diff --git a/Jenkinsfile b/Jenkinsfile
new file mode 100644
index 0000000..acef5e1
--- /dev/null
+++ b/Jenkinsfile
@@ -0,0 +1,25 @@
+node {
+    stage('Preparation') {
+        properties([
+            parameters([
+                string(
+                    defaultValue: 'Hello World!',
+                    description: 'Tekst do wyświetlenie',
+                    name: 'INPUT_TEXT',
+                    trim: false
+                )
+            ])
+        ])
+    }
+    stage('Hello') {
+        //Wypisz wartość parametru w konsoli (To nie jest polecenie bash, tylko groovy!)
+        echo "INPUT_TEXT: ${INPUT_TEXT}"
+        //Wywołaj w konsoli komendę "figlet", która generuje ASCI-art
+        sh "figlet \"${INPUT_TEXT}\" | tee output.txt"
+    }
+    stage('Goodbye') {
+        echo 'Goodbye!'
+        //Zarchiwizuj wynik
+        archiveArtifacts 'output.txt'
+    }
+}
\ No newline at end of file
diff --git a/main.py b/main.py
index 87c2101..f28b7a2 100644
--- a/main.py
+++ b/main.py
@@ -12,18 +12,12 @@ with zipfile.ZipFile('./data/netflix-shows.zip', 'r') as zip_ref:
 
 netflix = pd.read_csv('./data/netflix_titles.csv')
 
-netflix['release_year'] = (netflix['release_year'] - netflix['release_year'].min()) / (netflix['release_year'].max() - netflix['release_year'].min())
-
 netflix.dropna(inplace=True)
 
 random_seed = 42
 train_data, test_data = train_test_split(netflix, test_size=0.2, random_state=random_seed)
 train_data, dev_data = train_test_split(train_data, test_size=0.25, random_state=random_seed)
 
-train_data['release_year'] = (train_data['release_year'] - train_data['release_year'].min()) / (train_data['release_year'].max() - train_data['release_year'].min())
-dev_data['release_year'] = (dev_data['release_year'] - dev_data['release_year'].min()) / (dev_data['release_year'].max() - dev_data['release_year'].min())
-test_data['release_year'] = (test_data['release_year'] - test_data['release_year'].min()) / (test_data['release_year'].max() - test_data['release_year'].min())
-
 train_stats = train_data.describe(include='all')
 print(f"\nTraining set statistics:\n{train_stats}")
 dev_stats = dev_data.describe(include='all')