diff --git a/server/nightr/strategies/nightness_classifier.pkl b/server/nightr/strategies/nightness_classifier.pkl
new file mode 100644
index 0000000..554fa03
Binary files /dev/null and b/server/nightr/strategies/nightness_classifier.pkl differ
diff --git a/server/nightr/strategies/svm_strat.py b/server/nightr/strategies/svm_strat.py
index 78a760c..58a17f6 100644
--- a/server/nightr/strategies/svm_strat.py
+++ b/server/nightr/strategies/svm_strat.py
@@ -6,10 +6,11 @@ import json
 import numpy as np
 
 
-from server.nightr.strategies.strat_utils import write_json
+from .strat_utils import write_json
+from ..util import Context, Prediction
 
 
-def find_data(time):
+def write_data(time):
     write_json("https://portal.opendata.dk/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c", "parking_aarhus", time)
 
 def load_data():
@@ -18,7 +19,7 @@ def load_data():
     Y = []
 
     for filename in glob.glob("parking_aarhus*"):
-        p_class = '2330' in filename
+        p_class = '2235' in filename
 
         with open(filename) as file:
             data = json.load(file)
@@ -32,13 +33,26 @@ def load_data():
 
 def train():
     X, Y = load_data()
-    classifier = svm.SVC(C=10, gamma=0.01, probability=True)
+    classifier = svm.SVC(gamma=0.01, probability=True)
     classifier.fit(X, Y)
     joblib.dump(classifier, "nightness_classifier.pkl")
 
 def predict(X):
     classifier = joblib.load("nightness_classifier.pkl")
-    prob = classifier.predict_proba(X)
+    prob = classifier.predict_proba(np.array(X).reshape(1, -1))
     return prob[0, 1]
 
-train()
+
+def perform_svm_pred(context: Context) -> Prediction:
+    p = Prediction()
+    data = requests.get('https://portal.opendata.dk/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c')
+
+    records = data.json()['result']['records']
+    X = [house['vehicleCount'] / house['totalSpaces'] for house in records]
+    X = [min(x, 1) for x in X]
+    p.reasons.append("Since we only have two data points")
+    p.reasons.append("Since our only two data points have 11 dimensions")
+    p.reasons.append("Since we are using a SVM")
+
+    p.probability = predict(X)
+    return p