--- a
+++ b/prediction_api/src/utils.py
@@ -0,0 +1,26 @@
+from numpy import array, zeros_like, reshape
+from sklearn.preprocessing import MinMaxScaler
+
+def is_float(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+def preprocess_sequences(sequences):
+    sequences = array(sequences)
+    # Shape: (SeuenceSize, 51)
+    scaler = MinMaxScaler()
+    normalized_sequences = zeros_like(sequences)
+    for i in range(sequences.shape[0]):
+        for j in range(sequences.shape[1]):
+            # Flatten the landmarks for each set within the sequence
+            landmarks_flattened = reshape(sequences[i, j], (-1, 1))
+            # Normalize tshe landmarks
+            landmarks_normalized = scaler.fit_transform(landmarks_flattened)
+            # Reshape the normalized landmarks back to the original shape
+            normalized_landmarks = reshape(landmarks_normalized, sequences[i, j].shape)
+            # Update the normalized landmarks in the sequences array
+            normalized_sequences[i, j] = normalized_landmarks
+    return normalized_sequences