alt="Dimensionality Reduction With Kernel PCA" href="https://machinelearningflashcards.com">
    <img src="dimensionality_reduction_with_kernel_pca/Kernel_PCA_print.png" class="flashcard center-block">
</a>
Preliminaries
End of explanation
# Create linearly inseparable data
X, _ = make_circles(n_samples=1000, random_state=1, noise=0.1, factor=0.1)
Explanation: Create Linearly Inseparable Data
End of explanation
# Apply kernal PCA with radius basis function (RBF) kernel
kpca = KernelPCA(kernel="rbf", gamma=15, n_components=1)
X_kpca = kpca.fit_transform(X)
Explanation: Conduct Kernel PCA
End of explanation
print('Original number of features:', X.shape[1])
print('Reduced number of features:', X_kpca.shape[1])
Explanation: View Results
End of explanation
<END_TASK>