Python Tutorial : All models are wrong but some are useful

Want to learn more? Take the full course at https://learn.datacamp.com/courses/fundamentals-of-ai at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this chapter, we will explain what AI models are and how to build them. A predictive model is a simplified representation of the actual process we are dealing with. It is neither feasible nor necessary to ever create a perfect model -- we need one that is good enough for our application. A toy car can be a great car model for child's play, but for building self-driving vehicles, we will need something more. How do we actually build a model? We start by defining the specific problem we need to solve and the associated measure of success. Then we collect the data from our process inputs and outputs. Based on that, we select the model to address the problem at hand and fit it using the available data. And finally, we use our amazing model to tackle the monster-problem that motivated us to build it in the first place! But let's not rush. The top predictors of failure of AI projects are the lack of a clear problem definition, no value proposition, and no success metric. To minimize the chance of failure, make sure to clearly answer these questions upfront. First, what is the pain point? For example, we want to automate manual, repetitive tasks with an AI solution. Second, how do we create value by solving this problem? In our automation example, we would create value by freeing up resources for more complex and productive work. Finally, how do we define and measure success and failure? In our automation example, we would define success by how many hours of work we save. Now that our BUSINESS problem is clearly defined, we can start working on the technical aspect of our AI solution. We do so by answering the following questions. First, what is the technical nature of my problem in the broadest sense? Second, what is the appropriate theoretical mo