Outlier Detection Tool

Overview

The Outlier Detection Tool is a powerful and efficient Python package designed for data scientists, statisticians, and analysts who require a precise method to detect anomalies in two-dimensional datasets. By leveraging the statistical rigor of Cook's Distance, this tool performs regression diagnostics on (x, y) data, identifies influential data points, and flags significant deviations from the general trend.

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Purpose and Motivation

Outliers can significantly impact statistical models, distort trends, and bias predictions. Addressing these anomalies is essential for ensuring the accuracy and reliability of data-driven insights. Traditional methods like standard deviation thresholds or IQR (Interquartile Range) often fall short in complex datasets where more nuanced techniques are required.

The Outlier Detection Tool, developed by Yisong Chen, provides a more sophisticated approach by utilizing Cook's Distance, a metric designed to quantify the influence of each observation in a regression model. This tool enables professionals to:

• Detect and quantify data points that disproportionately affect regression outcomes.
• Evaluate the statistical influence of each observation and determine its impact on overall model performance.
• Make informed decisions about whether to retain or exclude anomalous data.

By automating the use of Cook's Distance, the Outlier Detection Tool bridges the gap between theoretical rigor and practical application, making it indispensable for any professional working in data preprocessing, anomaly detection, or predictive modeling.

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Key Features

• Cook's Distance Calculation: Precisely measures the influence of each data point in regression models.
• Automated Outlier Flagging: Flags influential data points based on a user-defined threshold.
• Customizable Sensitivity: Allows fine-tuning of the outlier detection threshold to fit different datasets.
• Comprehensive Summary Statistics: Provides detailed insights, including the number of outliers detected, their proportion, and their influence on the dataset.
• CSV File Support: Ingests CSV files with (x, y) data and outputs enriched datasets with diagnostic metrics and anomaly flags.
• Robust Error Handling: Includes validation for missing files, malformed data, or incomplete input structures to prevent processing failures.
• Flexible Data Cleaning: Offers built-in utilities for removing duplicates, handling missing values, and filtering datasets based on custom criteria.
• Advanced Data Processing: Supports additional functionalities such as feature scaling, sorting, renaming columns, and computing correlation matrices.

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Installation

To install the Outlier Detection Tool, ensure that Python and pip are installed on your system. You can install the required dependencies using:

pip install -r requirements.txt

or manually install the key libraries:

pip install pandas numpy statsmodels

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Usage

1. Detecting Outliers

The primary function of the tool is to detect outliers in (x, y) datasets using Cook’s Distance. To run outlier detection on a CSV file, use:

from detect_outliers import detect_outliers

file_path = "data.csv"
outlier_data = detect_outliers(file_path, threshold=0.5, output_file="output_with_outliers.csv")

2. Summarizing Outlier Results

from detect_outliers import summarize_outliers

summary = summarize_outliers(outlier_data)
print(summary)

3. Cleaning Data

from data_analysis import clean_data

cleaned_data = clean_data("data.csv", output_file="cleaned_data.csv")

4. Feature Scaling

from data_analysis import scale_features

scaled_data = scale_features(cleaned_data, ["x", "y"])

5. Sorting Data

from data_analysis import sort_data

sorted_data = sort_data(cleaned_data, column="x", ascending=True)

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Example Dataset

To test the tool, use a simple CSV file (data.csv) structured as follows:

x,y
1,2
2,4
3,6
4,8
5,10
100,200

This dataset contains five standard points and one extreme outlier (100,200). Running the tool will flag 100,200 as an influential data point.

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Contributing

We welcome contributions to enhance the functionality of the Outlier Detection Tool. If you would like to contribute:

1. Fork the repository.
2. Create a new feature branch.
3. Implement and test your changes.
4. Submit a pull request for review.

For major changes, please open an issue first to discuss your proposed modifications.

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License

This project is licensed under the MIT License.

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Author

Yisong Chen
For inquiries or collaborations, please reach out via GitHub or email.