Data Preprocessing

What Does Data Preprocessing Mean?

Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, lacking in certain behaviors or trends, and is likely to contain many errors.

Data preprocessing is a proven method of resolving such issues. Data preprocessing prepares raw data for further processing.

Data preprocessing is used in database-driven applications such as customer relationship management and rule-based applications (like neural networks).

In Machine Learning (ML) processes, data preprocessing is critical to encode the dataset in a form that could be interpreted and parsed by the algorithm.


Techopedia Explains Data Preprocessing

Data goes through a series of steps during preprocessing:

Data Cleaning: Data is cleansed through processes such as filling in missing values or deleting rows with missing data, smoothing the noisy data, or resolving the inconsistencies in the data.

Smoothing noisy data is particularly important for ML datasets, since machines cannot make use of data they cannot interpret. Data can be cleaned by dividing it into equal size segments that are thus smoothed (binning), by fitting it to a linear or multiple regression function (regression), or by grouping it into clusters of similar data (clustering).

Data inconsistencies can occur due to human errors (the information was stored in a wrong field). Duplicated values should be removed through deduplication to avoid giving that data object an advantage (bias).

Data Integration: Data with different representations are put together and conflicts within the data are resolved.

Data Transformation: Data is normalized and generalized. Normalization is a process that ensures that no data is redundant, it is all stored in a single place, and all the dependencies are logical.

Data Reduction: When the volume of data is huge, databases can become slower, costly to access, and challenging to properly store. Data reduction step aims to present a reduced representation of the data in a data warehouse.

There are various methods to reduce data. For example, once a subset of relevant attributes is chosen for its significance, anything below a given level is discarded. Encoding mechanisms can be used to reduce the size of data as well. If all original data can be recovered after compression, the operation is labeled as lossless.

If some data is lost, then it’s called a lossy reduction. Aggregation can also be used, for example, to condense countless transactions into a single weekly or monthly value, significantly reducing the number of data objects.

Data Discretization: Data could also be discretized to replace raw values with interval levels. This step involves the reduction of a number of values of a continuous attribute by dividing the range of attribute intervals.

Data Sampling: Sometimes, due to time, storage or memory constraints, a dataset is too big or too complex to be worked with. Sampling techniques can be used to select and work with just a subset of the dataset, provided that it has approximately the same properties of the original one.


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