Support vector machine (SVM) is a supervised machine learning method. As previously seen, we can use this method for regression, but also for classification. The principle of this algorithm is to find a hyper plan that separates the data into two classes.

Let's have a look at the following code, that implements the same:

# Fit the model
svc=SVC()
svc.fit(X_train, Y_train)

# Forecast value
goog_data['Predicted_Signal']=svc.predict(X)
goog_data['GOOG_Returns']=np.log(goog_data['Close']/
                                goog_data['Close'].shift(1))

cum_goog_return=calculate_return(goog_data,split_value=len(X_train),symbol='GOOG')
cum_strategy_return= calculate_strategy_return(goog_data,split_value=len(X_train))

plot_chart(cum_goog_return, cum_strategy_return,symbol='GOOG')

In this example, the following applies:

• Instead of instantiating a class to create a KNN method, we used the SVC class.
• The class constructor has several parameters adjusting the behavior of the method to the data you will work on.
• The most important one is the parameter kernel. This defines the method of building the hyper plan.
• In this example, we just use the default values of the constructor.

Now, let's have a look at the output of the code: