Continue to fetch data for SL feature mining models and organize codebase

Author: thammo4

ridge_regression_example.py

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+#
+# RIDGE Regression Example (for temporary reference during development)
+# 	• NB: for python, `alpha` is the ordinary lambda tuning parameter
+#
+
+df = pd.read_csv("https://raw.githubusercontent.com/Statology/Python-Guides/main/mtcars.csv")[['mpg', 'wt', 'drat', 'qsec', 'hp']];
+
+# Define covariates (X) and response (Y)
+# For stock trading:
+# 	• df_X = Factors that contribute to / affect the closing price of a stock
+# 	• df_Y = Closing price of a stock
+df_X = df[['mpg', 'wt', 'drat', 'qsec']];
+df_Y = df['hp'];
+
+# Perform 10-fold cross validation three times
+# For stock trading:
+# 	• This can probably stay the same
+xval = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1);
+
+# Define model and fit to training data
+# For stock trading:
+# 	• This can probably stay the same
+model = RidgeCV(alphas=np.arange(.01, 1, .01), cv=xval, scoring='neg_mean_absolute_error');
+model.fit(df_X, df_Y);
+
+print('Tuning parameter (lambda) corresponding to lowest test MSE: {}'.format(model.alpha_));
+
+# Prediction with RIDGE Model
+# For stock trading:
+# 	• Update the df_new to contain the future values of the covariates from df_X
+df_new = pd.DataFrame({'mpg':[24], 'wt':[2.5], 'drat':[3.5], 'qsec':[18.5]});
+df_predicted = model.predict(df_new);
+
+print(f'Predicted HP: {df_predicted[0]}');