Developing a churn analytics pipeline

In ML, we observe an algorithm's performance in two stages: learning and inference. The ultimate target of the learning stage is to prepare and describe the available data, also called the feature vector, which is used to train the model.

The learning stage is one of the most important stages, but it is also truly time-consuming. It involves preparing a list of vectors, also called feature vectors (vectors of numbers representing the value of each feature), from the training data after transformation so that we can feed them to the learning algorithms. On the other hand, training data also sometimes contains impure information that needs some pre-processing, such as cleaning.

Once we have the feature vectors, the next step in this stage is preparing (or writing/reusing) the learning algorithm. The next important step is training the algorithm to prepare the predictive model. Typically, (and of course based on data size), running an algorithm may take hours...