Key Differences Between Normalization and Standardization

1. What They Mean:

   • Normalization: This method changes the features to fit within a range, like [0, 1] or [-1, 1]. It uses a formula to do this: $$X' = \frac{X - X_{min}}{X_{max} - X_{min}}$$
   • Standardization: This method changes the data so that it has an average of 0 and a standard score of 1. The formula used is: $$Z = \frac{X - \mu}{\sigma}$$ Here, $\mu$ is the average, and $\sigma$ is how much the data varies.

2. When to Use Which:

   • Normalization: It works best for methods that calculate distances, like K-means and KNN.
   • Standardization: This is better when the data has a normal distribution or when using methods like logistic regression.

3. Effect on Outliers:

   • Normalization can be affected by outliers, which are extreme values in the data.
   • Standardization helps reduce the impact of outliers because it takes into account the average and how much the data spreads out.

4. Data Features:

   • Normalized data fits within a set range.
   • Standardized data does not have a specific range, which helps it deal with different types of data better.