au 0.0.7

Filtering outliers

au: Outlier Detection Toolkit

Filtering outliers to find the golden nuggets that standout from the rest.

To install: pip install au

Outlier detection is a fundamental step in data analysis, particularly relevant in statistics, data mining, and machine learning. This toolkit provides a set of functions and classes in Python for identifying outliers - observations in data that are significantly different from the majority. The toolkit is designed to accommodate various methodologies, ranging from statistical methods to machine learning-based approaches.

Features

1. Z-Score Based Outlier Detection

   • Detects outliers by measuring how many standard deviations an element is from the mean.
   • Suitable for datasets where the distribution is expected to be Gaussian.

2. Interquartile Range (IQR) Based Outlier Detection

   • Utilizes the IQR, which is the difference between the 75th and 25th percentile of the data.
   • Effective for skewed distributions.

3. Isolation Forest Based Outlier Detection

   • Implements the Isolation Forest algorithm, a machine learning method for anomaly detection.
   • Ideal for high-dimensional datasets.

Installation

Ensure that you have Python installed on your system. This toolkit requires numpy and scikit-learn. They can be installed via pip.

pip install numpy scikit-learn

Features

1. Z-Score Based Outlier Detection

   • Detects outliers by measuring how many standard deviations an element is from the mean.
   • Suitable for datasets where the distribution is expected to be Gaussian.

2. Interquartile Range (IQR) Based Outlier Detection

   • Utilizes the IQR, which is the difference between the 75th and 25th percentile of the data.
   • Effective for skewed distributions.

3. Isolation Forest Based Outlier Detection

   • Implements the Isolation Forest algorithm, a machine learning method for anomaly detection.
   • Ideal for high-dimensional datasets.

Installation

Ensure that you have Python installed on your system. This toolkit requires numpy and scikit-learn. They can be installed via pip:

pip install numpy scikit-learn

Usage

1. Z-Score Based Outlier Detection

   from outlier_detection import detect_outliers_zscore
   
   outliers = detect_outliers_zscore([10, 12, 12, 13, 12, 11, 40])
   

2. Interquartile Range (IQR) Based Outlier Detection

   from outlier_detection import detect_outliers_iqr
   
   outliers = detect_outliers_iqr([10, 12, 12, 13, 12, 11, 40])
   

3. Isolation Forest Based Outlier Detection

   from outlier_detection import IsolationForestOutlierDetector
   
   detector = IsolationForestOutlierDetector()
   outliers = detector.detect_outliers([10, 12, 12, 13, 12, 11, 40])
   

Documentation

Each function and class in this toolkit comes with a detailed docstring, explaining its purpose, parameters, return values, and examples.

Contributing

Contributions to this project are welcome! Please fork the repository and submit a pull request with your changes.