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AutomatedCleaning is a Python library for automated data cleaning. It helps preprocess and analyze datasets by handling missing values, outliers, spelling corrections, and more
Features
Supports both large (100+ GB) and small datasets
Detects and handles missing values and duplicate records
Identifies and corrects spelling errors in categorical values
Detect outliers
Detects and fixes data imbalance
Identifies and corrects skewness in numerical data
Checks for correlation and detects multicollinearity
Analyzes cardinality in categorical columns
Identifies and cleans text columns
Detect JSON-type columns
Performs univariate, bivariate, and multivariate analysis
https://lnkd.in/gmaStAsp
Features
Supports both large (100+ GB) and small datasets
Detects and handles missing values and duplicate records
Identifies and corrects spelling errors in categorical values
Detect outliers
Detects and fixes data imbalance
Identifies and corrects skewness in numerical data
Checks for correlation and detects multicollinearity
Analyzes cardinality in categorical columns
Identifies and cleans text columns
Detect JSON-type columns
Performs univariate, bivariate, and multivariate analysis
https://lnkd.in/gmaStAsp
AutomatedCleaning is a Python library for automated data cleaning. It helps preprocess and analyze datasets by handling missing values, outliers, spelling corrections, and more
Features
Supports both large (100+ GB) and small datasets
Detects and handles missing values and duplicate records
Identifies and corrects spelling errors in categorical values
Detect outliers
Detects and fixes data imbalance
Identifies and corrects skewness in numerical data
Checks for correlation and detects multicollinearity
Analyzes cardinality in categorical columns
Identifies and cleans text columns
Detect JSON-type columns
Performs univariate, bivariate, and multivariate analysis
https://lnkd.in/gmaStAsp
Features
Supports both large (100+ GB) and small datasets
Detects and handles missing values and duplicate records
Identifies and corrects spelling errors in categorical values
Detect outliers
Detects and fixes data imbalance
Identifies and corrects skewness in numerical data
Checks for correlation and detects multicollinearity
Analyzes cardinality in categorical columns
Identifies and cleans text columns
Detect JSON-type columns
Performs univariate, bivariate, and multivariate analysis
https://lnkd.in/gmaStAsp
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