Have you heard of machine learning but haven’t found a way to implement any algorithms? Do you use R for all of your machine learning models and are wondering how to scale and deploy your models to production quickly and efficiently? Do you solely use R, or caret, for your machine learning models and want to diversify your skillset?
No judgement if you do, but let me introduce you to a, in my opinion, superior way to craft and deploy machine learning models using Python and scikit-learn.
So far, the Hot Stove has been, well, rather cold. Derek Jeter’s Miami Marlins are in pursuit of building a winning team, while shedding payroll, that will hopefully help increase the fan base and future revenue from ticket sales, advertising revenue, and merchandise sales. Currently, the Marlins have the 10th lowest payroll in Major League Baseball (MLB), one of their highest rankings in the last ten years. By trading Giancarlo Stanton, reigning MVP, as well as other large players like Ozuna and Yelich, the Marlins can decrease their payroll, however, can they still field a competitive team?
Corporations have recently caught on that using sponsorships and influencer marketing techniques within sports products to promote their products is one of the savviest and most reliable ways to reach their target market. It is a great opportunity for them to show consumers their interest in a cause or event and gain their trust, especially in the case of men aged 18-34, a usually hard demographic to reach.
The average National Basketball Association franchise is now worth $1.36 billion, a 350 percent increase over the last five years. We took the time to perform an investment analysis on two teams valued below average for the NBA, the Milwaukee Bucks and the Orlando Magic, to look for any beneficial investment opportunities. We considered the asking price for the team, based off of recent valuations, anticipated revenues and expenses, thought of as cash inflows and outflows, as well as an investment horizon of five years.
In multiple regression, machine learning, and predictive analytics in general, a common goal is to determine which independent variables contribute significantly to explaining the variability in the dependent variable. Unfortunately, many people fall into the same trap, classifying variable importance by comparing the smallest relative P-values. In this post, we caution modelers of biased and misleading statistics and provide alternatives to discover what predictors could be the best fit for your model.