This project was done in respect to the #DataCleaningChallenge organized in data tech space by data experts. Data cleaning process can significantly help to improve data’s accuracy, dependability and validity. After the cleaning process, the data you keep may apply to similar data sets in the system, because cleaning data is about maximizing the dataset’s accuracy without compromising the valuable data. Data errors may occur during data entry and transmission because of storage damage or differing dictionary definitions of comparable entities. The process may entail identifying and eliminating incomplete, duplicate and irrelevant data and replacing it in a computer-readable format for analysis. Data cleaning involves detecting, revising, editing and organizing raw data within a data set to make it uniform and ready for analysis. Data cleaning also known as data cleansing as defined by Wikipedia is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, irrelevant parts of the data and then replacing, modifying, or deleting the dirty data or coarse data.

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