Package: data-cleaning
DATA CLEANING## DescriptionIn any Machine Learning process, Data Preprocessing is the primary step wherein the raw/unclean data are transformed 
into cleaned data, So that in the later stage, machine learning algorithms can be applied. This python paackage make the 
data preprocessing very easy in just 2 lines of code. All you have to do is just input a raw data(CSV file), this library
will clean your data and return you the cleaned dataframe on which further you can apply feature engineering, 
feature selection and modeling.What this does?Cleans special characterRemoves duplicatesFixes abnormality in column namesImputes the data (categorical & numerical)Data CleaningData-cleaning is a python package for data preprocessing. This cleans the CSV file and returns thecleaned data frame. 
It does the work of imputation, removing duplicates, replacing special characters, and many more.How to use:Step 1:
Install the libaraypipinstalldata-cleaningStep 2:Import the library, and specify the path of the csv file.fromdatacleaningimportDataCleaningdp=DataCleaning(file_uploaded='filename.csv')cleaned_df=dp.start_cleaning()There are some optional parameters that you can specify as listed below,Usage:fromdatacleaningimportDataCleaningDataCleaning(file_uploaded='filename.csv',separator=",",row_threshold=None,col_threshold=None,special_character=None,action=None,ignore_columns=None,imputation_type="RDF")ParametersParameterDefault ValueLimitExamplefile_uploadednoneProvide a CSV file.filename.csvseparator,Separator used in csv file;row_thresholdnone0 to 10080col_thresholdnone0 to 10080special_characterCheck the list belowSspecify the characterthat is not listed in default_list (see below)[ '$' , '?' ]actionnoneaddorremoveaddignore_columnsnoneProvide list of column namesto ignoring the special characters operation.[ 'column1', 'column2' ]imputation_typeRDFSelect your preferred imputationRDF,KNN,mean,median,most_frequent,constant.KNNExamples of using parameters- Appending extra special characters to the existing default_listThe DEFAULT SPECIAL CHARACTERS included in the package are shown below,default_list=["!",'"',"#","%","&","'","(",")","*","+",",","-",".","/",":",";","<","=",">","?","@","[","\\","]","^","_","`","{","|","}","~","â€“","//","%*",":/",".;","Ã˜","Â§",'$',"Â£"]How to remove a special character, say for example if you want to remove "?" and "%".Note:- Do not forget to giveaction = 'remove'fromdatacleaningimportDataCleaningdp=DataCleaning(file_uploaded='filename.csv',special_character=['?','%'],action='remove')cleaned_df=dp.start_cleaning()How to add a special character, say for example if you want to add "Ã©" that is not in the default_list given above.Note:- Do not forget to giveaction = 'add'fromdatacleaningimportDataCleaningdp=DataCleaning(file_uploaded='filename.csv',special_character=['Ã©'],action='add')cleaned_df=dp.start_cleaning()- Ignoring a particular columns and adding a special characterSay for example, column named "timestamp" and "date" needs to be removed and a special character needs to be added 'Ã©'fromdatacleaningimportDataCleaningdp=DataCleaning(file_uploaded='filename.csv',special_character=['Ã©'],action='add',ignore_columns=['timestamp','date'])cleaned_df=dp.start_cleaning()- Changing threshold to remove null rows/columns above this given threshold valuefromdatacleaningimportDataCleaningdp=DataCleaning(file_uploaded='filename.csv',row_threshold=50,col_threshold=90)cleaned_df=dp.start_cleaning()- Imputation methods availableRDF (RandomForest) -> (DEFAULT)KNN (k-nearest neighbors)meanmedianmost_frequentconstant# Example for KNN imputation.fromdatacleaningimportDataCleaningdp=DataCleaning(file_uploaded='filename.csv',imputation_type='KNN')cleaned_df=dp.start_cleaning()>> THANK YOU <<