THE IMPORTANCE OF DATA CLEANING IN DATA ANALYSIS

To‘xtasinov Adxamjon Ilxomjon o‘g‘li; Sodiqov Vali Salim o‘g‘li

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To‘xtasinov Adxamjon Ilxomjon o‘g‘li ##default.groups.name.author##
Sodiqov Vali Salim o‘g‘li ##default.groups.name.author##

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Keywords: Data cleaning, data preprocessing, data quality, missing values, outliers, data analysis

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Abstract: Data cleaning is a fundamental step in the data analysis pipeline that ensures the accuracy, consistency, and reliability of analytical outcomes. Poor-quality data can significantly skew analysis results, leading to incorrect conclusions and ineffective decision-making. This paper examines the role of data cleaning within the broader context of data analysis, identifies common data quality issues, outlines standard cleaning techniques, and evaluates the impact of cleaned data on analytical model performance. By exploring case studies and real-world datasets, the study highlights how data cleaning contributes to more robust and trustworthy insights.

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