Preparing the dataset

The dataset preparation is a crucial phase in any ML project because it has implications for the effectiveness of the resulting trained model.

In this recipe, we will create a dataset from the physical quantities acquired earlier by adopting two techniques to make it suitable for training an accurate model. These two techniques will allow us to obtain a balanced dataset and bring its values into the same numerical range.

Getting ready

The dataset required for our task must be prepared to train a binary classification model, meaning the output can only belong to two classes: Yes, it snows or No, it does not snow. Therefore, mapping the snowfall in cm into these two classes is the first thing we must do. In this project, we have considered the snow formation only when the snowfall in cm is above 5 cm.

The next step for building the dataset concerns the selection of input features to predict the snow. Considering our goal of forecasting...