diff --git a/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb b/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb
index d24b8b25767bcf5d36c080f5c5553e09a34a2d2b..03d83b82f723ded52834598ee44019d438cf7de1 100644
--- a/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb	
+++ b/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb	
@@ -4,79 +4,90 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
+    "include": true,
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "# Analyse der Bewegung und Aktivität von freilaufenden Rindern"
+    "# Analyse der Bewegung und Aktivität freilaufender Rinder"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "Paragraph": "Geschäftsverstandnis",
     "editable": true,
-    "include": false,
-    "paragraph": "Business",
+    "include": true,
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "# 1. Business Understanding"
+    "# 1. Geschäftsverstandinis\n",
+    "Landwirte geraten immer weiter unter wirtschaftlichen Druck. Beispielsweise kam es durch die Coronapandemie zu teuren logistischen Problemen. Gleichzeitig stellen immer mehr Konsumenten höhere Anforderungen an eine artgerechte Tierhaltung. Diese Marktbedingungen erscheinen für Landwirte problematisch, da letzterer Aspekt natürlich mit gewissen Kosten verbunden ist und ein generelles Umdenken notwendig macht. Durch neue Technologien wie z.B. Künstliche Intelligenz und der Verwendung von Sensoren können die Prozesse effizienter gestaltet und gleichzeitig die Anforderungen des Tierschutzes besser erfüllt werden. Ein Problem der Landwirte ist, dass sie ihre Tiere nicht gezielt bzw. individuell umsorgen können. Das Wissen darüber, welche Aktivitäten z.B. besonderen Einfluss auf die Milchproduktion oder Fleischqualität haben, kann bisher nicht umfänglich erfasst werden. Somit sind auch keine gezielten Maßnahmen zur Förderung bestimmter Aktivitäten möglich. Denkbare Maßnahmen wären die gezielte Motivation zu mehr Bewegung einzelner Tiere oder die Optimierung der Zucht. Bisher ist es jedoch schwierig kontinuierliche Aktivitätsprofile der Tiere zu erstellen. Ein weiteres Problem der Landwirte ist, dass eine frühzeitige Erkennung von Veränderungen des Gesundheitszustandes einzelner Tiere oft nicht möglich ist. Zwar ist die regelmäßige Kontrolle des Zustandes einer Herde rechtlich vorgeschrieben, jedoch können gerade in größeren Betrieben auf Grund der großen Anzahl an Tieren, gesundheitliche Veränderungen erst spät erkannt werden. Oft bleibt im Krankheitsfall nur die Behandlung mit Antibiotika oder anderen teuren Medikamenten. Gewissen Verhaltensänderungen können jedoch frühzeitig erste Hinweise liefern. Gesunde Rinder beispielsweise bewegen sich normalerweise recht viel und reduzieren ihre Aktivität, wenn sie erste Krankheitszeichen entwickeln. Eine frühe Erkennung dieser Bewegungsreduzierung wäre von großer Bedeutung für einen Landwirt. Healthy Cattle plant, zur Lösung der oben genannten Probleme, eine Aktivitätsklassifizierung zur Erstellung von Aktivitätsprofilen und zur frühzeitigen Erkennung von Krankheiten zu entwickeln. Die Hypothese ist, dass mit Hilfe von typischen Sensoren eines Smartphones und der Verwendung eines Machine Learning Algorithmus, die Aktivitäten klassifiziert werden können. Healthy Cattle möchte für die geplante Lösung zunächst testen, ob mit Hilfe von typischen Sensoren eines Smartphones eine ausreichend genaue Klassifizierung von Aktivitäten möglich ist. Um dies möglichst kostengünstig testen zu können, werden für einen ersten Prototypen keine eigenen Daten verwendet, sondern im Internet zugängliche Daten aus einem ähnlichen Setup genutzt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "include": true,
-    "paragraph": "Business",
+    "Include": true,
+    "Paragraph": "Datenverständnis",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": []
    },
    "source": [
-    "Landwirte geraten immer weiter unter wirtschaftlichen Druck. Gleichzeitig\n",
-    "stellen immer mehr Konsumenten höhere Anforderungen an eine artgerechte\n",
-    "Tierhaltung. Diese Marktbedingungen erscheinen für Landwirte problematisch,\n",
-    "da letzterer Aspekt natürlich mit gewissen Kosten verbunden ist und ein\n",
-    "generelles Umdenken notwendig macht.\n",
-    "\n",
-    "Durch neue Technologien wie Künstliche Intelligenz und die Verwendung\n",
-    "von Sensoren können Prozesse effizienter gestaltet und gleichzeitig die\n",
-    "Anforderungen des Tierschutzes besser erfüllt werden.\n",
-    "\n",
-    "Ein Problem der Landwirte ist, dass sie ihre Tiere nicht gezielt bzw. individuell\n",
-    "umsorgen können. Das Wissen darüber, welche Aktivitäten besonderen\n",
-    "Einfluss auf die Milchproduktion oder Fleischqualität haben, kann bisher nicht\n",
-    "umfänglich erfasst werden. Somit sind auch keine gezielten Maßnahmen zur\n",
-    "Förderung bestimmter Aktivitäten möglich. Denkbare Maßnahmen wären die\n",
-    "gezielte Motivation zu mehr Bewegung einzelner Tiere oder die Optimierung der\n",
-    "Zucht. Bisher ist es jedoch schwierig kontinuierliche Aktivitätsprofile der Tiere zu erstellen.\n",
-    "\n",
-    "Ein weiteres Problem der Landwirte ist, dass eine frühzeitige Erkennung von\n",
-    "Veränderungen des Gesundheitszustandes einzelner Tiere oft nicht möglich ist.\n",
-    "Zwar ist die regelmäßige Kontrolle des Zustandes einer Herde rechtlich\n",
-    "vorgeschrieben, jedoch können gerade in größeren Betrieben auf Grund der\n",
-    "großen Anzahl an Tieren, gesundheitliche Veränderungen erst spät erkannt\n",
-    "werden. \n",
-    "\n",
-    "Verhaltensänderungen können jedoch frühzeitig erste Hinweise liefern. Gesunde Rinder beispielsweise bewegen sich normalerweise recht viel und reduzieren ihre Aktivität, wenn sie erste Krankheitszeichen entwickeln. Eine frühe Erkennung dieser Bewegungsreduzierung wäre von großer Bedeutung für einen Landwirt."
+    "# 2. Datenverständnis\n",
+    "Der Datensatz dieser Hausarbeit stammt von Anguita, Ghio et al. und wurde abgerufen von Kaggle.com unter: https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones. Der Original Datensatz ist auf https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones. In einem Experiment haben 30 Probanden mit einem auf Hüfthöhe befestigten Smartphone einen festgelegten Bewegungsablauf durchgeführt. Dabei wurden mit Hilfe der verbauten Sensoren die linearen Beschleunigungs- und Winkelgeschwindigkeitssignale auf jeweils drei Achsen erfasst. Die einzelnen Datenpunkte wurden je nach Aktivität klassifiziert bzw. gelabelt. Durch verschiedene Filterverfahren wurden diese anschließend von Rauschen befreit und weitere zeit- der frequenzbezogene Features extrahiert. Aus diesen wurden weitere Features wie z.B. Mittelwert, Standardabweichung, Minimal- und Maximalwert, Median usw. berechnet. Das zu Grunde gelegte Machine Learning Problem ist in den Bereich Supervised Learning einzuordnen, da auf gelabelten Daten gelernt wird. Jeder Datenpunkt im Datensatz besitzt eine Reihe von Features - berechnet aus der Sensorwerten - und ein Target - die Aktivitätsklasse. Der zur Verfügung stehende Datensatz besteht aus 10299 Beobachtungen (Observations). Es gibt keine fehlenden Werte und es existieren auch keine Duplikate. Die Anzahl der Features ist sehr groß, was eine umfängliche Analyse der einzelnen Features stark erschwert. Eine grafische Überprüfung auf Multikollinearität mit Hilfe der Korrelationsmatrix ist deshalb nicht sinnvoll. Eine Überprüfung des Variance Inflation Factor (VIF) ergibt, dass sehr viele Features mit einem VIF>10 eine hohe Multikollinearität aufweisen (siehe Tabelle). Eine hohe Multikollinearität hat negative Auswirkungen auf die Interpretierbarkeit der später erstellten Modelle\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Include": true,
+    "Paragraph": "Import von Relevant Module",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
    "source": [
-    "# 2. Daten und Datenverständnis\n",
-    "## 2.1. Import von relevanten Daten "
+    "## 2.1. Import von Relevant Modules  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {
+    "Include": false,
+    "Paragraph": "Import von Relevant Module",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'plotly'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \u001b[38;5;66;03m# standard for data processing\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \u001b[38;5;66;03m# standard for data processing\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_objects\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgo\u001b[39;00m \u001b[38;5;66;03m# creates plots\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np # standard for data processing\n",
     "import pandas as pd # standard for data processing\n",
@@ -106,17 +117,25 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragragph": "Daten Auslesen",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Daten lesen"
+    "## Daten Auslesen"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "Include": true,
+    "Paragraph": "Daten Auslesen",
     "editable": true,
-    "include": true,
-    "paragraph": "DataUnderstanding",
     "slideshow": {
      "slide_type": ""
     },
@@ -125,29 +144,37 @@
    "source": [
     "Quelle der Daten:\n",
     "\n",
-    "Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra und Jorge L. Reyes-Ortiz. Ein öffentlich zugänglicher Datensatz für die Erkennung menschlicher Aktivitäten mit Smartphones. 21. Europäisches Symposium über Künstliche Neuronale Netze, Computational Intelligence und Maschinelles Lernen, ESANN 2013. Brügge, Belgien 24-26 April 2013.\n",
-    "Heruntergeladen von Kaggle: https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones\n"
+    "Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra und Jorge L. Reyes-Ortiz. Ein gemeinfreier Datensatz zur Erkennung menschlicher Aktivitäten mithilfe von Smartphones. 21. Europäisches Symposium zu künstlichen neuronalen Netzen, Computerintelligenz und maschinellem Lernen, ESANN 2013. Brügge, Belgien, 24.–26. April 2013.\n",
+    "Downloaded from Kaggle:\n",
+    "https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "Include": true,
+    "Pragraph": true,
     "editable": true,
-    "include": true,
-    "paragraph": "DataUnderstanding",
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "Es gibt zwei getrennte Datensätze aus der Testgruppe und der Trainingsgruppe. Diese werden für die weitere Analyse zunächst noch einmal zusammengefasst. Die Daten werden identifiziert, ob sie aus dem Trainings- oder Testdatensatz stammen, damit sie später wieder aufgeteilt werden können."
+    "Es gibt zwei separate Datensätze aus der Testgruppe und der Trainingsgruppe. Zur weiteren Analyse werden diese zunächst noch einmal zusammengefasst. Die Daten werden unabhängig davon, ob sie aus dem Zug- oder Testdatensatz stammen, identifiziert, sodass sie später wieder aufgeteilt werden können."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df_train = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Analysis_of_the_movement_and_activity_of_free-ranging_cattle_test.csv', delimiter=',')"
@@ -156,7 +183,14 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df_test = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Analysis_of_the_movement_and_activity_of_free-ranging_cattle_test.csv', delimiter=',')"
@@ -165,7 +199,14 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df_train['type']='train'"
@@ -174,7 +215,14 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df_test['type']='test'"
@@ -183,7 +231,14 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "df = pd.concat([df_train, df_test], axis=0).reset_index(drop=True)"
@@ -192,7 +247,14 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -421,15 +483,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "Include": true,
+    "Paragraph": "Datenvorbereitung",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "3. Datenexploration und -aufbereitung"
+    "# 3. Datenvorbereitung"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -445,15 +522,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "Include": true,
+    "Paragraph": "Suchen nach fehlenden Daten",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Auf fehlende Werte prüfen"
+    "### Suchen nach fehlenden Daten\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -484,7 +576,14 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -503,22 +602,44 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "--> **Keine fehlenden Werte im Data Frame"
+    "--> **no missing values** in the Data Frame"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "Include": true,
+    "Paragraph": "Suchen nach Duplikate",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Auf Duplikate prüfen"
+    "### Check for duplicates"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -589,29 +710,58 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "--> **keine Duplikatzeilen** in den Daten"
+    "--> **no duplicated rows** in the data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "Include": true,
+    "Paragraph": "Ziel Variable",
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Ziel Variable"
+    "### Target variable"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "Wir haben ein Klassifizierungsproblem und die Zielspalte ist die Spalte \"Aktivität\"."
+    "Wir haben ein Klassifizierungsproblem und die Zielspalte ist die Spalte „Aktivität"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -633,7 +783,14 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -664,15 +821,29 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "* Im Smart Farming Kontext dieser Aufgabe würde ein Rind normalerweise nicht auf einer Treppe laufen. Daher werden die Zeilen mit den Bezeichnungen \"WALKING_DOWNSTAIRS\" und \"WALKING_UPSTAIRS\" entfernt:"
+    "*Im Smart-Farming-Kontext dieser Aufgabe würde ein Rind normalerweise nicht über eine Treppe laufen. Daher werden die Zeilen mit den Bezeichnungen „WALKING_DOWNSTAIRS“ und „WALKING_UPSTAIRS“ entfernt:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "indexNames = df[(df['Activity'] == 'WALKING_DOWNSTAIRS') | (df['Activity'] == 'WALKING_UPSTAIRS')].index\n",
@@ -683,7 +854,14 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -714,23 +892,44 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "- Für jede Aktivität etwa die gleiche Anzahl von Beobachtungen. Wir könnten vor der Modellierung eine Überstichprobe aller Minderheitenklassen nehmen, um einen perfekt ausgewogenen Datensatz zu erhalten."
+    "- For each activity approximately the same number of observations. We could over-sample all minority classes before modelling to have a perfect balanced dataset"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "Paragraph": " Wie viele Beobachtungen gibt es zu jedem Thema?",
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Wie viele Beobachtungen gibt es von jedem Testobjekt??"
