Metadata-Version: 2.1
Name: Outlyzer
Version: 0.0.4
Summary: Outlier detection
Home-page: https://github.com/Devparihar5/Outlyzer
Author: Devendra Parihar
Author-email: devendraparihar340@gmail.com
Keywords: outliers,outlier-detection,data-science,machine-learning,statistics,zscore,iqr,visualization
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE

# Outlyzer -A Python package to detect outliers in a dataset


Outlyzer is a Python library that provides various methods for detecting outliers in a dataset. It includes implementation of Z-score, IQR, and Mahalanobis distance methods for identifying outliers, as well as visualization-based methods using scatter plots, box plots, and other types of visualizations.


## Installation
You can install Outlyzer using pip:
```
pip install outlyzer
```


Usage:

    - Import the desired method from the library, e.g.:
        from Outlyzer.zscore import detect_outliers_zscore        
        from Outlyzer.iqr import detect_outliers_iqr

    - Pass your dataset or data series to the respective function, e.g.:
        outliers_zscore = detect_outliers_zscore(data)
        outliers_iqr = detect_outliers_iqr(data)
    
    The functions will return a boolean array indicating whether each data point is an outlier (True) or not (False).


## 
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