diff --git a/Insurance/Prediction Interest for car insurance/neues_notebook.ipynb b/Insurance/Prediction Interest for car insurance/neues_notebook.ipynb
index 8d6a58f97d32335a77a448b68c1300a24654e617..00bf9db033152a991f4ea0dc3e87cb92921193d2 100644
--- a/Insurance/Prediction Interest for car insurance/neues_notebook.ipynb	
+++ b/Insurance/Prediction Interest for car insurance/neues_notebook.ipynb	
@@ -5121,9 +5121,8 @@
    "pygments_lexer": "ipython3",
    "version": "3.10.11"
   },
-  "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/Insurance/Prediction%20Interest%20for%20car%20insurance/notebook.ipynb?ref_type=heads",
+  "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/Insurance/Prediction%20Interest%20for%20car%20insurance/neues_notebook.ipynb?ref_type=heads",
   "dataSource": "https://www.kaggle.com/datasets/jassican/janatahack-crosssell-prediction",
-
   "skipNotebookInDeployment": false,
   "title": "Vorhersage für das Interesse an Auto Versicherungen",
   "Teaser": "Ein bedeutendes Ziel für jedes Unternehmen ist die Umsatzsteigerung, Kostensenkung und Verbesserung der Kundenzufriedenheit. Problematisch ist dabei oft die Identifikation potenzieller Interessenten für spezifische Produkte wie KFZ-Versicherungen. Dieses Machine Learning Modell nutzt Kundendaten und Algorithmen, um die Wahrscheinlichkeit des Interesses präzise zu berechnen."
diff --git a/Rating/What Quality does the Red wine have/neues_notebook.ipynb b/Rating/What Quality does the Red wine have/neues_notebook.ipynb
index 14aa14f3fb9ecad355026af21b115fdd446395b5..f123cce7f9168f1f1657a1e3c52ab26ec806280a 100644
--- a/Rating/What Quality does the Red wine have/neues_notebook.ipynb	
+++ b/Rating/What Quality does the Red wine have/neues_notebook.ipynb	
@@ -3875,7 +3875,7 @@
   "skipNotebookInDeployment": false,
   "title": "Effiziente Unterscheidung zwischen guten und schlechten Wein",
   "teaser": "Eine Weinmanufaktur möchte die Qualität und den Geschmack ihrer Weine durch den Einsatz eines maschinellen Lernmodells vorhersagen, um den teuren und subjektiven Prozess der professionellen Verkostung zu ersetzen.",
-  "repoLink": ""
+  "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/Rating/What%20Quality%20does%20the%20Red%20wine%20have/neues_notebook.ipynb?ref_type=heads"
  },
  "nbformat": 4,
  "nbformat_minor": 5
diff --git a/Success Predicition/Prediction of Successful or Failed Startups/notebook.ipynb b/Success Predicition/Prediction of Successful or Failed Startups/notebook.ipynb
index b09ec02f2d91c93940a5584b363cff8b23e61989..a7a68261ecadbdddad1c21664a931e759222f5ae 100644
--- a/Success Predicition/Prediction of Successful or Failed Startups/notebook.ipynb	
+++ b/Success Predicition/Prediction of Successful or Failed Startups/notebook.ipynb	
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
-    "include": true,
+    "include": false,
     "paragraph": "business",
     "slideshow": {
      "slide_type": ""
@@ -36,7 +36,7 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
-    "include": true,
+    "include": false,
     "paragraph": "daten",
     "slideshow": {
      "slide_type": ""
@@ -1356,7 +1356,7 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
-    "include": true,
+    "include": false,
     "paragraph": "datenmodell",
     "slideshow": {
      "slide_type": ""
@@ -7186,7 +7186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.3"
   },
   "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/Success%20Predicition/Prediction%20of%20Successful%20or%20Failed%20Startups/notebook.ipynb?ref_type=heads",
   "skipNotebookInDeployment": false,