diff --git a/predictions/k_nearest.py b/predictions/k_nearest.py
index 1b4c03e..2bf95d9 100644
--- a/predictions/k_nearest.py
+++ b/predictions/k_nearest.py
@@ -1,6 +1,9 @@
 import numpy as np
 from collections import Counter
 
+# TODO scaling van distance tov intra distances?
+# TODO efficientere manier om k=1 te doen
+
 
 def minkowski_distance_p(x, y, p=2):
     x = np.asarray(x)
diff --git a/predictions/svm_model.py b/predictions/svm_model.py
new file mode 100644
index 0000000..a79dad2
--- /dev/null
+++ b/predictions/svm_model.py
@@ -0,0 +1,39 @@
+from sklearn import svm
+
+class SVM:
+    def __init__(self, type="ovo"):
+        self.label_name_to_label = None
+        self.clf = None
+        self.embeddings_list = None
+        self.labels = None
+        self.type = type
+
+    def set_embeddings(self, embeddings):
+        # convert embedding from string to list of floats
+        embeddings["embeddings"] = embeddings["embeddings2"].apply(lambda x: [float(i) for i in x[1:-1].split(", ")])
+        # drop embeddings2
+        df = embeddings.drop(columns=['embeddings2'])
+        # to list
+        self.embeddings_list = df["embeddings"].tolist()
+        self.labels = df["labels"].tolist()
+        self.label_name_to_label = df[["label_name", "labels"]]
+        self.label_name_to_label.columns = ["label_name", "label"]
+        self.label_name_to_label = self.label_name_to_label.drop_duplicates()
+        print(self.label_name_to_label)
+
+        self.train()
+
+    def train(self):
+        self.clf = svm.SVC(decision_function_shape=self.type, probability=True)
+        self.clf.fit(self.embeddings_list, self.labels)
+        
+    def predict(self, key_points_embeddings):
+        label = self.clf.predict(key_points_embeddings)
+        score = self.clf.predict_log_proba(key_points_embeddings)
+        # TODO fix dictionary
+        label = label.item()
+        print("test")
+        print(self.label_name_to_label.loc[self.label_name_to_label["label"] == label]["label_name"].iloc[0])
+        print("test2")
+        print(score)
+        return self.label_name_to_label.loc[self.label_name_to_label["label"] == label]["label_name"].iloc[0], score[0][label]
diff --git a/webcam.py b/webcam.py
index 6acee10..acf4855 100644
--- a/webcam.py
+++ b/webcam.py
@@ -2,15 +2,23 @@ import cv2
 
 from predictions.k_nearest import KNearestNeighbours
 from predictions.predictor import Predictor
+from predictions.svm_model import SVM
 
 if __name__ == '__main__':
     buffer = []
-
     # open webcam stream
     cap = cv2.VideoCapture(0)
 
-    k = 3
-    predictor_type = KNearestNeighbours(k)
+    type_predictor = "svm"
+    if type_predictor == "knn":
+        k = 10
+        predictor_type = KNearestNeighbours(k)
+    elif type_predictor == "svm":
+        predictor_type = SVM()
+    else:
+        predictor_type = KNearestNeighbours(1)
+
+
 
     # embeddings_path = 'embeddings/basic-signs/embeddings.csv'
     embeddings_path = 'embeddings/fingerspelling/embeddings.csv'
@@ -39,7 +47,7 @@ if __name__ == '__main__':
             label, score = predictor.make_prediction(buffer)
 
             # draw label
-            cv2.putText(frame, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
+            cv2.putText(frame, str(label), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
             cv2.putText(frame, str(score), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
 
         # Show the frame