Support Vector Regression in Python – Step 6.) Visualize Results with Support Vector Regression Model

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#Import Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Import data
dataset = pd.read_csv(‘SVR Data.csv’)
x = dataset.iloc[:,1:2].values
y =dataset.iloc[:,2].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.2)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
x =sc_x.fit_transform(x)
y =sc_y.fit_transform(y)

# Training SVR to dataset
from sklearn.svm import SVR
regressor = SVR(kernel = ‘rbf’),y)

#Predict using Regression Model ,transform 6.5 to x ,inverse transform result to y
y_pred = sc_y.inverse_transform(regressor.predict(sc_x.transform(np.array([6.5]))))

#Visual SVR
plt.scatter(x,y, color = ‘red’)
plt.plot(x, regressor.predict(x), color = ‘blue’)
plt.title(‘SVR Regression Model’)
plt.ylabel(‘Account Balance’)

#Visual SVR
x_grid = np.arange(min(x), max(x),0.1)
x_grid = x_grid.reshape((len(x_grid),1))
plt.scatter(x,y, color = ‘red’)
plt.plot(x_grid, regressor.predict(x_grid), color = ‘blue’)
plt.title(‘SVR RegressionModel’)
plt.ylabel(‘Account Balance’)

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