When you're working on a machine learning project, the most tedious steps are often data cleaning and preprocessing. Especially when you're working in a Jupyter Notebook, running code in many cells can be confusing. The Scikit-learn library has tools called Pipeline and ColumnTransformer that can really make your life easier. Instead of transforming the dataframe step by step, the pipeline combines all transformation steps. You can get the same result with less code.

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to apply a suite of common and commonly useful data preparation techniques to the raw data in parallel and combine the results of all of the transforms together into a single large dataset from which a model can be fit and evaluated. This is an alternative philosophy for data preparation that treats data transforms as an approach to extract salient features from raw data to expose the structure of the problem to the learning algorithms.

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to apply a suite of common and commonly useful data preparation techniques to the raw data in parallel and combine the results of all of the transforms together into a single large dataset from which a model can be fit and evaluated. This is an alternative philosophy for data preparation that treats data transforms as an approach to extract salient features from raw data to expose the structure of the problem to the learning algorithms.

Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and highly specific to the project. Nevertheless, there are enough commonalities across predictive modeling projects that we can define a loose sequence of steps and subtasks that you are likely to perform. This process provides a context in which we can consider the data preparation required for the project, informed both by the definition of the project performed before data preparation and the evaluation of machine learning algorithms performed after. In this tutorial, you will discover how to consider data preparation as a step in a broader predictive modeling machine learning project.