Support_Vector_Regression.py

# Support Vector Regression: 

#1 Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#2 Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:,1:2].values.astype(float)
y = dataset.iloc[:,2:3].values.astype(float)

#3 Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)

#4 Fitting the Support Vector Regression Model to the dataset
# Create your support vector regressor here
from sklearn.svm import SVR
# most important SVR parameter is Kernel type. It can be linear,polynomial or gaussian.
#SVR. We have a non-linear condition so we can select polynomial or gaussian but here
#we select RBF(a gaussian type) kernel. 
regressor = SVR(kernel='rbf')
regressor.fit(X,y)

#5 Predicting a new result
y_pred = sc_y.inverse_transform((regressor.predict(sc_X.transform(np.array([[6.5]])))))

#6 Visualising the Support Vector Regression results
plt.scatter(X, y, color = 'magenta')
plt.plot(X, regressor.predict(X), color = 'green')
plt.title('Truth or Bluff (Support Vector Regression Model)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

#6 Visualising the Regression results (for higher resolution and smoother curve)
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('Truth or Bluff (Support Vector Regression Model(High Resolution))')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()