Z-score normalization in detail and syntax.pptx

Z-score normalization with example

1. Z-score normalization  refers to the process of normalizing every value in a dataset such that the mean of all of the values is 0 and the standard deviation is 1. We use the following formula to perform a z-score normalization on every value in a dataset: New value = (x – μ) / σ where: x : Original value μ : Mean of data σ : Standard deviation of data

2. Here , A = Type of cat and B = Height of cat

3. Standardization (Z-Score Normalization) Standardization is the concept and step of putting different variables on the same scale. This concept allows comparing scores between different types of variables. Equation of Standardization: Add alt text                                                                                         An Example where Standardization is used Lets there are two-dimensional data-set with two different features — height and BMI. Height is in inch and BMI is in its value and here the value of height is very large in comparison with BMI. So, height will dominate over the BMI feature and will have more contribution to the distance computation. Height(inch) = [165, 172] BMI = [18.5. 25] This problem can be solved by applying the technique of Standardization (Z-Score Normalization). Conclusion There are two common approaches to bringing different features onto the same scale: normalization and standardization. Most often, normalization refers to the rescaling of the features to a range of [0, 1], which is a special case of min-max scaling. Using standardization, we center the feature columns at mean 0 with standard deviation 1 so that the feature columns take the form of a normal distribution, which makes it easier to learn the weights. Standardization maintains useful information about outliers and makes the algorithm less sensitive to them in contrast to min-max scaling.

4. : Calculate the variance of cats’ height

5. Now, calculate each cat’s height difference from the derived mean: Calculate variance: Standard Deviation Standard Deviation is the square root of variance. Equation of Standard Deviation:                                                                                                 Now, calculate standard deviation from the value of variance: