A faulty measurement is an error in a dataset caused by inaccurate readings or malfunctions from the sensors or instruments collecting the data. Along with outliers and recording errors, faulty measurements are a primary source of poor data quality in real-world datasets and must be addressed before the data is fed into a machine learning model.

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Before data can be fed into any machine learning model, its overall quality must be evaluated and improved. Real-world datasets are often plagued by flaws such as extreme outliers, faulty sensor measurements, and human recording errors. Addressing these discrepancies is an essential part of the data preparation process to ensure the model learns from reliable information.

Data Quality

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Data visualization tools, such as seaborn, Bokeh, and matplotlib, are essential for manually inspecting datasets. By plotting the data, practitioners can visually identify underlying data quality issues—like extreme outliers or recording errors—and develop an intuition for the type of problems they may need to address before feeding data into a model.

Data Visualization Libraries

Faulty Measurement

A recording error is a data quality issue that occurs when information is incorrectly logged, transcribed, or stored during the data collection process. In real-world datasets, recording errors—alongside outliers and faulty sensor measurements—can introduce noise and bias, necessitating thorough data inspection and cleaning before the dataset can be reliably used to train a model.

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