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Author: jaydipkumar

Support Vector Regression Position_Salaries.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[61]:
+
+
+#import libabry
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVR
+
+
+# In[62]:
+
+
+#read dataset
+dataset = pd.read_csv('~/Downloads/Data Science/data set/Position_Salaries.csv')
+X = pd.DataFrame(dataset, columns = ['Gender', 'Age'])
+y = pd.DataFrame(dataset, columns = ['EstimatedSalary'])
+
+
+# In[68]:
+
+
+#Feature Scaling
+sc_X = StandardScaler()
+sc_y = StandardScaler()
+X = sc_X.fit_transform(X)
+y = sc_y.fit_transform(y)
+
+
+# In[69]:
+
+
+#build model
+regressor = SVR(kernel = 'rbf')
+regressor.fit(X, y)
+
+
+# In[ ]:
+
+
+#predicte new value
+
+y_pred = regressor.predict(6.5)
+y_pred = sc_y.inverse_transform(y_pred) 
+view raw
+
+
+# In[70]:
+
+
+# Visualize SVR
+X_grid = np.arange(min(X), max(X), 0.01) #this step required because data is feature scaled.
+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 (SVR)')
+plt.xlabel('Position level')
+plt.ylabel('Salary')
+plt.show()
+
+
+# In[ ]:
+
+
+
+