+    "### How many observations from each subject exists?"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "scrolled": true
+    "editable": true,
+    "include": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -762,22 +961,44 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "Die obige Abbildung ist interessant. Normalerweise haben alle Versuchspersonen die gleiche Versuchsreihe durchgeführt. Daher würde man annehmen, dass die Anzahl der Beobachtungen von jedem Probanden nahezu gleich sein muss. Es gibt jedoch eine Spanne von 200 bis 300 Beobachtungen über alle Versuchspersonen hinweg. Ein Grund dafür könnte sein, dass der Wechsel von einer Aktivität zur nächsten in der Sequenz nicht scharf oder deutlich genug war und die Beobachter die unklaren Beobachtungen in diesen unstabilen Phasen nachträglich gelöscht haben.  "
+    "Die obige Abbildung ist interessant. Normalerweise haben alle Probanden den gleichen Versuchsablauf durchgeführt. Daher würden wir erwarten, dass die Anzahl der Beobachtungen von jedem Probanden nahezu gleich sein muss. Es gibt jedoch eine Spanne von 200 bis 300 Beobachtungen über alle Themen hinweg. Ein Grund könnte sein, dass der Übergang von einer Aktivität zur nächsten in der Sequenz nicht scharf oder klar genug war und die Beobachter die unklaren Beobachtungen in diesen instabilen Phasen nachträglich gelöscht hatten.  "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Gesamtzahl der Beobachtungen",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Gesamtzahl der Beobachtungen"
+    "### Total number of observations"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -793,16 +1014,31 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "PCA fuer Visionalisation",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### PCA zur Visualisierung\n",
-    "- **Die PCA ist eine einfache Methode zur Visualisierung hochdimensionaler Daten in einem niedrigdimensionalen Raum. (Vorsicht: wir bezahlen dafür mit einem Informationsverlust-->aber für Visualisierungszwecke ist es OK)"
+    "### PCA fuer Visualisation\n",
+    "**Die Hauptkomponentenanalyse ** ist eine einfache Möglichkeit, hochdimensionale Daten in einem niedrigdimensionalen Raum zu visualisieren. (Achtung: Wir zahlen dafür mit einem Informationsverlust -> aber für Visualisierungszwecke ist es in Ordnung)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "data_visualisation = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
@@ -811,7 +1047,14 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "s = StandardScaler()\n",
@@ -821,7 +1064,14 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "#We want 3 \n",
@@ -833,7 +1083,14 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -850,7 +1107,14 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -867,7 +1131,14 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -887,7 +1158,14 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -927,7 +1205,14 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -958,7 +1243,14 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -994,7 +1286,14 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -23165,6 +23464,26 @@
           "y": 0.8999775657179135,
           "z": 0.6377980341399246
          },
+         "camera": {
+          "center": {
+           "x": 0,
+           "y": 0,
+           "z": 0
+          },
+          "eye": {
+           "x": 2.3987812973900966,
+           "y": 2.398781297390096,
+           "z": 2.3987812973900966
+          },
+          "projection": {
+           "type": "perspective"
+          },
+          "up": {
+           "x": 0,
+           "y": 0,
+           "z": 1
+          }
+         },
          "domain": {
           "x": [
            0,
@@ -24001,7 +24320,7 @@
         }
        }
       },
-      "image/png": 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zzkKhEQNShXYqyXyqw0LUrhouxO0QrGoC+plT22VwKNi/OroTBTPD0UbcTZvDENi4ETBStXHfP9u9IdByBKjAGxiKTu+FNwN5QSsFqSIi1YFmKl8sm6qrdDFhYqYaKTyqEKnH1WCxBhEz5ls3MC6YR9flEyyNfoWjaFQNrlw3vF50zYTuLX8UbUFDYNIhYKRq0t0S25AhMPkQiJveY+cqOm9vy8gIFgeeFQL6p1yhGCtVpyiEiRkpQwhRdydVehmXhlvZO+xSiXFSeMw7rbvNkaJyOiwlWJCsYrHo2uQwP4Ry3uwuWbJysOJNMqH75HuGbUeGQCsQMFLVCpRtDUNgI0VAo0GkxKpFkYjU+KJzrbTzz4tLquJ4VDGvVvGRuqPKbyQX+FSF/azCe0+iw4JgcW0I2xlUGq7qHam6hq7JHtFvdXdmzdF9I/0c2LYNgbgIGKmKi5QdZwhsJggkSe+NR6UCg85qonMiRBCgcqJy5kNEjoiduap5VHFLNHKkVXrqU6WEMIpgzZ6WPGXIWqQNezqpICw6wXulNaIel862jEztzjpCZoajm8kHaiO6zKjGyNqbL/ze9//5NPm/V98gjz75/HpXWJSivG7rLeWVV5dHXv3Xv/hxefDRv8qNt93t3tc1onr2+T0EaZisQ3sH+gtc9b0zxT9mQ0BvpGpDoG5rGgKTDAHSe6lUoDGqVkVHFAlyAXlBH0VEqto5erlRESLfWsHprSBTIef0cnCV85uqRrDK6aKq3RYVuGNeyr7157gEK6zlMqF7NcTt/VYhENUoGUJz6Y9/IfTuo/8fxOqcRZfLeWedJDtuN6+0tahz9c1y70X1DIRU/einN5WIFgQpTKoq9fVbdPHVctZpJ7i9bqhhpGpDIW/rGgKTAAEiLdgJEBGq5vcUpW1KaoXgV+oljXJFwQWxmT2to6LfFAQLMkOFHilCrndKZ7as8Wel21JOi1WOxIXb5VRKOxKd63Tu8OboPgk+GpvdFiA0F112rVx6/pfKkpJWkKrrfnW7vOewN8kNt/7ZNUNmnHvhFaINk+Psc0PePCNVGxJ9W9sQ2AAIqMt52JwTUsXwnckRgUNAKvXfS3IJkIpCIWiGXI/hp67J/vCNitNMmXM0yjalK+uc2xGgE2WLSw7jaLHCBMvvRzhnWoes7hupWqnoG45ay5wkT9imf2xxoE9G77pNMttuL5mFezXsgjW9d+Q7DpQvf/bYyHlbRaqINhF1gkjtsXCHCaTqO5dd6/ZWbo8NA6TGiYxU1QicnWYIbEwIkN6DROF0Xo5AKKkiegWJgkwx4vTfq4aFCs+ppqMX37r+0dhExp+baBlzUbVHpZ9qsFb3jlTbQul9zlcbBr1O9aiqZgK6xfR4pEgX8wkWUTJIIPYP1dbxL0aF7mDH9Vrj59i3epM8cOS2X8nAZd+W1Jy5Mv2H1wXVGg0aUZoqX6fUKlJFhOqxp54XolY+wSIdCKlasO1c+eCRb3VX7Wur9txth4qRtgbBVHEaI1WtQNnWMAQ2EAJ+eq/aFoje4CnFiCM6rzYf72vKUJsjM3/vQHzNFHP4miv2hSUDhA8ilMe9vUKD5qg9dralpaszKz4RC3tUlXNZDzdhjoOBHgOhIlKllYq0y0ni5s48fH/OnNLumk2bo3sS9DedY3NPPSr93/u6ZHd/g/Sc9o9NvTAIyyU/vl4uu+B0p6FqJaniwkj7aSpQ03/lIlWTJS1opKqpj6RNbgi0HgFN75HaqubbpGJrJVMIxZNEfaKurpIdQpKqO38eyAfRKT/KBhGikg6RuKbY4gjcfbF51P7LESyOrdSEudqd9smctsvBa4uKwLiRMtbwRfYmdK+Gur1fDwJhkXkrSVVXZ7tECdfLkScjVfXcaTvXEDAEJiBAeg8ShUdTHH2QLzrXRsbqCE5lWy1DSUMlOwRIFfNX2iPzYKugLuyVKgtJWVK1yHxqpUC6korEcg7rUdqxqOv1iQ8mo5iAstbytUNVyWrUfFEu7hp9ghjGIVjlqh01osccJnSv5em1cyAld9zzyAStUpiotJpUaaUf1guahixX/Wekyp5hQ8AQqAuBQqHo+s9p9V61yaoZakJmqD5LQqrCqTnfPT1qP+VIFWQBzRCEhxRftXl07rDvlVb6YdRZLr1WzSurHMGa3t3m9lgoSslkNFzdV+kexBG5c36ldjkanasmzPeF7tm0NX6u9tmw90U0KuX7TvkapbDeytdaJbVU8MkS2Ps+Veio0FQRqWKod1XYgyrsUzUZ9FTul6Qiv37ZMAQMgY0GAU3vVWtirNELFWPzRVvJUNNZHHTGI1W+HUJUaq4cmGFSpUQvk0k5fVQ189DwvJWISpicoOWCBM2YMt4QOclN1wgXkTBIWxyTUX/+WryxwtfAfETlkhBfFboTwcJWwoTuSe66HWsIJEPASFUyvOxoQ2CDIEB6r38o79JacdJ7YQ8oiEC1hsPVKun8qFStdghKLFzvvo6MqzKESEW1tIkD9Myp7VXNQjWNp/or5l0bw+Q0DoGrZjLqzxGnZ2Cla+b+QCIRqRMt0whlHB2ZzuuwyKadm7s5usd5wuwYQyAZAkaqkuFlRxsCLUNAmxgnSe+RntI2KqqVirvhcqkljSbxZYy3U9JoUjhaQ+88IkZJ98c8pCixZND4ehLhO+erJglSiD4KbNWeoRpO1ewUfJNRNFJqRQHpiWNSWm193qf6cPna4VJFJBEz8EzaLoe5IKQkKrSnYZz17RhDwBCojICRKntCDIFJhoB+Cb+2eijWzhCdQ6YQiNdjhRAmVWE7hLitaMKb1qgU82UzaeG64kTb/OiK6q2oTuyA3OULzpqBVN7KdcOJ5tOIkTZKVnuGavYGSdJ341WVgdcX+i6iS/VUVpYjZrW0y1GCtqZ/RGZN7Yj1nNlBhoAhUB0BI1XVMbIjDIGmI0BUCmNOdTknIrG0AqnyRecQjUEq3kYLde1TTTGJRjXCQd3fI9EgSEu1ljL+BVTSWykBRCc0MJSLLWwvR0xUT0aVH4Qt3H+wUtVdNdD9vaKH4x7HjY75cyt5rkTMuD4IaFjvFRbU43EFmRocycnUrrZql2DvGwKGQEwEjFTFBMoOMwQajYCm9/oHR11jYn9EkSqtkEOLpHYDRKaSRH3KXYNGpUhboTeqZ95KES6iPcvWVI7A+WSskrCeayHqRBNo9EGFIs2dg2rIcuU3kAnMM0mhRY1yGikwTkIIw3OTaoP4Eq2C9HAPk3hTMV+5voPl7mmldjmk/FQjZ21wGv3Jtvk2ZwSMVG3Od9+ufYMggChbNTDlNoB+Z1XviCNM46LzjIyMBucmESeXWyOIaqRds2FIFFolohbVSE/UfHGtFSpF4HxxPWSqWroR0rDF9HGSppVykBYw5v8wTklsI3x7Bq4Z/dGKdcM1eVRFpQ7jurgr3ujHkrrR67lhgsXrPEe46NswBAyBxiFgpKpxWNpMhkBZBPhyJ00VbmJc7gS+QEdzgaGlVtrVWiEXXqOcHUKYpMS5nUlF7FGRKo1ssR5pt7hpzHKpPJ9AUCmHKaqm21iLaBzRrSSDKBF2BKypIvu496Na6jBsMor+Skm3H3Grp0WOf62st/WsLld0QPGBDUPAEGgcAkaqGoelzWQITECgUnqvHFTqSk46CzJQa+Ph8Pxx7RDipOeYW+0X1D29WlRJ9+PPD8FRs89aKgpJ5U3vaZNKRpjhaJAz7hzTNCV5XDX1RmoRUqb2DHGq7qqlHMOER9ODNGAeyQUu+QyutVzaMsm1qMYLiw2ilDYMAUOgcQgYqWocljaTIeAQ0C9aIhlxRlh0znm4YEMAiLDUM/hCp7pNhefVyEs1UqVEiOhZLakooi29g6OllGO1/fjXriJs1UwlMSslOkPV4LSewOcJu4MkYnHVRPlRtHBKjTkhl2GNW7Veg+Xurz8/gnwiS2v7R+tO/ZLyAwOeCdNT1fPpsnMNgfURMFJlT4Uh0AAE1OU8bnovLDoPG3uW6xMXd6t8kUOmGPplH+fcKIPKcHubJERI19Q5iMJAZiCecQX2YdLpGiiP5J0wHdKYxF1cyVE2m3b4aKWf730VhVM1OwX2wbVF2TMkFZhHrY/pJ75aeFJx/XEiZOXuN6ll7oFZKcT5RGw+x1RqNaMoaMuYb37lU/LBI9/qXqZdzN0PPDGhtQyvf+eya0vg/einN7m/H/XOg9xxjz31vHzyi+eXfqYljbauOeboQ2X/fRaWzg23o+GNcMuayXSXjFRNprthe9moEND0Xu9A5QbB/kWpnknF1OVE55oGTOJrVK23XxxwfSF5WDheS0WgvyeulaqzuEJ4XR/dj08MVUAOgQkiZqMTDEErXadfAMBxnZ1ZeTU7KH/sWyKH92wnPcNpyeXWt6aoZnERvseBrUHG+VMRFWKPcbVi5Ujdqt5h54pfyWS02j1WfRfEmHSiDUNAEYhDqpQoLV22agKJ4vUF284tES3Il9/DT8/78mePdcvxvhIt+v5BosKkqlzjZPa56OKr5azTTpCZ06dOuhtopGrS3RLb0GRHAHIQ1+Wca/H1TAiP1Wm7UqQmbuNc5lehdyNsFojIkJ6DsBAJqsX1nD3p/nVPpMX4OxGXas2A9VzmqZRiRI/FnPQNVAfzaik9rm/52qFSBd/y7hHZ54mfuEdufvtU+fNux8jsYnaCNUOtbugajaQRM6PW6FIloXvYZJTnkiheuZZEpqea7P+6bLj9VSNVSmb+5yfeJxf88KdyxinHyY7bzXMbptnyOYsul/POOklmzZgqJ5/5XTn9s8eWIk5RpArS9Z7D3iQ33PpnR9AY5154hWikCuJ10WXXyqXnf2lSkqdyd8pI1YZ7hm3ljQgBTe8hHo/bgtyV77cH/e2I8sTpv6eQqBFnOWHy+JcpNgtBtVg9Ngtqr4ClAl/KSfbq38ZKmqtqpErJodOSDeaqXg9pNdJ3EDY/eqVu6xPwSAW7nDtj3FQ1nU3Lf+dekFNe/H3pEv648MOyW27amIFmUCyQc6QtJX29I1II+YlVe4SVEL22ZsjpyChAYPgVidXmiEuwww7xUSajYAZZxicLd3sbGx8Ca/Mj8tNVz8he3bPlzT1zG3YB1UgVJOeOex4Rok3hyBSb0DTgXrvvKMtWrHbH6ShHqog2EXWCSO2xcIcJpCp8TsMutMkTGalqMsA2/caLAOk9vphwOk+i/+HL07dCiFsZF0YqKuVUzg6hVpT99BzGmXzZQuTiXq+uG6eSrxypinNu1PUR9Roazq+XVlMSAmmAHD6f75Mn82tkWrpd3tkzT9YNjEh7d5v0Fkdl8UivvOev1wtfVESqbt/5Q9LRH6yWTqfkufZeOfml37v3f7jd2+WN+dlShPXFHFGEKKk/VS36unJrzJra7gg4Bqg2Nk4E/nX5E3LS4j/I/PYeeXHPj8vY7wt1X0w1UgXJOeTAvVz0KSqKpOezkXB0qRypUn0VUSufYLFGmLj52qo9d9th0kawjFTV/SjaBJsSAoVC0X1JJ0nvcf1+Ck5TZkmJSRSpIsKhlVqku9Szqlai5pMg35ld5wtrjirdW0gSX/j4N8VptBwmVbWSKd0TovNyEa1sZ0Z+O/qK7Nk120Wi7uxb4k67esfDZY/O2fIvyx+Rj85e6IgU49HBFTI90yG7t82U3EhBCqN5IZL1qaW3yY1rX3THMNc9ux8r6dF87MrHaoRoPE06bp8QjjhynUQPa73nPsGCKPKLAvfMxsaJwF19r8qxz98qb586T/5j+79r2EVUIlWk9y685BpZdPZJLhVX7liIz4svL50QpWKDlUgV75P201Sgpv/KRaome1rQSFXDHkmbaGNGgKgUOpTVfSOx03thu4J6U3Bh/ND/kMqqt1GyzhvH9ZzKMKrpKhHCJG1k/GsCr6ndQXoxqT+VrpkrFEtWCGECmEqnZFnnsDw+vEoeHVjhloY47fXYf7i/n7zlnnLw1G3kxjUvyJlb7y939r4iH52z0B3Lax+dvasjYESvfrXze2WAMntZAAAgAElEQVSbdI98YvEtJVJ18JR58qud3isFNGddWcmOWTNE2Sjodcet/IuyZyCtC8GqVnkY93NHOhr8Ieno0GwYAj4ClUhVVAUe5yIy99N8tZAqKv+ihOvlyJORKntuDYFJigBESq0M