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Data Pre-processing in Machine Learning

In this article, we will cover all the data pre-processing steps that need to be taken to convert raw data into a processed form.

Data Pre-processing

Data pre-processing includes the steps we need to take to transform or encode the data so that it can be easily analyzed by the machine.

The main goal for a model to be accurate and precise in predictions is for the algorithm to be able to easily interpret the characteristics of the data.

Data pre-processing steps

A. Data cleaning

Data cleaning is particularly done as part of data preprocessing to clean the data by filling in missing values, smoothing out noisy data, resolving inconsistencies, and removing outliers.

B. Data Integration

Data integration is one of the data preprocessing steps used to merge data present in multiple sources into a single larger data store, such as a data warehouse.

C. Data Transformation

After erasing the data, we need to consolidate the quality data into other forms by changing the value, structure or format of the data using the data transformation strategies : Generalization, standardization, Attribute Selection, aggregation.

D. Data Reduction

The size of the dataset in a data warehouse may be too large to be handled by data analysis and data mining algorithms.


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