The data cleaning process include eliminating extraneous data, such as patient ID numbers, and enhancing the readability and comprehensibility of the data by organizing it from its raw form to a formatted structure. The data cleaning process also involves removing any duplicate values and filling in missing information. It also includes normalizing the data and converting various types of data into the same scale for comparison purposes.

Data cleaning is the process of ensuring that a set of data is correct, consistent, and usable by identifying and correcting or removing errors and inconsistencies in the data to improve its quality. The process is primarily used in databases or files and is considered one of the most crucial steps in the data preparation process as it significantly impacts the output of any data-driven process or analysis.

In general, there are three to four key tasks in data cleaning: removing duplicates, identifying and addressing missing values, and correcting incomplete or inaccurate data. Data cleaning is the first and a crucial step in any type of analysis. It should be given ample attention and time before proceeding with the analysis; otherwise, all the efforts put into the analysis might be wasted. Moreover, clean data can reveal insights on its own, even without detailed analysis. Once the data is cleaned, simple plotting of this clean data can reveal trends and patterns, making future analyses easier and more effective.

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https://www.researchgate.net/profile/Azam-Amir

Data cleaning, also known as data cleansing or data scrubbing, is a crucial step in the data preparation process. It involves identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure that the data is accurate, reliable, and suitable for analysis. The process of data cleaning typically includes the following steps:

Importance of Data Cleaning:

In summary, data cleaning is a critical step in the data preparation process, contributing to the reliability and validity of analyses and decision-making based on the data. It is an essential practice for anyone working with data in various domains, including business, research, healthcare, and machine learning.

Data cleaning, also known as pre-processing data, is the first and most important step in the data analysis pipeline. The main purpose of this step is to refine the data for further analysis, ensuring that the findings derived from it are accurate and meaningful. This step consists of outlier removal, normalization, and removing missing values.

Beyond this, data cleaning also opens the door to more advanced techniques, such as feature extraction and/or selection. Given that datasets frequently contain numerous features relative to the number of samples, undertaking feature extraction becomes a strategic move. This additional step involves filtering the dataset to retain only the most relevant and significant features. By doing this, the following analyses become simpler and better because they concentrate on the most important parts of the data. So, feature extraction helps make the data cleaning process even better, improving the overall quality and making it easier to understand the results of the analysis.

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