Data cleaning | Web Scraping Tool | ScrapeStorm
Abstract：Data cleaning, also known as data preprocessing or data purification, is an important step in the data analysis and mining process. ScrapeStormFree Download
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Data cleaning, also known as data preprocessing or data purification, is an important step in the data analysis and mining process.It involves identifying, correcting, and removing inaccurate, incomplete, redundant, or inconsistent portions of a data set to ensure data quality and reliability. The main goal of data cleaning is to make the data suitable for further analysis and modeling to increase the accuracy and credibility of the analysis.
In market research, customer relationship management and advertising campaigns, cleaning data ensures the accuracy of customer information, allowing for better targeting of target audiences. Banks and financial institutions need to clean transaction data for fraud detection, credit scoring and risk management. In an IoT environment, large amounts of sensor data need to be cleaned and processed to monitor and control device performance.
Pros: Cleansing removes errors, duplications, and inconsistencies in data, thereby increasing data accuracy and aiding in more reliable decision-making. By removing invalid or corrupted data, data quality is improved and data inaccuracies are reduced. Cleansing ensures that the data is consistent across the entire dataset, allowing for appropriate comparison and analysis.
Cons: Data cleaning often requires significant time and resources, especially when working with large data sets. Improper data cleaning process can result in useful information being accidentally deleted. Cleaning data often requires subjective decision-making, and different data cleaning methods and standards may lead to different results. When handling personal or sensitive data, cleaning requires special attention to ensure privacy and security.
1. Data cleaning cycle.
2. Data cleaning steps.