Image by Unsplash

In the machine learning process, data scaling falls under data preprocessing, or feature engineering. Scaling your data before using it for model building can accomplish the following:

- Scaling ensures that features have values in the same range
- Scaling ensures that the features used in model building are dimensionless
- Scaling can be used for detecting outliers

There are several methods for scaling data. The two most important scaling techniques are Normalization and Standardization.

When data is scaled using normalization, the transformed data can be calculated using this equation

where and are the maximum and minimum values of the data, respectfully. The scaled data obtained is in the range [0, 1].

## Python Implementation of Normalization

Scaling using normalization can be implemented in Python using the code below:

```
from sklearn.preprocessing import Normalizer
norm = Normalizer()
X_norm = norm.fit_transform(data)
```

Let X be a given data with and . The data X is shown in the figure below:

Figure 1. Boxplot of data X with values between 17.7 and 71.4. Image by Author.

Figure 2. Normalized X with values between 0 and 1. Image by Author.

Here, is the mean of the data, and is the standard deviation. Standardized values should typically lie in the range [-2, 2], which represents the 95% confidence interval. Standardized values less than -2 or greater than 2 can be considered as outliers. Therefore, standardization can be used for outlier detection.

## Python Implementation of Standardization

Scaling using standardization can be implemented in Python using the code below:

```
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_std = stdsc.fit_transform(data)
```

Using the data described above, the standardized data is shown below:

Figure 3. Standardized X. Image by Author.

In summary, we’ve discussed two of the most popular methods for feature scaling, namely: standardization and normalization. Normalized data lies in the range [0, 1], while standardized data lies typically in the range [-2, 2]. The advantage of standardization is that it can be used for outlier detection.

**Benjamin O. Tayo** is a Physicist, Data Science Educator, and Writer, as well as the Owner of DataScienceHub. Previously, Benjamin was teaching Engineering and Physics at U. of Central Oklahoma, Grand Canyon U., and Pittsburgh State U.