Introduction
Normalization is the process of adjusting the range, or distribution, of individual variables so that they have the same or nearly the same range. It is a process used to make data more consistent and readable in various fields of mathematics, computing, statistics, and machine learning. In particular, normalization is a tool used to create data sets that have the same range, or distribution. Normalization can also be used to normalize features in data sets that have different ranges or variations in their values.
Definition
Normalization is a data preprocessing technique used to transform a dataset into a standard scale, where the values of all variables range from 0 to 1. This technique is especially useful when applied to data sets with highly varied ranges of values for different variables. Normalization is the process of converting different values into the same range by subtracting the minimum value and dividing by the total range.
Normalization Methods
There are two common types of normalization techniques: Min-Max Normalization and Z-Score Standardization.
Min-Max Normalization: Min-Max normalization, also known as scaling, is used to scale the numerical data within a particular range, usually between 0 to 1. This technique scales the values of each feature so that they lie between 0 and 1. It is done by subtracting the minimum value from each field and then dividing by the range.
Z-Score Standardization: Z-Score standardization, also sometimes referred to as ‘standard scoring’, is used to center the data around a mean value of zero. This technique is useful in situations when the mean is a better measure of the middle of the data than the median or mode. It subtracts the mean of each feature from its data point, and then divides by the standard deviation of the feature.
Applications
Normalization has a wide range of applications including data cleaning, data preprocessing, data transformation, and data visualization.
Data Cleaning: Normalization can be used to help find and clean up inconsistent data. Inconsistent data often have different ranges or variation in their values, which can make it difficult to compare the data. Normalization can help to reduce the differences in ranges by scaling the data to have the same range.
Data Preprocessing: Normalization is an important step in many machine learning algorithms. This includes clustering algorithms, linear models, decision tree models, deep learning models, etc. It helps to scale the data in order to reduce the complexity of the algorithm, which in turn helps to improve accuracy.
Data Transformation: Normalization is often used to transform the data from its raw form into a new form that is easier to analyze and interpret. This transformation can be used to reduce the dimension of the data, making it easier to analyze.
Data Visualization: Normalization can be used to improve the readability and interpretability of data visualizations, such as bar charts and histograms. By scaling the data to have the same range, different variables can be compared easily in the same chart.
Conclusion
Normalization is an important data preprocessing technique used in many fields, including machine learning, data science, and data analysis. It has many applications, such as data cleaning, data preprocessing, data transformation, and data visualization. Normalization helps to reduce the complexity of algorithms and improve the interpretability of data.
