diff --git a/JenkinsfileDatasetStats b/JenkinsfileDatasetStats
index f5a440b..e86ee27 100644
--- a/JenkinsfileDatasetStats
+++ b/JenkinsfileDatasetStats
@@ -29,6 +29,11 @@ pipeline {
             copyArtifacts projectName: 'z487183-create-dataset'
             sh './prepare-stats.sh'
             archiveArtifacts 'stats.txt'
+            sh 'python3 property_model.py'
+            sh 'python3 predict_values.py'
+            archiveArtifacts 'test_predictions.csv'
+            archiveArtifacts 'train_predictions.csv'
+            archiveArtifacts 'dev_predictions.csv'
          }
       }
    }
diff --git a/Lab4.dockerfile b/Lab4.dockerfile
index 68da029..214a2a6 100644
--- a/Lab4.dockerfile
+++ b/Lab4.dockerfile
@@ -5,6 +5,8 @@ RUN apt-get install -y python3 python3-pip unzip
 RUN pip install pandas
 RUN pip install scikit-learn
 RUN pip install kaggle
+RUN pip install keras
+RUN pip install tensorflow
 
 ARG DEBIAN_FRONTEND=noninteractive
 
diff --git a/predict_values.py b/predict_values.py
new file mode 100644
index 0000000..29e015a
--- /dev/null
+++ b/predict_values.py
@@ -0,0 +1,38 @@
+import pandas as pd
+from tensorflow import keras
+import csv
+
+model = keras.models.load_model('model.h5')
+X_test = pd.read_csv('X_test.csv').values
+X_train = pd.read_csv('X_train.csv').values
+X_val = pd.read_csv('X_val.csv').values
+
+with open('test_predictions.csv', "a") as file:
+  writer = csv.writer(file)
+  predictions = model.predict(X_test)
+  writer.writerow(['PredictedPriceAboveMedian'])
+  for pred in predictions:
+      result = [0]
+      if pred[0] > 0.5:
+          result = [1]
+      writer.writerow(result)
+
+with open('train_predictions.csv', "a") as file:
+  writer = csv.writer(file)
+  predictions = model.predict(X_train)
+  writer.writerow(['PredictedPriceAboveMedian'])
+  for pred in predictions:
+      result = [0]
+      if pred[0] > 0.5:
+          result = [1]
+      writer.writerow(result)
+
+with open('dev_predictions.csv', "a") as file:
+  writer = csv.writer(file)
+  predictions = model.predict(X_val)
+  writer.writerow(['PredictedPriceAboveMedian'])
+  for pred in predictions:
+      result = [0]
+      if pred[0] > 0.5:
+          result = [1]
+      writer.writerow(result)