Data Cleaning in Data Science

Data cleaning is an integral part of data preprocessing viz., removing or correcting inaccurate information within a data set. This could mean missing data, spelling mistakes, and duplicates to name a few issues. Inaccurate information can lead to issues during analysis phase if not properly addressed at the earlier stages.

Data Cleaning vs Data Wrangling :
Data cleaning focuses on fixing inaccuracies within your data set. Data wrangling, on the other hand, is concerned with converting the data s format into one that can be accepted and processed by a machine learning model.

Data Cleaning steps to follow :

1. Remove irrelevant data
2. Resolve any duplicates issues
3. Correct structural errors if any
4. Deal with missing fields in the dataset
5. Zone in on any data outliers and remove them
6. Validate your data

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