Data Cleaning and Preprocessing are two fundamentals of data mining and data science management. The former is a segment of the latter that contributes to enhancing data quality for better understanding.
While adopting data mining, companies often initiate the procedures of cleaning and structuring complex datasets so that they can integrate them appropriately. Raw data may contain additional and inaccurate information, which is not beneficial for organizations. Moreover, such insights create complications for marketers with their ambiguousness. Cleaning and preprocessing data addresses all these challenges.
But how do data cleaning and preprocessing work? This blog shall simplify these terminologies by highlighting their steps alongside their importance.
What is Data Cleaning?
Data cleaning is the method for transforming raw data into measurable information. Simply put, it is the process of eliminating irrelevant insights and adding missing elements in datasets. It has various techniques, such as missing value insertion, binning, regression, clustering, and ignoring tuples.
Cleaning data is a fragment of data preprocessing that can be beneficial in removing duplicate data, enhancing accuracy, boosting data reliability, identifying structural errors, analyzing data conveniently, and others.
What is Data Preprocessing?
Data preprocessing includes all the stages of cleaning, transforming, preparing, improving, integrating, and making data understandable. The process commences by assessing the raw datasets and identifying gaps in them. It depends on machine learning algorithms to identify and enhance applicable information, followed by eliminating negligible insights.
The method of data preprocessing has three broad segments- data cleaning, transformation, and reduction. These stages have further segments that contribute to successful data mining.
Why are Data Cleaning and Preprocessing Important in Data Mining?
Cleaning and preprocessing data are two influential stages in data mining that elevate the quality of gathered data. High-quality data helps in making better decisions and gaining a competitive advantage. Raw datasets often contain unnecessary information. Many times, there are occasions when information is missing or wrong.
The role of data mining does not end only after collecting data; instead, it includes filtering and preparing data for its enhanced usability. Analysts determine high-quality data by assessing its accuracy rate, relevance, consistency, completeness, and other characteristics. We know this method as data cleaning and preprocessing. The procedures assist in removing errors and unnecessary information alongside filling the gaps or adding missing information to the data sets.
In a way, both stages of data mining simplify complex data without deteriorating its quality. These ensure the data is accurate so that businesses can rely on it and make better decisions.
How is Data Cleaning and Preprocessing Done?
As mentioned before, cleaning data is a stage in data preprocessing, and both are stages of data mining; therefore, each step is important in making data better and more standard. Let us discuss the steps of data cleaning and preprocessing to comprehend both terminologies appropriately.
Steps of Data Preprocessing:
Data cleaning:
Cleaning data includes the stages of managing missing and noisy data.
Missing data management occurs when a dataset is incomplete or has missing elements. If important variables or insights do not exist in a database, its results can be vague to people. Therefore, filling the gaps is important. To execute this stage, techniques of filling in missing values and ignoring the tuples are mostly adopted.
Noisy Data management is the process of identifying and eliminating meaningless and irrelevant data. Machines or set algorithms usually fail to process such insights as they create errors. Analysts generally utilize binning, regression, and clustering techniques to manage noisy or unnecessary data.
Data transformation:
Transforming data mainly focuses on giving a structure or shape to datasets. It basically organizes data using rules and filters. The method further has the following methods:
- Normalization
- Attribute selection
- Discretization
Data reduction:
In data mining, insights are collected from various sources; hence, it is larger in size and can consume much time while integrating. Data reduction chiefly reduces data size without removing any important information. Different techniques of reducing data are:
- Dimensionality reduction
- Sampling
- Compression
- Feature selection
- Discretization
Wrapping Up
The methods of data cleaning and preprocessing are advantageous in many ways, such as elevating data accuracy, tracing consistency, validity, uniformity, verifying data, and cleaning backflow in data. Data mining is not about gathering data from various sources; instead, it cleans, transforms, and improves data for better integration with tactics such as cleaning and preprocessing. Read our blogs to stay up to date with the latest technological trends.
Also Read:
Decoding Data Mining Vs Machine Learning: Unveiling Distinctions
Comparing Data Mining and Machine Learning: Top Use Cases to Determine the Best Tactic
