Whether you should use normalization or standardization depends on the specific characteristics of your data and the requirements of the machine learning algorithm you are using. Here’s a simple guide:

**Use Normalization when:**

- You have features with different ranges.
- Your algorithm relies on the magnitude of values, such as in k-nearest neighbors (KNN) or neural networks.
**Example:** - If one feature represents people’s ages (ranging from 0 to 100) and another represents income (ranging from 0 to 100,000), normalizing would scale both features to a common range, like 0 to 1.

2. **Use Standardization when:**

- Your features have different units or different magnitudes.
- Your algorithm is not sensitive to the scale of features, such as in linear regression, logistic regression, or support vector machines.
**Example:** - If one feature is measured in inches and another in pounds, standardization would make them comparable by scaling them based on their mean and standard deviation.

In many cases, both normalization and standardization can work, and the choice may depend on the specific characteristics of your dataset and the behavior of the machine learning algorithm you are using. It’s often a good practice to try both and see which one gives better results for your particular task.

Let’s dive a bit deeper into the differences between normalization and standardization:

**Normalization:**

**Objective:**The main goal of normalization is to scale the values of different features to a standard range, usually between 0 and 1.**Formula:**(X_normalized) = {X – X_min}/{X_max – X_min})**Effect:**This ensures that all features have the same scale, making them directly comparable. It’s particularly useful when features have different ranges.**Example:**- If you have features like height and weight, where height might range from 150 to 200 centimeters and weight from 50 to 100 kilograms, normalization would bring both features into a common scale, say, between 0 and 1.

**Standardization:**

**Objective:**Standardization aims to center the data around zero and scale it based on the standard deviation, typically resulting in values with mean 0 and standard deviation 1.**Formula:**(X_standardized) = ({X -mean}\standard deviation)**Effect:**This transformation ensures that the features have similar scales, but it also takes into account the distribution of the data. Standardization is suitable when the features have different units or when the data distribution is not necessarily uniform.**Example:**- If you have features measured in different units, like temperature in Celsius and distance in kilometers, standardization would make these features comparable by scaling them based on their mean and standard deviation.

**When to Choose:**

**Normalization:**Use when the features have different ranges or when the algorithm you’re using (like k-nearest neighbors or neural networks) benefits from having features on a similar scale.**Standardization:**Use when features have different units, and the algorithm you’re using (like linear regression or support vector machines) is not sensitive to the scale of features.

In practice, the choice between normalization and standardization often depends on the characteristics of your data and the requirements of the specific machine learning algorithm you are applying.

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