Support Vector Regression (SVR)

Today we will code Support Vector Regression (SVR)

Check this out on Google Colab Notebook.

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

# Importing the data set
dataSet = pd.read_csv('Position_Salaries.csv')
x = dataSet.iloc[:, 1:2].values
y = dataSet.iloc[:, 2].values

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
x = sc_x.fit_transform(x)
sc_y = StandardScaler()
y = sc_y.fit_transform(y.reshape(10,1))

# Fitting SVR into the dataset
from sklearn.svm import SVR
regressor = SVM(kernel = 'rbf')
regressor.fit(x,y)

# Predicting the new results
y_pred = sc_y.inverse_transform(regressor.predict(sc_x.transform(np.array([[6.5]]))))

# Visualizing the SVM results
plt.scatter(x, y, color='red')
plt.plot(x, regressor.predict(x), color='blue')
plt.title('SVM')
plt.xlabel('Position levels')
plt.ylabel('Salaries')
plt.show()

Data-set is available here.

NOTE: Detailed explanation will be coming soon.

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