diff --git a/Jenkinsfile_evaluation b/Jenkinsfile_evaluation
new file mode 100644
index 0000000..066c982
--- /dev/null
+++ b/Jenkinsfile_evaluation
@@ -0,0 +1,41 @@
+pipeline {
+    agent {dockerfile true}
+	parameters {
+		buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'BUILD_SELECTOR'
+        )
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'WHICH_BUILD_TRAIN'
+        )
+	}
+    stages {
+		stage('copyArtifacts') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's434804-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts fingerprintArtifacts: true, projectName: 's434804-training/master', selector: buildParameter('WHICH_BUILD_TRAIN')
+            }
+        }
+        stage('evaluation') {
+			steps {
+                sh 'chmod +x tensor-eval.py'
+                sh 'python3 "tensor-eval.py" >> evaluation.txt'
+			}
+		}
+		stage('archiveArtifacts') {
+			steps{
+				archiveArtifacts 'evaluation.txt'
+			}
+		}
+        stage('sendMail') {
+            steps{
+                emailext body: currentBuild.result ?: 'EVALUATION SUCCESS',
+                subject: 's434804',
+                to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+            }
+        }
+    }
+}
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diff --git a/tensor-eval.py b/tensor-eval.py
new file mode 100644
index 0000000..a4cd9a3
--- /dev/null
+++ b/tensor-eval.py
@@ -0,0 +1,21 @@
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error as rmse
+
+reg = LinearRegression()
+
+alldata = pd.read_csv(
+    'country_vaccinations.csv', header=0, skipinitialspace=True,
+     usecols=['total_vaccinations', 'people_vaccinated', 'daily_vaccinations' ,'daily_vaccinations_per_million']).dropna()
+
+X = alldata[[c for c in alldata.columns if c != 'daily_vaccinations']]
+y = alldata['daily_vaccinations']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 6)
+lin_reg = reg.fit(X_train, y_train)
+score = lin_reg.score(X_test, y_test)
+prediction = lin_reg.predict(X_test)
+print("RMSE:", rmse(y_test, prediction, squared=False))
+print("Score:", score)
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