Stock_4.py

import matplotlib.pyplot as plt 

f,(ax1,ax2) = plt.subplots(1,2,figsize=(30,10))

# Linear Regression
ax1.scatter(range(len(y_test)),y_test,label='data')
ax1.plot(range(len(y_test)),y_pred_lr,color='green',label='LR model')
ax1.legend()

# Support Vector Machine
ax2.scatter(range(len(y_test)),y_test,label='data')
ax2.plot(range(len(y_test)),y_pred_svr,color='orange',label='SVM-RBF model')
ax2.legend()

f1,(ax3,ax4) = plt.subplots(1,2,figsize=(30,10))

# Random Forest Regressor
ax3.scatter(range(len(y_test)),y_test,label='data')
ax3.plot(range(len(y_test)),y_pred_rf,color='red',label='RF model')
ax3.legend()

# Gradient Boosting Regressor
ax4.scatter(range(len(y_test)),y_test,label='data')
ax4.plot(range(len(y_test)),y_pred_gb,color='black',label='GB model')
ax4.legend()