Data Quality And EDA For Machine Learning
Before choosing an algorithm, start with the data. Machine learning models learn from examples. If those examples are incomplete, inconsistent, mislabeled, or poorly formatted, the model can produce unreliable predictions. Data quality and exploratory data analysis, or EDA, are the first real skills to build in practical machine learning. The practical goal is not only to make a dataset look clean. The goal is to understand whether the data can support the prediction task, what risks it contains, and what should be fixed before the model learns from it. ...