svr_self.py

#svr regression


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

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

# Splitting the dataset into the Training set and Test set
"""from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""

# 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)

#svr regression model                 note: svr model donot do feture scaling with its own as it is less known class
from sklearn.svm import SVR
regressor=SVR(kernel='rbf',gamma='auto')
regressor.fit(X,y)

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

# Visualising the Regression results
plt.scatter(X, y, color = 'red',label='actual stat')
plt.plot(X, regressor.predict(X), color = 'blue',label='predicted stat')
plt.legend()
plt.title('Truth or Bluff (SVR Model)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()