Data Peculiarities: 5 Strategies for Identifying and Handling Anomalies

I. Characteristics of Outliers

II. Detecting Outliers:
  1. Visual Inspection:
    a. Scatter Plots
    b. Histograms
    c. Box Plots
    d. Violin Plots
    e. Residual Analysis
    f. Leverage-Residual Plot
    g. Cook's Distance
    h. Visualizing Removal and Transformation
  2. Summary Statistics
  3. Z-Score
  4. Interquartile Range (IQR)
  5. Tukey's Fences

III. Addressing Outliers:
  1. Remove Outliers:
    a. Z-Score Method
    b. Cook's Distance
    c. Tukey's Fences
    d. IQR Method
    e. Visual Inspection
  2. Transform Data:
    a. Log Transformation
    b. Square Root Transformation
    c. Box-Cox Transformation
    d. Rank Transformation
    e. Winsor-Mean Transformation
    f. Quantile Transformation
  3. Winsorizing
  4. Imputation:
    a. Mean, Median, Mode Imputation
    b. Constant Imputation
    c. Regression Imputation
    d. K-Nearest Neighbors Imputation
    e. Interpolation Imputation
    f. Random Imputation
  5. Robust Statistics:
    a. Median
    b. Robust Standard Deviation (MAD)
    c. Robust Regression (RANSAC)
    d. Trimmed Mean and Winsorized Mean
    e. Huber's T-Statistic
    f. Percentile Absolute Deviation (PAD)
    g. Tukey's Biweight Midvariance
    h. Quantile Regression
  6. Binning
  7. Modeling Techniques:
    a. Robust Regression
    b. Quantile Regression
    c. Support Vector Machines (SVM)
    d…