3Jd2d7aqiNonJlxWtd50tVy62iGwFsJr1qhnJHE9r0SqkraRCe8ZIsWXOs7ycW0ZwgSOn6kghJiFU5XDPSKHPvOfjhytzQ/LyVvuJXf2LnGEiUjVI3v8vWz38BUu4nTmvP1lbW7YkarFw+tkfsc0R644j0EEi5Tf9Ey7nPXyXe7vZ87bT+a3T3NRLY47tnsn6ezKBumX0byMRvQdBE+8pcoJx6Puq+rltLqR6sdXVw3GJvvlnhUleNwHIlY2DIE4pEqr8A564+6l6j7OC0eveK1WUqVr3Hjb3SW7hHLVf0aq7Lk1BCYRAuXSe9VaicS1Qqj1UqPsELo7M5GaobhrhF3P41giRPWX89vIJK0GDEhQINZnEAGL4ysVJlN+6kujOlgCUECgFYK/k1flYy/eLESUbtj1fXLCs7+Wo2ZuL2tzI3Lw1HmOKEGwSPPxOn/n2EuXPeJIlk+q2OueXXNkerYjIGW9pA+Lsni4txS5IvLFXO78rfeXL83ZU9pSE6v86m0tQ/qwpzPjoksqwOfPuMUS/rNCZA+Nmemp4n6CNq/jNFL16JPPT7jwUz/9Qbn9rodcZZ9W+3FAlK9UFKkijRflU+VbLjCfemCFPajCUbLJrKfiOiz9t3l9bjbbq9XqPUhBucGXDuk/jSr4FXaN0jKF165kh0BEoRZXdb8CLykJ8kmVr3lKmtpUvVVbNlWqTnQEqzNTkVRVIlNh7CAsNJ3WljFPjvTKkc/80kWUIEREmIhWnfD8b9yfkKqz/sZre7qf3/PXX8olC97uXj//1ftdhIsBSYI83bDL+9zPP1n5tDuPYy997RF5dHCle13J2yFPBCaHty34gOSGchOq/LgedHG1kCDm9NvjaPuaWsw/zZ9qs/2nzy68xQgYqWox4LZc6xAgvUflXlyXc3XbRpujIm6iO3FTVXGvTAkHFYKV7BCqiZz99cKRrlr3TEVdoSCuerGWa6+kt6rUViYJmeK6w95RLno1o1MWj/Y5WE547tcu+sQgAjW/Y6qcv+R+p5e6YZf3usgTZAmiRCRrera99D6Ea8/u2e5YiBPRKNKHpAIfHVjptFikC0kjotE6/9X7HAn7r22PlFEvVatRJpEgyhTHJiL8DEVFDn3zT/oDUlRRzZ8LggwpI+nHnzYMAUOgOQgYqWoOrjbrBkJAXc6TVu9pc156rOEhNDiUq8sBO+ry/XQc9gV8EVYzAO3pykolV3Vf58R8zFtL1aFPakbzxcSNnOOQIr7YibKsGxgtwRPnvCgsOQ97APXxSmdS0jWjMyBQ7VOddurMrfdzxArCw4D8kMpDW8WfMAyiWYte9xZHoIhW6bh6h8PllJd+J5cseIc7hveZj2gWx6kei8gVkbFt26bKlkPtE3yslBRjpooODKIa16RU91EtfRjXnsH0VBvoHyRbdrNDwEjVZnfLN80LRrjM/5XSe+ErV9E5gmB++4eQkK6q5vidBEFf2J40hVjJ9FHThtocuNZy+zCpIUJHRCVuS5UkpIg90yiaKFqS8xRviK+mZsPYZNrSMtwdHIkOCrJDdApCBMlCU3XUjO2dwJwoFdEoxo1rX3CpQsjYKS8FJqBEpCBTjniNpf/4k3OYR6sFeY3qQogWpIuI1tx8ZylaRdSPKKl/b8IkqJJjfDgaV+25Gyft6fUiY1QQ9g+NyvQe86eqhqO9bwjUg4CRqnrQs3M3KAKUm1NJFje9p5stJzqvJlZPcrF+BKmWNBprhaMxPkGr10U9qo0Ma/ou5ZWutxZSBKnqyAaCdYTrkKs4ZFC1XeiScAOHiFC81tkxUZ9FyxkMPffsnuPWIJqk/lOaysNeARKFX9UJz/3GpfWIVJHug2w5wrXVXvKTFU+5OUjzobFiXLLd293xzEvab/HIOkeqIGE3rnnRrQXB2j+1heSG8xLW6Pl48qyBBZFIjV6F28s4vNrSsYT94bn9yFjQczHj8LZ+f0k+xXasIZAcASNVyTGzMzYgApre6x8cFdJUcQckAC0JlVQaMeJLLCwg3nJGh0sp1SIs1l5vCMy1x18c0lDpGubN7pLXVg85Xydtd1OrXop1fBF7VESKdfCCKrfvWm0VwH9aT5vz3IpLppT45fPFkq5NXyNNS/EBQnXCjLico4Xrmt4hjw2tdJEj0n4IzBmQKMgUpAqhOtYI+h5pPfRVaKQYRJwWbfsWF6ki2hVU/gXDTxGueePJjmCpkJ2IGOcdMXU7GRkYlS162mOJ1MMtdoiYkr6mZRDPKmndWoeS3+7O4JlkLRuGgCHQPASMVDUPW5u5gQhoao8/kwy/ui5OxChKGFxtvSg7hHr68Ol67J1mvC46EzOqU26vcd3Ly5Eqv8lxrelBSHAuFwj/Kw3FE4PKckakmG92dWblr6O98h8rn5a3TJknB7VtJffkXnNVfUSfXBXfSKCn0ko9Td1BgDTdx7E/WfmUq+rjdaJNRLuIZDEgYaQOiUZp1IroFNosJVqcD3HDPJQUIxYLp83ZU9asGar2+Ex4X3EmesVYOzAaK5pXaRHSkBBTNF7mT5XodtjBhkBiBIxUJYbMTmgVArWm93ySk1THBKlgVPvi55hKdgi1YuRHu9g7minf5iHJvOoRxT7jEErmDl+/GpFq2i0uWYxKD1aLgvnnQKYqrcVca9oLMv/hKxwkkJyX9vm0I0dU+UFw0FNpexoVrZ/y4u8CbVVu2AnViUpxLn+qhxVpPMiVelAxP2J09/NrjzjSRNrvqBkL5NJlj7o/iWZhEKqki3NW7fNZlwZM4WDenpH8aN5FsCRGgBUsSB/yDIC9Rq9qiaCip0Isb/5UST49dqwhUBsCRqpqw83OahICmt7rHajcKiW8vJIRtUJQp/SklXC08iACUq7irllRqXKWCPVGzkjjoamJi4OLZqTEpQCTtpLhnlTSWpUjVbXos4i+/GLw5VI0CdL0scJ+kt+i10WUtAUNKUBIE0QKAbtqpTTCpFV9SpS0uTLvo6Ei/QdZ0gpC5uE1F0XKDTsiRkoQ0oXdgh/JQuweOLMPu9Qja72+Y5a8NNQr01Jt0j2SnlAt6D/TvhBfxe2kr/OFQuy+g3o/Zk/rcKlS7q0NQ8AQaC4CRqqai6/NHhMBvviT2iAwtWp80IwgWk9qUhneHhVm/Eavpfr6vpItNEFx7BBiXnZp/yrcDruexxWO+1gkEYGH98l6pNaStJKpRqZ0jTCpqoVM6VyQqheLA3LEM78seU+tuHWubLNNSvbYKyWnv/b7sVTewrFTii5qpQ2U/VQeB2gqT/sE8hpEDdJEWvCO3Y4pObJDnDAGDSJa01x0LCBn+5f+7lKIXUEKESKmpqPMS3QLoveHXT8sWw51OD1YeER5lEX1B6zmT8U8VLRmM2n3rNkwBAyB5iJgpKq5+NrsFRBQl/Ok1Xt+FZwKwuO0YIl7M9QbiNQbBAUykDSNWG2tuK7ncdKR4TRkLeJ4v8IO3Vqc9GdcMhUmVdwrIoqsGUcr5mus1IKACF7fSF761r0q6/LDMv91u8ity16R/jvnyuBgUY78SN4RGUTk6jlFxIkBQYqyUNAmzJAmUn2kEA958jp3jmq0nFlofkRII7p+gGNeWHru+UvucxEx0oGcA5Gjt+CNa190+8DdXQcWDf84bT8p5ArrPS5Rdgz+QVHWDFHta5hnNFe0qr9qH0h73/Xtu/uBJ+QbZ3xKaHIc1fOvXL+/r19wRalnn0JZqUdfeC3O4fizFl0ul11wulz/mzsjW9t88ovny1HvPGi9PR5z9KGy/z76C1TQg5A9+SPc/qZZt9xIVbOQtXkjESC9B4nC6TxuSkon8qNFfDHTxyxJo9q4twQtCzoWvszjapHizM18RA7UrTzO/qMMM3Utn5hV8jsqtzdfv6XXCYkMG3RGnV9LlIkoGBYBYBunfU5YY8U+aEnDQHA98NTDUvzGqcH29j9E1n7xf8mMtg73I6k5DEAZap+g1wExQtfkKgRHep3/FJEjCBhkShsyIzrX6BZVfUSdIGQQJ0gSg/OYH3E8Tu38qYN5IF2kB517+3Zvd39X8Tw/f7h9exkdXZ9UoYNauW646mckbM0ArpBi1aPxCwJid9NTxfmEbt7HhAkTP3/2KxfJke84UL782WMdOFHE68JLr3HvTenuLB3Hz0lIVdSx9Axk6Nocoz0EP338kY5EhfsPlmvCTF/DRRdfLWeddoLMnB6k75s1jFQ1C1mbdwICtab36hGdJ7kFPsEgQsUX05r+cefvJHOFjyWliMEohCVp6lANHVXjFfaqqsVeoZKAHeIa9n/yr6cWMsX5SgDjXL+/v6hIlpuru036Lv2WyB9+LbLFXJFPnCay5daSWrBzabt7PfofzqwTYgPZwYsKgTmVgFrZx8FBZd8LJVE7JIv/iT7RvgaixfGQIWwUIFVqrUBlIGk/BulA5oFA8RoCds7ldfRWaLGwbeB9V2U4YxeZmh5vYaOGq7X6pYWtGfjlBb8q01PV8+ndfM6FeJz1rcvljFOOc42TITEPPfaMPPvCKyUyAtFZsO1c+eCRb3XAQLyu/q/fygePPER+cMV/y6KzTyqRlrikaslrK+TCS66ZcC5zR5EqmjC/57A3yQ23/tlFqxjnXniFaKSq0pqtupNGqlqF9Ga4Dv+Yt2XTzmcpSdVSWHQe54u4VniVJCACVtsGZyzZnqmbVDUiLaftc6iGIxUInqNjtgRJI31+Gk3NQ8NzlOvPVy+ZYj0sEqpF1BSzcpGs0vsjBUndd6vIJd8SOeVskasuduRq9ZlXyiuvFGXPvdKydt1Kke4pgvicSBMpOciQRpeo5CNVB+kizafpQSJMEC9IECafiN4hRZCvwEw06AGoHlaByH1XR7yUkDGfiuAhVh+ZvrM779Bn/tM9qqz1y52Oli0G26U9ky41hSZ6yT3h+avUnqja8859xOiT+8YwK4VqiG08748Oiiy+tygz5qVk9vjvEA25AJ808ff99t5Vbv/zw3LCB/9O5m01ZwKBYUEiV4wj3nHQeu/FIVVf+h/HyDe+8+MSkfMvohypItpE1AkitcfCHSasGz6nIaAknMRIVULA7PDKCJDeo3fewFBQcUYqbW1/5fJ4nbGc03kzMK9kh1BOrB5nH410PWc99kIqCFyJ2tSiHfMJUbVqwDCp0qgRUbY4KTvFKMoXC30Pz0WUVYIeX25/Ue9nOzOSffUFST/9sMiPL5bVZ14h5/14e7eFc/5hSGbml8pNM7KO7KBvUrNP3of8QITQT6l4HcLF30kPQsB4jwo+jWrx3iN7/r3TSOG0jmWDVvtBwiBuVAlCpvy1XtzrU9LRX5SlbcOy71M/KT1GkLnTuvYsCdX9SBOfnbifm3LPJQ3CIeCkXW1sOgi88IeCPPDveemaJXLUt9ucNUijBiTpxZeXysmfeL9c+uNfyInHHSG/v+shN/3er99pQkSJVBupPwgXka1warAaqbrlD0GvzV12eN2EtKFeSzlSRYTqsaeeF6JWPsEiHRiOpPnaqj1320EuPf9Llv5r1MNi8zQPgUKh6HrFRVXv6T/ofhNdfyc+CeH1WolDnKtLYoeAk/mSlYNxpnXHhCNetaTl/MX85stUbiXZi85TS3RJS/mJjCEm913c44BRyWQ0ilSxHq+X065xDTixUyEXhSku6p2rFkvqm6fJY1++Sa76UV523CklJ34mI129S2Xx9B6X+lNtk+qZtImy34KGiBJRJyJQECmq9/g76Tod6qI+7W+L5ejhJ0opQyVj6LBYy+8jiOZqm/QUmd8+RfZ96qclTdUvdzxa3liYvZ5vFSJ8Unfoz5I2YPbvEXoqa00T56nduI5Z8UxR7v6XnGy5W0oO+ExjbTJUV4Vm6de/v1fOOPk4R2DuuOcROeTAvRyRUSE7pMn/mXPPWXS5nHfWSaX04UWXXRtJZCA7l/z4ell01knCMccefWgppRiHVHEMaT9NBWr6r1ykqpVpQYtUbVyfp0m1W63eq9TEuJJFASkOP+0W11gyKQjohOixxhd03FRipb5t/vr+3I0ghFFVgUSqlq+Nn0KthUzpNUFyZhHhyBcc0YnrcRXVUiZ8nyBPpP+IwugelUiH05D6PulOWtFUSnWms2npfPlxWb31vvLYowVZtUqctcJ+g78RWbCzrJs1SxZnA9sCSBRRpQ/07OpsF5QwIRpXIqSCciVKpPru7AtSgDfs+j4Xjdr24m/JY5873aUWIWMqdmcOSBXpPQgaa7EmPz+46/FOQP9kfrW8LjtF5o52CntnYBKq1gpafUrKPEkD5jApJ/3HZ8r8qZL+i7H5Hq+6qoMP2FO6uzoc0VGR907bbyNzZk0vkR8IjArHfcS++ZVPuWOqRaq00hBNFYJ4CJZfwVcpUkV1YpRwvdyaRqo232d60l85aaikxpr001u5Lihn1x52lfrvNQIEPwJWix3CjJ42Gc4VIluEhF3Pk6TFyl2bH+EJe20RuSAVVK3SsR4ypWk+SC6jGpEJR8Pi6KXUyFQjYOVc03lG4tot+HhmOzJSyLTJY48UnVfVqpUi8x6+TuT1+waHdU+R1JZbyx9vz8u8bVKyPNMrPyj8KWiKnA10UqTt0D6R9oMU4c6u2ihn/InTerZDirffJI+9fnfZ4/EnRPr7ZO3C3WXd6+a7NjVUDELM1GGd85nzhT1OlJeGe+XPQ6/Ku7vmy5+Hlsq3l6Lx6pAfzD/UeVZBsTDrXBZqb+N7VFFIQYq9kkeV+lxRaUqk04YhEBcBJUtqQaAVdTfednfJNgGidfKZ35XTP3vsBCLkR6+IcFWKVPn2Db6dAqlERjVSFbWvctV/Rqri3n07riUIqMt5LeacbJAvUxXMNtKiIOri/Ya/9awVZb4YTh/Wm+KrVIXnXxvaGPrtlYvkaZSI+eL4Pvlz+5op8CLthG2BkuByD1hSAsfxRMAgB+X2qDo3CgYg7klHW1dWfv+HtMycJTJrVkp22jkgE88+U5D77y26tOD+BwavXfL9nKxaVZRDv7ysZIOAxopBNGnRtgfL+a/eNyH1h50CWquTt9pLpL9Xijf9f8J23ZgXzoKdJHXBVVJ8/CFJLdhJpGeq3Ln0aTlh6Z0lPysqEdVyAd0WmixfHP+dWW+R9nTKpf0gnOWG77AOVr6Fgp7DZ47Xp/e0J4XRjt/MEdDUHH5RSnB47dpf3V5K5ZUjKX4F4arV6wRfKX9svdVs50P18OPPTvDE4hjVP0HmSDdqFEx9qVRHpelHzmEfrBH2oAr7VLVKT8WeLP23mX+AKl1+nPReufN9GwHtYRd2KW8U9H5USs1AazHA9PcTbhOi0RWiA6QQk1behYmMRuziEL9yrup+k+Ok0bIwmdI0n5KfcvcqKZniuns6gzSvpl/pQ+dXg5JCRTcVB4uoZ4YIFdqq0UJGXnhe5MofBYTskLel5d1HpOVrZ443cD758xlHtiBV7/tARs7LjacAOQfNFOahXV0p1+dPBedYIfxxziFBtWHPFJFjPiVy+00iECvGP3zLieVl+dLg5x9cJzLQJ2u3me+IJNEtiNiNoyscmQprvEgf/q+Z+0t3Ju2eLZ6zakOF7V3tmfX6A5JC7B/OydQuE6lXw9HeNwQaiYCRqkaiuQnMRVQKY86kLudceiXROf/I80VdDxkJw6vErVEtavz51SsIo0pG0ghQ5Je/51eVRH8V1eRYXd6rWRSE91GOTOlxXPcW0zvXSz/VQqbCFXtgSQQQ7ySinkRS8O9SfGt5NohOnbvyHtfvD2Jy2Lo9ZMkrRaeruuMPBYFEXfqD8ajXcR/NlKJVP706J8V3P1vSUqF/OvX598gtvynIiZ/OyMBgUR5Y8LhLA5L62+PKywJfLAbRqH9YFJCsJx4KbB34uw58s266VuTQI+Xl937YESln3zBtoTyaHg56E85e6DyveP2sufvJ7IE2mTOto6YG2kqwEbYPYWWSSbt0MffAhiFgCLQOASNVrcN6Uq8EmcIXZzS/fh+yahuP03+PFBZfovVGkNhLJTuEanut9r6fkkO7gpYJ/Uo9IyxmT4qBuqojHtcmx0l7HFYjU/71IYxXTY+PR1xiWS3yBBmGPOCdRDQU/VYSHzPdK4Tq3vx4+o7Xr9v2vfKb87Z0Uao99gxSgJAnTf+dePyg00DJlnNdBIvjeg5eKiPtI9L1yixZ9/QUN31Xl8jeB+fk9Nd+57yqji9Ml+kwNcYl5wV/HvMpeflNhzgROwSp+LkPj0eqNHJ15LFy9E4dpSpBUozO72pghZw04/UypdhGukCmdWQcEQKTV1cN1oQHW4IUk2btaDMyVc9n1s41BGpFwEhVrchtgudh0hk3WsCXrZKbOKJzjq3H0DCJHUIttyZKL0VKapB0X0QbkThr+JV8aKJqnQcihYN40ibH7DEJmdJrUlIVTlFWu2bWosIvn4+2P+B8nVPbqfiRq2rNgf318ai6/Y8Z6d3nJfnMkptLb92y4Bj52T9Ndz9/+StZl+aDXB1/QjZoL7PmhVJfv5t+lnFki2jWNq8LzH4GB8Rprfj5d8MvOmuFG7Z5h7zlm+cEhKl7isgZi1wqz+mpDnirrPvACTLtztsktfu+UnziIVm8005y9tBz8h9/KzoC99btcVtf6eY/avoCF5nStjUfaNu+1PtPyShkHgKdNAqpIAR6qoL1+6v2wNr7hkATEDBS1QRQN9YpicpU03L4UaIkGhi+cLEpWLp6KBE8/npx7RCSLBCOehFN06gJX/gYUsdtLqwkRpswJ8Enas9Kyogeso9q4nF/Dj/ClBQ3UrWk6qoZhep6PiEtV9HHtZDqi2rWrNog0oL9QzyD1SODxbZ2OffrBRdpeuqgB52AHCH4wrvf4NJ+nV0ipPrwrWJ8/pyC7PvyeINVqvN2fm4Xmfe6lNxxe8HpqNBYXXRhToYGxYndEbFTEfg/F68RufDscXjP+FYQ7YJkve0IkfvucNYNP52al1OW31c6jl6ARLluWPW8nP3Kn1wFICJ3Fai7hspT3yiFseiwFkegOQMLKvcY3D/SpXGieZq+HRwxPVWSfwfsWEOgUQgYqWoUkpvAPKQAl60ZXu9KwqLzpKJo/zfoOL99R0XBkqbMKt2OsN1CuSiSL1avdnsbVRno701JGQQnSucUtSe+VCEvkJikpE5JHCX4caKWcXRWPDuko+LsxRde066l0j3vntYu532rIKtXiYtEHXdCRu67p+AE5kSa9tgzLVf+KOfejyJVmH+iv+rqTsljjxTkuWc5JyXX//d4c+Mvn5GVX7Y9KkeOdsr8r53mhOfOluGCK6V407Wy+qBj5JWRubLHZceKHHmsPHbAAXLI4kBzBYF6ZI+/lxnZDkE1Ri0f5OmyFY+6qkJsFFybmoHx6jyifGgZ/ev2tVJgUi2ap8dDwNQeo9qza+8bAoZA4xAwUtU4LDeJmVasG3ZfgPrlzhc0X3ZKpOKmB6PA4DfxtkyqbE+9RtkhlLsRSgJU2F7NEiH4rR/PoPWJZlSUJm5kJ2p/1ewV4ji8V3Izr/Rwhs+L8kkKn++n8aLIj2JNVd66/tHYaWXWqUSudF6oz7rRtDzwQFCp94Y3ivzbVRh/Bj+/6/C0E6wHZCkt++/RL5f2veDSf7ikfz71ZvmvqzKOdO13QMpZMOBdhUj9XYcMyDYz+6Vru7kuBTilb4oc0NkpnY/dIbL7vs7r6uZf5+W+ewvy1rel5ZD+/wxSf/ffIXcd8ha5s7DOtcChlY2zXqAvIYRs932lcNiHSpGp0aFcyfCT6ybtunJddDFHuFlyuV9OqBSFhPO5tX5/m8Q/yXYRGxkCRqo2shvW7O2SnqHtTDMq6vhCRO/hk5Rm2CGEMfJbviSpumMe393an9cngHEF3OXIFAQF88xKWqRKruqNIlNKmH2henjP1Xr0xYlexX2GIRJYAuDbRGqaP1kfkqb6NEhbkbAM7uPdbfLairR0doqsWYPYPCXzcs+KXHiWiySt3usIue+xbudX9dijRZcm1PHPXx+SrjlTZfDJZ6TzB2cFqb1TzpbrV727lE48/YyszJodaK9WrSw6UjVt