0. Data Preprocessing

0.1 Importing the libraries 

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

0.2 Importing the dataset 

dataset = pd.read_csv('Position_Salaries.csv')
dataset
Position Level Salary
0 Business Analyst 1 45000
1 Junior Consultant 2 50000
2 Senior Consultant 3 60000
3 Manager 4 80000
4 Country Manager 5 110000
5 Region Manager 6 150000
6 Partner 7 200000
7 Senior Partner 8 300000
8 C-level 9 500000
9 CEO 10 1000000

0.3 Split into X & y 

X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

y = y.reshape(len(y),1)

0.4 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)
X

array([[-1.5666989 ],
       [-1.21854359],
       [-0.87038828],
       [-0.52223297],
       [-0.17407766],
       [ 0.17407766],
       [ 0.52223297],
       [ 0.87038828],
       [ 1.21854359],
       [ 1.5666989 ]])

1. Training the model on the whole dataset 

from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

C:\VIVEK\1.PYTHON_DEV\env\tensor\lib\site-packages\sklearn\utils\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  return f(**kwargs)

SVR()

2. Predicting a new result with SVR 

sc_y.inverse_transform(regressor.predict(sc_X.transform([[6.5]])))

array([170370.0204065])

3. Visualising the SVR results 

X_grid = np.arange(min(sc_X.inverse_transform(X)), max(sc_X.inverse_transform(X)), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(sc_X.inverse_transform(X), sc_y.inverse_transform(y), color = 'red')
plt.plot(X_grid, sc_y.inverse_transform(regressor.predict(sc_X.transform(X_grid))), color = 'blue')
plt.title('SVR')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()