Data Pre-Processing In Machine Learning

 

Machine learning requires the transformation of raw data into a format appropriate for modelling and analysis, which is known as data pre-processing. Preparing the data for machine learning algorithms to use in making correct predictions or classifications is the aim of data pre-processing.

Here are a few typical methods for pre-processing data:

1. Data cleaning is eliminating or fixing any mistakes or discrepancies in the data, such as missing values, duplicate records, or outliers.
2. Data transformation entails converting the data into a format that is better suited for analysis or modelling. To make it simpler to compare and analyse the data, you might normalise or standardise it, for instance.
3. Feature engineering is the process of using the existing data to generate new features or variables that may be more advantageous for modelling. For instance, with a person's birthdate, you could determine their age.
4. Data reduction is the process of lowering the number of dimensions in the data by only keeping the most important features or variables. This can aid in the model's simplification and increase its precision.
5. Converting continuous variables into discrete categories or bins is the process of data discretization. It may be simpler to examine the data and create models as a result.

Converting continuous variables into discrete categories or bins is the process of data discretization. It may be simpler to examine the data and create models as a result.