Test_SVR_Model

"""# Test Stock Price"""

testcolumns = ['SecuritiesCode', 'Open', 'High', 'Low', 'Close', 'Volume']
testdata = pd.read_csv('test_stock_prices.csv', usecols=testcolumns)
testdata.shape

log_testdata = np.log(testdata)   # log() == loge()
plt.plot(log_testdata)
plt.show()

log_testdata = log_testdata.replace([np.inf, -np.inf], np.nan)
log_testdata = log_testdata.fillna(log_data.mean())

Std_Scaler = StandardScaler()
Std_feature_transform = Std_Scaler.fit_transform(log_testdata)
Std_feature_transform = pd.DataFrame(Std_feature_transform, columns=log_testdata.columns, index=log_testdata.index)

test_svr_prediction = svr.predict(Std_feature_transform)
print(len(test_svr_prediction))

print(test_svr_prediction)

test_svr_prediction = np.exp(test_svr_prediction)
print(test_svr_prediction)

x = 0
for i in test_svr_prediction:
  print(pd.Timestamp(i, unit='s'))
  x=x+1
print(x)