diff --git a/scripts/handle_outliers.py b/scripts/handle_outliers.py
new file mode 100644
index 0000000000000..7b945a6ddf327
--- /dev/null
+++ b/scripts/handle_outliers.py
@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+
+
+"""
+Detect and handle outliers in a DataFrame.
+
+Parameters:
+    data: DataFrame
+    method: str, default 'z-score'. The method used for outlier detection. Options: 'z-score' or 'IQR' (Interquartile Range).
+    threshold: float, default 3. The threshold for identifying outliers. Data points beyond this threshold are considered outliers.
+
+Returns:
+    DataFrame: DataFrame with outliers handled (replaced or removed).
+"""
+def handle_outliers(data, method='z-score', threshold=3):
+    if method == 'z-score':
+        z_scores = np.abs((data - data.mean()) / data.std())
+        data_no_outliers = data[(z_scores < threshold).all(axis=1)]
+    
+    elif method == 'IQR':
+        Q1 = data.quantile(0.25)
+        Q3 = data.quantile(0.75)
+        IQR = Q3 - Q1
+        data_no_outliers = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis=1)]
+
+    else:
+        raise ValueError("Invalid method. Use z-score or IQR")
+    
+    return data_no_outliers
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