Adds more explanation and removes SGD regression

- explanation about the preparation of data for scikit-learn fit methods
- removed SGD regression because its prediction is very bad and makes the plot really ugly

scikit-learn-.py

@@ -32,6 +32,25 @@ ts = pd.read_csv(csv_file, sep=';',
                  squeeze=True,
                  index_col='date')
 
+# All scikit-learn `fit` methods require a 2D array of size
+# [n_samples x n_features]. So we transform our DateTimeIndex
+# into an array of time deltas describing how many days have
+# passed since the first observation and call the days method
+# to get just the numbers of days instead of a TimeDelta object.
+# Something like this:
+#
+# >>>  [1 ,2 ,3 , 4, ..., n_samples]
+#
+# Then, we reshape it using (-1, 1) shape, where -1 will tell
+# numpy to infer the array size and use it there. This reshape is
+# used because we have only 1 feature (the number of the day
+# of the order). The result will be an array like below:
+#
+# >>> [[1], [2], [3], ..., [n_samples]]
+#
+# If we had more features, each element of the outter array
+# would have more than one element.
+#
 X = (ts.index - ts.index[0]).days.reshape(-1, 1)
 y = ts.values
 
@@ -55,12 +74,6 @@ mtl = linear_model.ElasticNet(alpha=0.1, normalize=True)
 mtl.fit(X_train, y_train)
 plt.plot(X_test, mtl.predict(X_test), color='green', linewidth=2)
 
-# SGD Regression
-
-sgd = linear_model.SGDRegressor(shuffle=False, eta0=0.25)
-sgd.fit(X_train, y_train)
-plt.plot(X_test, sgd.predict(X_test), color='red', linewidth=1)
-
 plt.xticks()
 plt.yticks()
 plt.show()
\ No newline at end of file