One should know the importance of Data cleaning, it removes unwanted data sets and especially helps machine learning projects. Data cleansing tools help to remove duplicate data, error data, inaccurate data, and unmatched data for a set of data. As businesses are moving online and require data for business growth, they collect huge amounts of data, where data do not match the organization's needs; this data needs to be cleaned properly to find accuracy in decision-making.

Data Cleaning and its Processes

Removing unstructured data, incomplete, damaged, and error data are the process of data cleansing. When combining various data sources, there are several possibilities for data to be duplicated or improperly categorized. Even though results and algorithms seem to be accurate, faulty data renders them unreliable. There is no definite method to define the specific steps in the data cleaning process because the procedures will vary from dataset to dataset. But in order to ensure that you are performing your data cleaning operation correctly each time, it is crucial to build a template.

Here are the Data cleaning Processes

  • Importing Data
  • Merging data sets
  • Rebuilding missing data
  • Standardization
  • Normalization
  • Deduplication
  • Verification & Enrichment
  • Exporting data

Data Cleaning cycle