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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:  Load Iris Flower Data
Step2:  Standardize Features
Step3:  Train Support Vector Classifier
Step4:  Create Previously Unseen Observation
Step5:  Predict Class Of Observation
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Python Code:
# Load libraries
from sklearn.svm import LinearSVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
Explanation: Title: Support Vector Classifier  
Slug: support_vector_classifier 
Summary: How to train a support vector classifier in Scikit-Learn 
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Support Vector Machines
Authors: Chris Albon  
<a alt="Support Vector Classifier" href="https://machinelearningflashcards.com">
    <img src="support_vector_classifier/Support_Vector_Classifier_print.png" class="flashcard center-block">
</a>
There is a balance between SVC maximizing the margin of the hyperplane and minimizing the misclassification. In SVC, the later is controlled with the hyperparameter $C$, the penalty imposed on errors. C is a parameter of the SVC learner and is the penalty for misclassifying a data point. When C is small, the classifier is okay with misclassified data points (high bias but low variance). When C is large, the classifier is heavily penalized for misclassified data and therefore bends over backwards avoid any misclassified data points (low bias but high variance).
In scikit-learn, $C$ is determined by the parameter C and defaults to C=1.0. We should treat $C$ has a hyperparameter of our learning algorithm which we tune using model selection techniques.
Preliminaries
End of explanation
# Load feature and target data
iris = datasets.load_iris()
X = iris.data
y = iris.target
Explanation: Load Iris Flower Data
End of explanation
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
Explanation: Standardize Features
End of explanation
# Create support vector classifier
svc = LinearSVC(C=1.0)
# Train model
model = svc.fit(X_std, y)
Explanation: Train Support Vector Classifier
End of explanation
# Create new observation
new_observation = [[-0.7, 1.1, -1.1 , -1.7]]
Explanation: Create Previously Unseen Observation
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# Predict class of new observation
svc.predict(new_observation)
Explanation: Predict Class Of Observation
End of explanation
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