Scaling Numerical Data, Explained: A Visual Guide with Code Examples for Beginners

Data Preprocessing: Scaling Numerical Data Understanding Scaling: - Scales numerical features to make them comparable and improve model performance. - Benefits features with wide ranges, different units, or significant magnitudes. Methods of Scaling: Min-Max Scaling: - Transforms values to a fixed range (e.g., 0-1) to constrain features or preserve relationships. Standard Scaling: - Centers data around a mean of 0 and scales it to a standard deviation of 1 to standardize features. Robust Scaling: - Uses the median and interquartile range to handle outliers and maintain data order. Log Transformation: - Compresses large values by applying a logarithmic function to right-skewed data. Box-Cox Transformation: - Optimizes a power transformation to normalize feature distributions. Application Examples: - Min-Max Scaling used for temperature with a natural range. - Standard Scaling used for wind speed with a normal distribution. - Robust Scaling used for humidity to mitigate outliers. - Log Transformation used for golfers' count with a right-skewed distribution. - Box-Cox Transformation used for green speed to approximate a normal distribution. Conclusion: Scaling is crucial for preparing numerical data for machine learning models. By choosing the appropriate scaling method based on data characteristics, we enhance model accuracy and reliability.