Data Preprocessing in Machine Learning: A Simple Guide
Introduction to Data processing Data preprocessing refers to the procedures we must follow to alter or encode data so a machine can quickly and readily decode it. The algorithm's ability to quickly analyze the properties of the data is essential for a model to be accurate and exact in its predictions. Why Data Processing is Important Due to their heterogeneous origin, most real-world datasets used for machine learning are likely to contain missing data, inconsistent results, and noise. Data mining methods would not produce high-quality results when applied to this noisy data because they would be unable to find patterns successfully. Therefore, data processing is crucial to raising the general level of data quality. Missing or duplicate values could present an inaccurate picture of the data's overall statistics. False predictions are frequently the result of outliers and inconsistent data points disrupting the model's overall learning process. Quality data is required for...