diff --git a/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb b/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb
index 943859f408d989e72c9f0567da5921843b2e9c2f..d4095e82608932e6c61ded9b2553f2f72839d99d 100644
--- a/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb	
+++ b/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb	
@@ -5,6 +5,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "Geschäftsverständnis",
     "slideshow": {
      "slide_type": ""
     },
@@ -68,13 +69,12 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
-    "jp-MarkdownHeadingCollapsed": true,
+    "include": true,
+    "paragraph": "Import von Relevant Module",
     "slideshow": {
      "slide_type": ""
     },
-    "tags": [
-     "active_ipynb"
-    ]
+    "tags": []
    },
    "source": [
     "# 2.1. Import von Relevant Module"
@@ -83,7 +83,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -102,7 +109,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Daten Auslesen",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## 2.2. Read Data"
    ]
@@ -110,7 +125,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -345,7 +367,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def attribute_description(data):\n",
@@ -370,7 +399,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -418,7 +454,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -840,7 +883,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Daten Vorbereitung",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## 2.3. Data Cleaning"
    ]
@@ -848,7 +899,14 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -875,7 +933,14 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -927,7 +992,14 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['reservation_status'], axis=1)"
@@ -936,7 +1008,14 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['stays_in_weekend_nights'], axis=1)"
@@ -945,7 +1024,14 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['reservation_status_date'], axis=1)"
@@ -954,7 +1040,14 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['arrival_date_day_of_month'], axis=1)"
@@ -963,7 +1056,14 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['arrival_date_year'], axis=1)"
@@ -972,7 +1072,14 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['arrival_date_month'], axis=1)"
@@ -981,7 +1088,14 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['arrival_date_week_number'], axis=1)"
@@ -990,7 +1104,14 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['required_car_parking_spaces'], axis=1)"
@@ -999,7 +1120,14 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['previous_bookings_not_canceled'], axis=1)"
@@ -1008,7 +1136,14 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['total_of_special_requests'], axis=1)"
@@ -1017,7 +1152,14 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['agent'], axis=1)"
@@ -1026,7 +1168,14 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['company'], axis=1)"
@@ -1035,7 +1184,14 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.drop(['adr'], axis=1)"
@@ -1044,7 +1200,14 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = df.dropna(axis=0)"
@@ -1052,7 +1215,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Test auf Multikollinearität",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## 2.4. Test for Multicollinearity"
    ]
@@ -1060,7 +1231,14 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
@@ -1073,7 +1251,14 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -1174,7 +1359,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Deskriptiv Analyse",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## 2.5. Descriptive Analysis"
    ]
@@ -1182,7 +1375,14 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -1228,6 +1428,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "Datenaufbereitung",
     "slideshow": {
      "slide_type": ""
     },
@@ -1260,6 +1461,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "Erfassung kategorialer Variablen",
     "slideshow": {
      "slide_type": ""
     },
@@ -1278,9 +1480,7 @@
     "slideshow": {
      "slide_type": ""
     },
-    "tags": [
-     "active_ipynb"
-    ]
+    "tags": []
    },
    "outputs": [
     {
@@ -1600,6 +1800,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "Modellierung und Auswertung",
     "slideshow": {
      "slide_type": ""
     },
@@ -1639,6 +1840,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "Test- und Trainingsdaten",
     "slideshow": {
      "slide_type": ""
     },
@@ -1657,9 +1859,7 @@
     "slideshow": {
      "slide_type": ""
     },
-    "tags": [
-     "active_ipynb"
-    ]
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -1927,6 +2127,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "DecisionTree",
     "slideshow": {
      "slide_type": ""
     },
@@ -1975,9 +2176,7 @@
     "slideshow": {
      "slide_type": ""
     },
-    "tags": [
-     "active_ipynb"
-    ]
+    "tags": []
    },
    "outputs": [
     {
@@ -2066,6 +2265,7 @@
    "metadata": {
     "editable": true,
     "include": true,
+    "paragraph": "Logistik Regression",
     "slideshow": {
      "slide_type": ""
     },