Python Tutorial : Linear modeling with financial data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-for-finance-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we have features and targets, we can fit our first machine learning model -- a linear model. For machine learning, we usually split our data into train and test sets. With time-series data, we want to break up our train and test into continuous chunks. The training data should be the earliest data, and the test data should be the latest data. We fit our model to the training data and test on the newest data to understand how our algorithm will perform on new, unseen data. We can't use sklearn's train_test_split because it randomly shuffles the train and test data. For linear models, we need to add a constant to our features, which adds a column of ones for a y-intercept term. statsmodels has add_constant() for this. Then we split the data into train and test sets. First, we get the index we'll split at by using the train set fraction and the number of rows in our data. We get the number of rows from the dot-shape property and convert this to an integer. Finally, we split features and targets into train and test sets using Python's indexing. Remember Python indexing goes [start:stop:step]. Here, we start from the beginning and go to train_size for the training dataset, then go from train_size to the end of the data for the test set. Now that we have our train and test sets, we can fit a linear model. We first create the model with the OLS() function from statsmodels, giving it our train_targets and train_features. Then we use the fit method which returns an object with the results of the fit. Printing out the summary of the fit results will yield a lot of information. We see the R-squared value in the upper right and many other metrics. We can compare this value with R-squared from other models we try. An R-square