21/10/19 30 minutes read 200 Naren Allam
In this article, you will learn practically how to get clean data set after data_cleaning or data pre_processing for analysis, using python packages, creating meaningful data visualizations much more..!
follow data analysis articles :Data Analysis For Machine Learning with Python (part-1)
Data Analysis For Machine Learning with Python (part-2)
Data Cleaning or Data pre_processing
Data cleaning (or) Data pre_processing is Improving the quality of data by removing errors,handling missing values,Droping Unnecessary Columns,Create new variables,Transform data,Rename Variables,Merge two datasets and resolving inconsistencies etc...!
in previous article (part 2) we discussed NA ,missing values and duplicate values how to handle those data etc...
Data Cleaning (or) Data Pre_processing packages in Python :Pandas,NumPy.
continuation of the artcle Data Analysis For Machine Learning with Python (part-2).
mobile names displaying to know how many company mobiles we have in this dataset.
in the above image, we have 148 mobile names.
Next process we are going clean dataset, like removing the unwanted string from each columns etc...
in this article, we did data_cleaning or data pre_processing successfully..!
Building a model on this mobile data_set Data Analysis For Machine Learning with Python (part-4)