Metadata-Version: 2.1
Name: MissingValues-Arsh
Version: 0.0.2
Summary: Treating Missing values in a dataset
Home-page: UNKNOWN
Author: Arshpreet Singh
Author-email: asingh9_be17@thapar.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

Data in real world are rarely clean and homogeneous. Data can either be missing during data extraction or collection. Missing values need to be handled because they reduce the quality for any of our performance metric. It can also lead to wrong prediction or classification and can also cause a high bias for any given model being used.
Depending on data sources, missing data are identified differently. Pandas always identify missing values as NaN. However, unless the data has been pre-processed to a degree that an analyst will encounter missing values as NaN. Missing values can appear as a question mark (?) or a zero (0) or minus one (-1) or a blank.