This article investigates how to create meaningful features using date-related information. For example, you can often find such features in traditional regression or classification tasks. And this is not restricted to time series forecasting problems. Many data science projects contain some information about the passage of time. But, ideally, we find features that have a strong yet simple relationship with the target variable. The features do not have to be very complex. You are effectively shifting the complexity from the model to the features. Alternatively, you can try to come up with some more meaningful features and continue to use the current model (at least for the time being).įor many projects, both enterprise data scientists and participants of data science competitions like Kaggle agree that it is the latter – identifying more meaningful features from the data – that can often make the most improvement to model accuracy for the least amount of effort. One possibility would be to increase the complexity of the machine-learning model you have used. There are many ways in which you could follow up. The score is acceptable, but you believe you can do much better. You have already received some data from the stakeholders/data engineers, did a thorough EDA, and selected some variables you believe are relevant for the problem at hand. The goal is to build a model predicting Y, the target variable. Imagine you have just started a new data science project.
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