Machine Learning

Machine learning models are only as good as the data they are trained on. Before feeding data into a model, it must be cleaned, transformed, and prepared  a process known as "data preprocessing."  Let’s explore what preprocessing is, why it’s important, and how it’s done with a practical example. What is data preprocessing? Data preprocessing is the process of converting raw data into a clean and usable format for machine learning algorithms. It involves handling missing values, scaling features, encoding categorical variables, and splitting data into training and testing sets. In simple words, preprocessing makes sure that your data is in the right shape and scale for your model to understand. Why is preprocessing important? Without proper preprocessing: Models may produce inaccurate results . Algorithms can become biased due to inconsistent data. Features with large ranges can dominate the training process. Missing or invalid data may cause errors ...