It is common knowledge among data scientists that data cleaning and preprocessing is an important part of a data science project. And you'll probably agree with me that this isn't the most exciting part of the project.

Automating data cleansing is easier said than done, as the steps required depend heavily on the form of the data and the domain-specific use case. Still, there are ways to automate at least a significant portion of it in a standardized way.

AutoClean automates data preprocessing and cleaning for your next Data Science project in Python.
It is common knowledge among data scientists that data cleaning and preprocessing is an important part of a data science project. And you'll probably agree with me that this isn't the most exciting part of the project. Wouldn't it be great if this part could be automated?

AutoClean helps you do just that: it performs data preprocessing and cleaning in Python in an automated manner, so you can save time when working on your next project.

AutoClean supports:
Duplicate handling
Different imputation methods for missing values
Outlier handling
Categorical data encoding (OneHot, Label)
Extraction of data time values

Other search terms:
– Data cleaning in Python
– AutoClean data in Python
– Automated data cleaning
– Automated EDA
– Data analysis in Python
– Automated data preprocessing

#datacleaning #datascience #datapreprocessing #technology #python #eda #dataanalytics #matplotlib #datascience #google #colab #machinelearning #ai #bard
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Library link – https://github.com/elisemercury/AutoClean
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