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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Title
Step1:  Create Data
Step2:  Detect Outliers
EllipticEnvelope assumes the data is normally distributed and based on that assumption "draws" an ellipse around the data, classifying any observation inside the ellipse as an inlier (labeled as  1) and any observation outside the ellipse as an outlier (labeled as -1). A major limitation of this approach is the need to specify a contamination parameter which is the proportion of observations that are outliers, a value that we don't know.
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Python Code:
# Load libraries
import numpy as np
from sklearn.covariance import EllipticEnvelope
from sklearn.datasets import make_blobs
Explanation: Title: Detecting Outliers
Slug: detecting_outliers   
Summary: How to detect outliers for machine learning in Python. 
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Structured Data
Authors: Chris Albon
Preliminaries
End of explanation
# Create simulated data
X, _ = make_blobs(n_samples = 10,
                  n_features = 2,
                  centers = 1,
                  random_state = 1)
# Replace the first observation's values with extreme values
X[0,0] = 10000
X[0,1] = 10000
Explanation: Create Data
End of explanation
# Create detector
outlier_detector = EllipticEnvelope(contamination=.1)
# Fit detector
outlier_detector.fit(X)
# Predict outliers
outlier_detector.predict(X)
Explanation: Detect Outliers
EllipticEnvelope assumes the data is normally distributed and based on that assumption "draws" an ellipse around the data, classifying any observation inside the ellipse as an inlier (labeled as  1) and any observation outside the ellipse as an outlier (labeled as -1). A major limitation of this approach is the need to specify a contamination parameter which is the proportion of observations that are outliers, a value that we don't know.
End of explanation
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