Data preprocessing is essential before applying data to machine learning algorithms. Information available mostly is noisy, and it has missing or inappropriate facts, so data preprocessing is necessary before implementing it in machine learning.
It may affect the outcome of final data processing which can be interpreted. It is the most critical phase of machine learning, especially in computational biology.
Understand Data pre-processing
There are two methods of data preprocessing which are feature selection and feature extraction. Feature selection is choosing some regularity and selecting relevant features like while choosing bike the essential factors to consider are mileage, engine, not its colour and n feature extraction (m) attributes to (n) attributes are compared in which m could be lesser, greater or equal to n.
Series of steps in data preprocessing are data cleaning, data integration, data reduction, data discretisation. In feature extraction, it has picture data where feature extraction is mostly dependent on data where mostly colour histograms and symmetry are considered in picture data, and in text data counter vectorizer TFIDF vectorizer, word embedding, the bag of words are used.
Its main objectives recognition of the importance of data preparation in machine learning, identify the meaning and aspects of feature engineering and standardize data sets and its examples.