1z759/QDzmUdInX8uV8Lpjz9W+Jyto8/FFgtjI3sFb+Wzq4e90uKH3XSatNq6XHfBJQpw6lBPl/8UoQGz4YhYAi0HgEjVa3HfNKvuLpvpKbGvXEuDHd1qr0Y/DbdDPKm+4hq+RJnj/4x6gKuxpuqDapkYhlnDZ98xIlwRbmq10qmfH8rNFDh6GMUgYuzlurTak0Pl8NNhfqQfef0nhbJZDOO/OeGc+v1zmMeyBb/pzMp6RhYKbLl1i49iNaK6BVtbK75SaC3Qj/1tXMDElL84Xkl24TV/3SdnHfpnNK2iH69654viux/iKQOPcK9ftaqR0pNlXFbf3hoviNS+eM/L5k/3xxorzxSVfi/N0oh1en6KpKe05ReRzYtXZ1ZV4Ebd4RTgzxHRFat319cBO04Q6DxCBipajymG/2Ma/pG3G/SzRhYK/AFQooiadQozn7C2ia+tKq1d6k0L1/ohaK4/dbqDeXPH47k+ML4SvvwXdXjEJyouXRt+ukR+XFEMSWS7QhSvPSfY0yf1u7u/8hQTjKp6hV91aJXce5bpf3ynhYL9Extl/OWPeCq5yAxv9j+PTJcgYhAqp59qV3uv68gJ34mK7/4r7wjVAjTZ85KuRQh6cGubnHvnbjTLeN+U+d+X877+R4lXRbC9/1+/QVZ8tHzZN6av7ho1tl7bVMiVdqKJp8rSHqoX9JrlgWX5dJ/vSL7v1WGDz9B8iNBhMxP6UFsqfir5KReDkd/Ho7heUXLZsMQMARaj4CRqtZjPulX5Iteo0mN2KxPJIJmutmpBucAACAASURBVOn1zCXrXacZeiz2jQ5sSle2JjuDSmQqqfAerQwpQjRpcUTfsdbme3ekXZ7896Jku0S2f0/wRfzMdcHPCz+SllmzRNb2jRGw0E0CH9d8OVdwpKcevV34/mv/Pz/VxzHPtvfJ2/7689LhP5z/dnl/ZrvIaJUjGNk2OX9R0VX0EWmCTKGfQpBO1Ao/qncfnpGbf5N3f6LJOkRulq6+pbJ6z8NlcMrWrkkz5+y3+CoZnDJXUvsfIp2nBJGqxd+5Qj7X/6QzCP3CzP3lHW3bycx7fj4enTrlbCkcfLizTciP5kuEyr9eSBG+XZBdfLviFHNEfV54RtqytLkxj6p6/z2x8w2BWhEwUlUrcpv4eXGqv6pB4NsV+EJ3bbDciC/hRqXjokgI6RmaKmfTKecGX8tohMbIT2NGpevK7ava2tnOrDx5ZUqW/yUwfJ2yrci8N6flrz8LIilbvzklu350LII1Vs9PBZsf8YrbeDkudp0dGenuysroCD5NE/vfkc5b3jUiez9+dWm6q3c8XD7YM1/6BqJ7I0Kq/tc/BtcHifrauVnXcFlTf7yuruv6J5orqgnRTWn/QI4rXneFDB1whBSXvypdF5wW7GHMt2rJ9H3klt/k5V2HZ2TeomPGTUB331dGv/IdGR2s3HZGtXsYgNJ7EYyxlohrpcBWSP3xC5HpqeI+bXacIdB4BIxUNR7Tps5470NPygH77tbUNZi81hRgHDuEGT1tjqwkjdboRbMGHj5EkfBwqtRsOAlQYRJCRAjiV06sXmnuepoc67x+mi9XDHTPRG6qjSgylWlLSyqTlsJovlR9Vo1UQbDm/11aOueIPP/LgGht+46UzJktrpWRf//S2bS0dQYRkiJZxbRIPlcM1isUJZVKuXQXpAxB+cjalGQ6i1JM5cXtbez9dS+lZeq2KSnmi5LLjYxXxxFEy3XI0ruLcsuBj7sqPnypvrLVfpIpFiWbTUtuNC9Dw3kXFVLvJ+a+98Gs/PEPQaUeqb5nnw2sGPg/EK0X5JVXinL6V7Lyyt+KsuP012Ro6lx5trjSmX8uHul1vfq+MHM/Zw46M79U5OdXiNz+a1fRRzrwuSU9bo5jP5KW9GWLxtvZHHmMFI49RYYqkKqggIMq03EPt6RWCip0x/2fz4UNQ8AQ2DAIGKnaMLgnWvWVV5fLfQ8/LT+86hdywD4L5auf/6hMm9KdaI6kB6O3WZEgOpMk/aZtW5KYWbJ/JQtEkNREshHRrmptZMJi9UpYVpsrzn1w7vNdWelbnZKh/qL0zCvKa/cGVW5bvKFQNurh2zL4FYm4j//ttoysfrooW+yTktcdGkSfiPzkB7Ly+JV5GR0oyh4nZmV0sChPXxNoq3Y9LiPLHwqiPIt/G5CqmbumZM+TSWUVnAjcBbCKRVl2X1vpmP3OyErXnCCV2Lu4KJ2zRdp6gp+LhaK8fFtRXvpt3h3DsRzDGFxZdOste6ggr/9URrbYO5i/kMs7QvjQdwvuGtgD/+/43oDEvXRrXrbcNy3ZbpG27pS7FpGc5IfzTgdXSKdkJJ2SzHC/PPtsUVYPTXEE6P0fzDgSha6KKBaRphOPH3Ipz/se65Ert7rdabcY6KVe2ufTLj0485FfB/3+dt83uJ38uXypFHbdW4bWDktnZkTSd93sGi/n93yTTJk2rWLkiftNZWOUnso3Rh0ZJXKVD7RwoaEdC1w1bTYwDbVhCBgCrUfASFXrMU+04qlfu1ieenaxI1OnfOJ9ss3WWyQ6v56Dq6UAa7VD4Iti61ldsXucVe31lxJJE4XJF8pqa6JwiFsdiFYF8lbJbd6fqyZNTEqkvavNReAQh7/056I8dmXekaD5h6Ud2YDY7PKRtGx1YM6RGn9AHtIdGVnx/x0EeuaJ5PIjQbQmJTK8vEMeuHC88ODg87OOnEA+5r0lLUMrRKbOT8ngioBQQVYgKL0vF92fRKnQWHFMbiAgW5AjSJmO3586Klvsm3Lkpmt2SlY9XXB/Zyz5U0EW31pwRAiyBPHxB+sy77K/FNxemIe1OZ6oFe9BwB67IudwyA2Kw4X9cB1E03RNXuPvjDmzxLUZGiwUpf3nl4rcdF2QrrvgSnlu7VYutUdF4C/+O+80V4z9D0jLce8fkEuu6JJ1H3pIzn/1fvf6nl2z5Y7dj3UkjBY2xdtvClJ8+x8isuxVkZeelfx7Py7DfUEkEcLJ0IhZpchTnOermk8Vc0C6LfVXz794dq4hUD8CRqrqx7CpM0CqiEqdd+ZnmrpO1OT8VkwaMDzCDZRrqbAj+lOpH17Y9bxcmT5fXqNr22X5w0WZuXNKurYeIxNl0Ao7lsdpTAxhQuuyDo2P8oGxvtNJyJTzVKI0KzTQEfWtyMgTPy44ArHLRzLy6p8KJRID6VDSw/u7fbJQqtTj3GlokFIp+dNFeUcyIEAHfD0jkhl2++1/qUMe/uGYfcCuKaebIjrFIIoE0Zm1a9oRn+d+mZcd3huQIUgcZIafNYIEgXHEaU7KRZQgXRAf3idNyBy8xvvshbUgR+ybwXXcf2HORacOOjdIUxGh4lhNMbIn3uM1SB4kjr8zx9ZvSZVee/gHeUew3nhGxr0HEWMvkCr20NGdk+62tIyODEjuxEBY7saRx8hzbzlVdtwxJc89F+ir1Hnd2SZ03Sy3DL7bidj/tMuDsjY/Iu8d3UPm51IymJriyFjxqv8zTqpIAy5/VeTks2Vwm90j77EurcUPRFvRS0HUEftTjRm3SlWjocypPlU47jPX9B7zp2r5P5S2oCHgIWCkapI8Dr/4zZ3C/6T6Pn7Mu+VjH36X29nv7nxQzl50udx946WCnur+h5+WqVO65X2HH9z0FGC4bU1YFK6ao1ogdCmPbHo9d/WkKb72zk65/QvjIuA3f2uMTIQ2FbZaSFKtRnn69CntsnKVyNpnRDpniUxfUJBp7emKlXiQqDacrbMpKYymZcldBZmzNx4Gwy6iVurt9/+/5++9tDBBME60CILgSFK3ONKyy3FpRx7SbQVJtxelrT0to8MpyXaKIxeLbyuUiAlEaN7bck5f1N7TJg9/X1wkClLU93IQPdIBKYGIQG4gVwxID8dAVDgHgsVeGHt/LuOIDXshfQdR4to4nggSe4HYQZLYx9DKgHgRGdvusIybm6gUhEtTdsynKUbOPfjbWRe14n1e53/mYp8aYWNvbV1jZpxjkTEIG+ux5wVH5yU3lHcaruw/HDcuHv/EaTLytvcH0b72DhkaIvVXkHnbiOw/eLN03nDFWNPkPkkt2EmkZ6o8+khBtmlf6gxFu7bbWopf+aTIdju7FJ+LgDG2mCv571xTilZV+lz4aT0CfnQyiEuqdF4/+oV7Or8g4H9lwxAwBDYcAkaqNhz2buV1fQPyoc/8o0vvve/db3HaqUuu+oV8/59Pk3cc/AZ3zGHH/YP09vbLwp23k23mzpHb7nhA9t93N3dMswdGoJAfmgRDouJEduLsKeyu7ruexzHD1DX6F49HYXjt9SdmZPrC8Uq9esiUfx0zZnfJ77466r6wdZ05++RldDjniBMDnVIpEpUS6ZrWIS/cFERhIBuQAP5+8D+nZQo6n9HAiiDVlpZnrk3Lq38KSAuRnTeenpG+v4n85Yc5F/VRYsN7W+6TdgQIUgPB4H0iURwDCYE8QcraZ+A0XpS/3S4lYkNqDYLmSNFgQD40MgUxovIPUkP0Ssmc4wv7puTxK/JuHkiYphPZD8SKdJ9GozjepQ8XF0spQH5WPZSmGwPtUzB4DXyIdrl03+wgvcdgbt5naISNSkU0YBA5vW7OJ1JGNSPRLvYF+SwUR6VjcKWk7viNIz6Zgw+XvnXDTq8F8e2a3iGPP1Z0bWqmZ9ZJ+pXnAxd2WstsMVfSP/y5DPYXZeVqkXntSwM9Fak/3NK3mDsuSt9iruT+9zUyEqparPR5UOuIdCrlfKpqSR37PRvjfPbsGEPAEGgeAkaqmodtrJmJTF1/811yyiffXzr+k188XxbuNF/O/PxH3WtEsPbfe9eSnopz3nX8GXLld7/a9EpAvnh6B0ddpVcjROE+KJSA00AWHRGjlnYvHVPb5f5FxeALebbIAWenXdVYuI1MPVE1iFk22y6//9K4LgkisuDIlAytKDqfJ8bOx6SkbfqoI1ZUww0ubZugZVIycvCirKSyw5IfsylwJ4+0yzM/D/RCu34k4wgF6TWN9kBoeI8BcXQEYkw8jvUBA1KGXor3OJ45IBak9JjHVfLNDo5j/2ibIFDMw3uOvKwUF3Xy9VDMreJw/g7hgcyonon0HGtAfCBKqpt65a68i3xBhCCVOic/K/FjXuaBJCpZ5DX3PIxFopRAQrj+ek0QPWMQMYNMKsHiehGtQwxVj8VxOxwdPF+pbEHot9eZTUlPR1aolHOtYpwGKu0IKLek6+HfjRuAcuK535eBuQulIz0smS8dF5CtI49xZp6OVOGY3t8nxcM/LMMLDyzpqKr9A4AOCoE6thSYy3Z3ZKWnM+M+Z0nIFY2YaXFkeqpqiNv7hkDzETBS1XyME69wzvn/6s6ppKM67COny+dO/IC8//CDE8+f5ATK4V9bMxRUeTVo+NEj5i25e9cy/5jAe3hNSjpmFCVbKEhXe8ZVW9VC0vwt+NV0FLvf853xFBgECcJANEa/5Pnyn39Y0UUqIFW9z7eVtExKAiAwe57iSuZk7V+zLiozc/eCi6Zk2zNSGGpzrxGhIroDKSJdB+FRzRFidQiWr0FCcE6UZu/PB/ojCArER/VMpN+YjwiV+lJBqCBEkDfE8DofaTmuhWvzPaw4lmuGEDG3zsm1OQH5gJSiXrzmImmDweuaLoRwKWFifa6DeVSYDjGDPEHcNPUJydN9KJGCFLIPCCLzcN26N01jQiwhXOq7BRllkL6dMj8nHamUIzKqbdJnvGt0taROOzZ4FLqnSOF7P5OhfLt0Dq+S9Bc/Mv6IfOK0QEt15LEyOmXOxEhlhWdZo7T8oqJO8f7hfhuhSrpDPYe2QoMjOdcn0YYhYAhsWASMVG1Y/CNXP+iok+X7531B9t9nYeT7Gqm66ntnlj2mkZdVq2dVeA9h2wVsEab1tE1osFzrvn3BeJwvokrrROm6EBZnu7Ky+m9Baosv5tXPFEuRKBV8ZzqKpWhVz7R2uf97RfflT5SGFBpjyvy8vHzrOIkh0rTThwP/qLaODhdlgkQc9I9ZR66IHnGMir8hFBAVFZ9Dsog6QaAgJxASIkcMiJBGelSjpSk05tHoEKSI8yFvrEP6ELLFXrhefnakcUxfBYFTkbiKzNnjdn8XRNm4ZvYA0SF6pRV/zAkWECe/IpFrhYAxF/tkQKbcPv4yTighRrzP+Xd+Nef2wPVr1Z9GvVQjxjxKFiFYTpD/24K84ctp6Zw74uoO8HVScuWeySntMvTys66iD5uE4c7ZLvrY1pWVtqsuCPypiFCdco7IN04VedsRMvKJM0rFA5WeLaKykB+KO6r5tPleZ+EGzLqGelzhas9nwIYhYAhsWASMVG1Y/Ndb/b6/PCWnnvN/nDDdH7z+ytIVsmTpCvnv39wpHzj84Akpw2ZeBq0zknpK+fsJWyL4/e7qcVf3I0lJW7dE4eVH0MK6Lv2CWz2QC/yZCkV55fZs6Qub+YgqPXVNTg46Ky1TxtrJ9BeKzgtKI0Sq9YGgILJWj6gDzyWqk5OuGR2uChBy5LubMz/EA38nV+E2J4g8MfiZCA/kg6gNkS0IHKSECBYEjPUgKBAc/VnJy+2njQv9iQRBiJhfq+hUDwVhgRTxHuk8IkAQGl7ntRIZ+lPBnUulnr8/5uF8F1FaWZwQxePauGb+Z98QPuYHL4ic/h2MmUNJmqYKeQ1i5aoQZwcaMsgTJMxPVXJtEFb2vcV+QaqW4beKgaAgHOfFcLUmxIqIYurO34z19OtztgojJ3+jKqlS/RSfpSSp9Ep2DBBCWtNwTJYUpg1DwBDYoAgYqdqg8K+/OHoqIlSfG9NYIWTHUoEqwH/7+S1Oa/WxDx3WUr8qdlnNsyp8JWFLhHLRI9zVnSv6UOU2Hv78Sn66O+vvyce8PpliH5Sph7/01LF66eox1+uUOCuH+84LquiULEGS9j8jK4PLCzLcL9K5RU5W/iUw2GRo5ZwK3kkj9r4EgcpLN6aNnVl57emCS71BHPQ8yBI2A5ARyAuEQb2jIGcQKl7X6BVroa/SVCLkCpLmRPP7BhYHEBhI1hNXjlcCQkIgKhAg5lKLBtJrfmQJUgJZgeSoiJz5VADP+urIrtE2TdnxHgRPK/U0yse5XDMkCvLDNbJPKgEdcRvzv+J8IlSQL9KJRNOUPGo0UO0WmEvJHueBJ+u94fTxKlG9//RWJBVNVDIqLajPoBO3//VekX+72FX/Ff7HWTI0a35ZjzTmJyKL7gl9Yq2p9Cg7BiwUTE81yf4Rt+1s1ggYqZpEt1+jVLf87CK5/y9PyW13Pij3/uUpufWa/x25SwgXo9nu6qwRNwWYlPDob+FxImGVIkm13Maodi6V5kFY7/e6I2KVbeuQ1X8Noh2ku6hIc0LxK/MuZafmmrxPlIn0mBIlXoOc7HJYRrL5gjOpxG8K53AIE+QGkgGRgqBohIafeY+0ltocqF7In1sJHClACJL6OSnx0so/JU/qDwWRgfRBenRAiFhL19O5NM24z+cCwucTL+ZnqKDeJ5Xqd8VaELi7vxGk8hi6L9aHfOm1QpggcCpqV1KITQPkULVtHANBY17I0/BqccQLHCBmbdMLUigEUSqeP4TeYX2Tb3kQ1lyxR3fvO4Kqz5HB0YqEynmyDeFJNV7oUMvzWiJ1qSBlOQW7jnTKdReACNowBAyBDY+AkaoNfw9KO8Do876HnpSpU3tKFgtRff7QVJ3z7R/JU8+8JL39gy6q5VcPNuOSwp5V4TWqup5X2BS99WhYXC4l0og+ev7y4crActoWZ9Y53Ca5oZR0bZmXKVT0EckaczNPp9tkcFl6ApGA3EB+1PXbb04MweJLXSM07Gm/MzIyZ0dxlWgv/zbwsoKgEHniWLRDRK1Ibak2CGKjgm29LkgXxIfoEcf5TuyQErUo+MsPchOE6m//fpubHz2TelTpnBzLdfA+kSr+dCL4MX0YREgjbqzB+hzjomjdgeWBEkzmhLSpNkuNQXnft2vgOOexNSaWhxwRDWNO9F9gx554TUlfGIsDv56Rri2D6BvVfhCklGSDdJ4E3l2MOOk4n1z1D40mIkZU8kF+kqb74n5+dX6IFZYMNgwBQ2DDI2CkasPfg9IO8KeaN3fOBPuEqO196NNfdz5VWC7QwuaTX1gkZ556QtMrAZevGXKpOv83Zn5D5ssJQlTO9bwaxDOntrvftsPkRsXnnF9vJR9zJCJnKZHcunZ58KJxgrTfyRkpDOckVyjKlK6svPhIcGW+3YFL542RD97zK+0gAk//LPCTcl/q2wa2CWig1NKAyBREA8E4A3LBaxxLpEuF3xA0n9RALDQN5sjNmO2A71SulgrsiUgYxIfoDUNTbfzp2smcGFQRqrZL7QrQTnGMVv75FYPMQxQJSwhtwqyWCbyn1w1RYl7X9+/lQMjPNapDO9emES/W4dq0XQ7HaKpPI2B4ejEoHGC97nmjkh9Zvz+ePofVqu+inlc4y/TuNufXVu1Z5FjE6KQTaYCdRD9V7bOi7yshxJLEGijHRc2OMwSaj4CRquZjXNcKuKjT7w/TTx2vP/ST8vPLvyG77bydewkyhnj9qu9+talaK21bEzbUrNcQVJvBru4dcVEFn6gl8espB3Q4ilat6op5sERYdk9Wnv9VQKoYf3dxVmZ0p2RgKOe+WAeWtTsio9EXFxGai51CSgqDbY6UaDpOiYpaB+CC/vwNQVRJIziQJgbzQTyIAhFxesPnsy69lelOyR1jBqSqsVJDTY1QUeEGUSLiRaqR9JwSO9V9KfnRc9Vs8+5/mihYV7KomicIDhV32C/wdyWFjni9JS2+jkmjY5Ar9sIIa7B4jQgZBEoxYB4G5InhWt7QdqYnL9h7ZLJBk+XnfxGQMKJm27838J+K0/9Rozs0L45qTFztw1otZVwLYau2Zvh9CBWfEX7BMQf1pOjZ8YZAcxEwUtVcfGueHcPPH171C2dIuOS1lRNSfJAq33GddCCu7M2OVpECpMpO3dXLtXqBkKA5yY3kYzU4VhE40apqcycBNG7D5Kg5s50ZJzDXL3ZIziHnZYSCO1KVDCrBXr0raMNCCosISSZLpCotnbPwzSrIwKvpklWAWgSoD5O2X0FXBfFRvZISEkjIrh9Jy4JDii6KN7ymTe755rguh2iSM930XM41daZ98CAe2pKGaJBW5SmRgYBBqtgTWiRfUB82HFU3dgie7/0EEdI2M0rAwId5IV5Er5ib62I/2kaG1wMcUw4jXV8d3dUslKrIsEt5pi0t2Y6MazWDk321oWSI45K0KCo3r4rPaTGjxF/bONXlu1bhQviczJjS7oTpmpasdt32viFgCLQWASNVrcU71mr//vNbXKWfRp74+fwf/ERu+emFLhJFhSCRK98cFMNQLBfwrmrmQHDLl0a5gXbl+etTwn/bvjMlmalBn7tKX0785k0lH7qiRqRLkjQ5jtoXhHDJH9tKGiYE57scEzi1bzW9Y4IZKsdCIiGQtKt55IeBaSVj90+mXasZv6ed6183J2gKTPqLyjjIgy8CJzpFtGpoZVGm75KT0bH+dUPLMyXfKubXlJ96NbnU4Fjkh/dV8I3NAESI94lGqQYJUuZX62FroJ5WECAVjzMP/0PgIGFRWiqtsIO4QR45lutT4sQ6pDI1qqUCdbWDIN0JTr5Ga+9TJSBTdRrPKtmJ4w2V9LOjKWU0TRCtStrApHP7x2sELPilJiP0o7RhCBgCkw8BI1WT7564psoMCJQOPzpFFAsSpSSLY/zGy828JE0BRpKRbFqW/GHcu8lVk30jJSMRJCwsFs+mgy+lNRUIW7XrUjJVb4/Czp42eej7Qa86Btfxpm+mZbh3RKjkKpeS7JjSJr//3DgDgIhALInqQHwgFQyIBmk/LA9cj7wx/ROGmvy8/XvS0tGdk2EvAkNT4Kd/Ki6lp1EoP7Wn1X/sOdwsWfVJ2s5Gq+mwIwjbJHA+hI7IFWSMQYSK/UKWEKzzp/YphCQ609GxPn9q4QD50hSmOp4T+cIWgjX1mg/9P1lJdxSlMBLYQSjmYLfdkbmg6XGNQ7VNGG42SyzOMztrartrt4RfFKMRKeswoWINfukg3Wd+VDU+EHaaIdACBIxUtQDkepfAOuFN7zllgo6KJsu+AWirSBXXUs6ziojN4l8HlVoMvkwPuSggIzrK6ZvCDZaTYOaTqXpSOzpPLpOWF+4qlFJhpOfmHRy0n6FfG8LjKF+t7mntcufXxqMtEJ0Zu49IBrNIKtCKgUZLxeB7fz64yvsvKAaGnWP98eYeKC7yFVSupYRUF3qh0b7ABBPRuLNbGEubMQekRysPNTVHFAgipT+rrkvNPHnf94qCTEHUID7ORHOsXYySQPV84j3mUDNPCJY2Vtbee37TZfane9v7lIzTkmn136y9AlE56dZcb8Y1f2beeYcUne1BraMV2iZ+McAnyo+A+S1mGkGu/CgbEV0jVLU+EXaeIdAaBIxUtQbnulYhMnX+96+e4LKu0SoqAPfbe1f52rd/5ExDtQlzXQtWObmcZ5WzIBhtlyd/XHDpJiIzWx4QlLDH0TfhA7W6b0RyXoVhua000k19wt5GC7Lm5YwjLJAJJSALjhAZWjcinW1p6erMCqJ6SKRz106LdBTFNcdd3VeUFY8S3UrJjJ1okJwKGvWmc1LMFyWdbpfelwsybXtsDHLSjS7roSCqo02LiTSRRtvt4zCxlDxzbUBSd3pvRrq2Kojg1r4m2J8OFZvzs77uomCLx/VPar65x6eCJsvom7T6j/PVgR2CRkUekSztv4edwWNX5hz5Yxx6cdYdr4SN1w74WsaZndLTEO2Wmn7q8SN9RZk2rSBtbWlZN5iT3GhhomN5SiSTScvU7qxQz4eYvJbKuThWCfV+Pqqt0QhyZRV+9d4lO98QaD0CRqpaj3niFcMu6zqB015dd7Mz//zYMe9uuqWCrlupbQ3ECgExERYpFIQWr5R8x0nJEQVirENHU2Y0i0xpVEHTbJAa37SSiFs+N+K0LDOntMvKvhFZdl9b6RhX1t824kgCGBBdWvrnTIl0oGOasiB4n+FH1/pG8pLpzEpxNC13njnRBFPbqnCORpoQk/O6pul4D4KDNgty5DdIJlrI65zLeepxRfoNooSWCZE8kaT26QW392euC1zbVQTP/H56kblm75OXNU+n5ZFLxqOSb/7nQHdGQz28kzKZdlnxiMj0HYvSMa0gM7vbpH9g1EV2qrmKqylnuZ53UY+HRqdwxK/HubzSB1QF6tzHtQPV3dFrJVd8FrBkgFRi8mnDEDAENg4EjFRN8vvku6xDnp585iV5+rmXIwkUWqxLfny9uyJfxN6MS6zUtiaqIXGciIOmU7S6zt93Uqf2StdcScgOIXz00kCYrZog/r7g6LzkxhyxMStdVyjK7V/Kl+wWiMrt+OFxDRDkjCpAKto0TbbzsQVJ54vOwTuqV2Hn9A556t+C/noqPverAkkPqk8UZIkokxIfolDOB6oozuGdNX29lIrSsUNQ4sX56jm14wclSNOmxJHi9q42+e3/GLdYUI8rrjM3OuyaP2fa07L2mawT4889KCWpMVLpYw+56unIOBKZVNeUxHizmWJ0vZ56UopxyZVqtKiEddgZoWrGP182pyHQNASMVDUN2sZMrJV+pPawWKDq7/2HHzyBVOFldf3Nd7mWNrir836zB5EAojtRxKceW4Rwg2XfF4gIBFqmOAQt6vrjVgVms+2y9vmUi/DkR/OuZJ//fWLXly/I7WcHdgYMZ2XwtnG3bua491uBvko9pbpnFKUrJbI2wiNJ7f9vxwAAIABJREFU7Rm0GTIRpW0PK0gxnymlUyFB6JkgQhAsV2n3p3Eh92H/2uYIGek7SBVEiOOJLEF68sNB+xY0UOjEGJA3IlbZ6SMlp3FeJ9L2t99l5IUbggbFe52cdi7l4BBuMkwatJAvrFelV64FTNJnM8q+wCc6pMmIsDWicrTc3tTfijZFcdLT5ebRZxCPqfB+rcIv6ZNhxxsCkw8BI1WT755M2BFVf7isH7DPQjnlE+9bz9yT6NS7jj+jJa1q/I35bWvCTuVEYWolPjRYHs4Fflh8WULQ8GgiDVTrnHHJlH99fEm7DCYRmXTKVV1huEgEIZtNO+PINa+l5ZXfB5Go7Q4XyefHBfmF/k5nX6CD9B+EZ8ERKUl3Bn3ndGDLQCpR9UmQmDefl5GhtcOOLGDVgPFlfiQvxZE2+dvvgzNJ0amvFKafVAZCmqjQU3f1+X9XkJ42UpdFGSwUZHRlkGLtmFuQ/iWQqpRM3S7v9F3hgXgczRgY8H6YTJX76PhEOGl0qtLHUSOZVNpB6tsyQd++RvbVi1qfVByVfbVqvKLmDBdX8KyRVuY5h8CZIH2S/8Ns2zMEyiBgpGqSPxpEoaL6//nbxl6BCkEMQRn4VU2d0t30RstoYxBuM6q17ogLM182tAMpFIt1z6lfXOh3ajVkVLGwn65jXr7Q1w1i9pl2+iDtJ1e6zly73PvP427sWg1HRGvrN5FjG9NfpUS6utrkr9dHa7iicCO1SGQo25mVF28qunQeaTkc2YlCEaFCM7X9USnpnDviIkgQA4ghuDaS6IT3p5iHGxTHvf9xjoN00AaG0SxfKOauJ90X5zo4hmuZ3tPuDl/bP+LSfUao4qJnxxkCkw8BI1WT756U3RHE6bSvXbyewadGq0j7kQJcsnSFm6PZjZYhE6t7h0sNhuuB0vet4kvl1VWDVcXM5dZrRM/ASjYNiNUppYecRA2+KDEzXbkKfVMQySIlB9Eh7UZ13g7vLUq2UHTROEdA+jLy4EVBOhHi9bp3jmu4dA20TkMrMtL/Skpm71GU/qUpF5FiftKGvqnnnL2CyBlRJiWG/UNBFKQZQm6NTiGubmREJ4yvT3QohsCDqtaWM5WeV7X+IAoGXs0aem/4LLVlg19QbBgChsDGi4CRqo3o3tHjD10VIvSwbkq1V5oi1GN9g9BGX6qfAqx17qgmxzRYHuTLLKHxo2p42EutkbO4nleI1ZeuHppw2VHnojda90xWXr07EI+jg6Jy76DPZyU/1kOQtKam+egbqBquCZOnRIZf65AHLgpasmivPE3/8RrRMITxc/YN0nl+1Egr7nxDzHr1Qbo/JSC1NtSO++woAfFJlC8AbxSZq2aXEHe/1Y6zCr9qCNn7hsDGh4CRqo3knt1254PONZ3wzb0PPy23XvO/q+78oKNObno/wHKeVdU2p9VafMmHCRDvdWTTsd3Vo4hZtfXD78clU3qe76lFCnRaT3RFH8ejmeqYGtgLIECf/46Us19AJxV3QM58t3rOo63M0z8LbBJI+dFYGEJGvKOaeNu3LOiL0FPF2Vej++mVW5N10E7R866cuWtw/9pkYDjnNFbVLBui1vLd0SvZesTBptIxVuFXL4J2viEweREwUjV5703kzjTVd+V3v1pVa9UKUoVwm4hH3BEnNceXDqQlHAkKr9EIMqWEiGgRuqu4lV1E00ZGaQCNvipIeSFeL/dlDelKZVICgRmJ0QB4vXlSIrl17fLgRYFOi4o+euM5IX02VWosrFGWOJE6jVphWso9TFII4FKcHdkJbuJxn4EkxyWJgo1bMGSc11mStB0pXVrBNFv0bhV+Se6+HWsIbHwIGKna+O6Z6/vHKOdFhZcVFgtEt/7zX/+p6YL1Sp5VCm/SCrxKPfYaQab8OShtT5Jq1MgJf1YjL1rBCPlE01TPcM2b0+3S94rItAUFGRkcbzYcleqLu5ZW1QVRnsp79ElBPS2Bqu0NgkTBAjqjpIRP7w9rxCHKrUr3qXGsVfhVu/v2viGw8SJgpGojvHeQpg+fdO6EhspchrauwYLhnQe/QT72ocPWs2BoxuVWSgEmJVO6P6qgXATIa7DcCDLll/tXI0RhrPxz+WLsas+UbauTNJ1Y633RPdXr0wSJmTGlXdKp8u1hkkTBar0eztOoUb0VhJriLCfMV+JWL3ZxrlV9roigoaVqRoUfUexzvv0jwTAYXzsKVfjThiFgCLQOASNVrcO6oSv5wnQm3mbrLdz82ClgEArxwl39qWdekvcfcYj7B7ZZg7TXirXDE6avl1ToF+uyNcOyockUF6aEwvfM0shDuKFuOcf0RuPfDJITfPnj/TRailr57V/qMV+Nc/2NjhqVc2VvhV2CXi/XhMcZn5NmOaRTGfyhz/yjLNxpvpz5ueNlyWsr5ezz/1Wu+u5XW/KLVZx7a8cYApsDAkaqNtK7fP4PfiL0/iMqddbnPyrvOPgNpSvR1jZnnnqCS/0t+sFPXOSqmc2WSQEy8EKCUEW1YUkKNRV2rjIuQswed656IlOsUY0c+uJmojyYlTajxN+/3npSfXFw0/QZOvrcWDPsZl+TT9ya0bfPTwliHkqUsVbvsjgYcoxGwvg7artm9vCjKphItZ/uP/VrF8tuO82XU5r4C1VcLOw4Q2BzQcBI1UZ4p7FLuO2OB8o2UUZzhfmnkqgk4vZa4YD8FIvFhpApjUx1tGVkYCgXuwrQ37tPpmpJI1UjU7qWtlChWhFjTcwoa6k8i4N7o1J9cdbiHiDcZqzpH0kk+o4zv39Mq0TvrIlWj+dqcCQvpK2bea9Yi2ev2T38iFK96yOnr1fp+6FPf91FqT/24XclvSV2vCFgCNSIgJGqGoHbkKdBkjTdF+W4HnZYZ6+8Rmrwqu+d2bStxxGsV1o8nOajhH5qd7asyWbUXNpShjQLqaqk7W18EXq16IySAUTo6LManbryr68Zqb5y+Km4Xs1NSWeO5rAzGG0oCWmVJQPXGU73aZoTcX6tlhLlnmU/6kbajzRxM4dGqXybFaLVSAR+fvk3ZLedt2vm8ja3IWAIeAgYqdqIHwd+Q33Te05ZzwyUNMD5379a7r7x0tLVQb5O/NK31xO3N/LyV6wbdpGqpKOSZooUYNxWJFEtZeLuJUmasFIUS/2SiIKUs1iIuyeO07XqbSYdZ81KLWamdGVdarca0Yyzjl4X6bA4FYdx5yx3HK7reFj52jeO9Y1QG3VduhYke1p3a1rOkOZDS+XrJiFUjGb+ElXvfbHzDYFNEQEjVRvpXSVa9ckvfVt6e/tl4c7bTfjHs1w64LDj/kE+/uF3NS0dQFQIMhF3xBGga4Nl0ijlRtxUXdT5SciU6qdyhaJgw1DO16kRnke6r1osBeLir8f5GFTqC6jXVU+bG5/INLMHoV5bnOhho1zZda1mVvhF3VvSfB875t2lLguYBEO0IFRW/Zf002DHGwL1IWCkqj78NtjZkCod7zr+DAmbgZLuow+gnxLgt9cD9lnYNOEqDuGvrRmqmiJSHRKWCdVsDTDn7OrMyuoIg9F6yBTYRVX01Uu8fKKCJglRdFJ37lal+mqJGNUT3WllxZ2mcZOkLf2UYFJXdq3wIxU8tTto9tyqQWSaFOC3zvyM9PYNyNmLLpePH/Pupn3OW3Vdto4hsDEiYKRqY7xroT1HmYFqJOuAvXeV8846SfiZkuvvn/eFpv72WsmzKklUSC+RL/GtZ3XJkpWDpatWMjWarxwxqjeypfos1qtG/qLW0uovmgxjYFlNFN3KVF+9ovekbW6i+vY166NXj8+VTxrj3HP190L/R9MhrnNDDIjVv193s1v6nYe80QjVhrgJtqYhgKygSMmWjY0aAa3uCzdPfurZxS4NQIpw6tQe+cDhBzf9H9vh0fx6wvJayJR/Q6ii6h/rT0e7F57YWsrhk0S2fH1RUrF7+GGqloJqZaqPvTWq2i5OmxtNmY7mCmX79jXyw1cN67hr+RYMvQO5SH2cH3lrdoVf3H3bcYaAIbBhETBStWHxb9jqagYa1bqGqj+YiFYMNmzRMhNpFWC9ZEqnx4EaATAjTvQgvK0kFX1JiFcSHNn/tO52pznzBeytTPU1i+AEUSv65o0bhoJNkr59SbCMOtaPCiI6T9LHsNLa5VzZfULV0Z5xBrU2DAFDwBAwUrWJPANa3ff+ww+eYJ2geotWRKkUyt6BUVfCjhFmLSRI5/FJWTqVqtpgOYpMqT1AtX0o8WKdpL3m4j5C+kWMXgdjTfbWiqo+9tds8ua3uSGyQ6Ugac9GEpxyODdbqxV2ZYewUU3Yygq/uM+YHWcIGAIbFgEjVRsW/4asTvqPljQI0yFPb3/LvnL/w0/Lv/38lkCY/on3tSxKpRe0um/EGR/WMqIiXFtM74id8ksSIUtybC3XEj4Hvc+WMzrdy/X6esXZjxKORjjcx1mPas3uzqxEpYHjnJ/0GKJhWDPgoQVBbeYAS55DUn384sDazejh18xrsLkNAUOguQgYqWouvi2ZnRJqbBSIUjFUpN5M+4RqF7Z8zZAgJE8yKhEcUoCMapV0cSv6iD7wZcwXI5Gj/qHaCGCS6/OjRW2ZtHS0pZsWFWNfzY5Oha/dF6Nj2spoZqSqUfqpuPeQZ5B7NpovSHfHhhGkx92rHWcIGAIbBgEjVRsG96av6lsqQLKuv/ku17rmfYcf7PoBNnsk8azyyZTfsNjfozYvxgg0aiTRQiU5thE4lavqC0r4s45Y5RIS0Er7aoSfVJLr1mgYkSKf9KpFwbqBxra5UW1YLXYVSa5Lj/XtGTZkhV8te7dzDAFDoLUIGKlqLd4tW00rAhfuuK2s6x90jsv3PfSkqwL0vauataFcviDL1kQTIP/LCu0NxEJbvVQSGG85o8NVFvrHJCFIeF5RPdiqVFicqj4VeZO+qjVdqni22lgzTjSsFr+oSs8keE3vaWuJEzv7sAq/Zv0LYfMaApsmAkaqNs376q6KxstEp7ShqhItKgQ1VdjMyycCA1mKGhqlSUJwfHd1jVxBJKq1GPEjYdWObRQeSVJvjRBaV2ox06hr8udhz87eokBPwFzVartGtLlpdbpPnzFa6XR1ZK3CrxkPks1pCGxiCBip2sRuaLXLwbdqm7lz5MzPf7TaoXW/H5UCTBJZCm9AoxR4HsWpLGy1CJ391np9kEMc2DGRXDsQv3Gxf42taPui11hL375a05I+gUuCTT0PsJL+dQON7eFHo2Pcz2kfwy87rUjF14ODnWsIGALJEDBSlQyvjf7og446Wc489YSWRKoASyvcaiUbCriSByrL1vaPVBWW+21DiKQ0ezSK3KgYOo6tQ6ujU41ILyZtc9OIKF7Se9/MHn5Ei+97+Gm57c4HBXPeq7771ZZX5ibFw443BAyB+AgYqYqP1UZ5JP+IY/655LX/1975xfhVlnn8nZnO9M+0pbvCqmxcLlxiyV4IdeXGkmwgFLyyBAxaV2yD3EBbIWhaLGh2EWmjBKWAF0CKuqJECJBoXEvW3QSNBncVLzbW4F7gZo27gJa2U2Y603bzfafPcObX32/Ov/ec874zn5OQQuec9zzn85zh9/097/Pndf8NWS0W+jUIberhFK0aWzbkt4fUBb1sQrY+VJV3pXlq1v9Kth6emO5rcl3xVoVDma2+IuvnJbAXydUqcp8y54QWN9mmmoMqOvM4lLG/6LkSteqvpW1p+VXtE5o69j74hM9zfPqxu5u6BetCAAItE0BUtQy87dtJVO288wF/WyWs//4Pr7k14yvdLduumcu1atImDVlWF/HJ6fI9hAYJJOVW9Qor+5CuOg+wCoMmBdygBHbry6Q8nzbaQIiLicbQVYrZMTfqa2aCu8j4myr+Wugaqyj0OYBDQ63M8PvmUwfdQweecT/7/tdCPw7rQQACHRFAVHUEvu3b7rn3EX/Lm7du9lGrHXu+2tok+7JNLouIFQkrdSVXj6miXdNDMQ+11ZdnTzY6pIjfutWjPueqSGJ43tpFft5W41Ab4aMxN2rLoFmPqoRsY9tWHLqo8LNecioYuWXrZu+OX7/8ivvXn/zSV+pevnFDERdxDgQgEBkBRFVkDmnKHOVSffGOm+b+Z63xNepl1TuEuYn7q12ARpfkHRZt8lGoY9N9h9jaGtbdWv+t5OW67QjybLOfh97qy7uvojbq4q3O3UVyyfLWK/rzUEOXi94v+5za5m1LUFmF37HJGd/ao8kZfhJNmnSgfColrEtQ7dq+xSerq1JX2/M2ZkrcHvjCThLZi75AnAeBSAggqiJxRNNmXHvjXe7jH75qXoL6lR/5dCs5Vnk9q8pW6VmujYTUirGRuR5XTTIsEj0Lff9spEhROUV0mq7wM1+0NbevV6wqIqdu5b3DmUOz1Xr2HknAKwrYxMgZRaT27HvMHXr5Ff8I6y+8wF2xcYO7/AOXzCWoayKCqnKzrU76/b42wYA1IQCBsAQQVWF5RruaolL6H3s2KdaiVT/93sONfyN+7ciUT/7NHmXFVD9howjHuWuXNyas2trq631x+kXELJ9KCf8nZsrnqOW9nLPbcKOtb72plYS6oyuiefr07HacRI6OpsbcNFnhl+WsiNRn9z7q1IT3njtuOuv3TOOlNl1/u9v8wcvmtTmRyLror//Kb9dzQAAC6RBAVKXjq1qWDmr8qW1BbTNceslFfn0JLW1D3LPrxrm/q3XjMxdne1b1VvTpZwt1UrckYi3Vr9VAU8Kq7a0+PV9eHpP1ego9r1Biqo1IWPZdsu3eQc/SxJgbvSvnrBp1Q8PtVPjpeSWclJSu360v7v6k71Flh37XvvndH7qDT943J7hMaO2/51Pzzg3xe8gaEIBAswQQVc3yjWr17DxAM+zK62/3lYDnv/1tfptCSbK7b/lo8N45qgL8w58mSzXHLBPJClny38VWX5n+TSYyQ8y+M269c/uafnGLdke3Z505U0WqSFbVI/uOtFXhl7XVtgJVfSvBpEO/f4pSWbK6/k6tFp79wQtUBVZ1NNdBoEMCiKoO4bd9a6s48sJp+xanXI5vPHXQi6idd+13+7+ws9Gqo1OnT7vJM1VdeZEp9aaSuCkTkakrrLra6ss28VTCdBHhYBEX5T4pelfkmt73LbsFJlHVxmGMFSk6MjGdO97GbKo75qaLCr9BPNU3TlMNdPzN3211B+7fNRcVVkPQaz/5Of/7aeOl2vAL94AABMIQQFSF4ZjMKvZtWX9qy+/mT3zI2771tn3+27Kqj7T9oPyrv3zneXP/8w/xgOoh9erhyQWXqhslqiqsutrqs3YQVRPQi0Z8stAtZ6nN9gy6f1XfmO1Vx9xYywblbDVd4Vf290TFIjdct8kLqOyXHn3B4YAABNIjgKhKz2eNWKycD0Wt1HFduR/aDlQ/KwmtkMmyKpVXyXzvUVdM9YoG9TrSvfJaLYS8bxnHhBwxM7vWqG+ympfA3kXzUHF5a5bebC+qqkeZbVLdw0RnkxV+VZ9F1ykypaT0teMrfXNetVc48JXdjReO1LGZayEAgcEEEFW8HXMErMWColeKUkloKb8jdC+rrLCyqIn+zOtNVcZVFhUZJKy62upr6r55CexN3beIT5pIgs+OubGqwV5bbP6jfi4bmmiZUOT5885RZPjfXzrkVo+vDFockndffg4BCIQngKgKzzTZFfU/d31Tzh7WNPQ9736XHwSrXJBs9VLVh1VbgNGRITc2OlwoolTlPoOEVRdbfbI/ZHSqH49BCewmQNrsUi776m735fl8oTE361aPudOnTjttOTc9wy/PTn4OAQgsHQKIqqXj64FP2k9M6WQr7VZi+//87+u+MlADYG/48FVBtgSPvTnjBg3TDeUW+2BXMvfosmH/Aat+WW2NejFx0XTfJePVOzfPEv6r5mxV9YPymLQl2caMQt1r3bi2e+ePuemiwq8qL66DAAQWBwFE1eLwY6WnWKgnlUZqPPz153zC+tFjx+f66Fh1Urb7c6WbO+fUab2ND/sVo8NOkQtVH7ZxvyyPtse92L213aWKuTdPnPS5VlWqA6v6tUryfNV7ZcWkmsBKRKtidHh4yCelc0AAAhBokwCiqk3akdzLxJSS0i1/ykx78Ze/dttu2+fOf8e5c71zHjrwjHv+yfvmrNecsmf++cfu8ft31e5nJWH16htTjX3o2wf8xORJ/yH7x6NTbuZkjWZHBX2Y18Sz4DKVTsuO8Vm5fJmP4OQl7Fe6Uc9F2apCzWNsU8hZZGx65pRbhZgK4U7WgAAEKhBAVFWAluolC4kpPZNKuo9OvOkFleVWKWJ13U2fn5esbqXf1oKhLo/JEyd9