Stock_3.py

# Linear Regression model
from sklearn.linear_model import LinearRegression 
clf_lr = LinearRegression()
clf_lr.fit(x_train,y_train)
y_pred_lr = clf_lr.predict(x_test)

# Support Vector Machine with a Radial Basis Function as kernel 
from sklearn.svm import SVR
clf_svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
clf_svr.fit(x_train,y_train)
y_pred_svr = clf_svr.predict(x_test)

# Random Forest Regressor
from sklearn.ensemble import RandomForestRegressor
clf_rf = RandomForestRegressor(n_estimators=100)
clf_rf.fit(x_train,y_train)
y_pred_rf = clf_rf.predict(x_test)

# Gradient Boosting Regressor
from sklearn.ensemble import GradientBoostingRegressor
clf_gb = GradientBoostingRegressor(n_estimators=200)
clf_gb.fit(x_train,y_train)
y_pred_gb = clf_gb.predict(x_test)