Normalization is a data preparation technique that is frequently used in machine learning. Normalization is the process of converting the values of numeric columns in a dataset to a similar scale without distorting the ranges of values or losing information.
Normalization provides each variable equal weights/importance, ensuring that no single variable biases model performance in one direction simply because it is larger. Clustering algorithms, for example, utilise distance measurements to determine whether or not an observation belongs in a certain cluster.
Simply put, data normalisation assures that your data appears, reads, and can be used in the same way across all of your customer database's records. This is accomplished by ensuring that the formatting of specific fields and records in your client database are consistent.
Figure out the data set's range.
Subtract the value of this data point from the minimum x value.
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