r6WQR7+qvtlk7uXu9SNThfsjVbGpq5wtS0bPzu1rOq/J+FiifNt5W7p/7BV+Vd4hroEABNIkgKhK02+lrZYQ0nZeb2Qqu5Aq/f7lx79wz3/ny/PW7x28rLXULFQ5V08/+o9Byr8HtVoo+6B5VW42366JbcAuo1MLJcErz0rz9dSXqokIkokaCeM2ooDZd0LbnMt9sUPcFX5l32POhwAE0iSAqErTb8GttijW0aMTbveOj/kmoHbY4GW1VvjRT345v5/VO84Nsg2oe9UVVkUjRCZAQgkriZbVK5b56r4ivbFCOi/btynveUyA9JufWMWmbFf3pgYfD7LLthqnZ0773Dw9m/KoOCAAAQh0SQBR1SX9iO6tXCq1S3juhz/x+VK9Eaitt+51Gq8h0WVzy3bs+apbs2bcR6p0fohDH/jaDixzVGngGUpYZfslKUF6ofE7ZZ6pyLlVtvYs36puVKnKvYs8U5Fzup7hV8RGzoEABJYmAUTV0vT7gk+t7b5rrt44r22C5VH9/g+v+T5VEljKqXr/e9/jNn30My5ENaCM0uBlJa4XESfZrb4qIsEERl6Epx+s7L1DNi0t+jpm84jyuqj3rjkrBGdbEFRJYLeKRmthUNTmEOdlm5yqmScVfiGosgYEIBCKAKIqFMlFtI62+/bu/5Zbf+EFPjK1+YOX+dlkyqHSsFcd2cq/PXsfdS++dOisXKyqSIq0Wnirqm/GqbKv6lGl/L/pJp4LPUuovK2qkaYmuqMX9Z2JYInYteOjTuKQAwIQgEBMBBBVMXkjElvUMuGhx5/1ESn9o/+2SJTNCMyKKkWxFK06cP+uYGM2BrVaqLLVl4e1qLDKS4LPu0/dn9uMv4nJ6VpC0uwok8BuIkxz+5pu2NqPExV+dd8erocABNoggKhqg3LE91BUSjlRl2/c4K1UCwVV9mn7T8JK7RV0KN/q8a/s9v+u7cErNm5wu7dvmXsy5VwpuT2b4F73sbOtFupu9eXZYh/ag3pmdR2dkn0a6VNlqzLv2fOiT1YxqZyxOlHBPDsG/Vz2jS6bberZ1gw/5RiqKEONb3dt3xKkwrXq83MdBCCQDgFEVTq+CmppXgPQPfsec9YcVMnr2t4zUWXVgBa9UqRK24JKYA8xFzD7oKqmUxdJiZqmP9RtAO9rR95qRtp1dKqtuX2DEtiLRvGCvpxnFsvOMmy6wk/vsGZbqqWIRjFp61vTBHQcfPI+RFUTDmZNCCxCAoiqRejUhR5JHx4STDru2XVj347oOsdHp955nv9TOVNrVq+aF5myLcL1736XOzLx5lmJ7SGxKmn9tYLJ63XvmxVWmps3vmLUhdpuK2tb24ImmwSubT7lLek4MjFdqHCg7PMtdH6bFX4/f+mQUyXrFZe9z0dg//bi9e43v/2dU/R1/xd2zkVxQz4fa0EAAouTAKJqcfr1rKdSkvm+B5/wEaeyndCvvP5298U7bjorCqUKQH0gqQLQBFhTOBU90iDkNg6bmzc1rbl53QkKCRtV2LU57sXEzPDQ7HabjxS2fEjc/dnqMXdscsYnozdd4ad5ljvufMALqpu3bvZPq8irBohLVNmhCK1/3y9eH3Sbu2W83A4CEGiQAKKqQbixLa0PhbI5T/Yt3rZAtIbaKtiHT1vPWKbVQh2bLDo0ffK0O33qtDs8MV1nudLXFm1gWnrhghdkn1+9NNWqok1RZ9uQfzp6wp2zeqy1Cj996VD0Vdt/ElMSWtlebXvufcS9+Kvf+IisvpjosO3wgmg5DQIQWAIEEFVLwMlFH9FG2ShXyg4lq0tEfeiqD/htw35DmIuuX/e8Iq0Wqt6jX6uCdWe2v9oQVtYh/NTpbrbbrDO7Rr5Yx/W2tx9jqPDT+y5xpRYiVohh45uyFa/X3niXu2XbNWwNVv2F4zoILFICiKpF6tgyj2Viqt/WoHVS13oP3L3DXXThBWWWDn6uZsu9+sZk0OjJQtGhNoRVl5WFctBCPausjcPhYydc2SajRZ1v426Ghofc0ePTfk6hGnu2fVgftkvf+x4/KUDRWMutUlQqW4QhUfXxD19VOvLb9jNxPwhAoF0CiKp2eUd1N32I/NNTB/1YmkF5VvrWrrYKZbcN9aA/+vEvfOf1yz9wSdCcq+NTynU6UZulVdYpV0u5Q4O6uEtY+QjS8bBbgSYmmmqVUASQiaaFuqtnE9hDt1SwCj+1z9CWq1h3NcNP7+u9Dz4xb9tP4un9l1w0r0hDW+L6snHwO1/2rUY4IAABCBgBRNUSfRfsA0TJuR+/9sqgokdiTRWD6vGjQx9C2krRlkqoo87wZX2QK/lZ/ZeKDECW+Dl37XI/kzBU4rYJFY2JCbVmWbZltvey7Q1Cics2K/zKstH56tl23U2fdz/93sNzLRX0bm+7da//opFNYq+yPtdAAAKLjwCiavH5tNATSeic//a3BRVTdmN1XVfCryXy6r+Vl3Lw218Ker83JtRZvFx1mm21la2sCyms6sztK+TcnJNMIE3PnHJvHC9eXWiRtZGRodoJ7LMNRcfckeMn3OiyEScmsR3+i8f+b7nnn7xvzjS9x8/+4AUfzWq64jU2HtgDAQjkE0BU5TPijJIEFKXSN/rsN/nQ8wHNpKKtFkI08awrrELN7Svpjnmnh2gmWibC1c/fHpvrAAAPOUlEQVRWu14VfmtWjboVYyN1Hqmxa3ub2lpvtt78qsYMYGEIQCA5Aoiq5FwWn8ESUBp1Y4d1a3/+O1+e+zv7gNq942OV8rMGPXWRVgshE8ElrM47Z7kru2032ypg1Ped0rVdHCZmVN2nhP86h+ViKdKkqF/RI4YKv6K26jzrYaUKWG353bF9CxV/ZQByLgSWGAFE1RJzeOjH7ddtXQJq62373A3XbZqXR6Vzlbgeur/PoFYLIaJT/XgtVC3Xe35TNpTxo2xQd3T1nQrZzLRMXpjE6LrVY97sLiv8ynDLnqv3lqT0qvS4DgJLhwCiaun4OviTWvTp6MSbZ+VLKVq1d/+35uWeaEjtttv2uf/8t8eD29LbasGaSJaNKBU1rIiwChkhK2pXP1H3trVjpSNrRe9XJD8ry6rrCr+iz8V5EIAABKoQQFRVocY1noAaJWpEjQ1czm736edXfuTTflvwwFd2+z91/o9e+A/39GN3N0JQrRYUBVm3WlGZ2WTqQW0SQhhgYqF30LM10lQy9utHmrVhoecwYalqvTJbdFXYaLRPtnGorZFlNDw8HGVCepXn5RoIQAAC/QggqngvahNQxGrTRz/j1Ik9289Kf7/zrv1Of66/8AK/9afkdY0BaerQSBXl+YTupzTIXhMN1prB/rtsdWFIHl2Jut6cLavwU0+x5WNxVviF5M5aEIAABBBVvANBCKgySk1Es6M8tLCS2FWarkjV5Rs3BLlX3iJVWi3krbnQz01IaWtrdGSoUO+rOvcrYotEXah+UmVsNSE1ffKUZxF7hV+ZZ+NcCEAAAnkEEFV5hPh5YQLa7tNsQEWslD916SUXFb429IlFWy2EuK/lFY0uG3ZtC7qs/crhWr1imTs+NdNapK4fP3VFX3XGDrVw6GLkTAi/sgYEIACBsgQQVWWJcb4noC293uaHSk5XhZ9Kz01cdYWrSKuFELZJyJyzarZVwtT0Kffna5a7Px6dqt2yoKxtdXtHlb1fv/O17ai5fdMzp72w+4t1K0IsyxoQgAAEkiGAqErGVXEYaj2orrl6ox84a4e2+bQFqE7qg+YItv0Eg1othLBjUKuE2TYD7QmrItV3IZ43bw0q/PII8XMIQGApEEBULQUvB3hGRaYe/vpz7sWXDp2VN2WRK1UBZoVWgNvWXqK31ULtBZ1zeV3J2xJWZfpEhXjuQWtkBy5T4dckadaGAARiJ4Coit1DEdhnSeixRKDKIlGrBVWghTiKzu2T8NJW2KtvTDXS1iFkd/Q6XKxtg9pXrFy+jJYJdWAWvFaFH/pykx2Ebl96NLS8yeragiZyGgSWLAFE1ZJ1fbEH33rrXt9J+uZPfCjpAbJqeaAeVlWPKnP7rPlnyF5VNtRYSfFN9+HKY5XKDL+850jt5zaxQL+XNp1Av6dHjx33zXY5IACB7gggqrpjz51bJlC1Ms/Eg/WiKmN2SGFVpIt7GdvqnGvNPsVUzVap8KtDs/y11htO0WMd3/juD+dNLyi/IldAAAIhCCCqQlBkjWQIvHp40qmfVJFDIiZEd/YQwiqGgcxiZonxqnRUJ3mNwEFQFXmbwp+jbXlNKdChiNX7L14f/iasCAEIlCKAqCqFi5NTJ1C0IjD03L46LQ8UFep65I0JKpsjyAy/7n8TLFq1ZnwlUaru3YEFEPAEEFW8CK0QUMuFQy+/4sfVqLt6l8dCwqrJES9lhVUMI2/MT1T4dfnGnn1v/T5d+8nP+XxHjX/K5lfFZSnWQGBpEUBULS1/d/K0qla698En3Nrxle7Qf/2372PVdesFtVr4v8OT83i0IWKKCqvZcS+jfoutrTmGg16OLiv8fv3yK27tmnEvGjjeIrDn3kfci7/6jY9QHT064WdvxvB7hY8gsNQJIKqW+hvQ8PPrG/Wm6293j3/1Dl/q/fOXDjlVKsXwASDBokRrHXWS0csitHup3YIGQPceRYVX2ftWOb+rCj9rMivBsGb1Kl95unv7FtoFOOe++dRBt/fBJ7ygsvYJ9nfkVlV5y7kGAuEIIKrCsWSlPgQ0A3DnnQ+4n33/a3M/tQ+Ag9/+UudtGiSslo8OuxPTp/wg5JOniiWx13W2xMqKsRGnGYUmrCwxXiYcmZhuzZZ+z2KtG0ZGhrzwVM+tthLSLQFbIkp9l3RIjH/23ke8OCdqVfft43oIQKApAoiqpsiyridgybQH7t81b8CyZgQqF8T67HSJa/LESd/zqe0jK6ysqu7NqZNe3HV5dFnhp63iHXc+4Na/+11uzZpxH9G0qrZ+8ya75MS9IQABCPQSQFTxTjROQNt9OrICysTWU4/8g7vowgsatyHvBmVaLeStVebntr2ma44cn3aTJ06VuTz4uV3O8LOt4s0fvMxv9fno1N5H3R3bt7jLN24I/qwsCAEIQCA0AURVaKKsdxYBE1DZ7RydJLG1+eqN/p+uj6KtFkLaaVts2gacmjnl/tRBtCz7PLPJ8WPuyPETbnTZSOsjZ5SUvu3Wve7gk/fNVYhqq/jZH7zgnn7s7jlTlW+lfxZDp/+Q7xNrQQAC3RNAVHXvgyVhgfJk1PXZEtYtKrH/nk9F07SwTWGVrTRUhGrd+Kh/Dw6fSZxv+6XIJsevWtHNDL9++XfaJtbfP//kfU7vjESX/rzhuk3+T4mrx+/f1XluXtv+4n4QgECcBBBVcfplUVplwuqKy97nB8JesXGD3+aJ6ejXaiG0fbONRdUuYXpeu4SuhFVXFX79uF75kU/790IJ6sqvUpWbRTgV2fR5eBkRJdHlI1ZnxrWE9hXrQQACEChDAFFVhhbn1iagPBn9o1LwWPNksq0Waj9wzwJ57RLOO2e5U+J8G8nq2n5ct3rMW6hh021W+A3iquiTqkX1jpz/jnPdNVdv9IJJ24APHXhmLtJp10to6V2KTZyHfm9YDwIQSIMAoioNP2FlywQkaiQ0Qh223afWDW8cn+7bn0r3ktA5d23zwiqbkH586mTUM/xsq/iWbdfMtVgQK+t5Rm+mUG8p60AAAnUJIKrqEuT6RUtA/ZkUtap7jC0b9oOZi7ZLaFpYdVnhV4Vlv2R1FT9svW1flFvIVZ6RayAAgcVBAFG1OPzIUzREoG6rBdvuUx8s5WsVPSSstBVYVIgVXbfrCr+idmbPs2o/a8khQbXzrv0+UV1dxbueJVnlmbgGAhBYnAQQVYvTrzxVIAJVKwIVDVo7PuqGh5w7fKxad/RsRClEjlUMFX5V3GLDg5XArqT0bzx10C9D1V8VmlwDAQg0SQBR1SRd1l4UBMoKq5BiKNRaGs6sKJV6Ya3x/z6SlG8krFQ9eui3v3OXXrze/f11m4hQJeVBjIXA0iCAqFoafk72KbXV8/DXn/Ol9BpXorElXRxFWy2Mrxhx4yuW+eo9bd2FOOoIK20jqqpP8wRjqfALwYQ1IAABCMRIAFEVo1ewCQIQgAAEIACB5AggqpJzGQZDAAIQgAAEIBAjAURVjF7BJghAAAIQgAAEkiOAqErOZRgMAQhAAAIQgECMBBBVMXoFmyAAAQhAAAIQSI4Aoio5l2EwBCAAAQhAAAIxEkBUxegVbIIABCAAAQhAIDkCiKrkXIbBEIAABCAAAQjESABRFaNXsAkCEIAABCAAgeQIIKqScxkGQwACEIAABCAQIwFEVYxewSYIQAACEIAABJIjgKhKzmUYDAEIQAACEIBAjAQQVTF6BZsgAAEIQAACEEiOAKIqOZdhMAQgAAEIQAACMRJAVMXoFWyCAAQgAAEIQCA5Aoiq5FyGwRCAAAQgAAEIxEgAURWjV7AJAhCAAAQgAIHkCCCqknMZBkMAAhCAAAQgECMBRFWMXsEmCEAAAhCAAASSI4CoSs5lGAwBCEAAAhCAQIwEEFUxegWbIAABCEAAAhBIjgCiKjmXYTAEIAABCEAAAjESQFTF6BVsggAEIAABCEAgOQKIquRchsEQgAAEIAABCMRIAFEVo1ewCQIQgAAEIACB5AggqpJzGQZDAAIQgAAEIBAjAURVjF7BJghAAAIQgAAEkiOAqErOZRgMAQhAAAIQgECMBBBVMXoFmyAAAQhAAAIQSI4Aoio5l2EwBCAAAQhAAAIxEkBUxegVbIIABCAAAQhAIDkCiKrkXIbBEIAABCAAAQjESABRFaNXsAkCEIAABCAAgeQIIKqScxkGQwACEIAABCAQIwFEVYxewSYIQAACEIAABJIjgKhKzmUYDAEIQAACEIBAjAQQVTF6BZsgAAEIQAACEEiOAKIqOZdhMAQgAAEIQAACMRJAVMXoFWyCAAQgAAEIQCA5Aoiq5FyGwRCAAAQgAAEIxEgAURWjV7AJAhCAAAQgAIHkCCCqknMZBkMAAhCAAAQgECMBRFWMXsEmCEAAAhCAAASSI4CoSs5lGAwBCEAAAhCAQIwEEFUxegWbIAABCEAAAhBIjgCiKjmXYTAEIAABCEAAAjESQFTF6BVsggAEIAABCEAgOQKIquRchsEQgAAEIAABCMRIAFEVo1ewCQIQgAAEIACB5AggqpJzGQZDAAIQgAAEIBAjAURVjF7BJghAAAIQgAAEkiOAqErOZRgMAQhAAAIQgECMBBBVMXoFmyAAAQhAAAIQSI4Aoio5l2EwBCAAAQhAAAIxEkBUxegVbIIABCAAAQhAIDkCiKrkXIbBEIAABCAAAQjESABRFaNXsAkCEIAABCAAgeQIIKqScxkGQwACEIAABCAQIwFEVYxewSYIQAACEIAABJIjgKhKzmUYDAEIQAACEIBAjAQQVTF6BZsgAAEIQAACEEiOAKIqOZdhMAQgAAEIQAACMRJAVMXoFWyCAAQgAAEIQCA5Aoiq5FyGwRCAAAQgAAEIxEgAURWjV7AJAhCAAAQgAIHkCCCqknMZBkMAAhCAAAQgECMBRFWMXsEmCEAAAhCAAASSI4CoSs5lGAwBCEAAAhCAQIwEEFUxegWbIAABCEAAAhBIjgCiKjmXYTAEIAABCEAAAjESQFTF6BVsggAEIAABCEAgOQKIquRchsEQgAAEIAABCMRIAFEVo1ewCQIQgAAEIACB5AggqpJzGQZDAAIQgAAEIBAjAURVjF7BJghAAAIQgAAEkiOAqErOZRgMAQhAAAIQgECMBBBVMXoFmyAAAQhAAAIQSI4Aoio5l2EwBCAAAQhAAAIxEkBUxegVbIIABCAAAQhAIDkCiKrkXIbBEIAABCAAAQjESOD/ATGJp0vgqX79AAAAAElFTkSuQmCC",
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\"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
@@ -24053,34 +24372,61 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Ergebnisse Visualisierung mit PCA\n",
-    "\n",
-    "* Nach der PCA/Transformation in 3 Hauptkomponenten können wir die 3 Klassen in angemessener Weise visuell trennen. \n",
-    "* Allerdings beschreiben die 3 Hauptkomponenten nur 62% der Varianz der ursprünglichen Daten. Das bedeutet, dass wir einen relativ großen Informationsverlust haben. \n",
-    "* Ein zweites Problem ist, dass es schwierig ist, Modelle zu interpretieren, die auf dem Ergebnis einer PCA basieren\n",
-    "\n",
+    "###Ergibnis von Visualisation mit PCA\n",
+    "Nach der PCA / Transformation in 3 Hauptkomponenten können wir die 3 Klassen visuell angemessen trennen.\n",
+    "Die 3 Hauptkomponenten beschreiben jedoch nur 62 % der Varianz der Originaldaten. Das bedeutet, dass wir einen relativ großen Informationsverlust haben.\n",
+    "Ein zweites Problem besteht darin, dass es schwierig ist, Modelle basierend auf dem Ergebnis einer PCA zu interpretieren\n",
     "-->wir verwenden die PCA nur zur Visualisierung und nicht zur Modellierung"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Funktionsübersicht",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Feature Ãœbersicht"
+    "### Funktionsübersicht"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "Aus dem Originalpapier über die Daten/Quelle der Daten können wir die Information erhalten, dass es die folgenden 17 Hauptmerkmale im Zeit- und Frequenzbereich des Signals gibt:"
+    "Aus dem Originalpapier über die Daten/Quelle der Daten können wir die Information entnehmen, dass es die folgenden 17 Hauptmerkmale von Zeit- und Frequenzbereichssignalen gibt "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "|Name|Time|Freq.|\n",
     "| --- | --- | --- |\n",
@@ -24099,7 +24445,14 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "data_temp = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
