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It provides implementations of the Interquartile Range (IQR), Z-Score, and Percentile methods to detect and treat outliers. The tool supports various outlier treatment techniques, including trimming, capping, and Winsorization.\n\nThis project is developed and demonstrated in a Jupyter Notebook for ease of use and understanding.\n\n## Features\n\n1. Outlier Detection Methods :-\n  - IQR Method: Detects outliers using the Interquartile Range.\n  - Z-Score Method: Identifies outliers based on the standard deviation.\n  - Percentile Method: Flags outliers based on specified percentile thresholds.\n    \n\n2. Outlier Treatment Techniques :-\n  - Trimming: Removes detected outliers from the dataset.\n  - Capping: Replaces outliers with the nearest valid value within bounds.\n  - Winsorization: Replaces extreme values with predefined percentiles.\n\n\n3. Customizable Parameters :-\n  - Configure thresholds for Z-scores.\n  - Set percentile ranges for trimming and Winsorization.\n  - Define the IQR multiplier for flexible outlier detection.\n    \n\n4. Visualization :-\n  - Generate boxplots and histograms to visualize the effects of outlier treatment.\n\n## Required Libraries :-\n- pandas\n- numpy\n- matplotlib\n- seaborn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Foutlier_exterminator","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkhushi130404%2Foutlier_exterminator","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Foutlier_exterminator/lists"}