What’s your favorite restaurant? It’s probably your favorite because of current experience – great food, atmosphere, value, and so on. But how do you anticipate future experience? How do you assess where a restaurant is trending?
A restaurant’s trend hints at future direction: what might it be like in 6 months or 1 year?
Picture this. Your mission is to secure great deals on vehicles sold at wholesale auto auction. An intriguing prospect comes on your radar. How do you make the purchasing decision? How do you quickly weigh 25 pieces of available information? I’m eager to help. I introduce and verify a machine learning model trained by 70,000 historical transactions.
Key Takeaways
Around 2012, Carvana – a car retailer – posted wholesale auto auction transaction data, prompting analysts to predict whether a purchased vehicle would turn out to be a lemon. Historical data of this sort may help auction buyers avoid future lemons.
Preliminary insights follow from visual exploration of the data.
.nobullet li { list-style-type: none; } Key Takeaways
A business case study may be played out using a replicable controlled experiment1.
Consider a hypothetical business case with these characteristics:
The objective is prediction of a numerical outcome. Many indicators are available to try and predict the outcome.
Key Takeaways
A linear model – a “line of best fit” – estimates the true relationship between predictors and an outcome.
Linear models come in classical statistics or machine learning varieties.
To learn about linear models’ relative performance/behavior, we use controlled experiments, powered by computer simulation.