@@ -24108,7 +24461,14 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -24125,7 +24485,14 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -24145,15 +24512,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "auf Multikollinearität prüfen",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Multikollinearität prüfen"
+    "### auf Multikollinearität prüfen"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "variables = data_temp\n",
@@ -24163,7 +24545,14 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -24764,7 +25153,14 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# add name of features to vif dataframe\n",
@@ -24777,7 +25173,14 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -24894,7 +25297,14 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "#Drop all features with a VIF > 10, as they are correlating to much with other features\n",
@@ -24904,7 +25314,14 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "data_with_important_features = data_temp.drop(features_to_drop, axis = 1)"
@@ -24913,7 +25330,14 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -25157,7 +25581,14 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -25720,7 +26151,14 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -25756,7 +26194,14 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -25794,44 +26239,58 @@
    "metadata": {
     "editable": true,
     "include": true,
-    "paragraph": "DataUnderstanding",
+    "paragraph": "Zusammenfassung des Datenverständnisses",
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "### Zusammenfassung des Datenverständnisses\n",
-    "* 1 Spalte mit der Subjekt-ID 'subject'-->nur zur Filterung, muss entfernt werden\n",
-    "* 1 Zielspalte 'Aktivität'-->4 Klassen (GEHEN, SITZEN, LIEGEN, STEHEN), etwas unausgewogen\n",
-    "* wir haben ein Klassifizierungsproblem mit mehreren Klassen ***überwachtes maschinelles Lernen***\n",
-    "* 1 \"support column\" source-->is the observation from the train or test group-->train test split along this value\n",
-    "* 1 \"Unterstützungsspalte\"-Quelle-->ist die Beobachtung aus der Trainings- oder Testgruppe-->Train-Test-Aufteilung entlang dieses Wertes \n",
-    "* Diese wurden nach Möglichkeit für jede der drei Achsen x, y und z berechnet\n",
-    "* In der Summe sind das 561 Merkmale\n",
-    "-->Die Analyse der einzelnen Merkmale im Detail ist aufgrund der großen Anzahl von Merkmalen sehr schwierig. Schließlich haben wir Multilinerarität in den Daten\n",
+    "### Summary of the Data Understanding\n",
+    "Zusammenfassung des Datenverständnisses\n",
+    "1 Spalte mit der Betreff-ID „subject“ – nur zum Filtern – muss entfernt werden\n",
     "\n",
-    "* Nach der Multicollineraty-Prüfung sind nur noch 39 Merkmale übrig. In den Boxplots sind bei einigen Merkmalen Ausreißer zu sehen, aber es ist schwierig, diese Ausreißer zu analysieren und zu interpretieren, da die Daten anscheinend bereits teilweise standardisiert sind und wir keine Messeinheiten haben. Daher akzeptieren wir im Moment diese Ausreißer\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Trainings Test Split"
+    "1 Zielspalte „Aktivität“ -> 4 Klassen (GEHEN, SITZEN, LIEGEN, STEHEN) etwas unausgewogen\n",
+    "\n",
+    "Wir haben ein Klassifizierungsproblem mit mehreren Klassen – überwachtes maschinelles Lernen\n",
+    "\n",
+    "1 Quelle „Unterstützungsspalte“ -> ist die Beobachtung aus dem Zug oder der Testgruppe -> Zugtest, aufgeteilt entlang dieses Werts\n",
+    "\n",
+    "Darüber hinaus gibt es viele Merkmale, die aus den 17 Signalmerkmalen im Zeit- und Frequenzbereich berechnet werden. Zum Beispiel Mittelwert, Min., Max., Standardwert, Signalstärkebereich usw.\n",
+    "\n",
+    "Diese wurden nach Möglichkeit für jede der drei Achsen x,y und z berechnet\n",
+    "\n",
+    "Zusammenfassend bedeutet das 561 Features -> Aufgrund der hohen Anzahl an Features ist es sehr schwierig, jedes Feature im Detail zu analysieren. Schließlich haben wir Multilinearität in den Daten\n",
+    "\n",
+    "Nach der Multikollinearitätsprüfung waren nur noch 39 Features übrig. Wir können in den Boxplots einige Ausreißer in einigen Merkmalen erkennen, aber es ist schwierig, diese Ausreißer zu analysieren und zu interpretieren, da es den Anschein hat, dass die Daten bereits teilweise standardisiert sind und wir keine Maßeinheiten haben. Im Moment akzeptieren wir diese Ausreißer\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph  ": "Train Test Split",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### "
+    "### Train Test Split"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 75,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "data = df.copy()"
@@ -25840,7 +26299,14 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "data_train = data[data['type']=='train']\n",
@@ -25850,7 +26316,14 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "X_train = data_train.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)\n",
@@ -25865,7 +26338,14 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -25887,15 +26367,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Data Sampling",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "### Oversampling von Daten"
+    "### Data Oversampling"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 80,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ros = RandomOverSampler(random_state=0)\n",
@@ -25905,7 +26400,14 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -25936,9 +26438,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Modellierrung",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "# Modellierung"
+    "# Modeling "
    ]
   },
   {
@@ -25946,26 +26456,40 @@
    "metadata": {
     "editable": true,
     "include": true,
-    "paragraph": "Modeling",
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "Um verschiedene Modelle zu erstellen, ist es eine gute Praxis, Pipelines und GridSearchCV zu verwenden. Diese beiden Tools sind ein einfacher Weg, um Hyperparamter-Tuning und k-fold Cross-Validation auf verschiedenen Modellen durchzuführen. In diesem Fall bestehen die Pipelines aus einem StandardScaler und einem One vs Rest-Klassifikator"
+    "Um verschiedene Modelle zu erstellen, empfiehlt es sich, Pipelines und GridSearchCV zu verwenden. Diese beiden Tools sind eine einfache Möglichkeit, Hyperparameter-Tuning und k-fache Kreuzvalidierung auf verschiedenen Modellen durchzuführen. In diesem Fall bestehen die Pipes aus einem StandardScaler und einem One vs Rest-Klassifikator"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "__Erstellen & Evaluieren verschiedener Modelle__"
+    "__Build & Evaluate some different Models__"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph ": " KNN",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## KNN\n",
     "K-Nearest Neighbors"
@@ -25974,7 +26498,14 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Pipeline for KNN\n",
@@ -25987,7 +26518,14 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -26007,7 +26545,14 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -26219,7 +26764,14 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -26238,7 +26790,14 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -26389,7 +26948,14 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "classification_report_test = classification_report(y_test, clf.best_estimator_.predict(X_test), output_dict=True)\n",
@@ -26399,7 +26965,14 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "#helper function\n",
@@ -26420,7 +26993,14 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -26593,7 +27173,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## Random Forest"
    ]
@@ -26601,7 +27188,14 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Pipeline\n",
@@ -26614,7 +27208,14 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "param_grid = {'ranfo__estimator__max_depth': [5, 15, 30]}"
@@ -26623,7 +27224,14 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -26823,7 +27431,14 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -26842,7 +27457,14 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "classification_report_test = classification_report(y_test, clf2.best_estimator_.predict(X_test), output_dict=True)\n",
@@ -26852,7 +27474,14 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27025,15 +27654,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Logistic Regression",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Logistische Regression"
+    "## Logistic Regression"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 96,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Pipeline\n",
@@ -27046,7 +27690,14 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27071,7 +27722,14 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -27090,7 +27748,14 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "classification_report_test = classification_report(y_test, clf3.best_estimator_.predict(X_test), output_dict=True)\n",
@@ -27100,7 +27765,14 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27273,15 +27945,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Decision Tree",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Entscheidungsbaum"
+    "## Decision Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 101,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Pipeline\n",
@@ -27294,7 +27981,14 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27319,7 +28013,14 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -27338,7 +28039,14 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "classification_report_test = classification_report(y_test, clf4.best_estimator_.predict(X_test), output_dict=True)\n",
@@ -27348,7 +28056,15 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "SVM",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27521,7 +28237,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## SVM\n",
     "Support Vector Machine"
@@ -27530,7 +28253,14 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Pipeline\n",
@@ -27543,7 +28273,14 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "param_grid = {\n",
