From 2e2622a29cb97351bf872ed0a2e6b7a4b6d7c922 Mon Sep 17 00:00:00 2001
From: Andreas Buzer <andreas.buzer@student.reutlingen-university.de>
Date: Tue, 25 Jun 2024 12:29:08 +0200
Subject: [PATCH] fixes for articles with errors in formatting
---
.../neues_notebook.ipynb | 3 +--
.../neues_notebook.ipynb | 2 +-
.../notebook.ipynb | 8 ++++----
3 files changed, 6 insertions(+), 7 deletions(-)
diff --git a/Insurance/Prediction Interest for car insurance/neues_notebook.ipynb b/Insurance/Prediction Interest for car insurance/neues_notebook.ipynb
index 8d6a58f..00bf9db 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 14aa14f..f123cce 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 b09ec02..a7a6826 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,
--
GitLab