Machine Learning สำหรับการเงินด้วย Python
Nathan George
Data Science Professor
print(feature_names)
['10d_close_pct', '14-day SMA', '14-day RSI', '200-day SMA', '200-day RSI', 'Adj_Volume_1d_change', 'Adj_Volume_1d_change_SMA', 'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4']
print(feature_names[:-4])
['10d_close_pct', '14-day SMA', '14-day RSI', '200-day SMA', '200-day RSI', 'Adj_Volume_1d_change', 'Adj_Volume_1d_change_SMA']
train_features = train_features.iloc[:, :-4] test_features = test_features.iloc[:, :-4]
ตัวเลือกการสเกล:
from sklearn.preprocessing import scale sc = scale() scaled_train_features = sc.fit_transform(train_features) scaled_test_features = sc.transform(test_features)
# create figure and list containing axes f, ax = plt.subplots(nrows=2, ncols=1) # plot histograms of before and after scaling train_features.iloc[:, 2].hist(ax=ax[0]) ax[1].hist(scaled_train_features[:, 2]) plt.show()
# create figure and list containing axes f, ax = plt.subplots(nrows=2, ncols=1)
# plot histograms of before and after scaling train_features.iloc[:, 2].hist(ax=ax[0]) ax[1].hist(scaled_train_features[:, 2]) plt.show()
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