@@ -27553,7 +28290,14 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27578,7 +28322,14 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -27597,7 +28348,14 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27685,7 +28443,14 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "classification_report_test = classification_report(y_test, clf5.best_estimator_.predict(X_test), output_dict=True)\n",
@@ -27695,7 +28460,14 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -27868,15 +28640,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Zusammenfassung",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Zusammenfassung"
+    "## Summary"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 113,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "frames = [results_KNN, \n",
@@ -27891,7 +28678,14 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -28501,7 +29295,14 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "final_results.sort_index(level=1).to_csv('Results_VIF_lower10.csv')"
@@ -28509,7 +29310,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Evaluation",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "# 5.  Evaluation"
    ]
@@ -28517,7 +29326,14 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "#helper function\n",
@@ -28549,9 +29365,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Genauigkeit erreicht",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Erzielte Genauigkeit"
+    "## Achieved accuracy"
    ]
   },
   {
@@ -28559,6 +29383,7 @@
    "execution_count": 117,
    "metadata": {
     "editable": true,
+    "include": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -28667,7 +29492,14 @@
   {
    "cell_type": "code",
    "execution_count": 118,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -28704,7 +29536,14 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -28860,32 +29699,46 @@
    "metadata": {
     "editable": true,
     "include": true,
-    "paragraph": "Evaluation",
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "* **KNN** --> Die Leistung auf Testdaten ist ca. 8% niedriger als auf Trainingsdaten. Wahrscheinlich passt sich das Modell zu sehr an die Trainingsdaten an und lässt sich nicht gut auf ungesehene Testdaten verallgemeinern.\n",
-    "* **Random Forest** --> Die Leistung bei den Testdaten ist 15% geringer als bei den Trainingsdaten. Das Modell **übererfüllt** die Trainingsdaten und verallgemeinert sich nicht gut auf die ungesehenen Testdaten.\n",
-    "* **Logistische Regression** --> Trainings- und Testleistung sind sehr ähnlich (diff=3.9%). Dies bedeutet wahrscheinlich, dass das erstellte Modell **gut** auf neue Daten verallgemeinert.\n",
-    "* **Entscheidungsbaum** --> Die Leistung bei den Testdaten ist 34% niedriger als bei den Trainingsdaten. Das Modell passt sich zu sehr an die Trainingsdaten an und verallgemeinert **nicht gut** auf die unbekannten Testdaten.\n",
-    "* **Support Vector Machine** --> Trainings- und Testleistung sind sehr ähnlich (diff=4.8%). Das bedeutet wahrscheinlich, dass das erstellte Modell **gut** auf neue Daten verallgemeinert.\n",
-    "-->Überprüfen wir die Konfusionsmatrix der beiden besten Modelle"
+    "KNN -> Die Leistung bei Testdaten beträgt ca. 8 % niedriger als bei Trainingsdaten. Wahrscheinlich passt das Modell zu stark auf Trainingsdaten und lässt sich nicht gut auf nicht sichtbare Testdaten verallgemeinern.\n",
+    "Random Forest -> Die Leistung bei Testdaten ist 15 % geringer als bei Trainingsdaten. Das Modell passt zu stark auf Trainingsdaten und lässt sich nicht gut auf nicht sichtbare Testdaten verallgemeinern.\n",
+    "Logistische Regression -> Trainings- und Testleistung sind sehr ähnlich (Differenz = 3,9 %). Dies bedeutet wahrscheinlich, dass sich das erstellte Modell gut auf neue Daten verallgemeinern lässt.\n",
+    "Entscheidungsbaum -> Die Leistung bei Testdaten ist 34 % geringer als bei Trainingsdaten. Das Modell passt zu stark auf Trainingsdaten und lässt sich nicht gut auf nicht sichtbare Testdaten verallgemeinern.\n",
+    "Support Vector Machine -> Trainings- und Testleistung sind sehr ähnlich (Differenz = 4,8 %). Dies bedeutet wahrscheinlich, dass sich das erstellte Modell gut auf neue Daten verallgemeinern lässt.\n",
+    "-->Lassen Sie uns die Verwirrungsmatrix der beiden besten Modelle überprüfen"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragragh": "Confusion Matrix-Logistic Regression",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Konfusionsmatrix-Logistische Regression"
+    "## Confusion Matrix-Logistic Regression"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 120,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -28906,15 +29759,29 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Konfusionsmatrix-SVM"
+    "## Confusion Matrix-SVM"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 121,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -28937,7 +29804,13 @@
    "cell_type": "code",
    "execution_count": 122,
    "metadata": {
-    "scrolled": true
+    "editable": true,
+    "include": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -29121,18 +29994,32 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragrph": "Funktionsbedeutung",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Wichtigkeit der Merkmale\n",
+    "## Funktionsbedeutung\n",
     "\n",
-    "* \n",
-    "Ein One vs Rest Classifier besteht aus n einzelnen Klassifikatoren (mit n=Anzahl der Klassen). Das macht es schwierig, die Merkmalsbedeutung für den gesamten OvsR-Klassifikator zu interpretieren. Eine Möglichkeit besteht darin, die Koeffizienten für jedes einzelne Modell zu bestimmen und den Mittelwert über jeden Koeffizienten zu bilden.\n"
+    "* Ein One vs Rest-Klassifikator besteht aus n einzelnen Klassifikatoren (wobei n = Anzahl der Klassen). Das macht es schwierig, die Bedeutung der Merkmale für den gesamten OvsR-Klassifikator zu interpretieren. Eine Möglichkeit besteht darin, die Koeffizienten für jedes einzelne Modell zu bestimmen und den Mittelwert über jeden Koeffizienten zu bilden."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 123,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def get_the_feature_importances(clfobject,est_name, functype):\n",
@@ -29152,7 +30039,14 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "feature_importance = pd.DataFrame({},[])\n",
@@ -29162,18 +30056,33 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "jp-MarkdownHeadingCollapsed": true,
+    "paragraph": "ergebnis",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Ergebnisse"
+    "## Results"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "* Das beste Modell ist der OvsR-Klassifikator mit logistischer Regression. \n",
-    "* Es hat eine gute Genauigkeit (0.77:test, 0.81:train) und eine gute Präzision (0.87:test) für die Klasse 'WALKING'.\n",
-    "* Die 10 wichtigsten Eigenschaften dieses OvsR-Klassifikators sind die folgenden:"
+    "* The best model is the Logistic Regression OvsR Classifier. \n",
+    "* It has a good accuracy (0.77:test, 0.81:train) and a good precision (0.87:test) for the class 'WALKING'.\n",
+    "* The 10 most important feautures of this OvsR-Classifier are the following:"
    ]
   },
   {
@@ -29290,20 +30199,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "-->Die meisten dieser wichtigen Funktionen stammen vom Gyroskopsensor im Smartphone."
+    "-->The most of this important features comes from the **gyroscope sensor** in the smartphone"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Modell Deployment",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Endgültiges Modell für das Deployment"
+    "## Final model for deployment"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 126,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "final_model = clf3.best_estimator_"
@@ -29311,16 +30235,31 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Deployment",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "# 6. Deployment\n",
-    "Teste die Prognose lokal"
+    "Test the prediction locally:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 127,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -29539,7 +30478,14 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -29564,7 +30510,14 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -29586,7 +30539,6 @@
    "metadata": {
     "editable": true,
     "include": true,
-    "paragraph": "Deployment",
     "slideshow": {
      "slide_type": ""
     },
@@ -29599,7 +30551,8 @@
  ],
  "metadata": {
   "branche": "Landwirtschaft",
-  "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Analysis_of_the_movement_and_activity_of_free-ranging_cattle_test.csv",
+  "category": "Forschung",
+  "dataSource": "https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones",
   "funktion": "Operations",
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
@@ -29616,12 +30569,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.3"
   },
-  "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/tree/main/Agriculture/Analysis%20of%20the%20movement%20and%20activity%20of%20free-ranging%20cattle",
+  "repoLink": "https://github.com/AlexRossmann/machine-learning-services/tree/main/Agriculture/Analysis%20of%20the%20movement%20and%20activity%20of%20free-ranging%20cattle",
   "skipNotebookInDeployment": false,
-  "teaser": "To be done",
-  "title": "Analyse der Bewegungsprofile von Kühen auf dem Feld"
+  "teaser": "Landwirte geraten immer weiter unter wirtschaftlichen Druck. Beispielsweise kam es durch die Coronapandemie zu teuren logistischen Problemen. Gleichzeitig stellen immer mehr Konsumenten höhere Anforderungen an eine artgerechte Tierhaltung. Diese Marktbedingungen erscheinen für Landwirte problematisch, da letzterer Aspekt natürlich mit gewissen Kosten verbunden ist und ein generelles Umdenken notwendig macht. ​ ",
+  "title": "Erkennung der Aktivitäten von Kühen auf dem Feld"
  },
  "nbformat": 4,
  "nbformat_minor": 4