Feature Scaling and Normalization
@genaiexp Feature scaling and normalization are critical in preparing data for predictive modeling. These techniques ensure that all features contribute equally to the model's prediction process. Feature scaling methods like min-max scaling and Z-score standardization adjust the range of features, making them comparable. Normalization transforms data into a common scale without distorting differences in the range of values. This process is especially important in algorithms that compute distances between data points, such as k-means clustering or k-nearest neighbors. By applying these techniques, you enhance the model's performance and ensure that it makes reliable predictions based on balanced data.