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Importance of data preprocessing in machine learning

                     Data Preprocessing

  • Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.

Need of Data Preprocessing

  • Inaccurate data
    • There are many reasons for missing data such as data is not continuously collected, a mistake in data entry, technical problems with bio-metrics and much more.
  • The presence of noisy data
    • The reasons for the existence of noisy data could be a technological problem of gadget that gathers data, a human mistake during data entry and much more.
  • Inconsistent data 
    • The presence of inconsistencies are due to the reasons such that existence of duplication within data, human data entry, containing mistakes in codes or names, i.e., violation of data constraints and much more.

Steps Involved in Data Preprocessing

  • Handling Null Values
  • Standardization
  • Handling Categorical Variables
  • One-Hot Encoding
  • Multicollinearity



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