الفهرس 
Duplicate DetectionOnly 14 pages are availabe for public view
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Abstract
The process of data cleansing involves identifying and correcting errors and inconsistencies in raw data to ensure its quality. In machine learning, classification models are used to predict the category or class of new data points based on training data. The accuracy and performance of classification models are heavily reliant on the quality of the training data. Consequently, data cleansing is considered a crucial and indispensable step in the modeling process. The meticulous examination and refinement of the data are essential to ensure its integrity, consistency, and relevance. By undertaking this imperative procedure, researchers and academics can enhance the reliability and validity of their classification models, thereby enabling more accurate predictions and insightful analysis. Data cleaning tasks include Outlier detection, removing duplicates, filling in missing values,correcting inaccuracies, and standardizing data formats to guarantee that the training data is complete, consistent, and accurate. Not only does data cleaning improve data quality, but it can also decrease the dimensionality of the data, resulting in a simpler and more efficient classification model