automated data cleaning tool
Automated Data Cleaning Tool.
The main goal is to develop a Python tool
datacleanbot such that:
Given a random parsed raw dataset representing a supervised learning problem, the Python tool is capable of automatically identifying the potential issues and reporting the results and recommendations to the end-user in an effective way.
$ pip install datacleanbot
Acquire data from OpenML:
>>> import openml as oml >>> data = oml.datasets.get_dataset(id) # id: openml dataset id >>> X, y, features = data.get_data(target=data.default_target_attribute, return_attribute_names=True) >>> Xy = data.get_data()
Autoclean data with datacleanbot
>>> import datacleanbot.dataclean as dc >>> Xy = dc.autoclean(Xy, data.name, features)
datacleanbot is equipped with the following capabilities:
- Present an overview report of the given dataset
- The most important features
- Statistical information (e.g., mean, max, min)
- Data types of features
- Clean common data problems in the raw dataset
- Duplicated records
- Inconsistent column names
- Missing values
The three aspects
datacleanbot meaningfully automates are marked in bold.
The user's guide can be found at datacleanbot.
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