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Why is data preparation so important in machine learning?

Randall Hendricks
Randall HendricksAnswered

Algorithms for Machine Learning (ML) help us identify patterns in data. We utilize these patterns to anticipate the behavior of additional data points. If we enter low-quality data into an ML model, the model will always be of poor quality, resulting in inaccurate predictions.

Real-world data is often insufficient or inconsistent, including several inaccuracies and omitting important behaviors. Therefore, the data preparation process is an essential ML step. The term “data preparation” refers to the steps taken to ensure a high-quality dataset is built and raw data is transformed into a workable format.

If just a few records are used to train a machine learning model, it will likely perform badly in the prediction stage due to overfitting or underfitting. Sadly, there is no effective remedy for inadequate data. There may be a need to obtain further data. We might select simpler models (minimal parameters to modify), such as the Naive Bayes classification or logistic regression, that perform generally well with fewer data. Additionally, these models may be less sensitive to overfitting.

Strange as it may seem, too much data and irrelevant attributes may potentially reduce the efficiency of machine learning systems (Curse of Dimensionality). Here are feature selection and feature engineering as the primary solutions.

Data preparation in machine learning is vital in the  a development cycle and has a significant influence on the predictive model performance. Consequently, we must first build an accurate dataset before moving on to the training step. We should also remember that data preparation approaches differ between datasets, and not all procedures are relevant in all circumstances.

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