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. Quick Answer Data quality means making sure the dataset is accurate, complete, consistent, timely, and usable. EDA means exploring the dataset with summaries and visualizations so you can find missing values, outliers, correlations, patterns, and potential modeling problems before training. ...