Python Tutorial : Machine Learning for Finance in Python

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. --- Hello, and welcome to the course! I'm Nathan George, an assistant professor at Regis University in Denver, Colorado, where I teach and develop data science courses. In this course, we'll learn how to use machine learning for finance. Machine learning has been used in finance for years. Here's an example from Zacks, which uses machine learning to predict future earnings of companies. A report by JP Morgan says investors who don't know machine learning will be left behind. Most data we'll use is stock prices, like the price of AMD stock shown here. We'll use many machine learning methods to predict future prices and select stock portfolios. In this course, we'll use a few stocks, including LNG and AMD. These are highly volatile, meaning their price moves around a lot. The data are stored in pandas DataFrames, such as amd_df. I only included two columns we will use as inputs in our machine learning algorithms -- the adjusted closing price and adjusted volume. The adjusted close price is the price at the end of each day, adjusted for things like stock splits. The adjusted volume is the number of shares exchanged that day. We always want to do exploratory data analysis, or EDA, on new data to understand it. EDA is broad, but we'll focus on plots of raw data, histograms, scatter plots, and correlations. The pandas library has some nice built-in plotting methods for DataFrames. For example, we can use dot-plot() to show line plots of the raw data, and plot-dot-hist() for histograms. Using machine learning for finance can be accomplished in many ways. We could predict the raw prices of our stocks, but typically we'll predict percent changes. This makes it easier to create a general-purpose model for stock price prediction. It will