diff --git a/model_training/MAD outlier removal/mad_outlier_removal.ipynb b/model_training/MAD_outlier_removal/mad_outlier_removal.ipynb
similarity index 100%
rename from model_training/MAD outlier removal/mad_outlier_removal.ipynb
rename to model_training/MAD_outlier_removal/mad_outlier_removal.ipynb
diff --git a/model_training/tools/mad_outlier_removal.py b/model_training/tools/mad_outlier_removal.py
new file mode 100644
index 0000000..4fadcc7
--- /dev/null
+++ b/model_training/tools/mad_outlier_removal.py
@@ -0,0 +1,23 @@
+import numpy as np
+import pandas as pd
+
+def mad_outlier_removal(df, columns, threshold=3.5, c=1.4826):
+    df_clean = df.copy()
+    masks = []
+
+    for col in columns:
+        x = df_clean[col].values
+        median = np.median(x)
+        mad = np.median(np.abs(x - median))
+
+        if mad == 0:
+            continue
+
+        z = np.abs((x - median) / (c * mad))
+        masks.append(z <= threshold)
+
+    if not masks:
+        return df_clean
+
+    final_mask = np.logical_and.reduce(masks)
+    return df_clean[final_mask]