The problem we will tackle is predicting the average global land and ocean temperature using over 100 years of past weather data. We are going to act as if we don’t have access to any weather forecasts.
The problem we will tackle is predicting the average global land and ocean temperature using over 100 years of past weather data. We are going to act as if we don’t have access to any weather forecasts. What we do have access to is a century’s worth of historical global temperatures averages including; global maximum temperatures, global minimum temperatures, and global land and ocean temperatures. Having all of this, we know that this is a supervised, regression machine learning problem
It’s supervised because we have both the features and the target that we want to predict, also our target makes this a regression task because it is continuous. During training, we will give multiple regression models both the features and targets and it must learn how to map the data to a prediction. Moreover, this is a regression task because the target value is continuous (as opposed to discrete classes in classification).
That’s pretty much all the background we need, so let’s start!
Before we jump right into programming, we should outline exactly what we want to do. The following steps are the basis of my machine learning workflow now that we have our problem and model in mind:
First, we need some data. To use a realistic example, I retrieved temperature data from the Berkeley Earth Climate Change: Earth Surface Temperature Dataset found on Kaggle.com. Being that this dataset was created from one of the most prestigious research universities in the world, we will assume data in the dataset is truthful.
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