Learn about prediction.
Hollywood loves making movies about prediction. There was Moneyball, which predicted athletes' performance in baseball. There are countless movies about someone trying to outwit the casinos or really their own luck. There's the story of the housing bubble and how one commodities trader predicted the recession and made billions buying against foreclosed mortgages. Predictions make for a great story and, in this chapter, I'm going to teach you a popular machine learning technique for predicting events and outcomes called decision trees. A decision tree takes a set of data and it splits that data continuously until it has a predicted outcome.
How many times have you heard someone say, "If I only had a crystal ball, I could predict the future"? Well, we're going to do just that, and walk you through the process step by step so you can grasp exactly how to go about it. Now brick and mortar retailers in the US do an estimated 4 trillion in sales each year. That number might be surprising given the power of e-commerce these days, but old school retail is still a significant economic engine. To that point, in this chapter, we're going to help a retailer determine where their next store and expansion of the retail business should be located.
And we're going to do so using, you guessed it, prediction. What our prediction algorithm is going to do is assess the ability for different predictors to influence the outcome. In the case of our retailer, the potential predictors that we have available to us in our data set are weather, the radius of complimentary establishments, the population, the number of cars that drive by each store, and the unemployment rate for the store's geographic location. These predictors can help us to understand the conditions necessary for our retailer to make a safe bet on their next location.
We just have to figure out which ones matter. So, let's get started with our prediction analysis. These predictors can help us to understand the conditions necessary for our retailer to make a safe bet on their next location. We just have to figure out which ones matter. So, let's get started with our prediction analysis.
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment