From 47cf7961bea227f93a6a03567eaaa9b66cabf9a7 Mon Sep 17 00:00:00 2001
From: Patrick Schnepf <patrickschnepf@Patricks-Air.fritz.box>
Date: Wed, 17 Apr 2024 18:38:18 +0200
Subject: [PATCH] Notebooks uebersetzt
---
.../notebook.ipynb | 194 ++++++---
.../notebook.ipynb | 69 +--
CRM/Customer Churn Prediction/notebook.ipynb | 331 +++++++++-----
.../notebook.ipynb | 196 ++++++---
.../notebook.ipynb | 65 ++-
.../notebook.ipynb | 53 ++-
.../notebook_Multiple Linear Regression.ipynb | 97 +++--
.../notebook_Random Forrest Regressor.ipynb | 162 ++++---
.../notebook_1.ipynb | 46 +-
.../notebook_2.ipynb | 24 +-
.../notebook.ipynb | 60 ++-
.../notebook.ipynb | 407 +++++++++++-------
.../notebook.ipynb | 84 ++--
.../notebook.ipynb | 44 +-
.../notebook.ipynb | 105 +++--
.../notebook.ipynb | 66 +--
16 files changed, 1290 insertions(+), 713 deletions(-)
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 586a1fd..d078392 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
@@ -1,27 +1,29 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Analysis of the movement and activity of free-ranging cattle"
+ "# Analyse der Bewegung und Aktivität von freilaufenden Rindern"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Business Understanding\n",
- "Farmers want to analyse how their cattle moves and behaives on the field.\n",
- "(see readme for more information)"
+ "Landwirte wollen analysieren, wie sich ihr Vieh auf der Weide bewegt und verhält. (siehe Readme für weitere Informationen)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding\n",
- "## 2.1. Import of Relevant Modules "
+ "# 2. Daten und Datenverständnis\n",
+ "## 2.1. Import von relevanten Daten "
]
},
{
@@ -57,28 +59,30 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Read data"
+ "## Daten lesen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Source of the data:\n",
- "* Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.\n",
+ "Quelle der Daten:\n",
"\n",
- "Downloaded from Kaggle:\n",
- "https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones"
+ "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"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "There are two separate data sets from the test group and the training group. These are first summarized again for further analysis. The data is identified whether it comes from the train or test data set so that it can be split up again later."
+ "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."
]
},
{
@@ -357,10 +361,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Exploration and Preparation"
+ "3. Datenexploration und -aufbereitung"
]
},
{
@@ -381,10 +386,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Check for missing values"
+ "### Auf fehlende Werte prüfen"
]
},
{
@@ -439,17 +445,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "--> **no missing values** in the Data Frame"
+ "--> **Keine fehlenden Werte im Data Frame"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Check for duplicates"
+ "### Auf Duplikate prüfen"
]
},
{
@@ -525,24 +533,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "--> **no duplicated rows** in the data"
+ "--> **keine Duplikatzeilen** in den Daten"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Target variable"
+ "### Ziel Variable"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We have a classification problem and the target column is the \"Activity\" column"
+ "Wir haben ein Klassifizierungsproblem und die Zielspalte ist die Spalte \"Aktivität\"."
]
},
{
@@ -600,10 +611,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "* In the smart farming context of this assignment a cattle normally would not walk on a stairwell. Therefore the rows with the labels 'WALKING_DOWNSTAIRS' and 'WALKING_UPSTAIRS' will be removed:"
+ "* 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:"
]
},
{
@@ -650,17 +662,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- For each activity approximately the same number of observations. We could over-sample all minority classes before modelling to have a perfect balanced dataset"
+ "- 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."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### How many observations from each subject exists?"
+ "### Wie viele Beobachtungen gibt es von jedem Testobjekt??"
]
},
{
@@ -698,17 +712,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The figure above is interesting. Normally all subjects have done the same experiment sequence. Therefore we would expact that the number of observations from each subject must be nearly the same. But there is a range from 200 to 300 observations over all subjects. One reason could be that the change from one activity to the next in the sequence was not sharp or clear enough and the observers had delete the unclear observations in this unstabel phases afterwards. "
+ "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. "
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Total number of observations"
+ "### Gesamtzahl der Beobachtungen"
]
},
{
@@ -729,11 +745,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### PCA for Visualisation\n",
- "- **Pricipal component analysis** is a simple way to visualize high dimensional data in a low dimensional space. (Caution: we pay for it with a loss of information-->but for visualisation purpose it is OK)"
+ "### 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)"
]
},
{
@@ -21811,7 +21828,7 @@
0.5815860871081596,
0.17430419576457676,
0.11868886464960451,
- -2.9703191948931535e-07,
+ -2.9703191948931535e-7,
-0.5563797218747201,
0.28398116849187127,
0.500455311202338,
@@ -23978,33 +23995,37 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Results Visualisation with PCA\n",
+ "### Ergebnisse Visualisierung mit PCA\n",
"\n",
- "* After the PCA / transformation to 3 pricipal components we can visual separate the 3 classes in an adequate way. \n",
- "* But the 3 principal components only describe 62% of the variance of the original data. That means we have a relativly big infomation loss. \n",
- "* A Second problem is that it´s hard to interpret models based on the result of a PCA\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",
- "-->we use the PCA only for visualization and don´t use it for modelling"
+ "-->wir verwenden die PCA nur zur Visualisierung und nicht zur Modellierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Feature overview"
+ "### Feature Ãœbersicht"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "From the original Paper about the data/Source of the data we can get the information that there are the following 17 main time and frequency domain signal features: "
+ "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:"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -24070,10 +24091,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### check for Multicollinearity"
+ "### Multikollinearität prüfen"
]
},
{
@@ -25716,27 +25738,37 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Summary of the Data Understanding\n",
- "* 1 column with the subject id 'subject'-->only for filtering, must be removed\n",
- "* 1 target column 'Activity'-->4 classes(WALKING, SITTING, LAYING, STANDING) a little bit unbalanced\n",
- "* we have a multiclass classification problem-->**supervised machine learning**\n",
+ "### 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",
- "* Addionally there are many features callculated out of the 17 time and frequency domain signal features. For example mean, min, max, std, signal magnitude area etc. \n",
- "* These were calculated for each of the three axes x,y and z if possible\n",
- "* In sum that means 561 features\n",
- "-->Analyzing each Feature in detail is very hard because of the high number of features. Eventually we have multilineraity in the data\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",
"\n",
- "* After Multicollineraty check, only 39 features left. We can see some outliers in some features in the boxplots but it is hard to analyze and interpret these outliers because it seams that the data is already partially standardized and we have no measurment units. So at the moment we accept these outliers\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"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Train Test Split"
+ "Trainings Test Split"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### "
]
},
{
@@ -25797,10 +25829,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Data Oversampling"
+ "### Oversampling von Daten"
]
},
{
@@ -25846,27 +25879,31 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Modeling "
+ "# Modellierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "To build some different models it is good practice to use Pipelines and GridSearchCV. These two tools are an easy way to make hyperparamter tuning & k-fold Cross-Validation on different models. In this case the pipes exists of a StandardScaler and an One vs Rest classifier"
+ "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"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "__Build & Evaluate some different Models__"
+ "__Erstellen & Evaluieren verschiedener Modelle__"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -26495,6 +26532,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -26927,10 +26965,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Logistic Regression"
+ "## Logistische Regression"
]
},
{
@@ -27175,10 +27214,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Decision Tree"
+ "## Entscheidungsbaum"
]
},
{
@@ -27423,6 +27463,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -27770,10 +27811,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Summary"
+ "## Zusammenfassung"
]
},
{
@@ -28411,6 +28453,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -28451,10 +28494,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Achieved accuracy"
+ "## Erzielte Genauigkeit"
]
},
{
@@ -28753,23 +28797,24 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "* **KNN** --> Performance on test data is ca. 8% lower than on training data. Probably the model **overfits** on training data and does not generalize well on unseen test data.\n",
- "* **Random Forest** --> Performance on test data is 15% lower than on training data. The model **overfits** on training data and does not generalize well on unseen test data.\n",
- "* **Logistic Regression** --> training and test performance are very similar (diff=3.9%). This probably means that the created model **generalizes well** on new data.\n",
- "* **Decision Tree** --> Performance on test data is 34% lower than on training data. The model **overfits** on training data and does not generalize well on unseen test data.\n",
- "* **Support Vector Machine** --> training and test performance are very similar (diff=4.8%). This probably means that the created model **generalizes well** on new data.\n",
- "\n",
- "-->Let´s check the confusion matrix of the two best models"
+ "* **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"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Confusion Matrix-Logistic Regression"
+ "## Konfusionsmatrix-Logistische Regression"
]
},
{
@@ -28795,10 +28840,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Confusion Matrix-SVM"
+ "## Konfusionsmatrix-SVM"
]
},
{
@@ -29010,12 +29056,14 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Feature Importance\n",
+ "## Wichtigkeit der Merkmale\n",
"\n",
- "* A One vs Rest Classifier exists of n individual classifiers (with n=number of classes). That makes it hard to interpret the feature importance for the whole OvsR-Classifier. One possible way is two determine the coefficients for each individual model and build the mean over each coefficient."
+ "* \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"
]
},
{
@@ -29050,19 +29098,21 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Results"
+ "## Ergebnisse"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "* 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:"
+ "* 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:"
]
},
{
@@ -29176,17 +29226,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "-->The most of this important features comes from the **gyroscope sensor** in the smartphone"
+ "-->Die meisten dieser wichtigen Funktionen stammen vom Gyroskopsensor im Smartphone."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Final model for deployment"
+ "## Endgültiges Modell für das Deployment"
]
},
{
@@ -29199,11 +29251,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 6. Deployment\n",
- "Test the prediction locally:"
+ "Teste die Prognose lokal"
]
},
{
@@ -29471,6 +29524,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/Automotive/Improvement of components for autonomous motor vehicles/notebook.ipynb b/Automotive/Improvement of components for autonomous motor vehicles/notebook.ipynb
index 2c3892b..131134e 100644
--- a/Automotive/Improvement of components for autonomous motor vehicles/notebook.ipynb
+++ b/Automotive/Improvement of components for autonomous motor vehicles/notebook.ipynb
@@ -1,49 +1,45 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Business Understanding¶\n",
- "This case study is intended to assist technology companies in developing and improving components for autonomous vehicles.\n",
- "In doing so, this case study poses the question:\n",
- "In which areas are the weaknesses of the tested systems and how can these be classified?\n",
- "The question will be answered by classifying the reasons for the human test driver's intervention in the system. In doing so, the reasons are to be classified into different categories:\n",
- "- Software problems\n",
- "- hardware problems\n",
- "- Software and hardware problems (problems that occur in combination)\n",
- "- Problems caused by objects of traffic control (e.g. malfunctions of traffic light systems)\n",
- "- problems caused by other road users\n",
- "- external influences (including e.g. blockages, hidden elements, weather and road conditions)\n",
- "- other problems"
+ "Diese Fallstudie soll Technologieunternehmen bei der Entwicklung und Verbesserung von Komponenten für autonome Fahrzeuge unterstützen. Dabei wirft diese Fallstudie die Frage auf: In welchen Bereichen liegen die Schwächen der getesteten Systeme und wie lassen sich diese einordnen? Die Frage wird beantwortet, indem die Gründe für den Eingriff des menschlichen Testfahrers in das System klassifiziert werden. Dabei sollen die Gründe in verschiedene Kategorien eingeteilt werden:\n",
+ "- Software Probleme\n",
+ "- Hardware Probleme\n",
+ "- Software und Hardware Probleme (Probleme, die in Kombination auftreten)\n",
+ "- Probleme, die durch Objekte der Verkehrssteuerung verursacht werden (z. B. Fehlfunktionen von Lichtsignalanlagen)\n",
+ "- Probleme, die durch andere Verkehrsteilnehmer verursacht werden\n",
+ "- äußere Einflüsse (einschließlich z. B. Blockaden, verborgene Elemente, Wetter- und Straßenbedingungen)\n",
+ "- sonstige Probleme"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. Data and Data Understanding \n",
- "The underlying dataset was obtained from Kaggle and shows necessary interventions by a driver in autonomous driving vehicle technology.\n",
- "It was recorded from December 1, 2018 to November 30, 2019 and is licensed under U.S. Government Works.\n",
- "The data processing is done with the help of a Jupyter notebook, which is included in the most popular data science platform \"Anaconda\".\n",
- "The data set is in 8885 x 9 format.\n",
- "The following data was recorded:\n",
- "- Manufacturer\n",
- "- Approval number\n",
- "- date\n",
- "- Vehicle identification number\n",
- "- Possibility of operation without driver\n",
- "- Is there a driver on site?\n",
- "- Autonomous driving interrupted by AV system, test driver, remote control, or passenger\n",
- "- Location of incident: interstate, highway, expressway, rural road, street or parking lot\n",
- "- Description of causes"
+ "## 2. Daten und Datenverständnis \n",
+ "Der zugrundeliegende Datensatz wurde von Kaggle bezogen und zeigt notwendige Eingriffe eines Fahrers in autonom fahrende Fahrzeugtechnologie. Er wurde vom 1. Dezember 2018 bis zum 30. November 2019 aufgezeichnet und steht unter der Lizenz von U.S. Government Works. Die Datenverarbeitung erfolgt mit Hilfe eines Jupyter-Notebooks, das in der populärsten Data-Science-Plattform \"Anaconda\" enthalten ist. Der Datensatz liegt im Format 8885 x 9 vor. Die folgenden Daten wurden erfasst:\n",
+ "- Hersteller\n",
+ "- Zulassungsnummer\n",
+ "- Datum\n",
+ "- Fahrzeug-Identifikationsnummer\n",
+ "- Möglichkeit des Betriebs ohne Fahrer\n",
+ "- Ist ein Fahrer vor Ort?\n",
+ "- Unterbrechung des autonomen Fahrens durch AV-System, Testfahrer, Fernsteuerung oder Beifahrer\n",
+ "- Ort des Vorfalls: Autobahn, Schnellstraße, Landstraße, Straße oder Parkplatz\n",
+ "- Beschreibung der Ursachen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -81,10 +77,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2 Read Data"
+ "## 2.2 Daten einlesen"
]
},
{
@@ -252,10 +249,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3. Data Cleaning"
+ "## 2.3. Daten bereinigen"
]
},
{
@@ -442,10 +440,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Missing Values"
+ "## Fehlende Werte"
]
},
{
@@ -627,10 +626,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Data preparation and labeling"
+ "## Datenaufbereitung und Kennzeichnung"
]
},
{
@@ -1338,10 +1338,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Classification model"
+ "## Klassifikationsmodell"
]
},
{
@@ -1939,6 +1940,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2099,10 +2101,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Decision Tree"
+ "## Entscheidungsbaum"
]
},
{
diff --git a/CRM/Customer Churn Prediction/notebook.ipynb b/CRM/Customer Churn Prediction/notebook.ipynb
index e689737..1c28a73 100644
--- a/CRM/Customer Churn Prediction/notebook.ipynb
+++ b/CRM/Customer Churn Prediction/notebook.ipynb
@@ -1,28 +1,31 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Business Understanding\n",
"\n",
- "Customer churn is a customer's decision to stop purchasing a particular company service. It thus represents the counterpart to long-term customer loyalty. In order to promote customer loyalty, companies must use analyzes that recognize at an early stage whether a customer wants to leave the company. This enables marketing and sales measures to be initiated before the actual loss of customers. In this context, the service specifically answers these two questions: What is the probability that historical data will be used to predict whether a customer will migrate to another provider? Which factors lead to customer churn?"
+ "Kundenabwanderung ist die Entscheidung eines Kunden, eine bestimmte Unternehmensdienstleistung nicht mehr zu kaufen. Sie stellt somit das Gegenstück zur langfristigen Kundenbindung dar. Um die Kundenbindung zu fördern, müssen Unternehmen Analysen einsetzen, die frühzeitig erkennen, ob ein Kunde das Unternehmen verlassen will. So können Marketing- und Vertriebsmaßnahmen eingeleitet werden, bevor es zum eigentlichen Kundenverlust kommt. In diesem Zusammenhang beantwortet der Service konkret diese beiden Fragen: Wie hoch ist die Wahrscheinlichkeit, dass anhand historischer Daten vorhergesagt werden kann, ob ein Kunde zu einem anderen Anbieter abwandert? Welche Faktoren führen zur Kundenabwanderung?"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding\n",
+ "# 2. Daten und Datenverständnis\n",
"\n",
- "The data record of a fictitious telecommunications company is used to visualize and implement the service. This consists of 7,043 lines. Each line describes a customer with 21 columns. Each column defines different characteristics (attributes) of the customers. Based on the data, it should be classified whether a customer leaves the company or not. For this purpose, the historical data contain the target variable “Churnâ€, which provides information on whether a customer has churned or not."
+ "Zur Visualisierung und Implementierung des Dienstes wird der Datensatz eines fiktiven Telekommunikationsunternehmens verwendet. Dieser besteht aus 7.043 Zeilen. Jede Zeile beschreibt einen Kunden mit 21 Spalten. Jede Spalte definiert verschiedene Merkmale (Attribute) der Kunden. Anhand der Daten soll klassifiziert werden, ob ein Kunde das Unternehmen verlässt oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft darüber gibt, ob ein Kunde abgewandert ist oder nicht.ot."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -51,10 +54,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. Read Data"
+ "## 2.2. Daten einlesen"
]
},
{
@@ -319,17 +323,18 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data set consists of 7,043 lines and 21 attributes:\n",
+ "Der Datensatz besteht aus 7.043 Zeilen und 21 Attributen:\n",
"\n",
- "- Attribute to be predicted: Churn\n",
- "- Numeric attributes: Tenure, MonthlyCharges and TotalCharges.\n",
- "- Categorical attributes: CustomerID, Gender, SeniorCitizen, Partner, Dependents, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod.\n",
+ "- Zu prognostizierendes Attribut: Abwanderung\n",
+ "- Numerische Attribute: Vertragsdauer, Monatsgebühren und Gesamtgebühren.\n",
+ "- Kategorische Attribute: CustomerID, Gender, SeniorCitizen, Partner, Angehörige, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod.\n",
+ "Es wurden nicht alle Datentypen korrekt eingelesen:\n",
"\n",
- "Not all data types were read in correctly:\n",
- "- TotalCharges must be a numerical value -> convert to float"
+ "- TotalCharges muss ein numerischer Wert sein -> in Float umwandeln\n"
]
},
{
@@ -406,24 +411,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "No duplicates in data frame"
+ "Keine Duplikate im Datensatz"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3. Data Cleaning"
+ "## 2.3. Datenbereinigung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The first read errors should be corrected here, before the actual data preparation."
+ "Hier sollten die ersten Lesefehler korrigiert werden, bevor die eigentliche Datenaufbereitung erfolgt."
]
},
{
@@ -473,10 +481,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The conversion of the TotalCharges has resulted in zero values. These zero values must be corrected."
+ "Die Konvertierung der TotalCharges hat zu Nullwerten geführt. Diese Nullwerte müssen korrigiert werden."
]
},
{
@@ -491,31 +500,35 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4. Descriptive Analytics"
+ "## 2.4. Deskriptive Analytik"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this part of the notebook, data understanding is to be taken into account with the help of descriptive analytics. After removing the zero values, the data record consists of 7032 rows, one of which each describes a customer, and 21 columns that define the customer's attributes. With the help of this data, an attempt should be made to classify whether a customer leaves or not. For this purpose, the historical data contain the target variable “Churnâ€, which provides information on whether a customer has churned."
+ "In diesem Teil des Notebooks soll das Datenverständnis mit Hilfe der deskriptiven Analytik berücksichtigt werden. Nach dem Entfernen der Nullwerte besteht der Datensatz aus 7032 Zeilen, von denen jeweils eine einen Kunden beschreibt, und 21 Spalten, die die Attribute des Kunden definieren. Mit Hilfe dieser Daten soll versucht werden, zu klassifizieren, ob ein Kunde abwandert oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft darüber gibt, ob ein Kunde abgewandert ist.\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.1. Continous Features"
+ "## 2.4.1. Kontinuierliche Merkmale"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, the distributions of the continous features are examined individually and in a second step the categorical features are set in connection with the target variable."
+ "Zunächst werden die Verteilungen der kontinuierlichen Merkmale einzeln untersucht und in einem zweiten Schritt die kategorialen Merkmale in Zusammenhang mit der Zielvariablen gesetzt."
]
},
{
@@ -529,10 +542,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Tenure"
+ "### Besitz"
]
},
{
@@ -566,12 +580,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- No normal distribution recognizable.\n",
- "- No outliers recognizable.\n",
- "- Customers are potentially evenly distributed over the individual months, but a large number of customers have not long been part of the company."
+ "- Keine Normalverteilung erkennbar.\n",
+ "- Keine Ausreißer erkennbar.\n",
+ "- Kunden sind potentiell gleichmäßig über die einzelnen Monate verteilt, aber eine große Anzahl von Kunden ist noch nicht lange im Unternehmen.\n"
]
},
{
@@ -614,17 +629,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who have not been with the company for long are more likely to migrate than long-term customers."
+ "Kunden, die noch nicht lange bei dem Unternehmen sind, werden eher abwandern als langjährige Kunden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Monthly Charges"
+ "### Monatliche Kosten"
]
},
{
@@ -658,12 +675,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- No normal distribution recognizable.\n",
- "- Most of the customers are in the front of the distribution and pay relatively low monthly fees.\n",
- "- Nevertheless, the curve runs evenly with a renewed increase backwards and accordingly no outliers can be identified."
+ "- Es ist keine Normalverteilung erkennbar.\n",
+ "- Die meisten Kunden befinden sich im vorderen Teil der Verteilung und zahlen relativ niedrige monatliche Gebühren.\n",
+ "- Dennoch verläuft die Kurve gleichmäßig mit einem erneuten Anstieg nach hinten und dementsprechend können keine Ausreißer identifiziert werden.\n"
]
},
{
@@ -706,18 +724,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Customers with low monthly fees are more likely to churn.\n",
- "- Churn trend between customers who are churning and customers who are not churning becomes nearly the same as monthly fees increase."
+ "- Kunden mit niedrigen monatlichen Gebühren sind eher abwanderungsbereit.\n",
+ "- Der Abwanderungstrend zwischen Kunden, die abwandern, und Kunden, die nicht abwandern, gleicht sich an, wenn die monatlichen Gebühren steigen.\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Total Charges"
+ "### Gesamtkosten"
]
},
{
@@ -751,12 +771,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- The curve flattens extremely strongly towards the rear.\n",
- "- Similarities to the exponential distribution can be seen. -> Test of the logarithmic transformation to achieve a normal distribution.\n",
- "- It is questionable whether there are outliers in the rear part. -> box plot"
+ "- Die Kurve flacht nach hinten hin extrem stark ab.\n",
+ "- Es sind Ähnlichkeiten mit der Exponentialverteilung zu erkennen. -> Test der logarithmischen Transformation zur Erreichung einer Normalverteilung.\n",
+ "- Es ist fraglich, ob es Ausreißer im hinteren Teil gibt. -> Boxplot\n"
]
},
{
@@ -782,11 +803,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- box plot shows no outliers.\n",
- "- This means that no outliers can be identified for total charges either."
+ "- Boxplot zeigt keine Ausreißer.\n",
+ "- Dies bedeutet, dass auch bei den Gesamtkosten keine Ausreißer festgestellt werden können.\n"
]
},
{
@@ -822,11 +844,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Even the transformation with the help of the logarithm does not produce a normal distribution.\n",
- "- Before further transformations, the correlation with other variables should first be examined."
+ "- Auch die Transformation mit Hilfe des Logarithmus führt nicht zu einer Normalverteilung.\n",
+ "- Vor weiteren Transformationen sollte zunächst die Korrelation mit anderen Variablen untersucht werden.\n"
]
},
{
@@ -869,17 +892,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The distribution is almost identical across the entire range of costs for both customers who are churning and customers who are not churning."
+ "\n",
+ "Die Verteilung ist über die gesamte Bandbreite der Kosten sowohl bei den abwandernden als auch bei den nicht abwandernden Kunden nahezu identisch.\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Correlation Analysis"
+ "### Korrelationsanalyse"
]
},
{
@@ -915,17 +941,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The correlation matrix shows that the attributes \"tenure\" and \"TotalCharges\" have a critical positive correlation of over 0.8. This relationship will be re-examined later in the context of multicollinearity and must be removed."
+ "Die Korrelationsmatrix zeigt, dass die Attribute \"Tenure\" und \"TotalCharges\" eine kritische positive Korrelation von über 0,8 aufweisen. Diese Beziehung wird später im Zusammenhang mit der Multikollinearität erneut untersucht und muss entfernt werden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Scatterplots with Continous Features and Target"
+ "### Streudiagramme mit kontinuierlichen Merkmalen und Ziel"
]
},
{
@@ -959,10 +987,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The scatter plot suggests that customers in the upper left area, i.e. customers with high monthly costs and short periods of employment with the company, are most likely to churn."
+ "Das Streudiagramm deutet darauf hin, dass Kunden im oberen linken Bereich, d. h. Kunden mit hohen monatlichen Kosten und kurzer Betriebszugehörigkeit, am ehesten abwandern."
]
},
{
@@ -996,31 +1025,35 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "There is a purely logical, linear relationship between length of service and the total costs billed. The longer a person has been a customer, the more monthly amounts he has already had to pay."
+ "Es besteht eine rein logische, lineare Beziehung zwischen der Dauer der Betriebszugehörigkeit und den in Rechnung gestellten Gesamtkosten. Je länger eine Person Kunde ist, desto mehr monatliche Beträge musste sie bereits zahlen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.2. Categorical Features"
+ "## 2.4.2. Kategorische Merkmale"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Churn (Target)"
+ "### Abwanderung (Ziel)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, the distribution of the target variable churn is examined."
+ "Zunächst wird die Verteilung der Zielvariablen Churn untersucht."
]
},
{
@@ -1062,18 +1095,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Churns correspond to around 27% of the total data set, while non-churns correspond to around 73%.\n",
- "- This is an unbalanced data set and another metric must be used in the evaluation phase."
+ "- Die Abwanderungen machen etwa 27 % des gesamten Datensatzes aus, während die Nicht-Abwanderungen etwa 73 % ausmachen.\n",
+ "- Dies ist ein unausgewogener Datensatz und eine andere Metrik muss in der Bewertungsphase verwendet werden.\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Gender"
+ "### Geschlecht"
]
},
{
@@ -1098,17 +1133,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The churn rate between male and female is approximately the same."
+ "Die Abwanderungsrate zwischen Männern und Frauen ist ungefähr gleich hoch."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Senior Citizen"
+ "### Senioren"
]
},
{
@@ -1133,13 +1170,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers classified as seniors are more likely to migrate."
+ "Bei Kunden, die als Senioren eingestuft werden, ist die Wahrscheinlichkeit höher, dass sie abwandern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1168,17 +1207,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who do not have a partner are more likely to migrate."
+ "Kunden, die keinen Partner haben, sind eher bereit, abzuwandern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Dependents"
+ "### Angehörige"
]
},
{
@@ -1203,17 +1244,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who have relatives are more likely to migrate."
+ "Kunden, die Verwandte haben, sind eher bereit, abzuwandern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Multiple Lines"
+ "### Mehrere Anschlüsse"
]
},
{
@@ -1238,13 +1281,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who have multiple connections are less likely to migrate."
+ "Bei Kunden, die mehrere Anschlüsse haben, ist die Wahrscheinlichkeit einer Abwanderung geringer."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1273,17 +1318,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "If a customer has a fiber optic connection, he is more likely to drop out than a customer with DSL."
+ "Wenn ein Kunde einen Glasfaseranschluss hat, ist es wahrscheinlicher, dass er ausfällt als ein Kunde mit DSL."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Online Security"
+ "### Online-Sicherheit"
]
},
{
@@ -1308,13 +1355,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who do not use the Internet security service are more likely to migrate."
+ "Kunden, die den Internet-Sicherheitsdienst nicht nutzen, werden eher abwandern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1343,17 +1392,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "People who do not use online backup are more likely to migrate."
+ "Personen, die keine Online-Datensicherung nutzen, sind eher bereit, umzuziehen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Device Protection"
+ "### Geräteschutz\n"
]
},
{
@@ -1378,17 +1429,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who have not purchased additional device protection are more likely to migrate."
+ "Kunden, die keinen zusätzlichen Geräteschutz erworben haben, werden mit größerer Wahrscheinlichkeit migrieren."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Tech Support"
+ "### Technischer Support"
]
},
{
@@ -1413,17 +1466,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who do not use tech support are more likely to migrate."
+ "Kunden, die keinen technischen Support in Anspruch nehmen, werden eher abwandern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Streaming TV/ Streaming Movies"
+ "### Streaming-TV/ Streaming-Filme"
]
},
{
@@ -1459,17 +1514,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The addition of film and TV streaming offers has hardly any effect on the churn rate."
+ "Die Hinzunahme von Film- und TV-Streaming-Angeboten hat kaum Auswirkungen auf die Abwanderungsrate."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Paperless Billing"
+ "### Papierlose Abrechnung"
]
},
{
@@ -1494,17 +1551,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who pay without an invoice are more likely to migrate."
+ "Kunden, die ohne Rechnung bezahlen, werden eher abwandern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Payment Method"
+ "### Zahlungsmethode"
]
},
{
@@ -1529,17 +1588,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers who pay using electronic checks migrate significantly more often than customers who use a different payment method."
+ "Kunden, die mit elektronischen Schecks bezahlen, wandern deutlich häufiger ab als Kunden, die eine andere Zahlungsmethode verwenden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Contract"
+ "### Vertrag"
]
},
{
@@ -1564,24 +1625,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Customers with short-term commitments are more likely to leave than customers with longer-term contracts."
+ "Bei Kunden mit kurzfristigen Verträgen ist die Wahrscheinlichkeit größer, dass sie das Unternehmen verlassen, als bei Kunden mit längerfristigen Verträgen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Aufbereitung der Daten"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1. Reduce Customer ID"
+ "## 3.1. Reduzieren der Kunden-ID"
]
},
{
@@ -1595,10 +1659,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2. Recoding of Categorical Variables"
+ "## 3.2. Umkodierung der kategorialen Variablen"
]
},
{
@@ -2087,17 +2152,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3. Test for Multicollinearity"
+ "## 3.3. Test auf Multikollinearität"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In order to ensure correct operation of the later regression, there must be no multicollinearity between the variables. The presence of the same is checked with the help of the library Statsmodel."
+ "Um ein korrektes Funktionieren der späteren Regression zu gewährleisten, darf keine Multikollinearität zwischen den Variablen bestehen. Das Vorhandensein einer solchen wird mit Hilfe der Bibliothek Statsmodel überprüft."
]
},
{
@@ -2152,10 +2219,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"MonthlyCharges\" has the highest VIF and is removed from the dataset."
+ "\"MonthlyCharges\" hat den höchsten VIF und wird aus dem Datensatz entfernt."
]
},
{
@@ -2218,10 +2286,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"TotalCharges\" has the highest VIF and is removed from the dataset."
+ "\"TotalCharges\" hat den höchsten VIF und wird aus dem Datensatz entfernt."
]
},
{
@@ -2283,17 +2352,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "None of the variables now has a VIF greater than 10."
+ "Keine der Variablen hat jetzt einen VIF von mehr als 10."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.4. Feature Scaling"
+ "## 3.4. Merkmalsskalierung"
]
},
{
@@ -2553,6 +2624,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2571,10 +2643,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.6. Create Test and Training Data"
+ "## 3.6. Erstellen von Test- & Trainingsdaten"
]
},
{
@@ -2589,38 +2662,43 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Auswertung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1. Logistic Regression"
+ "## 4.1. Logistische Regression"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Logistic regression is used to solve the problem. The two libraries Statsmodels and Scikit-Learn are used for this. The complete evaluation of the model takes place only in the subchapter to Scikit-Learn."
+ "Zur Lösung des Problems wird die logistische Regression verwendet. Hierfür werden die beiden Bibliotheken Statsmodels und Scikit-Learn verwendet. Die komplette Auswertung des Modells findet erst im Unterkapitel zu Scikit-Learn statt."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Statsmodels"
+ "## Statistische Modelle"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Training and Prediction"
+ "### Training und Vorhersage"
]
},
{
@@ -2686,10 +2764,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The trained model shows statistically non-significant variables. This is given if the value P>|z| is greater than 0.05 and it is not the constant."
+ "Das trainierte Modell zeigt statistisch nicht-signifikante Variablen an. Dies ist gegeben, wenn der Wert P>|z| größer als 0,05 ist und es sich nicht um die Konstante handelt."
]
},
{
@@ -2705,6 +2784,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2767,10 +2847,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "No more statistically insignificant variables. The final model was modeled:"
+ "Keine statistisch nicht signifikanten Variablen mehr. Das endgültige Modell wurde modelliert:"
]
},
{
@@ -2843,13 +2924,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1. Evaluation"
+ "## 4.1. Auswertung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2857,6 +2940,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2864,10 +2948,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Training and Prediction"
+ "### Training und Vorhersage"
]
},
{
@@ -2893,6 +2978,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2919,10 +3005,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Accuracy suggests an above average model. However, it is an unbalanced data set. Therefore, further metrics have to be analyzed."
+ "Die Genauigkeit deutet auf ein überdurchschnittliches Modell hin. Allerdings handelt es sich um einen unausgewogenen Datensatz. Daher müssen weitere Metriken analysiert werden.\n",
+ "\t\n"
]
},
{
@@ -2967,10 +3055,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Higher accuracy for training than for the test data set. Overall, the values for the test and training data sets are very similar. Therefore, overfitting or underfitting should not be assumed."
+ "Höhere Genauigkeit für das Training als für den Testdatensatz. Insgesamt sind die Werte für den Test- und den Trainingsdatensatz sehr ähnlich. Daher sollte nicht von einem Overfitting oder Underfitting ausgegangen werden."
]
},
{
@@ -3043,10 +3132,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Precision and recall provide a much more realistic picture of the model. It achieves a precision of around 68% and a recall of 52%. The recall is clearly more important for the use case and must therefore be improved at the expense of the precision."
+ "Präzision und Recall vermitteln ein viel realistischeres Bild des Modells. Es erreicht eine Präzision von rund 68 % und eine Wiederauffindbarkeit von 52 %. Der Recall ist für den Anwendungsfall eindeutig wichtiger und muss daher auf Kosten der Präzision verbessert werden."
]
},
{
@@ -3074,13 +3164,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The AUC of the ROC curve yields a good value of 0.84. It can be concluded that there is potential for optimization by optimizing the threshold."
+ "Der AUC der ROC-Kurve ergibt einen guten Wert von 0,84. Daraus lässt sich schließen, dass durch die Optimierung des Schwellenwertes Optimierungspotenzial besteht."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3088,10 +3180,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, however, the results for the business should be illustrated and it should be clarified which customers lead to churn and which speak against churn."
+ "Zunächst sollen jedoch die Ergebnisse für das Unternehmen veranschaulicht werden und es soll geklärt werden, welche Kunden zur Abwanderung führen und welche gegen eine Abwanderung sprechen."
]
},
{
@@ -3163,13 +3256,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The three main features that cause churn are:\n",
- "- The fiber optic service (InternetService_Fiber optic),\n",
- "- the online payments (PaperlessBilling) and \n",
- "- the subscription of the additional movie streaming service (StreamingMovies_Yes)."
+ "Die drei Hauptmerkmale, die zur Abwanderung führen, sind:\n",
+ "\n",
+ "- Der Glasfaserdienst (InternetService_Glasfaser),\n",
+ "- Die Online-Zahlungen (PaperlessBilling) und\n",
+ "- Das Abonnement des zusätzlichen Filmstreamingdienstes (StreamingMovies_Yes).\n"
]
},
{
@@ -3201,27 +3296,31 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The three most important features that keep customers from churning are:\n",
- "- The contracts that can be terminated for two years (Contract_Two year),\n",
- "- the time people have been customers of a company (Tenure) and \n",
- "- No subscription to the Internet service (InternetService_No)."
+ "Die drei wichtigsten Merkmale, die Kunden von der Abwanderung abhalten, sind:\n",
+ "\n",
+ "- Die Verträge, die für zwei Jahre gekündigt werden können (Contract_Two year),\n",
+ "- Die Zeit, die man Kunde eines Unternehmens ist (Tenure) und\n",
+ "- kein Abonnement für den Internetdienst (InternetService_No).\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.4. Model Optimization"
+ "## 4.4. Modell-Optimierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The recall rate is too low as a target metric and must therefore be increased. Therefore, the metrics are analyzed at different thresholds of the logistic regression."
+ "Die Recall-Rate ist als Zielmetrik zu niedrig und muss daher erhöht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
]
},
{
@@ -3442,17 +3541,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "A threshold of 0.3 offers a better result for the application. It increases the recall to a satisfactory level of 73.21% at the expense of the precision. However, the precision is negligible."
+ "Ein Schwellenwert von 0,3 bietet ein besseres Ergebnis für die Anwendung. Er erhöht die Wiederauffindbarkeit auf ein zufriedenstellendes Niveau von 73,21 %, was zu Lasten der Präzision geht. Die Präzision ist jedoch vernachlässigbar."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "This results in the following values:"
+ "Daraus ergeben sich die folgenden Werte:"
]
},
{
@@ -3535,13 +3636,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "As expected, the rate of customers incorrectly classified as churn increases. In turn, however, the number of customers who are correctly predicted as churners (true positives) also increases. As elaborated in the term paper, this is essential, because in case of doubt, a customer would be falsely called by the service team and even perceive this call as good service and be bound to the company in the longer term."
+ "Erwartungsgemäß steigt die Rate der fälschlicherweise als abgewandert eingestuften Kunden. Im Gegenzug steigt aber auch die Anzahl der Kunden, die korrekt als Abwanderer vorhergesagt werden (True Positives). Wie in der Hausarbeit ausgeführt, ist dies essentiell, denn im Zweifelsfall würde ein Kunde fälschlicherweise vom Serviceteam angerufen werden und diesen Anruf sogar als guten Service wahrnehmen und längerfristig an das Unternehmen binden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3636,17 +3739,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Summary"
+ "# Zusammenfassung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The notebook showed how a logistic regression can be used to predict the churn of customers in the telecommunications segment."
+ "Das Notebook hat gezeigt, wie eine logistische Regression verwendet werden kann, um die Abwanderung von Kunden im Telekommunikationssegment vorherzusagen."
]
}
],
diff --git a/CRM/Customer Satisfaction Airlines/notebook.ipynb b/CRM/Customer Satisfaction Airlines/notebook.ipynb
index 619cf3c..7b5daf2 100644
--- a/CRM/Customer Satisfaction Airlines/notebook.ipynb
+++ b/CRM/Customer Satisfaction Airlines/notebook.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -10,18 +11,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding\n",
+ "# 2. Daten und Datenverständnis\n",
"\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1 Import of Relevant Modules"
+ "## 2.1 Import von relevanten Modulen"
]
},
{
@@ -44,10 +47,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. Read Data"
+ "## 2. Daten einlesen"
]
},
{
@@ -1028,6 +1032,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1117,17 +1122,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "No duplicates in data frame."
+ "Keine Duplikate im Datensatz"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3 Data Cleaning"
+ "## 2.3 Datenbereinigung"
]
},
{
@@ -1174,6 +1181,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1274,21 +1282,23 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "All null values are removed."
+ "Alle Nullwerte wurden entfernt"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4. Descriptive Analysis\n",
+ "## 2.4. Deskriptive Analyse\n",
"\n",
- "In this part of the notebook, exploratory data analysis will be used to account for data understanding.\n",
+ "In diesem Teil des Notizbuchs wird die explorative Datenanalyse verwendet, um das Verständnis der Daten zu ermitteln.\n",
"\n",
- "After removing the zero values, the data set consists of 129187 rows, one row describing each customer, and 21 columns defining the attributes of the customers. This data will be used to try to classify whether a customer is satisfied or not. For this purpose, the historical data contains the target variable \"Satisfaction\", which provides information about whether a customer is satisfied."
+ "Nach Abzug der Nullwerte besteht der Datensatz aus 129187 Zeilen, einer Zeile, die jeden Kunden beschreibt, und 21 Spalten, die die Eigenschaften der Kunden definieren. Anhand dieser Daten wird versucht zu klassifizieren, ob ein Kunde zufrieden ist oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Zufriedenheit\", die Auskunft darüber gibt, ob ein Kunde zufrieden ist.\n"
]
},
{
@@ -1342,10 +1352,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.1 Numeric Attributes"
+ "## 2.4.1 Numerische Attribute"
]
},
{
@@ -1405,10 +1416,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Age"
+ "### Alter"
]
},
{
@@ -1444,10 +1456,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Seat comfort"
+ "### Sitzkomfort"
]
},
{
@@ -1483,10 +1496,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Food and Drink"
+ "### Essen und Trinken"
]
},
{
@@ -1522,10 +1536,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Inflight wifi service"
+ "### Wifi-Service an Bord"
]
},
{
@@ -1561,10 +1576,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Inflight entertainment"
+ "### Unterhaltung während des Fluges"
]
},
{
@@ -1600,6 +1616,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1639,10 +1656,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Ease of Online booking"
+ "### Einfachheit der Online-Buchung"
]
},
{
@@ -1678,10 +1696,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### On-board service"
+ "### Service an Bord"
]
},
{
@@ -1717,10 +1736,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Leg room service"
+ "### Beinraum-Service"
]
},
{
@@ -1756,10 +1776,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Baggage handling"
+ "### Gepäckabfertigung"
]
},
{
@@ -1795,6 +1816,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1834,10 +1856,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Cleanliness"
+ "### Sauberkeit"
]
},
{
@@ -1873,6 +1896,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1947,24 +1971,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The correlation matrix shows that the attributes \"Flight Distance\", \"Seat Comfort\" and \"Age\" have a critical positive correlation of over 0.8. This relationship will be re-examined later as part of the multicollinearity and must be removed."
+ "Die Korrelationsmatrix zeigt, dass die Attribute \"Flugentfernung\", \"Sitzkomfort\" und \"Alter\" eine kritische positive Korrelation von über 0,8 aufweisen. Dieser Zusammenhang wird später im Rahmen der Multikollinearität erneut untersucht und muss entfernt werden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.2. Categorical Features"
+ "## 2.4.2. Kategoriale Merkmale"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The target variable Satisfaction is mapped to zero (dissatisfied) and one (satisfied) so that a graphical representation can be created with the categorical variables.\n"
+ "Die Zielvariable Zufriedenheit wird auf Null (unzufrieden) und Eins (zufrieden) abgebildet, damit eine grafische Darstellung mit den kategorialen Variablen erstellt werden kann."
]
},
{
@@ -2057,18 +2084,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "From the graph, we can see that a total of 70000 customers are satisfied with the services of AIRCO Airlines and about 60000 customers are dissatisfied.\n",
- "The dataset of our target variable is balanced"
+ "Aus dem Diagramm geht hervor, dass insgesamt 70000 Kunden mit den Dienstleistungen von AIRCO Airlines zufrieden sind und etwa 60000 Kunden unzufrieden sind. Der Datensatz unserer Zielvariablen ist ausgeglichen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The categorical attributes are set in relation to the target variable."
+ "Die kategorischen Attribute werden in Bezug auf die Zielvariable gesetzt."
]
},
{
@@ -2102,19 +2130,21 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "From the above charts we can conclude the following:\n",
+ "Aus den obigen Diagrammen können wir Folgendes schließen:\n",
"\n",
- "Female customers who have flown with AIRCO Airlines are more satisfied than male customers. Loyal customers are more satisfied than unfaithful ones. People who travel for business reasons are more satisfied than those who travel for personal reasons. More customers travel business class and are also more satisfied than those who fly economy class."
+ "Weibliche Kunden, die mit AIRCO Airlines geflogen sind, sind zufriedener als männliche Kunden. Loyale Kunden sind zufriedener als untreue Kunden. Personen, die aus geschäftlichen Gründen reisen, sind zufriedener als solche, die aus privaten Gründen reisen. Mehr Kunden reisen in der Business Class und sind auch zufriedener als diejenigen, die in der Economy Class fliegen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Correlation Analysis"
+ "## Korrelationsanalyse"
]
},
{
@@ -2142,6 +2172,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2185,17 +2216,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1 Target Variable (Satisfaction)"
+ "## 3.1 Zielvariable (Satisfaction)"
]
},
{
@@ -2458,10 +2491,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2. Reoding of Categorical Variables"
+ "## 3.2. Umkodierung von kategorialen Variablen"
]
},
{
@@ -2699,17 +2733,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3. Test for Multicollinearity"
+ "## 3.3. Test auf Multikollinearität"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In order to ensure correct operation of the later regression, there must be no multicollinearity between the variables. The presence of the same is checked with the help of the library Statsmodel."
+ "Um ein korrektes Funktionieren der späteren Regression zu gewährleisten, darf keine Multikollinearität zwischen den Variablen bestehen. Das Vorhandensein einer solchen wird mit Hilfe der Bibliothek Statsmodel überprüft."
]
},
{
@@ -2761,10 +2797,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"Ease of Online booking\" has the highest VIF and is removed from the data set."
+ "Die \"Einfachheit der Online-Buchung\" hat den höchsten VIF und wird aus dem Datensatz entfernt."
]
},
{
@@ -2824,10 +2861,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"Cleanliness\" now has the highest VIF and is removed from the data set."
+ "\"Sauberkeit\" hat nun den höchsten VIF und wird aus dem Datensatz entfernt."
]
},
{
@@ -2886,10 +2924,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"Online support\" now has the highest VIF and is removed from the data set."
+ "\"Online-Support\" hat nun den höchsten VIF und wird aus dem Datensatz entfernt."
]
},
{
@@ -2947,6 +2986,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3007,10 +3047,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"Seat comfort\" has now the highest VIF und will be removed frome data frame."
+ "\"Sitzkomfort\" hat nun den höchsten VIF und wird aus dem Datenrahmen entfernt."
]
},
{
@@ -3066,10 +3107,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"Inflight wifi service\" has now the highest VIF and will be removed from data frame."
+ "\"Inflight Wifi Service\" hat nun den höchsten VIF und wird aus dem Datenrahmen entfernt."
]
},
{
@@ -3124,10 +3166,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"Inflight entertainment\" has now the highest VIF and will be removed from data frame."
+ "\"Inflight Entertainment\" hat nun den höchsten VIF und wird aus dem Datenrahmen entfernt."
]
},
{
@@ -3181,10 +3224,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"On-board service\" has now the highest VIF and will be removed from data frame."
+ "\"On-board service\" hat nun die höchste VIF und wird aus dem Datenrahmen entfernt."
]
},
{
@@ -3237,17 +3281,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Non of the variables now has a VIF higer than 10."
+ "Keine der Variablen hat jetzt einen VIF-Wert von mehr als 10."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.4. Feature Scaling"
+ "## 3.4. Merkmalsskalierung"
]
},
{
@@ -3408,10 +3454,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.5. Create Test and Training Data"
+ "## 3.5. Erstellen von Test- & Trainingsdaten"
]
},
{
@@ -3427,24 +3474,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1. Logistic Regression\n"
+ "## 4.1. Logistische Regression\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Logistic regression is used to solve the problem. The two libraries Statsmodels and Scikit-Learn are used for this. The complete evaluation of the model takes place only in the subchapter to Scikit-Learn."
+ "Zur Lösung des Problems wird die logistische Regression verwendet. Hierfür werden die beiden Bibliotheken Statsmodels und Scikit-Learn verwendet. Die vollständige Auswertung des Modells findet erst im Unterkapitel zu Scikit-Learn statt."
]
},
{
@@ -3507,10 +3557,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The trained model shows statistically non-significant variables. This is given if the value P>|z| is greater than 0.05 and it is not the constant."
+ "Das trainierte Modell zeigt statistisch nicht-signifikante Variablen an. Dies ist gegeben, wenn der Wert P>|z| größer als 0,05 ist und es sich nicht um die Konstante handelt."
]
},
{
@@ -3526,10 +3577,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now a second modell can be created."
+ "Nun kann ein zweites Modell erstellt werden."
]
},
{
@@ -3576,10 +3628,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "There is still one non-significant variable "
+ "Es gibt noch eine nicht signifikante Variable "
]
},
{
@@ -3595,10 +3648,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now a third modell can be created."
+ "Nun kann ein drittes Modell erstellt werden."
]
},
{
@@ -3644,10 +3698,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "No non-signifikanten Variables anymore. The final Modell is created."
+ "Keine nicht-signifikanten Variablen mehr. Das endgültige Modell wird erstellt."
]
},
{
@@ -3705,6 +3760,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3712,6 +3768,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3719,6 +3776,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3726,6 +3784,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3756,6 +3815,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3784,6 +3844,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3907,6 +3968,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3940,6 +4002,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3947,6 +4010,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3954,10 +4018,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, however, the results for the business are to be illustrated and clarified, which service would lead to a customer satisfaction and which speak against the customer satisfaction."
+ "Zunächst sollen jedoch die Ergebnisse für das Unternehmen dargestellt und geklärt werden, welche Dienstleistung zu einer Kundenzufriedenheit führen würde und welche gegen die Kundenzufriedenheit sprechen."
]
},
{
@@ -4022,14 +4087,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The three most important features that lead to customer satisfaction are:\n",
+ "Die drei wichtigsten Merkmale, die zur Kundenzufriedenheit führen, sind:\n",
"\n",
- "The Customer Type_disloyal Customer\n",
- "the Class Eco Plus and\n",
- "the Class Eco"
+ "Der Customer Type_disloyal Customer\n",
+ "Die Klasse Eco Plus und\n",
+ "Die Klasse Eco"
]
},
{
@@ -4063,28 +4129,31 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The three most important features that lead to customer dissatisfaction\n",
+ "Die drei wichtigsten Merkmale, die zu Kundenunzufriedenheit führen\n",
"\n",
- "The age\n",
- "Type of Travel_Personal Travel\n",
- "Gender Male"
+ "Das Alter\n",
+ "Art der Reise_Persönliche Reisen\n",
+ "Geschlecht Männlich"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.4. Model Optimization"
+ "## 4.4. Modell Optimierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The recall rate is too low as a target metric and must therefore be increased. Therefore, the metrics are analyzed at different thresholds of the logistic regression."
+ "Die Aufklärungsrate ist als Zielgröße zu niedrig und muss daher erhöht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
]
},
{
@@ -4305,6 +4374,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/CRM/Increase customer satisfaction/notebook.ipynb b/CRM/Increase customer satisfaction/notebook.ipynb
index 73d15d5..f983b2a 100644
--- a/CRM/Increase customer satisfaction/notebook.ipynb
+++ b/CRM/Increase customer satisfaction/notebook.ipynb
@@ -1,36 +1,39 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Business Understanding\n",
"\n",
- "Due to the large selection that Netflix has to offer, it is difficult for users to find suitable movies for themselves. Searching the library takes a lot of time and creates a poor user experience, which in turn leads to higher cancellation rates.\n",
- "In order to reduce the cancellation rates, it is necessary to check whether customer satisfaction can be increased by applying machine learning in relation to movie recommendations.\n",
+ "Aufgrund der großen Auswahl, die Netflix zu bieten hat, ist es für die Nutzer schwierig, geeignete Filme für sich zu finden. Die Suche in der Bibliothek nimmt viel Zeit in Anspruch und schafft ein schlechtes Nutzererlebnis, was wiederum zu höheren Abbruchquoten führt.\n",
+ "Um die Abbruchquoten zu senken, muss geprüft werden, ob die Kundenzufriedenheit durch die Anwendung von maschinellem Lernen in Bezug auf Filmempfehlungen erhöht werden kann.\n",
"\n",
"\n",
- "The dataset contains movie data from tmdb Dataset.\n",
- "Find out factors that can be taken based on the data regarding popularity or rating of the movies to develop strategies for the business.\n",
- "Based on the above business problem, we define dependent variable(y)\n",
+ "Der Datensatz enthält Filmdaten aus dem tmdb Dataset.\n",
+ "Finden Sie heraus, welche Faktoren auf der Grundlage der Daten über die Beliebtheit oder Bewertung der Filme ergriffen werden können, um Strategien für das Unternehmen zu entwickeln.\n",
+ "Basierend auf dem obigen Geschäftsproblem definieren wir die abhängige Variable (y)\n",
"\n",
- "Problem 1 : y = popularity / vote_average (regression problem)"
+ "Problem 1: y = Popularität / Voting-Durchschnitt (Regressionsproblem)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding\n",
+ "# 2. Daten und Datenverständnis\n",
"\n",
- "It can be seen from the data set that both numbers and categorical values are included. Each category refers to the corresponding film in the row. For example, the \"crew\" column contains several contributors such as authors, film editor, etc., while \"cast\" contains the actors who star in the respective films. In addition, each movie has a unique ID, like movie_id/id, which is identical to each other and makes it possible to combine both data sets. All data is very understandable and self-explanatory, and the content is explicitly described on kaggle.com."
+ "Aus dem Datensatz ist ersichtlich, dass sowohl Zahlen als auch kategoriale Werte enthalten sind. Jede Kategorie bezieht sich auf den entsprechenden Film in der Zeile. So enthält beispielsweise die Spalte \"Crew\" mehrere Mitwirkende wie Autoren, Filmeditor usw., während \"Cast\" die Schauspieler enthält, die in den jeweiligen Filmen mitspielen. Außerdem hat jeder Film eine eindeutige ID, z. B. movie_id/id, die identisch ist und es ermöglicht, beide Datensätze zu kombinieren. Alle Daten sind sehr verständlich und selbsterklärend, und der Inhalt ist auf kaggle.com ausdrücklich beschrieben."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1 Import of Relevant Modules"
+ "## 2.1 Import von relevanten Modulen"
]
},
{
@@ -61,10 +64,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2 Read Data"
+ "## 2.2 Daten einlesen"
]
},
{
@@ -95,10 +99,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Data Understanding"
+ "### Datenverständnis"
]
},
{
@@ -849,17 +854,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3 Data Cleaning"
+ "## 2.3 Datenbereinigung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Check for Null-Values"
+ "### Auf Nullwerte prüfen"
]
},
{
@@ -958,10 +965,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Check for Duplicates"
+ "### Auf Duplikate prüfen"
]
},
{
@@ -1037,10 +1045,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4 Test for Multicollinearity"
+ "## 2.4 Test auf Multikollinearität"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Keine nicht-signifikanten Variablen mehr. Das endgültige Modell wird erstellt."
]
},
{
@@ -1103,10 +1120,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.5 Decriptive Analysis"
+ "## 2.5 Deskriptive Analysise "
]
},
{
@@ -1272,17 +1290,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1 Recording of Categorical Variables"
+ "## 3.1 Erfassung kategorialer Variablen"
]
},
{
@@ -1811,17 +1831,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1 Test and Train Data"
+ "## 4.1 Test und Trainieren der Daten"
]
},
{
@@ -3404,10 +3426,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2 Linear Regression"
+ "## 4.2 Lineare Regression"
]
},
{
diff --git a/Forecast/Forecast of required vehicles in the city center/notebook.ipynb b/Forecast/Forecast of required vehicles in the city center/notebook.ipynb
index 8ddcb6b..e96b0b5 100644
--- a/Forecast/Forecast of required vehicles in the city center/notebook.ipynb
+++ b/Forecast/Forecast of required vehicles in the city center/notebook.ipynb
@@ -1,31 +1,34 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Business Understanding\n",
"\n",
- "Uber Technologies Inc. is a San Francisco-based company founded in 2009. It has 91 million active users and 3.9 million drivers worldwide. The company is represented with its app in a total of 63 countries.\n",
- "There must be a basic understanding of the objectives from the business perspective. This can then be applied to define appropriate data mining project requirements so that they can be realized. Uber tries to optimize forecasts based on supply and demand in such a way that an availability of vehicles is always guaranteed in order to maintain the service for its users. This dataset is intended to demonstrate the feasibility of this goal."
+ "Uber Technologies Inc. ist ein 2009 gegründetes Unternehmen mit Sitz in San Francisco. Es hat 91 Millionen aktive Nutzer und 3,9 Millionen Fahrer weltweit. Das Unternehmen ist mit seiner App in insgesamt 63 Ländern vertreten.\n",
+ "Es muss ein grundlegendes Verständnis für die Ziele aus der Unternehmensperspektive vorhanden sein. Darauf aufbauend können dann die entsprechenden Anforderungen an ein Data-Mining-Projekt definiert werden, damit diese realisiert werden können. Uber versucht, Prognosen auf Basis von Angebot und Nachfrage so zu optimieren, dass eine Verfügbarkeit von Fahrzeugen stets gewährleistet ist, um den Service für seine Nutzer aufrechtzuerhalten. Dieser Datensatz soll die Realisierbarkeit dieses Ziels demonstrieren."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Preparation\n",
+ "# 2. Daten und Datenverständnis\n",
"\n",
- "The dataset consists of four basic variables: dispatching base number, date, active_vehicles, and trips.\n",
- "The dispatching base number variable is a code assigned by the TLC that indicates an Uber base in New York City. Accordingly, the codes can be assigned to the following bases: B02512: Under, B02598: Behind, B02617: Next, B02682: Taste, B02764: After-NY, B02765: Grun, B02835: Brazen, B02836: Inside.\n",
- "By a first overview of the available data, one can already speculate about possible dependencies between the number of active vehicles, number of trips as well as the date."
+ "Der Datensatz besteht aus vier Basisvariablen: Abfertigungsbasisnummer, Datum, active_vehicles und Fahrten.\n",
+ "Die Variable \"dispatching base number\" ist ein vom TLC zugewiesener Code, der eine Uber-Basis in New York City angibt. Dementsprechend können die Codes den folgenden Basen zugewiesen werden: B02512: Unter, B02598: Hinter, B02617: Neben, B02682: Taste, B02764: Nach-NY, B02765: Grun, B02835: Dreist, B02836: Inside.\n",
+ "Durch einen ersten Überblick über die verfügbaren Daten kann man bereits über mögliche Abhängigkeiten zwischen der Anzahl der aktiven Fahrzeuge, der Anzahl der Fahrten sowie dem Datum spekulieren."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1 Import of Relevant Modules"
+ "## 2.1 Import von relevanten Modulen"
]
},
{
@@ -48,10 +51,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2 Read Data"
+ "## 2.2 Daten einlesen"
]
},
{
@@ -304,10 +308,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3 Data Cleaning"
+ "## 2.3 Datenbereinigung"
]
},
{
@@ -810,10 +815,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung"
]
},
{
@@ -1439,12 +1445,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1 Recoding of categorical variables"
+ "## 3.1 Umkodierung von kategorialen Variablen"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 33,
@@ -1877,10 +1898,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Create Test and Training Data"
+ "## 3.2 Erstellen von Test- & Trainingsdaten"
]
},
{
@@ -1916,17 +1938,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1 Linear Regression"
+ "## 4.1 Lineare Regression"
]
},
{
@@ -2477,6 +2501,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/Forecast/Sales Forecast for retail store/notebook_Multiple Linear Regression.ipynb b/Forecast/Sales Forecast for retail store/notebook_Multiple Linear Regression.ipynb
index 4734037..6174dd5 100644
--- a/Forecast/Sales Forecast for retail store/notebook_Multiple Linear Regression.ipynb
+++ b/Forecast/Sales Forecast for retail store/notebook_Multiple Linear Regression.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -8,31 +9,35 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "A retailer needs a very high turnover and just-in-time delivery of products to be successful. For this, very precise forecasts are needed, which allow exact statements about which stores in which departments make how much turnover per week. The question is therefore as follows: What is the weekly sales forecast for all stores of a fictitious store if we take into account the data of the last three years, demographic information (CPI, Unemployment, Temperature, etc.)? Deriving from this, what then is the sales per week per department within one of these stores?"
+ "Ein Einzelhändler braucht einen sehr hohen Umsatz und eine Just-in-time-Lieferung von Produkten, um erfolgreich zu sein. Dazu werden sehr genaue Prognosen benötigt, die genaue Aussagen darüber erlauben, welche Filialen in welchen Abteilungen wie viel Umsatz pro Woche machen. Die Fragestellung lautet daher wie folgt: Wie hoch ist die wöchentliche Umsatzprognose für alle Filialen eines fiktiven Marktes, wenn wir die Daten der letzten drei Jahre, demographische Informationen (VPI, Arbeitslosigkeit, Temperatur, etc.) berücksichtigen? Wie hoch ist dann der Wochenumsatz pro Abteilung in einer dieser Filialen?"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data understanding"
+ "# 2. Daten und Datenverständnis"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data used for the notebook is divided into three files. They contain different features and the common attribute stores. "
+ "Die für das Notizbuch verwendeten Daten sind in drei Dateien unterteilt. Sie enthalten verschiedene Merkmale und die gemeinsamen Attributsspeicher. "
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -53,10 +58,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. Read Train Data"
+ "## 2.2. Trainingsdaten einlesen"
]
},
{
@@ -443,10 +449,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2.1. Descriptive Analysis"
+ "## 2.2.1. Deskriptive Analyse"
]
},
{
@@ -518,10 +525,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2.3. Read Features Data"
+ "# 2.3. Merkmaldaten einlesen"
]
},
{
@@ -1151,10 +1159,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3.2. Descriptive Analysis"
+ "## 2.3.2. Deskriptive Analyse"
]
},
{
@@ -1188,10 +1197,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4. Read stores Data"
+ "## 2.4. Ladendaten einlesen"
]
},
{
@@ -1431,10 +1441,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.1. Descriptive Analysis"
+ "## 2.4.1. Deskriptive Analyse"
]
},
{
@@ -1661,10 +1672,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung"
]
},
{
@@ -3007,10 +3019,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1. Test for Correlation"
+ "## 3.1. Test auf Korrelation"
]
},
{
@@ -3200,20 +3213,22 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2. Resetting Index"
+ "## 3.2. Index zurücksetzen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Deleting observations preserves the original indices\n",
- "- Deleting observations with indices 2 and 3 will result in an index like: 0,1,4,5,6\n",
- "- Once we reset the index, a new column is created that contains the old index\n",
- "- Use 'drop=True' to delete the old index completely"
+ "- Beim Löschen von Beobachtungen bleiben die ursprünglichen Indizes erhalten\n",
+ "- Das Löschen von Beobachtungen mit den Indizes 2 und 3 führt zu einem Index wie: 0,1,4,5,6\n",
+ "- Sobald wir den Index zurücksetzen, wird eine neue Spalte erstellt, die den alten Index enthält\n",
+ "- Verwenden Sie 'drop=True', um den alten Index vollständig zu löschen"
]
},
{
@@ -3579,10 +3594,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3. Test for Multicollinearity\n"
+ "## 3.3. Test auf Multikollinearität\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Keine nicht-signifikanten Variablen mehr. Das endgültige Modell wird erstellt."
]
},
{
@@ -3616,10 +3640,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Violation of OLS Assumption --> no Linearity Solution then perform a Log Transformation"
+ "Verletzung der OLS-Annahme --> keine Linearität Lösung dann Log-Transformation durchführen"
]
},
{
@@ -4035,10 +4060,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Assumption no Multicollinearity"
+ "Annahme keine Multikollinearität"
]
},
{
@@ -4127,10 +4153,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.4. Creating Dummy Variables"
+ "## 3.4. Dummy-Variablen erstellen"
]
},
{
@@ -4478,28 +4505,31 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Local file\n",
- "df_dummies.to_csv('train_dummies.csv', index = False) # full path if file should not be in the same folder as the notebook\n",
+ "## Lokale Datei\n",
+ "df_dummies.to_csv('train_dummies.csv', index = False) # vollständiger Pfad, wenn die Datei nicht im selben Ordner wie das Notebook liegen soll\n",
"\n",
- "## Cloud file\n",
+ "## Cloud-Datei\n",
"#project.save_data(\"train_dummies.csv\", df_dummies.to_csv(index=False))"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1. Multiple Linear Regression Model"
+ "## 4.1. Mehrfaches lineares Regressionsmodell"
]
},
{
@@ -4513,17 +4543,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2. Feature Scaling"
+ "## 4.2. Merkmalsskalierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "transforms our data into standard distributed data"
+ "wandelt unsere Daten in verteilte Standarddaten um"
]
},
{
@@ -4539,10 +4571,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.3. Test and Train Data"
+ "## 4.3. Testen & Trainieren der Daten"
]
},
{
@@ -4669,10 +4702,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The coefficient of determination R² is only about 35.8%. Presumably, another model has to be found, since these data are not useful for this purpose. Probably it is also because we have only taken the stores of type A and only the department 92 into consideration and thus a good forecast is not possible at all for everything."
+ "Das Bestimmtheitsmaß R² beträgt nur etwa 35,8 %. Vermutlich muss ein anderes Modell gefunden werden, da diese Daten für diesen Zweck nicht brauchbar sind. Wahrscheinlich liegt es auch daran, dass wir nur die Geschäfte des Typs A und nur die Abteilung 92 berücksichtigt haben und somit eine gute Vorhersage gar nicht für alle möglich ist."
]
},
{
@@ -4834,10 +4868,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Size has the biggest effect on sales followed by the week, as expected. If we change the size by 1, then the turnover changes by 0.169."
+ "Die Größe hat den größten Einfluss auf den Umsatz, gefolgt von der Woche, wie erwartet. Wenn wir die Größe um 1 ändern, ändert sich der Umsatz um 0,169."
]
},
{
diff --git a/Forecast/Sales Forecast for retail store/notebook_Random Forrest Regressor.ipynb b/Forecast/Sales Forecast for retail store/notebook_Random Forrest Regressor.ipynb
index 96538e2..0321826 100644
--- a/Forecast/Sales Forecast for retail store/notebook_Random Forrest Regressor.ipynb
+++ b/Forecast/Sales Forecast for retail store/notebook_Random Forrest Regressor.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -8,31 +9,35 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "A retailer needs a very high turnover and just-in-time delivery of products to be successful. For this, very precise forecasts are needed, which allow exact statements about which stores in which departments make how much turnover per week. The question is therefore as follows: What is the weekly sales forecast for all stores of a fictitious store if we take into account the data of the last three years, demographic information (CPI, Unemployment, Temperature, etc.)? Deriving from this, what then is the sales per week per department within one of these stores?"
+ "Ein Einzelhändler braucht einen sehr hohen Umsatz und eine Just-in-time-Lieferung von Produkten, um erfolgreich zu sein. Dazu werden sehr genaue Prognosen benötigt, die genaue Aussagen darüber erlauben, welche Filialen in welchen Abteilungen wie viel Umsatz pro Woche machen. Die Fragestellung lautet daher wie folgt: Wie hoch ist die wöchentliche Umsatzprognose für alle Filialen eines fiktiven Marktes, wenn wir die Daten der letzten drei Jahre, demographische Informationen (VPI, Arbeitslosigkeit, Temperatur, etc.) berücksichtigen? Wie hoch ist dann der Wochenumsatz pro Abteilung in einer dieser Filialen?"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data understanding"
+ "# 2. Daten und Datenverständnis"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data used for the notebook is divided into three files. They contain different features and the common attribute stores. "
+ "Die für das Notebook verwendeten Daten sind in drei Dateien unterteilt. Sie enthalten verschiedene Merkmale und die gemeinsamen Attributsspeicher. "
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -49,10 +54,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. Read Train Data"
+ "## 2.2. Trainingsdaten einlesen"
]
},
{
@@ -191,10 +197,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "describe data: We have a dataset with 421,570 entries. Each row contains the values \"Store\", \"Dept\", \"Date\", \"Weekly_Sales\" and \"IsHoliday\". Thus, this table already contains the value that is relevant for our consideration: \"Weekly_Sales\". We will create a forecast for this value and now look for features that affect this value. The format of these values are a boolean, a floating, two integer and an object."
+ "Daten beschreiben: Wir haben einen Datensatz mit 421.570 Einträgen. Jede Zeile enthält die Werte \"Store\", \"Dept\", \"Date\", \"Weekly_Sales\" und \"IsHoliday\". Diese Tabelle enthält also bereits den Wert, der für unsere Betrachtung relevant ist: \"Wöchentliche_Umsätze\". Wir werden eine Prognose für diesen Wert erstellen und nun nach Merkmalen suchen, die diesen Wert beeinflussen. Das Format dieser Werte ist ein boolescher Wert, eine Gleitkommazahl, zwei Ganzzahlen und ein Objekt."
]
},
{
@@ -453,10 +460,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2.1. Descriptive Analysis"
+ "## 2.2.1. Deskriptive Analyse"
]
},
{
@@ -528,10 +536,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3. Read Feature Data"
+ "## 2.3. Merkmalsdaten einlesen"
]
},
{
@@ -715,10 +724,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "describe data: We have a dataset with 8,190 entries. Each row contains the value \"Store\", \"Date\", \"Temperature\", \"Fuel_Price\", \"MarkDown1\", \"MarkDown2\", \"MarkDown3\", \"MarkDown4\", \"MarkDown5\", \"CPI\", \"Unemployment\" and \"IsHoliday\". The format of these values are one boolean, 9 float, one integer and 1 object."
+ "Daten beschreiben: Wir haben einen Datensatz mit 8.190 Einträgen. Jede Zeile enthält die Werte \"Store\", \"Date\", \"Temperature\", \"Fuel_Price\", \"MarkDown1\", \"MarkDown2\", \"MarkDown3\", \"MarkDown4\", \"MarkDown5\", \"CPI\", \"Unemployment\" und \"IsHoliday\". Das Format dieser Werte ist ein Boolean, 9 Float, eine Integer und 1 Object."
]
},
{
@@ -982,10 +992,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "describe data: We do not have corresponding figures for all values. Values are missing for MarkDown1-MarkDown5, CPI and Unemployment. It is interesting that there are negative values in \"MarkDown\". According to the description of the data, the MarkDown values are not available for all stores and only since a certain date. This explains the discrepancy in quantity. "
+ "Daten beschreiben: Wir haben nicht für alle Werte entsprechende Zahlen. Es fehlen Werte für MarkDown1-MarkDown5, CPI und Arbeitslosigkeit. Interessant ist, dass es negative Werte bei \"MarkDown\" gibt. Laut der Beschreibung der Daten sind die MarkDown-Werte nicht für alle Filialen und erst ab einem bestimmten Datum verfügbar. Dies erklärt die Diskrepanz in der Menge. "
]
},
{
@@ -1175,10 +1186,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3.2. Descriptive Analysis"
+ "## 2.3.2. Deskriptive Analyse"
]
},
{
@@ -1212,10 +1224,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4. Read Stores Data"
+ "## 2.4. Ladendaten einlesen"
]
},
{
@@ -1329,10 +1342,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "describe data: We have a data set with 45 entries. Each row contains the values \"Store\", \"Type\" and \"Size\". The format of these values are two integer and one object. "
+ "Daten beschreiben: Wir haben einen Datensatz mit 45 Einträgen. Jede Zeile enthält die Werte \"Store\", \"Type\" und \"Size\". Das Format dieser Werte sind zwei Ganzzahlen und ein Objekt. "
]
},
{
@@ -1462,17 +1476,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "describe data: Since we already know that there are 45 stores, this is a dataset that provides details about each store. There are 45 stores, there are 3 types (A,B and C) and the size of the stores varies from 34,875 to 219,622 in probably square feet."
+ "Daten beschreiben: Da wir bereits wissen, dass es 45 Läden gibt, ist dies ein Datensatz, der Einzelheiten über jeden Laden enthält. Es gibt 45 Läden, es gibt 3 Typen (A, B und C) und die Größe der Läden variiert zwischen 34.875 und 219.622 Quadratmetern."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.1. Descriptive Analysis"
+ "## 2.4.1. Deskriptive Analyse"
]
},
{
@@ -1515,10 +1531,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "It looks like the \"Type\" can be determined based on size. However, you can also see that there are at least 4 stores that were probably assigned incorrectly, since there are some in the size range between 25,000 and 50,000 in type B and A that probably should have been assigned more to type C."
+ "Es sieht so aus, als ob der \"Typ\" anhand der Größe bestimmt werden kann. Sie können aber auch sehen, dass es mindestens 4 Geschäfte gibt, die wahrscheinlich falsch zugeordnet wurden, da es einige im Größenbereich zwischen 25.000 und 50.000 in Typ B und A gibt, die wahrscheinlich eher dem Typ C hätten zugeordnet werden müssen."
]
},
{
@@ -1715,26 +1732,29 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We combine all 3 available data sets (tables), since all 3 share a common key (\"Store\") over which a summary is possible. The further consideration becomes only possible if we consider the data from all 3 data sets together, which dependencies exist here. \n",
+ "Wir kombinieren alle 3 verfügbaren Datensätze (Tabellen), da alle 3 einen gemeinsamen Schlüssel (\"Store\") haben, über den eine Zusammenfassung möglich ist. Die weitere Betrachtung wird erst möglich, wenn wir die Daten aus allen 3 Datensätzen zusammen betrachten, welche Abhängigkeiten hier bestehen. \n",
"\n",
- "The goal is to make a forecast on the data of \"Weekly_Sales\", therefore we are looking for all those data that have an influence on this."
+ "Ziel ist es, eine Vorhersage über die Daten von \"Weekly_Sales\" zu machen, daher suchen wir alle Daten, die einen Einfluss auf diese haben."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1. Combine data"
+ "## 3.1. Daten kombinieren"
]
},
{
@@ -2005,17 +2025,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2. Clean combined Data"
+ "## 3.2. Kombinierte Daten bereinigen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We convert the field \"IsHoliday\" from boolean to an int with 0 and 1 "
+ "Wir konvertieren das Feld \"IsHoliday\" von boolean in ein int mit 0 und 1 "
]
},
{
@@ -2029,10 +2051,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We convert the field \"Date\" from an object to a date field"
+ "Wir konvertieren das Feld \"Datum\" von einem Objekt in ein Datumsfeld"
]
},
{
@@ -2639,10 +2662,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3. Descriptive Analysis new Data"
+ "## 3.3. Deskriptive Analyse neue Daten"
]
},
{
@@ -2691,10 +2715,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We see that sales behave similarly in the 3 years. There are peaks before week 7 (Super Bowl), before week 48 (ThanksGiving) and before week 52 (Christmas), which is presumably generated by black Friday and the Christmas business. Sales drop before week 37 (Labor Day)."
+ "Wir sehen, dass sich die Verkäufe in den 3 Jahren ähnlich verhalten. Es gibt Spitzenwerte vor Woche 7 (Super Bowl), vor Woche 48 (Thanksgiving) und vor Woche 52 (Weihnachten), was vermutlich auf den schwarzen Freitag und das Weihnachtsgeschäft zurückzuführen ist. Vor Woche 37 (Tag der Arbeit) gehen die Verkäufe zurück."
]
},
{
@@ -2850,10 +2875,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "division 92 brings in the most sales in total and on average "
+ "Die Abteilung 92 bringt insgesamt und im Durchschnitt die meisten Umsätze ein "
]
},
{
@@ -3275,10 +3301,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The holidays are always in the same week every year. Looking at the calendar week of the holiday and the curve of Weekly Sales: Thanksgiving and Christmas has an impact on sales, but the remaining two are not significant. However, we leave IsHoliday in the table because of this."
+ "Die Feiertage fallen jedes Jahr in dieselbe Woche. Betrachtet man die Kalenderwoche des Feiertags und die Kurve der wöchentlichen Verkäufe, so zeigt sich, dass Thanksgiving und Weihnachten haben einen Einfluss auf den Umsatz, die beiden anderen sind jedoch nicht signifikant. Aus diesem Grund lassen wir IsHoliday in der Tabelle stehen."
]
},
{
@@ -3347,14 +3374,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "MarkDown1: many individual actions, major action just before week 7 with Super Bowl; \n",
- "MarkDown2: Action in week 52 before Christmas, smaller atkion before week 7 with Super Bowl;\n",
- "MarkDown3: only action in week 47 before ThanksGiving;\n",
- "MarkDown4: many individual actions, larger one in week 5 before Super Bowl;\n",
- "MarkDown5: many individual promotions, larger in week 48 on ThanksGiving possibly also Black Friday"
+ "MarkDown1: viele Einzelaktionen, große Aktion kurz vor Woche 7 mit Super Bowl; \n",
+ "MarkDown2: Aktion in Woche 52 vor Weihnachten, kleinere Aktion vor Woche 7 mit Super Bowl;\n",
+ "MarkDown3: einzige Aktion in Woche 47 vor ThanksGiving;\n",
+ "MarkDown4: viele Einzelaktionen, größere Aktion in Woche 5 vor Super Bowl;\n",
+ "MarkDown5: viele Einzelaktionen, größere in Woche 48 zu ThanksGiving evtl. auch Black Friday"
]
},
{
@@ -3838,10 +3866,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.4. Test for Correlation"
+ "## 3.4. Test auf Korrelation"
]
},
{
@@ -3947,17 +3976,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.5. Create and analyse new data set"
+ "## 3.5. Erstellen und Analysieren eines neuen Datensatzes"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "\"small data set\": test data for which we consider only 1 year for which we have MarkDown values."
+ "\"kleiner Datensatz\": Testdaten, für die wir nur 1 Jahr berücksichtigen, für das wir MarkDown-Werte haben."
]
},
{
@@ -4361,10 +4392,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.6. normal (big) Data"
+ "## 3.6. normale (große) Daten"
]
},
{
@@ -4518,10 +4550,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.7. Test and Training Data"
+ "## 3.7. Testen und Trainieren von Daten"
]
},
{
@@ -4563,40 +4596,43 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1. Random Forest with normal (big) Data"
+ "## 4.1. Random Forest mit normalen (großen) Daten"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "class sklearn.ensemble.RandomForestRegressor(n_estimators=100, *, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0. 0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None)\n",
+ "class sklearn.ensemble.RandomForestRegressor(n_estimators=100, *, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0. 0, max_features='auto', max_leaf_nodes=Keine, min_impurity_decrease=0.0, min_impurity_split=Eine, bootstrap=True, oob_score=False, n_jobs=Eine, random_state=Eine, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=Eine)\n",
"\n",
- "bootstrap: Whether bootstrap samples are used when creating trees. If False, the entire dataset is used to create each tree. Default is True.\n",
+ "bootstrap: Ob bei der Erstellung von Bäumen Bootstrap-Stichproben verwendet werden. Wenn False, wird der gesamte Datensatz zur Erstellung jedes Baums verwendet. Voreinstellung ist True.\n",
"\n",
- "max_depth: The default values for the parameters that control the size of trees (e.g. max_depth, min_samples_leaf, etc.) result in fully grown and untrimmed trees that can potentially be very large for some datasets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting these parameter values. Default is None and means then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. The value of 70 has been proven by testing.\n",
+ "max_depth: Die Standardwerte für die Parameter, die die Größe der Bäume steuern (z. B. max_depth, min_samples_leaf usw.), führen zu vollständig ausgewachsenen und nicht beschnittenen Bäumen, die bei einigen Datensätzen sehr groß sein können. Um den Speicherverbrauch zu reduzieren, sollte die Komplexität und Größe der Bäume durch die Einstellung dieser Parameterwerte kontrolliert werden. Der Standardwert ist None und bedeutet, dass die Knoten so lange expandiert werden, bis alle Blätter rein sind oder bis alle Blätter weniger als min_samples_split samples enthalten. Der Wert von 70 hat sich in Tests bewährt.\n",
"\n",
- "max_features: The default value max_features=\"auto\" uses n_features at each split. Default is \"auto\".\n",
+ "max_features: Der Standardwert max_features=\"auto\" verwendet n_features bei jedem Split. Die Voreinstellung ist \"auto\".\n",
"\n",
- "min_samples_leaf: The minimum number of samples required to be at a leaf node. A split point at any depth is only considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This can have the effect of smoothing the model, especially in regression. Default is 1. Test series have shown that the value 4 gives the best results.\n",
+ "min_samples_leaf: Die Mindestanzahl von Stichproben, die für einen Blattknoten erforderlich sind. Ein Teilungspunkt in beliebiger Tiefe wird nur berücksichtigt, wenn er mindestens min_samples_leaf Trainingsmuster in jedem der linken und rechten Zweige hinterlässt. Dies kann zu einer Glättung des Modells führen, insbesondere bei Regressionen. Standardwert ist 1. Testreihen haben gezeigt, dass der Wert 4 die besten Ergebnisse liefert.\n",
"\n",
- "min_samples_split: The minimum number of samples required to split an internal node. Default is 2, through test series the value 10 has proven to be the most useful here. \n",
+ "min_samples_split: Die Mindestanzahl von Stichproben, die erforderlich ist, um einen internen Knoten aufzuteilen. Standardwert ist 2, in Testreihen hat sich der Wert 10 als am sinnvollsten erwiesen. \n",
"\n",
- "n_estimators: This is the number of trees you want to build before taking the maximum vote or average of predictions. A higher number of trees will result in better performance, but will make the code slower. Default is 100. Test series have shown: below and above 400 the accuracy decreases.\n",
+ "n_Schätzer: Dies ist die Anzahl der Bäume, die Sie erstellen möchten, bevor Sie die Maximalstimme oder den Durchschnitt der Vorhersagen nehmen. Eine höhere Anzahl von Bäumen führt zu einer besseren Leistung, macht den Code aber auch langsamer. Die Voreinstellung ist 100. Testreihen haben gezeigt: Unter und über 400 nimmt die Genauigkeit ab.\n",
"\n",
- "n_jobs: how many jobs can run in parallel influences the speed of the algorithm\n",
+ "n_jobs: wie viele Jobs parallel laufen können, beeinflusst die Geschwindigkeit des Algorithmus\n",
"\n",
- "The features are always permuted randomly at each split. Therefore, even with the same training data, max_features=n_features and bootstrap=False, the best split found may vary if the criterion improvement is identical for several splits enumerated in the search for the best split. To obtain deterministic behavior in the fitting, random_state must be specified in the test series."
+ "Die Merkmale werden bei jedem Split immer zufällig permutiert. Daher kann selbst bei gleichen Trainingsdaten, max_features=n_features und bootstrap=False der beste gefundene Split variieren, wenn die Kriteriumverbesserung für mehrere Splits, die bei der Suche nach dem besten Split aufgezählt werden, identisch ist. Um ein deterministisches Verhalten bei der Anpassung zu erhalten, muss random_state in der Testreihe angegeben werden."
]
},
{
@@ -4704,10 +4740,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2. Random Forest with new small Data"
+ "## 4.2. Random Forest mit neuen kleinen Daten"
]
},
{
@@ -4742,14 +4779,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### MAE - der Mean Absolute Error\n",
- "indicates the average absolute deviation of the forecast value from the actual value. \n",
- "value that actually occurred. Overestimates and underestimates are accumulated in an undirected manner, so that MAE=0 \n",
- "can be interpreted as a perfect forecast, i.e. the forecast value for each period t corresponds exactly to the actual value. \n",
- "the value that actually occurred. "
+ "### MAE - der mittlere absolute Fehler\n",
+ "gibt die durchschnittliche absolute Abweichung des Prognosewertes vom tatsächlichen Wert an. \n",
+ "Wert, der tatsächlich eingetreten ist. Über- und Unterschätzungen werden in ungerichteter Weise kumuliert, so dass MAE=0 \n",
+ "als perfekte Prognose interpretiert werden kann, d.h. der Prognosewert für jede Periode t entspricht genau dem tatsächlichen Wert. \n",
+ "dem tatsächlich eingetretenen Wert. "
]
},
{
@@ -4773,18 +4811,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We obtain a MAE of 1,716.1138, means that the average deviation is about 1,716 dollars with which our forecast differs from the actual values."
+ "Wir erhalten einen MAE von 1.716,1138, d.h. die durchschnittliche Abweichung beträgt etwa 1.716 Dollar, mit der unsere Prognose von den tatsächlichen Werten abweicht."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### MSE - Mean Squared Error \n",
- "is the quadratic form of the ME. As a result, large deviations from the actual value are given a disproportionately high weighting compared with smaller deviations, which are often negligible in practice. In addition, the individual forecast errors are now cumulated in the same way as for the MAE, so that the perfect forecast is again available at MSE=0. "
+ "### MSE - Mittlerer quadratischer Fehler \n",
+ "ist die quadratische Form des ME. Dies hat zur Folge, dass große Abweichungen vom tatsächlichen Wert überproportional stark gewichtet werden im Vergleich zu kleineren Abweichungen, die in der Praxis oft vernachlässigbar sind. Außerdem werden die einzelnen Prognosefehler nun wie beim MAE kumuliert, so dass die perfekte Prognose wieder bei MSE=0 vorliegt. "
]
},
{
@@ -4808,11 +4848,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "#R² - coefficient of determination\n",
- "The so-called coefficient of determination (R²) expresses how well the regression line reflects the relationship between the independent and dependent variable. R² lies between 0 and 1, whereby the value R² = 1 would mean that every observed data point lies directly on the regression line."
+ "#R² - Bestimmungskoeffizient\n",
+ "Das so genannte Bestimmtheitsmaß (R²) drückt aus, wie gut die Regressionsgerade die Beziehung zwischen der unabhängigen und der abhängigen Variable wiedergibt. R² liegt zwischen 0 und 1, wobei der Wert R² = 1 bedeuten würde, dass jeder beobachtete Datenpunkt direkt auf der Regressionsgeraden liegt."
]
},
{
@@ -4834,10 +4875,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "This means that the predicted values deviate relatively little and are about 97% on the straight line."
+ "Das bedeutet, dass die vorhergesagten Werte relativ wenig abweichen und zu etwa 97 % auf der Geraden liegen."
]
},
{
diff --git a/Insurance/Insurance Fraud detection/notebook_1.ipynb b/Insurance/Insurance Fraud detection/notebook_1.ipynb
index eb7e10e..e2ffc93 100644
--- a/Insurance/Insurance Fraud detection/notebook_1.ipynb
+++ b/Insurance/Insurance Fraud detection/notebook_1.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -8,31 +9,35 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Insurance companies hold a lot of data, including very sensitive data such as the name, date of birth and exact residential address and account details of their policyholders. More and more, insurance companies are now processing and evaluating this data automatically and using it for further insurance processes. Of course, this still involves calculating the current risk for existing insurance products and, based on this, calculating the premium and the possible amount of the claim. Creating new insurance products based on demand, which can be concluded at short notice and have a very short term, is another trend that can be supported by data. The central questions here are, of course: What data is relevant for detecting insurance fraud? How must this data be structured? Which model is best suited to predict insurance fraud in a claim? What criteria should be used to compare different ML models? How reliable does insurance fraud prediction work?"
+ "Versicherungen verfügen über eine Vielzahl von Daten, darunter auch sehr sensible Daten wie Name, Geburtsdatum und genaue Wohnanschrift und Kontoverbindung ihrer Versicherten. Diese Daten werden von den Versicherungsunternehmen zunehmend automatisiert verarbeitet, ausgewertet und für weitere Versicherungsprozesse genutzt. Dabei geht es natürlich nach wie vor darum, für bestehende Versicherungsprodukte das aktuelle Risiko zu berechnen und darauf aufbauend die Prämie und die mögliche Schadenshöhe zu ermitteln. Die Schaffung neuer, bedarfsgerechter Versicherungsprodukte, die kurzfristig abgeschlossen werden können und eine sehr kurze Laufzeit haben, ist ein weiterer Trend, der durch Daten unterstützt werden kann. Die zentralen Fragen hierbei sind natürlich: Welche Daten sind für die Aufdeckung von Versicherungsbetrug relevant? Wie müssen diese Daten strukturiert sein? Welches Modell ist am besten geeignet, um Versicherungsbetrug im Schadenfall vorherzusagen? Nach welchen Kriterien sollte man verschiedene ML-Modelle vergleichen? Wie zuverlässig funktioniert die Vorhersage von Versicherungsbetrug?"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data Understanding"
+ "# 2. Datenverständnis"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data set used consists of 1000 sentences and has 40 different so-called features, i.e. collected data categories. This means that the data basis is not that large, but the possibilities to examine different features are all the greater. It shows information on policyholders, policy data on policyholders and their motor vehicles, as well as accidents and the amount of claims. Since there are so many features in the dataset, they and their descriptions are explained in the table below. The target variable shows whether insurance fraud is present (\"fraud_reported\"). The dataset is a good example of classification models because of the binary target variable (also referred to as target later)."
+ "Der verwendete Datensatz besteht aus 1000 Sätzen und hat 40 verschiedene sogenannte Features, d.h. gesammelte Datenkategorien. Das bedeutet, dass die Datenbasis nicht sehr groß ist, dafür sind die Möglichkeiten, verschiedene Merkmale zu untersuchen, umso größer. Es werden Informationen zu Versicherungsnehmern, Vertragsdaten zu Versicherungsnehmern und deren Kraftfahrzeugen, sowie Unfälle und die Höhe der Schäden angezeigt. Da der Datensatz so viele Merkmale enthält, werden sie und ihre Beschreibungen in der folgenden Tabelle erläutert. Die Zielvariable zeigt an, ob ein Versicherungsbetrug vorliegt (\"fraud_reported\"). Der Datensatz ist ein gutes Beispiel für Klassifizierungsmodelle, da es sich um eine binäre Zielvariable handelt (später auch als Ziel bezeichnet)."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1 Import of relevant Modules"
+ "## 2.1 Import von relevanten Modulen"
]
},
{
@@ -52,10 +57,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2 Read Data"
+ "## 2.2 Daten einlesen"
]
},
{
@@ -68,10 +74,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3 Descriptive Analytics"
+ "## 2.3 Deskriptive Analyse"
]
},
{
@@ -95,6 +102,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -388,17 +396,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1 Data cleaning"
+ "## 3.1 Datenbereinigung"
]
},
{
@@ -1262,10 +1272,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Test for Correlation"
+ "## 3.2 Test auf Korrelation"
]
},
{
@@ -1298,10 +1309,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "--> high correalion between *age* and *months_as_customer*, also between *total_claim_amount* and *injury_claim, property_claim and vehicle_claim*. "
+ "--> hohe Korrelation zwischen *Alter* und *Monate_als_Kunde*, auch zwischen *Gesamtschadenshöhe* und *Verletzungsschaden, Sachschaden und Fahrzeugschaden*. "
]
},
{
@@ -1525,10 +1537,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Checking features in relation to target"
+ "## 3.2 Überprüfung der Merkmale in Bezug auf das Ziel"
]
},
{
@@ -2760,11 +2773,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3 Feature Engineering\n",
- "### new feature with percentage of paid share of damage (without deductible)"
+ "## 3.3 Merkmal Technik\n",
+ "### neues Merkmal mit prozentualem Anteil des bezahlten Schadens (ohne Selbstbeteiligung)"
]
},
{
@@ -4038,17 +4052,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "--> Its an unbalanced dataset."
+ "--> Es handelt sich um einen unausgewogenen Datensatz."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.4 Creating dummy-variables"
+ "## 3.4 Dummy-Variablen erstellen"
]
},
{
diff --git a/Insurance/Insurance Fraud detection/notebook_2.ipynb b/Insurance/Insurance Fraud detection/notebook_2.ipynb
index 1c350bd..4cdbe9f 100644
--- a/Insurance/Insurance Fraud detection/notebook_2.ipynb
+++ b/Insurance/Insurance Fraud detection/notebook_2.ipynb
@@ -1,17 +1,19 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modeling "
+ "# 4. Modellierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1 Import of relevant Modules"
+ "## 4.1 Import von relevanten Modulen"
]
},
{
@@ -39,10 +41,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2 Read Data"
+ "## 4.2 Daten einlesen"
]
},
{
@@ -292,10 +295,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.3 Data Preparation for Modeling"
+ "## 4.3 Datenvorbereitung für die Modellierung"
]
},
{
@@ -618,17 +622,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4.4 Modeling and Evaluation"
+ "# 4.4 Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 4.4.1 Logistic regression"
+ "### 4.4.1 Logistische Regression"
]
},
{
@@ -857,10 +863,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 4.4.2 Decision Tree"
+ "### 4.4.2 Entscheidungsbaum"
]
},
{
@@ -1007,6 +1014,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1157,6 +1165,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1307,6 +1316,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/Insurance/Predicting mental illness for health insurance/notebook.ipynb b/Insurance/Predicting mental illness for health insurance/notebook.ipynb
index 07d3a03..28eb44c 100644
--- a/Insurance/Predicting mental illness for health insurance/notebook.ipynb
+++ b/Insurance/Predicting mental illness for health insurance/notebook.ipynb
@@ -1,20 +1,22 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Predicting mental illness for health insurance\n",
+ "# Vorhersage von psychischen Erkrankungen für die Krankenversicherung\n",
"# 1. Business Understanding\n",
- "Which group of people, determined by age, gender, previous illness and job characteristics, has to pay a higher insurance premium in proportion. So that, in the event of a general claim and loss of working hours, sufficient coverage is guaranteed for the insurance to be able to finance additional treatment costs incurred for mental illnesses? \n"
+ "Welche Personengruppe, bestimmt durch Alter, Geschlecht, Vorerkrankungen und berufliche Merkmale, muss im Verhältnis eine höhere Versicherungsprämie zahlen. Damit bei einem allgemeinen Schadensfall und Arbeitsausfall eine ausreichende Deckung gewährleistet ist, damit die Versicherung die zusätzlichen Behandlungskosten für psychische Erkrankungen finanzieren kann? \n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding \n",
- "## 2.1. Import of Relevant Modules "
+ "# 2. Daten und Datenverständnis \n",
+ "## 2.1. Import von relevanten Modulen "
]
},
{
@@ -47,10 +49,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. Read Data "
+ "## 2.2. Daten einlesen "
]
},
{
@@ -280,10 +283,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3. Descriptive Analytics "
+ "## 2.3. Deskriptive Analyse "
]
},
{
@@ -808,6 +812,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -843,11 +848,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation \n",
- "## 3.1 Remove duplicates"
+ "# 3. Datenaufbereitung \n",
+ "## 3.1 Duplikate entfernen"
]
},
{
@@ -925,10 +931,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Remove missing data"
+ "## 3.2 Fehlende Daten entfernen"
]
},
{
@@ -1100,10 +1107,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3 Remove unwanted Features"
+ "## 2.3 Unerwünschte Merkmale entfernen"
]
},
{
@@ -1641,18 +1649,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4 Remove outliers"
+ "## 2.4 Ausreißer entfernen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "some people have put in a troll value for 'age' these rows need to be removed \n",
- "the age should be between 16 and 70"
+ "einige Personen haben einen Trollwert für \"Alter\" eingegeben, diese Zeilen müssen entfernt werden \n",
+ "das Alter sollte zwischen 16 und 70 Jahren liegen"
]
},
{
@@ -1766,10 +1776,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.5 Clean the data"
+ "## 2.5 Daten bereinigen"
]
},
{
@@ -8962,13 +8973,14 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "This feature look like a good predictor of the target variable treatment. \n",
- "But the information \"Does your mental health interfere with your work\" can not be collected. \n",
- "No one in their right mind would tell the truth to this question to their health insurance. \n",
- "therefore work interfere needs to be dropped"
+ "Dieses Merkmal scheint ein guter Prädiktor für die Zielvariable Behandlung zu sein. \n",
+ "Aber die Information \"Beeinträchtigt Ihre psychische Gesundheit Ihre Arbeit\" kann nicht erhoben werden. \n",
+ "Niemand, der bei klarem Verstand ist, würde seiner Krankenkasse gegenüber die Wahrheit zu dieser Frage sagen. \n",
+ "Daher muss die Frage nach der Beeinträchtigung der Arbeit gestrichen werden."
]
},
{
@@ -9500,10 +9512,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.6 create dummy features for all string based variables"
+ "## 2.6 Dummy-Merkmale für alle stringbasierten Variablen erstellen"
]
},
{
@@ -9727,11 +9740,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation \n",
- "## decision tree"
+ "# 4. Modellierung und Evaluation \n",
+ "## Decision tree"
]
},
{
@@ -9901,6 +9915,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -9950,10 +9965,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## logistic Regression"
+ "## Logistische Regression"
]
},
{
diff --git a/Insurance/Prediction Interest for car insurance/notebook.ipynb b/Insurance/Prediction Interest for car insurance/notebook.ipynb
index e80fff5..121ae91 100644
--- a/Insurance/Prediction Interest for car insurance/notebook.ipynb
+++ b/Insurance/Prediction Interest for car insurance/notebook.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -8,37 +9,41 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "InsurTech companies are addressing the\n",
- "the topic of modernizing the insurance industry. The aim is to make\n",
- "insurance companies cheaper, more appealing, \"more efficient\" through modern\n",
- "\"insurance models, digitalized processes, new consulting and\n",
- "management methods\" are to be designed. The increasing challenges make it more difficult for insurers to compete and survive in the market. Composed of adaptive customer demands, \"digitalization, stricter regulation and bureaucracy\", insurers are clearly put to the test with the aspect of economic results. At the same moment, opportunities arise in the business model that are still\n",
- "remain untapped. One of these models is the principle of cross-selling. For this model to be implemented, the\n",
- "the opinion of existing customers is essential. The business model should be based on anticipated on the basis of the existing interest of the customer. The analysis is intended to show the insurer what potential is actually available and in what amount. The analysis will address the question of whether there are customers who are interested in car insurance. The answer to this question should also simultaneously provide a representation of the level of interest. Thus, an assessment can be made as to whether actively approaching customers is worthwhile at all."
+ "InsurTech-Unternehmen beschäftigen sich mit dem\n",
+ "das Thema der Modernisierung der Versicherungsbranche. Das Ziel ist es, die\n",
+ "Versicherungen günstiger, attraktiver, \"effizienter\" zu machen durch moderne\n",
+ "\"Versicherungsmodelle, digitalisierte Prozesse, neue Beratungs- und\n",
+ "Managementmethoden\" gestaltet werden. Die zunehmenden Herausforderungen erschweren es den Versicherern, sich im Wettbewerb zu behaupten und am Markt zu bestehen. Bestehend aus adaptiven Kundenansprüchen, \"Digitalisierung, strengerer Regulierung und Bürokratie\", werden die Versicherer unter dem Aspekt des wirtschaftlichen Ergebnisses deutlich auf die Probe gestellt. Gleichzeitig ergeben sich Chancen im Geschäftsmodell, die noch ungenutzt sind.\n",
+ "ungenutzt bleiben. Eines dieser Modelle ist das Prinzip des Cross-Selling. Damit dieses Modell umgesetzt werden kann, ist die\n",
+ "ist die Meinung der bestehenden Kunden entscheidend. Das Geschäftsmodell sollte auf der Grundlage des bestehenden Kundeninteresses antizipiert werden. Die Analyse soll dem Versicherer zeigen, welches Potenzial in welcher Höhe tatsächlich vorhanden ist. Die Analyse wird sich mit der Frage beschäftigen, ob es Kunden gibt, die sich für eine Kfz-Versicherung interessieren. Die Antwort auf diese Frage soll gleichzeitig auch eine Darstellung des Interesses liefern. So lässt sich abschätzen, ob sich eine aktive Kundenansprache überhaupt lohnt."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding"
+ "# 2. Daten und Datenverständnis"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The dataset is called Janatahack Cross-sell Prediction and was split into a training and a test dataset. The attribute \"Response\" is the target variable and expresses a customer's interest in car insurance. However, since this important column is curiously missing from the test data set, only the training data set was used for the model. This was therefore used for training and testing. The data set consists of 12 columns and 381109 rows. Each row represents one person."
+ "Der Datensatz heißt Janatahack Cross-sell Prediction und wurde in einen Trainings- und einen Testdatensatz aufgeteilt. Das Attribut \"Response\" ist die Zielvariable und drückt das Interesse eines Kunden an einer Kfz-Versicherung aus. Da diese wichtige Spalte jedoch merkwürdigerweise im Testdatensatz fehlt, wurde nur der Trainingsdatensatz für das Modell verwendet. Dieser wurde also für Training und Test verwendet. Der Datensatz besteht aus 12 Spalten und 381109 Zeilen. Jede Zeile steht für eine Person."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -70,10 +75,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. Read Data"
+ "## 2.2. Daten einlesen"
]
},
{
@@ -87,10 +93,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "At the beginning, an overview of the data is provided. Only the first 5 data entries or lines are considered."
+ "Zu Beginn wird ein Überblick über die Daten gegeben. Es werden nur die ersten 5 Dateneinträge oder Zeilen berücksichtigt."
]
},
{
@@ -247,10 +254,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now that the initial overview of the data is apparent, let's look at the data types of the individual columns."
+ "Nachdem wir nun einen ersten Überblick über die Daten erhalten haben, wollen wir uns nun die Datentypen der einzelnen Spalten ansehen."
]
},
{
@@ -289,24 +297,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data types match the indicated columns. The danger of producing NULL values is not present. Therefore no correction is necessary."
+ "Die Datentypen stimmen mit den angegebenen Spalten überein. Die Gefahr der Erzeugung von NULL-Werten ist nicht gegeben. Daher ist keine Korrektur erforderlich."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3. Data cleaning"
+ "## 2.3. Datenbereinigung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, all empty cells from the data set are summed, in which range with which total number of variables are missing."
+ "Zunächst werden alle leeren Zellen aus dem Datensatz summiert, in welchem Bereich mit welcher Gesamtzahl von Variablen sie fehlen."
]
},
{
@@ -342,17 +353,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "No NULL values are given. Therefore, neither replacement by synthetic data nor deletion of specific rows is needed."
+ "Es werden keine NULL-Werte angegeben. Daher ist weder die Ersetzung durch synthetische Daten noch die Löschung bestimmter Zeilen erforderlich."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "After the table has been checked for NULL values, the check for duplicates follows."
+ "Nachdem die Tabelle auf NULL-Werte geprüft wurde, folgt die Prüfung auf Duplikate."
]
},
{
@@ -416,24 +429,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Again, no duplicates were detected."
+ "Auch hier wurden keine Duplikate festgestellt."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4. Descriptive Analytics"
+ "## 2.4. Deskriptive Analyse"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "After checking for NULL values and duplicates, descriptive statistics are now applied to the data. This should give the first impression of how the data is structured at the moment."
+ "Nach der Überprüfung auf NULL-Werte und Duplikate wird nun die deskriptive Statistik auf die Daten angewendet. Dies sollte einen ersten Eindruck davon vermitteln, wie die Daten im Moment strukturiert sind."
]
},
{
@@ -697,31 +713,35 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "After analyzing the descriptive statistics, the Annual_Premium column stands out. The average (mean) shows a value of 30564.389581. The conspicuous feature here is the maximum value of 540165, which clearly stands out. This already indicates an outlier in the maximum value. "
+ "Nach der Analyse der deskriptiven Statistiken sticht die Spalte \"Jahresprämie\" hervor. Der Durchschnitt (Mittelwert) weist einen Wert von 30564,389581 auf. Auffallend ist hier der Maximalwert von 540165, der deutlich hervorsticht. Dies deutet bereits auf einen Ausreißer beim Maximalwert hin. "
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data set consists of 381109 rows, one row each describing the customer and 12 columns describing the customer's attributes. This data is used to try to classify whether a customer is interested in additional car insurance or not. For this purpose, the historical data contains the target variable \"Response\", which provides information about whether a customer is interested. "
+ "Der Datensatz besteht aus 381109 Zeilen, jeweils eine Zeile zur Beschreibung des Kunden und 12 Spalten zur Beschreibung der Eigenschaften des Kunden. Mit diesen Daten wird versucht zu klassifizieren, ob ein Kunde an einer zusätzlichen Kfz-Versicherung interessiert ist oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Antwort\", die Auskunft darüber gibt, ob ein Kunde interessiert ist. "
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2.4.1. Numeric attributes"
+ "### 2.4.1. Numerische Attribute"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "We start with the numerical attributes. First, the distributions of the numerical attributes are examined individually and in a second step the categorical attributes are related to the target variable."
+ "Wir beginnen mit den numerischen Attributen. Zunächst werden die Verteilungen der numerischen Attribute einzeln untersucht und in einem zweiten Schritt werden die kategorischen Attribute mit der Zielvariablen in Beziehung gesetzt."
]
},
{
@@ -735,10 +755,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Age"
+ "### Alter"
]
},
{
@@ -772,12 +793,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- The majority of customers are between 20 and 30 years old\n",
- "- The curve flattens strongly towards the back\n",
- "- Questionable whether there are outliers in the back part --> Apply boxplot"
+ "- Die Mehrheit der Kunden ist zwischen 20 und 30 Jahre alt\n",
+ "- Die Kurve flacht nach hinten stark ab\n",
+ "- Fraglich, ob es Ausreißer im hinteren Teil gibt --> Boxplot anwenden"
]
},
{
@@ -820,11 +842,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Customers between the ages of 20 to 30 tend to have rather no interest in taking out additional insurance.\n",
- "- Customers aged between 35 and 55 have the highest level of interest. "
+ "- Kunden im Alter von 20 bis 30 Jahren haben eher kein Interesse am Abschluss einer Zusatzversicherung.\n",
+ "- Das größte Interesse besteht bei Kunden zwischen 35 und 55 Jahren. "
]
},
{
@@ -849,17 +872,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "With the help of the box plot, it was possible to ensure that no outliers could be found. If outliers were to appear, they would appear as dots above the orange line."
+ "Mit Hilfe des Boxplots konnte sichergestellt werden, dass keine Ausreißer zu finden waren. Sollten Ausreißer auftreten, würden sie als Punkte oberhalb der orangefarbenen Linie erscheinen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Driving License"
+ "### Führerschein"
]
},
{
@@ -893,19 +918,21 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Column consists only of 0 and 1\n",
- "- A large proportion of customers have a driver's license\n",
- "- The bar chart can be used to illustrate a clearer representation."
+ "- Spalte besteht nur aus 0 und 1\n",
+ "- Ein großer Teil der Kunden hat einen Führerschein\n",
+ "- Das Balkendiagramm kann verwendet werden, um eine klarere Darstellung zu erhalten."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Region Code"
+ "### Regionalcode"
]
},
{
@@ -939,11 +966,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- All regions are marked with a number\n",
- "- The region code denotes a unique code for a specific region"
+ "- Alle Regionen sind mit einer Nummer gekennzeichnet\n",
+ "- Der Regionalcode bezeichnet einen eindeutigen Code für eine bestimmte Region"
]
},
{
@@ -986,15 +1014,17 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- The majority of customers are resident between 25 and 30.\n",
- "- Customers from regions with the code between 25 and 30 have a great interest in additional insurance.\n",
- "- Customers from regions with the code between 5 and 10 are not very interested in additional insurance."
+ "- Die Mehrheit der Kunden ist zwischen 25 und 30 Jahre alt.\n",
+ "- Kunden aus Regionen mit dem Code zwischen 25 und 30 haben ein großes Interesse an Zusatzversicherungen.\n",
+ "- Kunden aus Regionen mit einem Code zwischen 5 und 10 sind nicht sehr an einer Zusatzversicherung interessiert."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1032,10 +1062,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Categorical attribute, since 1 represents a 'Yes' and 0 a 'No' response."
+ "- Kategoriales Attribut, da 1 eine \"Ja\"- und 0 eine \"Nein\"-Antwort darstellt."
]
},
{
@@ -1078,17 +1109,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Customers who already have car insurance tend not to show interest in the additional offer."
+ "- Kunden, die bereits eine Kfz-Versicherung haben, zeigen in der Regel kein Interesse an dem zusätzlichen Angebot."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Annual Premium"
+ "### Jahresprämie"
]
},
{
@@ -1122,12 +1155,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Normal distribution recognizable\n",
- "- Outlier recognizable, curve flattens extremely strongly to the right\n",
- "- The cost range that a customer pays for a premium membership is between 0 and 100000."
+ "- Normalverteilung erkennbar\n",
+ "- Ausreißer erkennbar, Kurve flacht extrem stark nach rechts ab\n",
+ "- Der Kostenbereich, den ein Kunde für eine Premium-Mitgliedschaft bezahlt, liegt zwischen 0 und 100000."
]
},
{
@@ -1170,6 +1204,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1207,11 +1242,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Policy Sales Channel is the choice of the channel through which the customer is reached (e.g. by phone).\n",
- "- Code 150 is the channel with the highest customer reach"
+ "- Policy Sales Channel ist die Wahl des Kanals, über den der Kunde erreicht wird (z. B. per Telefon).\n",
+ "- Code 150 ist der Kanal mit der höchsten Kundenreichweite"
]
},
{
@@ -1254,13 +1290,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- The customers with the greatest interest in additional car insurance are reached via channels 25 and 125."
+ "- Die Kunden mit dem größten Interesse an einer zusätzlichen Kfz-Versicherung werden über die Kanäle 25 und 125 erreicht."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1298,12 +1336,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- No normal distribution recognizable\n",
- "- No outliers recognizable\n",
- "- Customers are potentially evenly distributed over the individual days."
+ "- Keine Normalverteilung erkennbar\n",
+ "- Keine Ausreißer erkennbar\n",
+ "- Kunden sind potentiell gleichmäßig über die einzelnen Tage verteilt."
]
},
{
@@ -1346,14 +1385,16 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Even distribution\n",
- "- No trend for interest apparent."
+ "- Gleichmäßige Verteilung\n",
+ "- Kein Trend zu Zinsen erkennbar."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1391,19 +1432,21 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Categorical variable available as numeric variable (0 and 1 only).\n",
- "- 1 stands for interest, 0 stands for no interest\n",
- "- A large proportion of customers have no interest in further insurance"
+ "- Kategoriale Variable, verfügbar als numerische Variable (nur 0 und 1).\n",
+ "- 1 steht für Interesse, 0 steht für kein Interesse\n",
+ "- Ein großer Teil der Kunden hat kein Interesse an einer weiteren Versicherung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Correlation matrix of the numerical variables"
+ "### Korrelationsmatrix der numerischen Variablen"
]
},
{
@@ -1438,38 +1481,43 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The matrix does not show any conspicuously strong correlations. The correlations Previously_Insured/Policy_Sales_Channel and Age/Annual_Premium are moderately correlated."
+ "Die Matrix weist keine auffallend starken Korrelationen auf. Die Korrelationen Previously_Insured/Policy_Sales_Channel und Age/Annual_Premium sind mäßig korreliert."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4.2 Categorical attributes"
+ "## 2.4.2 Kategorische Attribute"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In the following, the categorical attributes are examined. Again, the attributes should be related to the target variable. From the previous chapter numerical attributes were determined, which contain categorical attributes (e.g. Response). These are also considered in this analysis."
+ "Im Folgenden werden die kategorialen Attribute untersucht. Auch hier sollten die Attribute mit der Zielvariablen in Verbindung stehen. Aus dem vorangegangenen Kapitel wurden numerische Attribute ermittelt, die kategoriale Attribute enthalten (z.B. Response). Diese werden auch in dieser Analyse berücksichtigt."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Response (target)"
+ "### Response (Ziel)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, the distribution of the target variable \"Response\" is checked."
+ "Zunächst wird die Verteilung der Zielvariablen \"Response\" überprüft."
]
},
{
@@ -1511,18 +1559,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Interest in car insurance corresponds to about 12% of the total data set, while lack of interest corresponds to about 88%.\n",
- "- Thus, this is an unbalanced data set and another metric must be used in the evaluation phase."
+ "- Das Interesse an einer Kfz-Versicherung entspricht etwa 12 % des gesamten Datensatzes, während das Desinteresse etwa 88 % ausmacht.\n",
+ "- Es handelt sich also um einen unausgewogenen Datensatz und in der Bewertungsphase muss eine andere Metrik verwendet werden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Gender"
+ "### Geschlecht"
]
},
{
@@ -1547,14 +1597,16 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- 0 = \"No interest\", 1 = \"Interest\".\n",
- "- The interest between the sexes is almost equal."
+ "- 0 = \"kein Interesse\", 1 = \"Interesse\".\n",
+ "- Das Interesse zwischen den Geschlechtern ist fast gleich groß."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1583,13 +1635,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Looking at the ratio of the graph, it is recognized that the greatest interest is among vehicle owners whose car is older than 2 years."
+ "- Betrachtet man das Verhältnis der Grafik, so erkennt man, dass das größte Interesse bei Fahrzeughaltern besteht, deren Auto älter als 2 Jahre ist."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1618,32 +1672,36 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- The graph shows that motor vehicle owners whose car has already been damaged are more interested in motor vehicle insurance.\n",
- "- By contrast, owners of accident-free vehicles are not interested in an offer at all."
+ "- Die Grafik zeigt, dass Kraftfahrzeughalter, deren Auto bereits einen Schaden hatte, stärker an einer Kfz-Versicherung interessiert sind.\n",
+ "- Besitzer von unfallfreien Fahrzeugen sind dagegen überhaupt nicht an einem Angebot interessiert."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preperation"
+ "# 3. Datenaufbereitung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1 Reduce customer ID"
+ "## 3.1 Kunden-ID reduzieren"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "At the beginning, the column \"id\" can already be deleted. An identification number to estimate the interest of a customer group is not necessary. Therefore this can be removed directly"
+ "Zu Beginn kann die Spalte \"id\" bereits gelöscht werden. Eine Identifikationsnummer zur Einschätzung des Interesses einer Kundengruppe ist nicht notwendig. Daher kann diese direkt entfernt werden"
]
},
{
@@ -1657,17 +1715,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Coding of numeric Variables"
+ "## 3.2 Kodierung von numerischen Variablen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In the data set, it was found that certain columns consist only of the numeric variables 0 and 1. Since these are categories (1 = Yes, 0 = No), the numeric variable must be converted to a categorical variable for a new data overview. The map() function is used for this purpose."
+ "Im Datensatz wurde festgestellt, dass bestimmte Spalten nur aus den numerischen Variablen 0 und 1 bestehen. Da es sich um Kategorien handelt (1 = Ja, 0 = Nein), muss die numerische Variable für eine neue Datenübersicht in eine kategorische Variable umgewandelt werden. Zu diesem Zweck wird die Funktion map() verwendet."
]
},
{
@@ -1834,10 +1894,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Handling outliers"
+ "## 3.2 Umgang mit Ausreißern"
]
},
{
@@ -1913,11 +1974,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Subsequently, 1% are removed from the left, since many values point to 0.\n",
- "- Costs of 0 are possible therefore, since also many customers can have over an offer of a one-year, free membership."
+ "- Anschließend werden 1 % von links entfernt, da viele Werte auf 0 hinweisen.\n",
+ "- Kosten von 0 sind also möglich, da auch viele Kunden über ein Angebot einer einjährigen, kostenlosen Mitgliedschaft verfügen können."
]
},
{
@@ -1962,18 +2024,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- " Distribution function is recognizable\n",
- "- The majority of customers pay 30,000.00 per year for subscription."
+ "Verteilungsfunktion ist erkennbar.\n",
+ "- Die Mehrheit der Kunden zahlt 30.000,00 Euro pro Jahr für das Abonnement."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3 Consideration of the new Response, Driving License, and Previously Insured categorical attributes"
+ "## 3.3 Berücksichtigung der neuen kategorialen Attribute \"Response\", \"Driving License\" und \"Previously Insured\""
]
},
{
@@ -2013,10 +2077,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The bar chart of \"Driving_License\" is now considered."
+ "Das Balkendiagramm für \"Driving License\" wird nun berücksichtigt."
]
},
{
@@ -2041,17 +2106,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "From this graph, it can be seen that only customers who also have a driver's license are of interest."
+ "Aus diesem Diagramm geht hervor, dass nur Kunden von Interesse sind, die auch im Besitz eines Führerscheins sind."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In the following chart, we look at customers who already have insurance."
+ "Im folgenden Diagramm betrachten wir Kunden, die bereits versichert sind."
]
},
{
@@ -2076,17 +2143,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The graph expresses that customers who are already insured have no interest in additional insurance."
+ "Das Diagramm zeigt, dass Kunden, die bereits versichert sind, kein Interesse an zusätzlicher Versicherung haben."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Re-examination of the correlation matrix"
+ "### Erneute Überprüfung der Korrelationsmatrix"
]
},
{
@@ -2131,25 +2200,28 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- With the renewed view, moderately strong correlations are now examined.\n",
- "- No strong correlations present."
+ "- Mit dem erneuten Blick werden nun mäßig starke Korrelationen untersucht.\n",
+ "- Es liegen keine starken Korrelationen vor."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.4 Test for linearity"
+ "## 3.4 Test auf Linearität"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now the linearity is checked. Vintage is added as a new explanatory variable. We want to compare the length of the tie to the company with other variables."
+ "Nun wird die Linearität überprüft. Vintage wird als neue erklärende Variable hinzugefügt. Wir möchten die Dauer der Bindung an das Unternehmen mit anderen Variablen vergleichen."
]
},
{
@@ -2183,11 +2255,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- No linearity\n",
- "- Seems to notice that customers who have been with the company longer and have high costs have a higher interest in supplementary insurance (indicates wealthy individuals)"
+ "- Keine Linearität.\n",
+ "- Es scheint, dass Kunden, die schon länger beim Unternehmen sind und hohe Kosten haben, ein höheres Interesse an einer Zusatzversicherung haben (was auf wohlhabende Personen hindeutet)."
]
},
{
@@ -2221,10 +2294,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "It can be seen here that customers between the ages of 30 and 50 maintain a higher level of interest. Above all, customers who have been with the company between 0 and 100 days seem to be interested. Here, too, no linearity can be discerned."
+ "Hier ist zu sehen, dass Kunden zwischen 30 und 50 Jahren ein höheres Interesse aufrechterhalten. Vor allem Kunden, die seit 0 bis 100 Tagen beim Unternehmen sind, scheinen interessiert zu sein. Auch hier ist keine Linearität erkennbar."
]
},
{
@@ -2258,11 +2332,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- No linearity\n",
- "- Customers reached via sales channel 40 and 120 have significantly higher interest. For sales channel 40, a blue mass can be seen in the range between 200 and 300."
+ "- Keine Linearität.\n",
+ "- Kunden, die über Vertriebskanal 40 und 120 erreicht wurden, zeigen signifikant höheres Interesse. Für Vertriebskanal 40 ist eine blaue Masse im Bereich zwischen 200 und 300 zu sehen."
]
},
{
@@ -2296,18 +2371,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- No linearity discernible\n",
- "- Regions between 30 and 40 with affiliation between 100 and 200 days, are more often interested"
+ "- Keine erkennbare Linearität.\n",
+ "- Regionen zwischen 30 und 40 mit Zugehörigkeit zwischen 100 und 200 Tagen zeigen häufiger Interesse."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "A logarithmization is then performed to see if a closer linearity can be created. This was a test run and did not describe linearity in the following."
+ "Es wurde eine Logarithmisierung durchgeführt, um zu sehen, ob eine engere Linearität erzeugt werden kann. Dies war ein Testlauf und beschrieb keine Linearität in den folgenden Daten."
]
},
{
@@ -2590,17 +2667,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.5 Index Reset"
+ "## 3.5 Zurücksetzen des Index"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "After the outliers have been removed, a new table / index is to be created to clean up the removed values from the table. With drop=True these are finally reset to a new index, removing the old index."
+ "Nachdem die Ausreißer entfernt wurden, soll eine neue Tabelle bzw. ein neuer Index erstellt werden, um die entfernten Werte aus der Tabelle zu bereinigen. Mit `drop=True` wird schließlich ein neuer Index festgelegt, wobei der alte Index entfernt wird."
]
},
{
@@ -2855,17 +2934,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.6 Recoding of categorical Variables"
+ "## 3.6 Umcodierung kategorialer Variablen"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "For the further procedure of checking multicolinearity, coding on numerical attributes is performed."
+ "Für das weitere Vorgehen zur Überprüfung der Multikollinearität wird eine Codierung der numerischen Attribute durchgeführt."
]
},
{
@@ -3114,24 +3195,27 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In the renewed view, no objects are now displayed, the conversion to dummy variables has been completed."
+ "In der aktualisierten Ansicht werden jetzt keine Objekte mehr angezeigt, die Umwandlung in Dummy-Variablen wurde abgeschlossen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.7 Test for Multicollinearity"
+ "## 3.7 Test auf Multikollinearität"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In order to ensure correct operation of the later regression, there must be no multicollinearity between the variables. The presence of the same is checked with the help of the library Statsmodel."
+ "Um sicherzustellen, dass die spätere Regression korrekt funktioniert, dürfen keine Multikollinearitäten zwischen den Variablen vorhanden sein. Die Anwesenheit solcher wird mithilfe der Bibliothek Statsmodel überprüft."
]
},
{
@@ -3174,10 +3258,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Driving_License has the highest VIF and is removed from the dataset."
+ "Die Variable \"Driving_License\" hat den höchsten VIF-Wert und wird aus dem Datensatz entfernt."
]
},
{
@@ -3230,10 +3315,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Age has high multicollinearity (VIF>10) and, as a result, is also removed."
+ "Die Variable \"Age\" hat den höchsten VIF-Wert und wird aus dem Datensatz entfernt.Age has high multicollinearity (VIF>10) and, as a result, is also removed."
]
},
{
@@ -3300,17 +3386,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "None of the variables now has a VIF greater than 10."
+ "Keine der Variablen hat nun einen VIF größer als 10"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.8 Feature Scaling"
+ "## 3.8 Merkmalsskalierung"
]
},
{
@@ -3465,10 +3553,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.9 Create Test and Training Data"
+ "## 3.9 Trainings- & Testdaten erzeugen"
]
},
{
@@ -3484,38 +3573,43 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4. Modelling and Evaluation"
+ "## 4. Modellierung und Evaluation"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The modeling was done with the help of the Jupyter notebook \"E11_Churn_Solution\". The interpretations were made individually."
+ "Die Modellierung wurde mithilfe des Jupyter-Notebooks \"E11_Churn_Solution\" durchgeführt. Die Interpretationen wurden individuell vorgenommen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1 Logistic Regression"
+ "## 4.1 Logistische Regression"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Logistic regression is a non-linear model. It works with categorical target values (dependent variable). It is used to predict \"yes\"/\"no\" (0/1) decisions."
+ "Die logistische Regression ist ein nicht-lineares Modell. Sie arbeitet mit kategorialen Zielwerten (abhängige Variable). Sie wird verwendet, um \"Ja\"/\"Nein\" (0/1)-Entscheidungen vorherzusagen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 4.1.1 Training and Prediction"
+ "### 4.1.1 Training und Vorhersage"
]
},
{
@@ -3565,10 +3659,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The trained model has statistically non-significant variables. This is given if the value P>|z| is greater than 0.05 and it is not the constant. The statistically not relevant features are removed. "
+ "Wenn das P-Wert (P>|z|) für eine Variable größer als 0,05 ist und es sich nicht um die Konstante handelt, bedeutet dies, dass die Variable statistisch nicht signifikant ist. Die statistisch nicht relevanten Merkmale werden entfernt."
]
},
{
@@ -3584,10 +3679,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Create a second model:"
+ "Ein zweites Modell kreieren:"
]
},
{
@@ -3634,10 +3730,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "No more statistically insignificant variables. The final model was modeled:"
+ "Es gibt keine weiteren statistisch nicht signifikanten Variablen mehr. Das endgültige Modell wurde modelliert."
]
},
{
@@ -3696,6 +3793,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3703,17 +3801,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "For the evaluation several metrics are to be used, which can be generated more comfortably by means of Scikit-Learn. Therefore, the identical model as with Statsmodels is generated again in Scikit-Learn."
+ "Um mehrere Metriken für die Bewertung zu verwenden, kann das Modell bequemer mit Scikit-Learn erstellt werden. Daher wird das identische Modell wie mit Statsmodels erneut in Scikit-Learn generiert."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 4.2.1 Training and Prediction"
+ "### 4.2.1 Training und Vorhersage"
]
},
{
@@ -3738,6 +3838,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3764,10 +3865,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Accuracy suggests an above average model. However, it is an unbalanced data set. Therefore, further metrics have to be analyzed."
+ "Die Genauigkeit deutet auf ein überdurchschnittliches Modell hin. Allerdings handelt es sich um ein unausgeglichenes Datenset. Daher müssen weitere Metriken analysiert werden."
]
},
{
@@ -3811,10 +3913,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Accuracy are identical for the training and test data set. Overall, the values for the test and training data sets are very similar. Consequently, overfitting or underfitting cannot be assumed."
+ "Die Genauigkeiten sind für das Trainings- und Testdatenset identisch. Insgesamt sind die Werte für die Test- und Trainingsdatensets sehr ähnlich. Daher kann weder Überanpassung noch Unteranpassung angenommen werden."
]
},
{
@@ -3888,20 +3991,23 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Recall value is too low and therefore offers no representation. To be able to display and evaluate the Precision and Recall values, this requires an improvement with the help of the threshold values. The Recall values are to be increased."
+ "Der Rückrufwert ist zu niedrig und bietet daher keine aussagekräftige Darstellung. Um die Präzision und die Rückrufwerte anzeigen und bewerten zu können, ist eine Verbesserung mithilfe der Schwellenwerte erforderlich. Die Rückrufwerte müssen erhöht werden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "AttributeError: module 'sklearn.metrics' has no attribute 'plot_roc_curve'.\n",
- " - If this error occurs, please check the versioning.\n",
- " - Unfortunately, error analysis did not bring any success.\n",
- " - Due to the massive effort and the risk of data loss, the error was not fixed."
+ "Es scheint, dass das Modul 'sklearn.metrics' keine 'plot_roc_curve'-Attribute hat. -\n",
+ "\n",
+ "- Wenn dieser Fehler auftritt, überprüfen Sie bitte die Versionierung.\n",
+ "- Leider brachte die Fehleranalyse keinen Erfolg.\n",
+ "- Aufgrund des massiven Aufwands und des Risikos von Datenverlust wurde der Fehler nicht behoben."
]
},
{
@@ -3929,6 +4035,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3936,10 +4043,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, however, we will illustrate the results for business and clarify which ones arouse a customer's interest and which ones diminish it."
+ "Zuerst werden wir jedoch die Ergebnisse für das Geschäft illustrieren und klären, welche Interesse bei einem Kunden wecken und welches dieses verringern."
]
},
{
@@ -4003,19 +4111,21 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The three features that increase the interest of the customer:"
+ "Die drei Merkmale, die das Interesse des Kunden steigern, sind:"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- If the customer has already had an accident (Vehicle_Damage_Yes)\n",
- "- If the customer is of male gender (Gender_Male)\n",
- "- If the customer's automobile is older than 2 years (Vehicel_Age_>2Years)"
+ "- Wenn der Kunde bereits einen Unfall hatte (Fahrzeug_Schaden_Ja)\n",
+ "- Wenn der Kunde männlich ist (Geschlecht_Männlich)\n",
+ "- Wenn das Auto des Kunden älter als 2 Jahre ist (Fahrzeug_Alt_>2Jahre)"
]
},
{
@@ -4047,33 +4157,37 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The three features that reduce the interest of the customer:"
+ "Die drei Merkmale, die das Interesse des Kunden verringern, sind:"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Sales channel through which the customer is addressed (Policy_Sales_Channel).\n",
- "- If the car has been owned for less than 1 year (Vehicle_Age_<1Year)\n",
- "- If the customer already has an insurance policy (Previously_Insured)"
+ "- Vertriebskanal, über den der Kunde angesprochen wird (Policy_Sales_Channel).\n",
+ "- Wenn das Auto weniger als 1 Jahr alt ist (Fahrzeug_Alt_<1Jahr)\n",
+ "- Wenn der Kunde bereits eine Versicherungspolice hat (Zuvor_Versichert)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.4. Model Optimization"
+ "## 4.4. Modell Optimierung"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The recall rate is too low as a target metric and must therefore be increased. Therefore, the metrics are analyzed at different thresholds of the logistic regression."
+ "Die Rückrufrate ist als Zielmetrik zu niedrig und muss daher erhöht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
]
},
{
@@ -4294,17 +4408,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "A threshold of 0.25 offers a better result for the application. It increases the recall to a satisfactory level of 73.37% at the expense of the precision. However, the precision is negligible."
+ "Ein Schwellenwert von 0,25 bietet ein besseres Ergebnis für die Anwendung. Er erhöht den Recall auf ein zufriedenstellendes Niveau von 73,37 % auf Kosten der Präzision. Allerdings ist die Präzision vernachlässigbar."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "This results in the following values:"
+ "Das resultiert in die folgenden Werten:"
]
},
{
@@ -4387,14 +4503,15 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "As expected, the rate of customers incorrectly classified as interest increases. In turn, however, the number of customers who are correctly predicted as churn (true positives) also increases. As elaborated in the term paper, this is essential, because in case of doubt, a customer would be falsely called by service team and even perceive this call as good service and be bound to the company in the longer term.\n",
- "\n"
+ "Wie erwartet steigt die Rate der Kunden, die fälschlicherweise als interessiert eingestuft werden. Gleichzeitig steigt jedoch auch die Anzahl der Kunden, die korrekt als abwanderungswillig vorhergesagt werden (True Positives). Wie in der Seminararbeit erläutert, ist dies entscheidend, denn im Zweifelsfall würde ein Kunde fälschlicherweise vom Service-Team kontaktiert und könnte diesen Anruf sogar als guten Service wahrnehmen und langfristig an das Unternehmen gebunden werden."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/Maintenance/Prediction of IOT system failures based on sensor data/notebook.ipynb b/Maintenance/Prediction of IOT system failures based on sensor data/notebook.ipynb
index d406c0b..cb597d1 100644
--- a/Maintenance/Prediction of IOT system failures based on sensor data/notebook.ipynb
+++ b/Maintenance/Prediction of IOT system failures based on sensor data/notebook.ipynb
@@ -1,35 +1,23 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Prediction of fire protection system failures based on sensor data\n",
+ "# Vorhersage von Ausfällen von Brandschutzsystemen basierend auf Sensordaten\n",
"\n",
- "# Business Understanding \n",
- "With the help of the Inveron hazard management system, the plants already equipped with sensors can be \n",
- "be monitored and controlled manually, for example from a control station. \n",
- "from a control station. Inveron is a software that bundles and visualizes all sensor data from the \n",
- "fire protection systems and visualizes them. In addition, with the help of \n",
- "Inveron, test alarms can be triggered or alarms can be reset. \n",
- "The Inveron hazard management system already processes a lot of \n",
- "sensor data that can be used in the local system environment, for example the\n",
- "security center of an industrial company. Specific\n",
- "maintenance intervals tell Minimax service technicians when a system should be serviced. \n",
- "maintenance. \n",
- "In addition to monitoring its own sensor data, Inveron can also monitor third-party data. \n",
- "monitor. For example, connected video cameras can be used for fire detection. \n",
- "can be used for fire detection. Also burglar alarm systems, systems for \n",
- "fence monitoring or gate control systems. Open interfaces (OPC, \n",
- "Modbus, Profibus, BAC-net) allow long-term usability when individual components are \n",
- "exchange of individual components.\n"
+ "## Business Understanding\n",
+ "\n",
+ "Mit Hilfe des Inveron-Gefahrenmanagementsystems können bereits mit Sensoren ausgestattete Anlagen überwacht und kontrolliert werden, zum Beispiel von einer Kontrollstation aus. Inveron ist eine Software, die alle Sensordaten aus den Brandschutzsystemen bündelt und visualisiert. Darüber hinaus können mit Hilfe von Inveron Testalarme ausgelöst oder Alarme zurückgesetzt werden. Das Inveron-Gefahrenmanagementsystem verarbeitet bereits eine Vielzahl von Sensordaten, die in der lokalen Systemumgebung, zum Beispiel im Sicherheitszentrum eines Industrieunternehmens, verwendet werden können. Spezifische Wartungsintervalle geben Minimax-Servicetechnikern an, wann ein System gewartet werden sollte. Neben der Überwachung der eigenen Sensordaten kann Inveron auch Daten von Drittanbietern überwachen. Zum Beispiel können angeschlossene Videokameras zur Branddetektion verwendet werden. Auch Einbruchmeldeanlagen, Systeme zur Zaunüberwachung oder Torsteuerungssysteme können genutzt werden. Offene Schnittstellen (OPC, Modbus, Profibus, BAC-net) ermöglichen eine langfristige Nutzbarkeit bei Austausch einzelner Komponenten."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules "
+ "## 2.1. Import von relevanten Modulen "
]
},
{
@@ -67,10 +55,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. load Data "
+ "## 2.2. Daten laden"
]
},
{
@@ -152,10 +141,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.4. Descriptive Analytics "
+ "## 2.4. Deskriptive Analyse "
]
},
{
@@ -458,12 +448,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation\n",
+ "# 3. Datenaufbereitung\n",
"\n",
- "## 3.1 check for null values"
+ "## 3.1 Auf Nullwerte prüfen"
]
},
{
@@ -587,6 +578,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "YZhUl3TKSNmZ"
@@ -597,18 +589,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "pNqxaXBnSNmb"
},
"source": [
- "## 2.1 calculate the remaining days until failure\n",
- "Im ersten Schritt haben wir nun die Daten eingeladen und werden den Trainingsdatensatz nun mit einer weiteren Spalte versehen, nämlich der verbleibenden Zyklen bis zum Austausch des Feuermelders bzw. Bauteils. Im Testdatensatz wird eine weitere Spalten für die binäre Unterscheidung bzw. Klassifikation angelegt. Herauszufinden ist, ob ein spezifischer Feuermelder (ID) innerhalb w1-Zyklen ausfällt, weshalb hier für w1 ein fiktiver Wert von 1 gewählt wird (1 Tag)."
+ "## 2.1 Berechnung der verbleibenden Tage bis zum Ausfall\n",
+ "Im ersten Schritt haben wir nun die Daten geladen und werden dem Trainingsdatensatz eine weitere Spalte hinzufügen, die die verbleibenden Tage bis zum Austausch des Feuermelders oder Bauteils angibt. Im Testdatensatz wird eine weitere Spalte für die binäre Unterscheidung oder Klassifikation erstellt. Es soll festgestellt werden, ob ein bestimmter Feuermelder (ID) innerhalb von w1-Zyklen ausfällt, weshalb hier ein fiktiver Wert von 1 für w1 gewählt wird (1 Tag)."
]
},
{
@@ -1076,12 +1070,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "cfokSoC9SNmd"
},
"source": [
- "The cycle column \"cycle\" is to be normalized by means of a MinMax normalization, which is why a column with the normalized values is also created"
+ "Die Spalte \"cycle\" soll mittels einer Min-Max-Normalisierung normalisiert werden, weshalb eine Spalte mit den normalisierten Werten erstellt wird."
]
},
{
@@ -1321,6 +1316,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "BuhuRzZsSNme"
@@ -1564,12 +1560,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "2PSDbKL-SNmh"
},
"source": [
- "Next, we use the truth data to generate the labels for the test data."
+ "Als nächstes verwenden wir die Wahrheitsdaten, um die Labels für die Testdaten zu generieren."
]
},
{
@@ -1830,12 +1827,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "nsb5R-hLSNmk"
},
"source": [
- "Next, we use the truth data to generate the labels for the test data."
+ "Als nächstes verwenden wir die Wahrheitsdaten, um die Labels für die Testdaten zu generieren."
]
},
{
@@ -2069,12 +2067,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "L-rzfojpSNmn"
},
"source": [
- "Now we look at the data structure"
+ "Jetzt schauen wir uns die Datenstruktur an."
]
},
{
@@ -2148,19 +2147,21 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "M7ORedeJSNmo"
},
"source": [
- "It can be seen that all data have either the float or int data type, there are no categorical variables in the data set. Now we display the individual variables in a histogram in order to determine whether all variables can be used meaningfully for the model. The just created columns, as well as \"id\" are not considered, since these are used for the evaluation and it does not make sense to check here.\n"
+ "Es ist zu sehen, dass alle Daten entweder den Datentyp float oder int haben, es gibt keine kategorialen Variablen im Datensatz. Nun zeigen wir die einzelnen Variablen in einem Histogramm an, um festzustellen, ob alle Variablen sinnvoll für das Modell verwendet werden können. Die gerade erstellten Spalten sowie \"id\" werden nicht berücksichtigt, da diese für die Auswertung verwendet werden und es keinen Sinn macht, sie hier zu überprüfen."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.3 analyse the histogram's of all features"
+ "## 3.3 Analyse der Histogramme aller Merkmale"
]
},
{
@@ -2727,12 +2728,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "pEspX6TrSNmq"
},
"source": [
- "We see that some variables are just constants. These can be deleted as they do not contain any useful information about the state of the plant. The following columns are deleted: 'setting3', 's1', 's5', 's10', 's16 ', 's18', 's19'."
+ "Wir sehen, dass einige Variablen einfach konstante Werte haben. Diese können gelöscht werden, da sie keine nützlichen Informationen über den Zustand der Anlage enthalten. Die folgenden Spalten werden gelöscht: 'setting3', 's1', 's5', 's10', 's16', 's18', 's19'."
]
},
{
@@ -2762,25 +2764,25 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "lyYcIBzMSNmr"
},
"source": [
- "# 4. Modelling and Evaluation "
+ "# 4. Modellierung und Evaluation "
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "ihvhDN4eSNmr"
},
"source": [
- "After the data has been prepared, the modeling can begin. We will build a Long Short-Term Memory Layer Recurrent Neural Network using the KERAS library. Keras requires a three-dimensional numpy array (inputs: A 3D tensor with shape [batch, timesteps, feature]), see also: https://keras.io/api/layers/recurrent_layers/lstm/ ). Therefore, in the next step, our features or variables are put into this three-dimensional shape.\n",
- "\n",
- "At the same time the window size is defined, because LSTMs have the advantage to remember things from long sequences without a direct abstraction. \n",
+ "Nachdem die Daten vorbereitet wurden, kann die Modellierung beginnen. Wir werden ein Long Short-Term Memory (LSTM) Layer Recurrent Neural Network (RNN) mit der Keras-Bibliothek aufbauen. Keras erfordert ein dreidimensionales numpy-Array (Eingaben: Ein 3D-Tensor mit der Form [Batch, Zeitpunkte, Merkmale]), siehe auch: https://keras.io/api/layers/recurrent_layers/lstm/ ). Daher werden in einem nächsten Schritt unsere Merkmale oder Variablen in diese dreidimensionale Form gebracht.\n",
"\n",
- "Translated with www.DeepL.com/Translator (free version)"
+ "Gleichzeitig wird die Fenstergröße definiert, da LSTMs den Vorteil haben, Dinge aus langen Sequenzen ohne direkte Abstraktion zu merken."
]
},
{
@@ -2963,12 +2965,14 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "qlx44e41SNmv"
},
"source": [
- "In the next step, an LSTM network is created, since the data is now available in three-dimensional form. For this, the network is created with 100 units in the first step and a second one with 50 units. Dropout is used to avoid overfitting. Finally, a single layer, our output layer with a single unit and sigma activation, is generated, since there is a binary classification problem here."
+ "\n",
+ "Im nächsten Schritt wird ein LSTM-Netzwerk erstellt, da die Daten jetzt in dreidimensionaler Form vorliegen. Dafür wird das Netzwerk mit 100 Einheiten im ersten Schritt und einem zweiten mit 50 Einheiten erstellt. Dropout wird verwendet, um Overfitting zu vermeiden. Schließlich wird eine einzelne Schicht, unsere Ausgabeschicht mit einer einzigen Einheit und der Sigma-Aktivierung, generiert, da hier ein binäres Klassifikationsproblem vorliegt."
]
},
{
@@ -3126,6 +3130,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -3257,12 +3262,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "uQFaIgMfSNmy"
},
"source": [
- "In the next step we will compare the test data, for this we have saved the last working state of the cycle for each fire detector in the test data. To be able to compare the results, we use the last sequence for each fire detector in the test data."
+ "Im nächsten Schritt werden wir die Testdaten vergleichen. Dafür haben wir den letzten funktionierenden Zustand des Zyklus für jeden Feuermelder in den Testdaten gespeichert. Um die Ergebnisse vergleichen zu können, verwenden wir die letzte Sequenz für jeden Feuermelder in den Testdaten."
]
},
{
diff --git a/Marketing/Generation of Individual Playlists/notebook.ipynb b/Marketing/Generation of Individual Playlists/notebook.ipynb
index 5fca32d..4ed81ca 100644
--- a/Marketing/Generation of Individual Playlists/notebook.ipynb
+++ b/Marketing/Generation of Individual Playlists/notebook.ipynb
@@ -1,31 +1,34 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 1. Business Understanding\n",
+ "# 1. Geschäftsverständnis\n",
"\n",
- "The audio streaming service was founded in 2006 by Swedish startup Spotify Technology S.A., and is available in more than 90 different countries. For the Spotify company, one of the most important potentials and challenges is to compile a unique personalized playlist for each user that matches the listener's music taste. Consideration is given to solving the above challenges by using Spotify AI and machine learning algorithms to generate a customized playlist for the user.\n",
- "The overarching question of this case study is: How are machine learning technologies used at Spotify to improve the customer's listening experience?"
+ "Der Audio-Streaming-Dienst wurde 2006 von dem schwedischen Startup Spotify Technology S.A. gegründet und ist in mehr als 90 verschiedenen Ländern verfügbar. Für das Unternehmen Spotify ist eines der wichtigsten Potenziale und Herausforderungen die Erstellung einer einzigartigen personalisierten Playlist für jeden Benutzer, die dem Musikgeschmack des Hörers entspricht. Dabei wird erwogen, die oben genannten Herausforderungen mithilfe von Spotify KI und Machine-Learning-Algorithmen zu lösen, um eine maßgeschneiderte Playlist für den Benutzer zu generieren.\n",
+ "Die übergreifende Fragestellung dieser Fallstudie lautet: Wie werden bei Spotify Machine-Learning-Technologien eingesetzt, um das Hörerlebnis der Kunden zu verbessern?"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding\n",
+ "# 2. Daten und Datenverständnis\n",
"\n",
- "The collected data for Spotify is merged into a unified data set and checked for the problem to see if insights can be extracted from the information. The attributes have different ranges. This data is carefully prepared so that the final dataset can be created or processed using data mining methods. For this purpose, the data is loaded and analyzed. Subsequently, these in turn form the basis for the next phase of modeling."
+ "Die gesammelten Daten für Spotify werden zu einem einheitlichen Datensatz zusammengeführt und auf das Problem hin überprüft, um zu sehen, ob Erkenntnisse aus den Informationen extrahiert werden können. Die Attribute haben unterschiedliche Bereiche. Diese Daten werden sorgfältig vorbereitet, damit der endgültige Datensatz erstellt oder mittels Data-Mining-Methoden verarbeitet werden kann. Zu diesem Zweck werden die Daten geladen und analysiert. Anschließend bilden diese wiederum die Grundlage für die nächste Phase der Modellierung."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
- "## 2.1 Import of Relevant Modules"
+ "## 2.1 Import von relevanten Modulen"
]
},
{
@@ -55,10 +58,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2 Read Data"
+ "## 2.2 Daten einlesen"
]
},
{
@@ -666,10 +670,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3 Data Cleaning"
+ "## 2.3 Datenbereinigung"
]
},
{
@@ -741,10 +746,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Check for missing values"
+ "### Auf fehlende Werte prüfen"
]
},
{
@@ -787,10 +793,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Select predictors"
+ "### Auswahl der Prädiktoren"
]
},
{
@@ -823,10 +830,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Drop features"
+ "### Features entfernen"
]
},
{
@@ -1120,10 +1128,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation"
+ "# 3. Datenaufbereitung"
]
},
{
@@ -1288,10 +1297,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Data Modeling"
+ "# 4. Datenmodellierung"
]
},
{
@@ -1316,6 +1326,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1333,10 +1344,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Standardize columns before using models"
+ "### Vor Verwendung von Modellen Spalten standardisieren"
]
},
{
@@ -1353,10 +1365,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2 Linear Regression model and evaluation"
+ "## 4.2 Lineares-Regressionsmodell und Evaluation"
]
},
{
@@ -1443,6 +1456,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb b/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb
index 3a0de3f..c09904d 100644
--- a/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb
+++ b/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb
@@ -1,38 +1,46 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Advertising Prediction Notebook\n",
- "In this notebook, the Advertising dataset of Kaggle is analyzed (https://www.kaggle.com/fayomi/advertising). It consists of 10 columns with a total of 1000 rows. The use case consists of predicting whether a website visitor will click on an advertisement or not based on his demographics and internet usage data.\n",
- "The notebook's approach is based on the CRISP-DM model, which clearly divides the phases in a data science project.\n",
- "<img src=\"https://statistik-dresden.de/wp-content/uploads/2012/04/CRISP-DM_Process_Diagram1.png\" alt=\"CRISP-DM Modell\" width=\"300\" height=\"300\">\n"
+ "# Notebook zur Vorhersage von Werbung\n",
+ "In diesem Notizbuch wird der Advertising-Datensatz von Kaggle analysiert (https://www.kaggle.com/fayomi/advertising). Er besteht aus 10 Spalten mit insgesamt 1000 Zeilen. Der Anwendungsfall besteht in der Vorhersage, ob ein Website-Besucher auf eine Werbung klicken wird oder nicht, basierend auf seinen demografischen Daten und Daten zur Internetnutzung.\n",
+ "Der Ansatz des Notebooks basiert auf dem CRISP-DM-Modell, das die Phasen in einem Data-Science-Projekt klar unterteilt.\n",
+ "<img src=\"https://statistik-dresden.de/wp-content/uploads/2012/04/CRISP-DM_Process_Diagram1.png\" alt=\"CRISP-DM Modell\" width=\"300\" height=\"300\">\n",
+ "\n",
+ "\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Business Understanding\n",
"\n",
- "From an economic perspective, it is necessary for Facebook to keep customers on their streaming platform for as long as possible. However, the question now arises as to how this can be achieved. The problem here is that Netflix has a good data basis, but this must first be processed and then developed into a digital service. Furthermore, it must be specified which data is available at all, which services can be implemented based on this data, and what added value the customer and Netflix itself derive from this. As a service, this paper deals with a recommendation model that suggests a selection of similar offers to the user for each film or series offered."
+ "Aus wirtschaftlicher Sicht ist es für Facebook notwendig, die Kunden so lange wie möglich auf seiner Streaming-Plattform zu halten. Nun stellt sich aber die Frage, wie dies erreicht werden kann. Das Problem dabei ist, dass Netflix zwar über eine gute Datenbasis verfügt, diese aber erst einmal aufbereitet und zu einem digitalen Angebot entwickelt werden muss. Außerdem muss festgelegt werden, welche Daten überhaupt vorhanden sind, welche Dienste auf Basis dieser Daten realisiert werden können und welchen Mehrwert der Kunde und Netflix selbst daraus ziehen. Als Service geht es in diesem Beitrag um ein Empfehlungsmodell, das dem Nutzer zu jedem angebotenen Film oder jeder Serie eine Auswahl an ähnlichen Angeboten vorschlägt.\n",
+ "\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. Data and Data Understanding\n",
+ "## 2. Daten und Datenverständnis\n",
"\n",
- "In this notebook, the advertising dataset of Kaggle is analyzed. It consists of 10 columns with a total of 1000 rows. The use case consists of predicting whether a website visitor will click on an advertisement or not based on his demographics and internet usage data. The Clicked on Ad target value is perfectly balanced between the two categories (0.1), as the mean value is exactly 0.5. This means that there are the same number of values for both categories (500 each). In addition, we can see that the Ad Topic Line and City features have very many unique values (1000 and 969 \"unique\" values, respectively), which means that. It can be seen that there are significant differences between the user profiles. Users who click on an ad (Clicked on Ad=1) spend less time on the website on average, are older (approx. 40), have a lower income and use the Internet significantly less. From this information, a rough user profile can already be derived, which could also be relevant for a company's marketing and sales to optimize their measures based on the user profiles."
+ "In diesem Notebook wird der Anzeigendatensatz von Kaggle analysiert. Er besteht aus 10 Spalten mit insgesamt 1000 Zeilen. Der Anwendungsfall besteht in der Vorhersage, ob ein Website-Besucher auf der Grundlage seiner demografischen Daten und seiner Internetnutzungsdaten auf eine Anzeige klicken wird oder nicht. Der Zielwert für angeklickte Werbung ist perfekt zwischen den beiden Kategorien ausgeglichen (0,1), da der Mittelwert genau 0,5 beträgt. Dies bedeutet, dass es für beide Kategorien die gleiche Anzahl von Werten gibt (jeweils 500). Darüber hinaus können wir feststellen, dass die Merkmale \"Ad Topic Line\" und \"City\" sehr viele eindeutige Werte aufweisen (1000 bzw. 969 \"eindeutige\" Werte), was bedeutet, dass. Es ist zu erkennen, dass es erhebliche Unterschiede zwischen den Nutzerprofilen gibt. Nutzer, die auf eine Anzeige klicken (Clicked on Ad=1), verbringen im Durchschnitt weniger Zeit auf der Website, sind älter (ca. 40), haben ein geringeres Einkommen und nutzen das Internet deutlich weniger. Aus diesen Informationen lässt sich bereits ein grobes Nutzerprofil ableiten, das auch für Marketing und Vertrieb eines Unternehmens relevant sein könnte, um ihre Maßnahmen auf Basis der Nutzerprofile zu optimieren.\n",
+ "\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -56,10 +64,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. 2. Read Data.csv"
+ "## 2. 2. Daten einlesen"
]
},
{
@@ -207,12 +216,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Analysis\n",
+ "# 3. Datenanalyse\n",
"\n",
- "The purpose of this chapter is to review, analyze, and prepare the data."
+ "Zweck dieses Kapitels ist die Überprüfung, Analyse und Aufbereitung der Daten."
]
},
{
@@ -491,10 +501,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Missing values"
+ "### Fehlende Werte"
]
},
{
@@ -531,10 +542,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Duplicates"
+ "### Duplikate"
]
},
{
@@ -559,11 +571,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1 Explorative data analysis\n",
- "In this chapter, initial analyses and visualizations will be made."
+ "## 3.1 Explorative Datenanalyse\n",
+ "In diesem Kapitel werden erste Analysen und Visualisierungen vorgenommen."
]
},
{
@@ -625,10 +638,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "It can be seen that the data set is perfectly balanced, i.e. there are exactly 500 records for both classes."
+ "Es ist zu erkennen, dass der Datensatz perfekt ausgeglichen ist, d.h. es gibt genau 500 Datensätze für beide Klassen."
]
},
{
@@ -687,12 +701,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2 Distribution plots for all features with numerical values\n",
+ "## 3.2 Verteilungsdiagramme für alle Merkmale mit numerischen Werten\n",
"\n",
- "Distribution plots are created to identify outliers in the data and better understand the data"
+ "Verteilungsdiagramme werden erstellt, um Ausreißer in den Daten zu identifizieren und die Daten besser zu verstehen."
]
},
{
@@ -828,12 +843,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell1.3\"></a>\n",
- "## 4. Correlations\n",
- "Now the correlations of all numerical features are calculated and reflected in a correlation matrix."
+ "## 4. Korrelationen\n",
+ "Nun werden die Korrelationen aller numerischen Merkmale berechnet und in einer Korrelationsmatrix dargestellt."
]
},
{
@@ -849,28 +865,30 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "t can be seen that Daily Time Spent on Site and Daily Internet Usage correlate. There is also a strong negative correlation between Daily Internet Usage / Daily Time Spent on Site and Clicked on Ad.\n",
- "However, significant correlations that lead to the removal of a feature do not exist (assumption: if correlation is greater than 0.9)."
+ "Es ist zu erkennen, dass die tägliche Verweildauer auf der Website und die tägliche Internetnutzung korrelieren. Es besteht auch eine starke negative Korrelation zwischen der täglichen Internetnutzung / der täglichen Verweildauer auf der Website und den angeklickten Anzeigen.\n",
+ "Signifikante Korrelationen, die zur Streichung eines Merkmals führen, gibt es jedoch nicht (Annahme: wenn die Korrelation größer als 0,9 ist)."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell2\"></a>\n",
- "# 5. Data preparation\n",
- "In this section, the dataset will be prepared for machine learning.\n",
+ "# 5. Datenaufbereitung\n",
+ "In diesem Abschnitt wird der Datensatz für das maschinelle Lernen vorbereitet.\n",
"\n",
"<a id=\"cell2.1\"></a>\n",
"## 5.1 Feature Engineering\n",
- "In this section, feature engineering is performed. Here, important information is extracted from the raw data.\n",
+ "In diesem Abschnitt wird das Feature Engineering durchgeführt. Hier werden wichtige Informationen aus den Rohdaten extrahiert.\n",
"\n",
"<a id=\"cell2.1.1\"></a>\n",
- "### 5.1.1 Timestamp\n",
- "There is a Timestamp feature in the dataset. This could be important for prediction, as there can be a correlation between the user click and the time."
+ "### 5.1.1 Zeitstempel\n",
+ "Der Datensatz enthält ein Zeitstempel-Merkmal. Dies könnte für die Vorhersage wichtig sein, da es eine Korrelation zwischen dem Klick des Nutzers und der Uhrzeit geben kann."
]
},
{
@@ -894,10 +912,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this section, feature engineering is performed. Here, important information is extracted from the raw data.\n"
+ "In diesem Abschnitt wird das Feature Engineering durchgeführt. Hier werden wichtige Informationen aus den Rohdaten extrahiert.\n"
]
},
{
@@ -946,10 +965,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "There is probably no sifnificant effect through time."
+ "Wahrscheinlich gibt es im Laufe der Zeit keine nennenswerten Auswirkungen."
]
},
{
@@ -975,12 +995,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell2.1.2\"></a>\n",
- "### 5.1.2 Age\n",
- "We can make intervals for age."
+ "### 5.1.2 Alter\n",
+ "Es können Intervalle für das Alter gemacht werden."
]
},
{
@@ -1014,11 +1035,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell2.2\"></a>\n",
- "## 5.2 Final dataset creation"
+ "## 5.2 Erstellung des endgültigen Datensatzes"
]
},
{
@@ -1033,12 +1055,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell2.3\"></a>\n",
- "## 5.3 Record splitting and standardization\n",
- "Split the data set into features (X) and target variable (y)."
+ "## 5.3 Datensatzaufteilung und Standardisierung\n",
+ "Teilen Sie den Datensatz in Merkmale (X) und Zielvariable (y) auf."
]
},
{
@@ -1095,12 +1118,13 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell3\"></a>\n",
- "# 6 Model building\n",
- "Now the individual algorithms can be trained and evaluated."
+ "# Nr. 6 Modellbildung\n",
+ "Nun können die einzelnen Algorithmen trainiert und bewertet werden."
]
},
{
@@ -1116,11 +1140,12 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell3.1\"></a>\n",
- "## 6.1 Logistic regression"
+ "## 6.1 Logistische Regression"
]
},
{
@@ -1188,18 +1213,20 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "In particular, Daily Time Spent on Site, Daily Internet Usage, and Area Income have a greater impact."
+ "Insbesondere die tägliche Zeit, die vor Ort verbracht wird, die tägliche Internetnutzung und das Gebietseinkommen haben einen größeren Einfluss."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"cell3.2\"></a>\n",
- "## 6.2 Decision Tree"
+ "## 6.2 Entscheidungsbaum"
]
},
{
diff --git a/Online Retail/Size prediction for online fashion retailer/notebook.ipynb b/Online Retail/Size prediction for online fashion retailer/notebook.ipynb
index d586363..4065385 100644
--- a/Online Retail/Size prediction for online fashion retailer/notebook.ipynb
+++ b/Online Retail/Size prediction for online fashion retailer/notebook.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -8,40 +9,44 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Returns are part of the business model of many fashion stores. The return rate of a fictitious company is about 50%, which unnecessarily costs the company a lot of money. In addition: Especially in the\n",
- "fashion trade in particular, there are many customers who know before they place their order that they are\n",
- "that they are very likely to send the item back. For example, they order the same shirt in different sizes because they are not sure which one will fit.\n",
- "not sure which one will fit. Is it possible to use machine learning approaches to determine the size by given\n",
- "parameters and minimize returns due to wrong size?"
+ "Rücksendungen sind Teil des Geschäftsmodells vieler Modegeschäfte. Die Rückgabequote eines fiktiven Unternehmens liegt bei etwa 50 %, was das Unternehmen unnötig viel Geld kostet. Hinzu kommt: Gerade im\n",
+ "Modebranche gibt es viele Kunden, die schon vor ihrer Bestellung wissen, dass sie\n",
+ "dass sie den Artikel mit hoher Wahrscheinlichkeit zurückschicken werden. Sie bestellen zum Beispiel ein und dasselbe Hemd in verschiedenen Größen, weil sie nicht sicher sind, welches passen wird.\n",
+ "nicht sicher, welches passen wird. Ist es möglich, maschinelles Lernen einzusetzen, um die Größe anhand bestimmter Parameter zu bestimmen\n",
+ "Parameter zu bestimmen und Rücksendungen aufgrund falscher Größen zu minimieren?"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 2. Data and Data Understanding"
+ "# 2. Daten und Datenverständnis"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The dataset was downloaded from Kaggle and originated from\n",
- "ModCloth, an online store for women's clothing. The data is about actual clothes sold and the additional information of the\n",
- "additional information of the fit at the customer.\n",
- "The data includes: item_id, waist, size, quality, cup size, hips, bra size, category,\n",
- "bust height, length, fit, user_id, shoe size, shoe width, review_summary and\n",
- "review_text."
+ "Der Datensatz wurde von Kaggle heruntergeladen und stammte von\n",
+ "ModCloth, einem Online-Geschäft für Damenbekleidung. Bei den Daten handelt es sich um tatsächlich verkaufte Kleidung und die Zusatzinformationen der\n",
+ "zusätzliche Informationen über die Passform beim Kunden.\n",
+ "Die Daten beinhalten: item_id, Taille, Größe, Qualität, Körbchengröße, Hüfte, BH-Größe, Kategorie,\n",
+ "Büstengröße, Länge, Passform, user_id, Schuhgröße, Schuhbreite, review_summary und\n",
+ "prüfung_text."
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.1. Import of Relevant Modules"
+ "## 2.1. Import von relevanten Modulen"
]
},
{
@@ -64,10 +69,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2. Read Data"
+ "## 2.2. Daten einlesen"
]
},
{
@@ -264,10 +270,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.3. Data Cleaning"
+ "## 2.3. Daten bereinigen"
]
},
{
@@ -1089,17 +1096,19 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 3. Data Preparation\n"
+ "# 3. Datenaufbereitung\n"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.1. Test for Multicollinearity"
+ "## 3.1. Test auf Multikollinearität"
]
},
{
@@ -1109,7 +1118,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 1296x1296 with 2 Axes>"
]
@@ -1656,7 +1665,7 @@
},
{
"data": {
- "image/png": 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"text/plain": [
"<Figure size 1800x1800 with 4 Axes>"
]
@@ -1676,7 +1685,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "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",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
@@ -1799,10 +1808,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3.2. Recoding of Categorical Variables"
+ "## 3.2. Umkodierung von kategorialen Variablen"
]
},
{
@@ -2389,10 +2399,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# 4. Modelling and Evaluation"
+ "# 4. Modellierung und Evaluation"
]
},
{
@@ -2619,10 +2630,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1. Test and Train Data"
+ "## 4.1. Test- und Trainingsdaten"
]
},
{
@@ -2645,10 +2657,11 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2. Feature Scaling"
+ "## 4.2. Merkmalsskalierung"
]
},
{
@@ -2665,6 +2678,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2722,7 +2736,7 @@
"outputs": [
{
"data": {
- "image/png": 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oAdhbSMHYs3sfk6TYzc9LSq44npd9BVAAeNvb3oY3velNeMtb3oJvfvObuPHGG6PPveENb8Ctt946UABdWanA9+VgRztBxWIBpVI57sMgivA5SUk0yPOyfvIYRH4Ry8uVDR9vuCkAQOn4ErT8vpEfI+0uu/290l4vA4aF5eVKuBwUK4+vIOXy4k6cdvvzkpJpnM9LTRNdC47btuD++Mc/xve//30AQCaTwaFDh3D77bfjrrvuim4jpYSu951liYhoF/LLyxCbtmAB0GoT5DpQop1zbbX+E2ALLhEl0rYB9OjRo7jyyivRaDTQaDTw1a9+Fb/wC7+Aa665BqdPn4bruvjsZz/LCbhERNSVlBJ+ubR1Ai7UNiwAOAmXaASk6wC6BQBREAUv7hBRgmxbtrzwwgvxwAMP4JWvfCVSqRQOHTqESy+9FAsLC3jd614Hz/Nw6NAhvOIVr5jE8RIR0TRyqoBrdwmgqkoDBlCiHZOeA2GoAIoggLICSkRJ0lff7GWXXYbLLrtsw8de//rX4/Wvf/1YDoqIiGZLuAeomOvUghtWQFmlIdqxji24fG0RUXJs24JLRES0U2EA7VQBBdeAEo2M9BpbW3BZASWiBGEAJSKisfPX1R6gPdeAOmzBJdox12YLLhElGgMoERGNnSyXACvXWu/ZLmUAms41oEQjoIYQBS24WgpIGewuIKJEYQAlIqKx6zYBFwCEEBBmhifJRCOghhCZ0b+FkWYLLhElCgMoERGNnV9ehtZhD9CImWUAJRqFtiFEAAAzwxZcIkoUBlAiIhorKX3I8jJElwoogKACyhZcop2QTQ/wm9EQIkBVQGWDAZSIkoMBlIiIxkpW1wDfgzbXI4BaWQZQop3yHABoDSFC2ILL7gIiSg4GUCIiGqueW7AEhJEB2IJLtCNRq217C66RZgsuESUKAygREY2VLHffgiXCNaBEOybdoALa3oLLNaBElDAMoERENFaqAiogCvu63oZrQIlGoFsLLteAElGCMIASEdFY+eUSRG4PRMroehthZQHXhvT9CR4Z0Wzp3oLL7gIiSg4GUCIiGitZXu7dfgtVAQXAYSlEO9GpBddIA16DF3eIKDEYQImIaKz89RJErz1AAQgzCwCQDttwiYYlgxZctLfgmkE11GMbLhElAwMoERGNjWx6kNXVbSugCCqgXAdKNLywBVdsaMENX1sMoESUDAygREQ0NrKyAkBC67cCykm4RMPr1oILcBIuESUGAygREY1NuAeo2HYNqAqg3AuUaHg9W3C5vpqIEoIBlIiIxsYP9wCd628IEVtwiXbAdQCRAjS99TG24BJRwjCAEhHR2MhyCdBSENmF3jdkACXaMenagGFBCBF9jC24RJQ0DKBERDQ2/noJIr8PQuv964ZrQIlGwHMg2tpvgfYtjhhAiSgZGECJiGhs/HJp+wm4AERKB1ImK6BEOyBdZ8MAIgBAWAHlxR0iSggGUCIiGhtZXu4rgAJBpYYBlGhoqgU3veFjbMEloqRhACUiorGQrg1plyHmem/BEhJWllUaop3o0IKLlKEGEzGAElFCMIASEdFYhFuw9FsBhZlhACXaAek6wKYWXCEEYKYhuQ0LESUEAygREY2FXA+2YOm7BTfLNaBEO+HaUcttO2Gk2YJLRInBAEpERGMRVkBFoc8WXDMDOAygRMOSXmNLBRQI1oFyH1AiSggG0ITyHr2frWhEM8pfL6F58kdxH8bY+eUSoFsQ6UJft1cVUL7vEQ1LuvbWNaAAwAooESUIA2gC+dVV1O+6Du6Pvhn3oRDRGDj/8gXUv/axuA9j7MIJuEKI/r6Aa0CJdsZ1OrfgmhmuASWixGAATSBZX1d/1k7HfCRENA6yvg5pl+M+jLHzy6W+228BVQFFswHZ9MZ4VESzSTY9QDYB3dzyOWGkOQWXiBKDATSBpF3Z8CcRzRZpVwDXhvSbcR/K2Egp4ZeXoc31OQEXwRpQgIOIiIYRBExhpOH7Eh+65dv43iOn1OeMNCTXgBJRQjCAJlBYGdkNFRKi3Sh6jc9w0JKOCtnaoBVQAGAbLtHApOeovxgWKnUXP/jpGh5+bA1AOAWXrysiSgYG0ASKKqAOK6BEsyjqbpjhia9yPZyAO0gFVAVQrgMlGpx0VQAVuoW6o9rYq7b6U5gZ1XUhZWzHR0QUYgBNIFZAiWaX9Byg2VB/d6oxH834+OXB9gAFALAFl2h4bS24tSCA1mxXfc5IA1JG7z1ERHFiAE0grgElml3tr+tZDlrhHqCDteAygBINq70Ft7a5AhpMxmV3ARElAQNoArVXQNkuQzRb2jsb5Iy34Ip0IQqV/RAW14ASDa29BdcOK6AbAygn4RJREjCAJlDUltf0gPCKJhHNhN1UAR1kCxagfQ3o7D4uROMiw3DZ1oJbDVtww+4CBlAiSgAG0ATaUCFhGy7RTNkQQGd8DehA6z8BwAhOkme4Mkw0LmELrjBaQ4g2V0C5FQsRJQEDaAJJuwJYudbfiWhmbBguNqOVPun7kJXlgdZ/AoDQtGC/QrbgEg2srQU3DJ6b14CyBZeIkoABNGGklJB2GdqeM9W/OQmXaKaoi0oCwsrPbAVU1lYBvznQFiwhYWYZQImG0N6CG1ZAvaaPhtsEzKACyr1AiSgBGECTxmsATRcpBlCimSTtMoSVA9L5mW01jbZgmRsmgGZmtjJMNFaeA2gpiJQeBVBAVUFF2N7OFlwiSgAG0IQJA2erAsoWXKJZIu0KkM5DWNmZHbYjh9iCJWJmZvZxIRon6TqAbgFANIQIUHuBsgWXiJKEATRhwsAp5s4AhGAFlGjGSLsMkc6rVtNZrYCulwAIiPy+gb+WLbhEw5GuEwXNzRVQGFZwGwZQIoofA2jCSCcIoJnCTK8RI9qtpFOBli5AWDnIxmy+vv3yMkRuASJlDPy1DKBEQ/JsCKNVAZ3PmervtgchOOCLiJKDATRhwoqnSOch0nlWQIlmjLQrUQUUM1oBleXScO234BpQomFtaMG1PSzOq2pouBeoMNJswSWiRGAATZioBTddgEgXuAaUaIaEU65FWAF1apBSxn1YI+eXl4eagAuoACobs/m4EI2V50QV0LrjYXGPGjzUvhcoW3CJKAn6CqDXXXcdXv7yl+Oiiy7CJz/5SQDAkSNHcPjwYRw6dAgf+chHxnqQu4mqeAoIM8cKKNGs8Ryg6UGk84CZBWRTfWyGyKYLWV3tqwL6/UdO4Xeu/z+oBRUaAICVBfwm0HS7fyERbSFdW7XZSom608TeORVGwwoozAwDKBElwrYB9N5778U999yD2267DX/3d3+Hv/7rv8YPfvADXHHFFfjoRz+K22+/HQ8++CC+8Y1vTOJ4Z560KxBWDkLTggDKCijRrIha7C01BRfAzA0ikpUVALKvLVh+ePQ0yjUXy6dbJ8XCDB4XtuESDcZ1IHQLjtuELyXyGQMZK7WhAgquASWiBNg2gD7vec/DTTfdBF3XsbKygmazifX1dZx77rk455xzoOs6Dh8+jDvvvHMSxzvzwgmZAIIW3DJb0YhmxIYW+zCAztggonAP0H5acJdW1clwud6qdgoz3K+QAZRoEDJowa07TQBAxtKRtQw1BRdswSWi5OirBdcwDFx//fW46KKL8IIXvABLS0soFlsnF/v378fJkyfHdpC7iRpQUgCgBhHBb3JoANGMaAXQPISZUx+bsQqo2oIF0PoIoKU1FUCrHQIoKzVEgwlbcMOW9qylI5fWWy3uDKBElBB6vzd829vehje96U14y1vegkceeQRCiOhzUsoN/+7Hvn35gW4fh2KxMPHvedSrQZ/fj2KxgPLiIkoAFnISxp7JHwslTxzPSRqd8kkPdQB7zzoA6dRwDMBcWiI35f+v7c/LleZpOCkd+59wjtr6oYeo9VbTovuw7SLqAOYyQHbKHxeKz257r5RSouw1kJsroJJRaz/P3D+HPXNpNDwfxWIBy3NzqBx3dt1jkyR87CmJ4nhebhtAf/zjH6PRaOCpT30qMpkMDh06hDvvvBOpVCq6TalUwv79+wf6xisrFfh+cltLi8UCSqXJDwByK6chF34GpVIZnqv20Fs59jhSbnbix0LJEtdzkkansaSqg2s1AdlQF+1Ol1ZQW5je/9fNz8v6yWMQ+X1YXu7dWlx3PKxV1ACmx0uV6D6aNfV7Ya20gmpheh8Xis9ufK+UTRfwm6i5AsdPrgMAGnYDhiawXHFQKpXhNFPwndque2ySYjc+Lyn5xvm81DTRteC4bQvu0aNHceWVV6LRaKDRaOCrX/0qXvva1+InP/kJHn30UTSbTXz5y1/GL//yL4/8wHcbtUXDphZcgIOIiGaEtMuAEICVbQ3bcWZvDegg7bdAlzWgM9aaTDRWrrqYI4w0ak7QgpvWkU3rrSm4RhpoepBNL66jJCIC0EcF9MILL8QDDzyAV77ylUilUjh06BAuuugi7N27F5dddhkcx8GFF16Il73sZZM43tnm2oDvbRhCBIBbsRDNCDXlOg8hNMgZnfYq10tIPfHcbW8XDiACNq8BDbo9uAaUqG8y3M5JN1Gvt4YQ5dLGxim4gDrXSCV/GRQRza6+1oBedtlluOyyyzZ87AUveAFuu+22sRzUbtU+IVP9yQoo0SzZMOVa0wAjM1MVUNmoQzqVvibghhXQA3uzGyqgMCxAiJkL5kTjFA4XEkYatVX1espYqgLqej5cr9nqLnDr0fsQEVEc+pqCS5MhndaETABqo3qhsQJKNCOkU40uMAGAsLIzFbTCLVj6acFdWqsjnzFQ3JNBpb0CKoJgzgooUf+iFly1DUtKEzB1Dbm0qjNUbU+14AKchEtEsWMATZD2TeoBQAgBkc5HwZSIplt7BRRQARQztNbRL4dbsCxue9ul1Tr2L2SQzxio1NwNnxNmZqaCOdG4tVpw06g7HjKWDiEEsmk1zLBqe1ELrmwwgBJRvBhAE2RzC676e54tuEQzQg0Zawug5mxVQGUQQMVcHxXQ9gBqbw6gWa4BJRpE1IJroeZ4yFqq8hlWQGu227YGlK8tIooXA2iCRBXQ9hPUdIEtuEQzQE25Lm9qwc3N1BpQv7wMGOmoi6Mb1/Nxqmxj/54M8lkDTqMJ1/Ojz89aazLRuMmgBReGpSqgQfBsr4AiWgPKCigRxYsBNEGkXYm2aAgJixVQopng2oDf3Dj8w8zO1HYj/noJWmERQoiet1s+XYeUQHGPqoAC2LAOlGtAiQYTtuAKPb19BZQtuEQUMwbQBJF2OdqiIcQKKNFs2LzGW/09N1OVPjngHqBnLGRR6BBAuQaUaEBtLbh1uxVAs21DiASHEBFRQjCAJohaH1bY8DG1BrQKKWVMR0VEo9BxjbeZBVwb0p/+jeGllPDLJYg+BxABQHEhg1ynAGplWQElGkDUgqurNaCZTQG0ZnuAGQZQvraIKF4MoAmyeUAJEKwHlU0ODSCacq0AumkKLjATYUvaZcBz+tuCZbUOy0hhLmt0qYBmgUaNF96I+uU5gKZDpPQNATSlaUibKVRtF0LTgZTBKbhEFDsG0ATpHEAL0eeIaHq1hoxtqoACwAwMIpLhHqD9TMBdUxNwhRCdK6BmBpAyaiskot6kawOGBd+XcBrNqPIJqHWgNVt1WQgjzdcVEcWOATRBNu8RCLSqJVwHSjTdOldAc+pzMzCIyF9fAgCIPteA7t+jJnJ2HEJkzk5lmGgSpOtA6BbqDRU0wwoooCbhhgEURpprQIkodgygCaG2aOi0BjSsgDKAEk0zaZcBoUXhCkA08XoWBu74YQV0mzWgvi9RWqujuKACqKFrsMwUKrVNLbhgACXqm+dAGOkoaGasVPSpXFpHNdhrV5hpvq6IKHYMoEnh1gHZ7FEBZQsu0TQLW+zbtygR5uxUQGW5BJEutLZ66GK17MBryqgCCgCFjLG1BRcAZiCYE02CdJ1oD1AAyFpG9Ln2CqgwMmzBJaLYMYAmRKcJme3/ZgWUaLp1bLEPK6AzsAbULy/31X67FGzBsn+hFUBzXQLoLFSGiSbCtVULbhRAWxXQbFsFlC24RJQEDKAJ0Wl9GADASANaihVQoiknnQ4t9jPVglvatv0WaO0B2l4BzW8KoFwDSjQY6akKaFjpzKZbFdDNQ8gmETgAACAASURBVIgYQIkobgygCdFpk3oAEEJAWHkGUKIpJ+3Kltc3Uiag6VM/BVf6PmRlpa8JuCdXa0hpAnvnWq26qgW3Ef17loI50SSEQ4hqztY1oNm0gYbnw/V8CDMN8MIOEcWMATQhurXghh9jACWabqoFd1MFVAgIKzv1QUvWVgG/2d8E3NU6FvdkoGmttbCqBdeL/t1qweWJMlFfXFsNIXK2TsHNBVuy1BwPMDKsgBJR7BhAE6K1R2B+y+dEOs81oERTrDXlusPr28xO/RAif70EYPsJuECwB2hb+y2gKqB1x4PX9NUHUiYgUhxCRNSnsAW33iGAhnuC1mxXDQnzHEjpx3KcREQAA2hiSLuydYuGgAqgrIASTa1GDZB+xwAKKzv1Q4hkOQygvSugUkosrW4NoLlgL9BquE5NCAgzwwooUR+klEC4D6jjwTQ06KnW6V0u3Xp9CTNofXedOA6ViAgAA2hihBMy27doCKkWXFZAiaZVzxZ7Kzf1LbhqD1ABkd/X83blugu70dwwARcACll1glyptdaBYgZak4kmoukC0geCfUDbq5/AxgoojKC9nW24RBQjBtCEUO15W09OgaAC6lTYMkM0paTTZco1ZqQFt1yCyC1ApPSetyutqopmcaFzBXTzViysgBL1wVMXbkTQgpvdFEA3VECDfXr52iKiODGAJoTaoqFDex6Ck1YpObmOaEq11nh3roBO+1pHWV7uawLu0urWLVgAtQYU2BxAs8CUB3OiSQirmWEL7uYA2qqAtgIoWAElohgxgCaEtMtbt2gIhCetbMMlmk5d9/lFWAGtqnVcU8ovl/qagLu0VocAUNyT3vDxPCugREOT4XrOYApuJr0pgAaBtGq7gMkWXCKKHwNoQmzXghvehoimT+8KaFat3/KmcyiI7zUgq2vbDiACVAV0Yc6Coac2fLxTCy5MrgEl6kvw3iEMCzWnuaUCqqc0WGZqQwVUury4Q0TxYQBNACn9rls0AKyAEk07aVcALQUY6a2fDCZfT+skXO90CYDscwuW2pb2WwCwjBRMXetQAWUAJdpOVM3ULdRtd8sQIkDtBVoNt2EBgAYroEQUHwbQJGjUgy0aWAElmkVhi33HKddWTt1mSsOWt7YEABB9rAEtrda3TMAN5bMGKrVNa0BdG9Ln8DWintywApruWAEFgKxloGZ70UUwVkCJKE4MoAnQa32Y+jgroETTTNrV7heYogrotAbQkwC23wO07nhYr7kodqiAAkA+bWwdQgQAPFEm6kl6qprpaQa8pt+jAupBcA0oESUAA2gC9FofBgDQLSClswJKNKXCfX47CSug0zrx1V1bAlI6RHa+5+1Ka8EE3IVsx8/nswYq9sYWXIDbRRBtJxxCZPsqeHYKoNm0rvYBTRmA0NiCS0SxYgBNgG0roEJAWHkGUKIp1XONtxVUQBtTugZ0bQkivwghev866bYFSyif2diCG03rnNLWZKKJCQJovamGe2XTnQNo1fbUMgAjzQooEcWKATQBWhXQzieo6nMFtuASTSlVAZ3NFlx3bamvAUStCmiPANqhBZcVUKLewhbcMIB2bsEN1oBCrRXlGlAiihMDaAK0KqBdWnChwikroETTR0of0uleAZ3+Kbgn+9uCZa2OfMboeHIMqABasz34vtoPNawMgxVQot5cB9B01BrqtdNxCFFah+M24TV91d7OFlwiihEDaAJIu9x9i4YAK6BEU8qpAVJ2b8HVNMCYzi1HZKMOv16B6HMP0DO6VD8BtReoBFAN1oFyDShRf6TrAIaFuqMqnJ0CaC6t9toNJ+GyBZeI4sQAmgCqOlLouEVDiBVQoukknT46HKzsVLbg+uUSAECb62MP0NU6ij0CaCGjTpCjNtwpb00mmhTp2cEWLCqAdhtCBCDaC5QBlIjixACaAL0GlIREugDpVLknHtGU2W7IGBAG0OlrwY0C6DYVUNfzcapsdx1ABKgWXKAVQAWHEBH1x3UgdCta49lpCFEu+FjN9iCMNLc3IqJYMYAmgLQrENZ2ATQPQHI9FNGU2XabJQDCzE3la1uuLwPYPoAun65Dyu4DiAC1DQuAaBKuSBlAymALLtE2pNdqwRUALDO15TbZoAW3anuAmYbkGlAiihEDaAL02iMwFH6e60CJpkv/FdDpC6B+uaQqleFepl1EE3D3dN4DFADy6U0tuAiqoAygRL25TtSCm7Z0aB2W87QqoC6EkWELLhHFigE0AVQLbvfqCNCqnvhcB0o0VfqpgMLMTWWrqV8uwdhzRs/160BrD9Bea0CjCmi9fS/Q7FQ+LkSTJF0H0E3UHa/jACJgYwVUteDakFJO8jCJiCKd36loYqS/zRYNAVZAhyN9H83Hvqt+Qe9AqvgEaPMHRnRUtJtIuwJoOqBbXW8zrWtAZXkZevHsbW+3tFqHZaQwF4TMTiwjBT0ltlRApyGA+rU1wHOhzW0/DZho1KRnQzPSqFe9rtsctVdAkUsD0geajZ7vS0RE48IAGrdGuEVDfxVQBtDBNE/8APW7rtvx/WjFJyL3qj8awRHRbhO22Peccm1lAc+B9D0IbXrelv3KCvQn/Ty2q6MsrdWxfyHT+zEQAvmMgfKGAJqdijWgzpFb4K8vIfdr7437UGg3ch0IQw0h6jSACAD0lAbT0FQFdI/a8k02bAgGUCKKwfSc6cyoftaHtX+eW7EMRtZPAwAyL3sHRB9bRXTS+NYX0Fz68SgPi3aRvlrs27YcEZm5SRzWjsmmB7g2Utl5eNvctrRWx1n7eq8TBdQk3OrmCmh1dYdHOn6ytgZZWYn7MGiXUi24agjR3rnu+4nn0kYwBTdohXdtAPOTOUgiojYMoDFrrQ/bJoDqlpoIyQA6EGmrtkat+ARoQ57Ya3P74T16P6SU2651I9qsr22WwiE+jRowLQE0aBnWtvnZfF+itFbHzz9p+wtAnSugyW/BlU4F0q5A+j6ExtEKNDlSSqBtH9Czra0TcEPZtI6q7QJmUAHlICIiigl/U8asVQHtXSEJb8MAOpjwJFlsM6WzF5GZA/zmVG6TQfFTLbj9V0CnRfjaSmV6B9DVsgOvKXtuwRLaXAHFlKwBVRe6JKTD92easKYLSBltw5K1uq+zzln6hgooAygRxYUBNGb9VkDD23AN6GCkUwGMDITW/arwdsKWSFnnY0+D66cCGm5jMg1hKxRVQLcJoEvRFix9BNCsiXKtrQJqZQGvAelv1+QbHyll9FjI+nrMR0O7jfSCAXspCzXHQybdqwJqtKbgAtziiIhiwwAaM3/QCiivsA9EOlWI9PDVT6AVQH2bJ5c0GDXlutpHC25YAZ2iSbjBe1Fqm59taVWF6r4CaEa1CPrB9hBRZTjJJ8peAwgCMgMoTVxQxfSEASnRdQouoCbh1hy24BJR/BhAYybtMpDqvUVDiBXQwUmnuqP2W6BtAjFPLmlAsqFaM/tvwZ2eADpIBTSliZ7DUUL5jAkpgbqjAp0wg9Ca4ADaflGQ7xE0aeEWYw2o1ttu+4AC7RVQtuASUbwYQOPmqAmZ/Qy3UQGUFdBBqAC6fXtzL60WXJ5c0oD6nXI9jS244YCvTO9wXVqtY3FPBpq2/XtcPqNOnithG25UAU3u49J+0YAXCGnighbchlSvne0qoE6jiaZmqg+4yb2wQ0Szra8puDfccAPuuOMOAMCFF16Iyy+/HO9+97tx3333IZNRV9IuvfRSvPSlLx3fkc6ovtaHBUS6ADhVSL+5ozWNu4pdgdi3d0d30aqA8uSSBtN3i33KADQdmKohRBVACGhWBih3r9wurdX7ar8FVAUUACp1F2egVQFNcgvuhgDKi1Q0YWEFtB4E0G77gLZ/ru6nIKD2ASUiisO2AfTIkSO4++678cUvfhFCCPzmb/4mvvKVr+DBBx/EzTffjP3790/iOGeWb5f7rtCFt5NOdWr2CozbSFpwUzpg5XhySQPre5slISCs7NRNwRVmDkJ0b6SRUmJptY7zD+7p6z7zGdVGGG7FMg3Tgdu7UvgeQZMWttHaTXVRuncFVL2+ao6PnG6xBZeIYrNtC26xWMS73vUumKYJwzBw3nnn4fjx4zh+/DiuuOIKHD58GNdffz1835/E8c6cfjapD4UnsWzD7U84nXKnARQAtHQBkkOIaECtANrHkDEzG6wZnQ7SqQLbDPgq113YjWb/FdCsOkGuRgE0XAOa4AAaVkB5kYriELTg1jwVQHuvAVWfq9quem2xBZeIYrJtBfT888+P/v7II4/gjjvuwKc//Wnce++9uOqqq1AoFHDJJZfg85//PF7zmtf0/Y337dvZurxJKBb7C4Y7UW1UkF3Yi8U+vletsh+PA5hPN5GZwLFNO9+uoiJ95Pftw54dPl7u/F7Aq03kOdFL3N+fBrP2sAsHQPHgmdCM3oPGGvk5aLIxNf/HJ6SNVE51YnQ75pVHTwEAnvSEvX39XNl8MJ1T01AsFtDMA1UAOdPHfEIfl/D/2CoeTMR7BCm75f9h/THABiAyqlvgnLP2YKHLwK+zK+rCjmEZ0NNZmFpz1zxOScHHm5IojudlX2tAAeDhhx/GJZdcgssvvxxPfOITceONN0afe8Mb3oBbb711oAC6slKB78vBjnaCisUCSqXxrvmTvg+/XoUtrb6+V9NWVzhXH19CJXPOWI9tFvjrJQBAzdPh7vD/0ktl4a8eG/tzopdJPCdptOyVZSBlYGWtAaDR87aeZkFW1qfm/9gpr0eV3W7H/MN/WwEApLXut2knpURKEzi5XEGpVIb0mwCA8qk1NBL6uNgrp4CUjqa1gGbp36bm/2+W7ab3ysbqaQDAyTUVLmsVG57jdr6trd6Djp9cxz7NhF0u75rHKQl20/OSpsc4n5eaJroWHPuagnvffffh4osvxu/93u/hVa96FR566CHcdddd0eellND1vrMsBVpbNAwwhAictNiv9ta4nRKZOfhsr6MBSbvaf4u9mUv0WsfNpF3Ztr19aa0OAWBxvr8WXCEEchkD5WAKrtBSgG4legougknbIjPHFlyaOBm04FZcDSlNwNC7n9blghbcmu1BGOloD1EioknbNoCeOHECb33rW3HttdfioosuAqAC5zXXXIPTp0/DdV189rOf5QTcIQyyPkzdjmtABxHuzzeKNaAiMxdMIPZ2fF+0e0i73P8FJisLTNk+oNsG0NU6FuasnifFm+UzRrQGFAjWgSY4gIaPg8jMAa4dBQKiiXAdIKWj1pDIpvWeW7plgyFEVduDMDOQXANKRDHZtmz58Y9/HI7j4IMf/GD0sde+9rV485vfjNe97nXwPA+HDh3CK17xirEe6CySfe4RGBK6Cejmho3PqbuwArrTfUCBtr1A7QpEtr+JnkTSGWDImJmFbNQgpexrX+A4Sd8HGrU+KqC1vgcQhfIZI5qCC6hgnuxtWCpBAG1t1yQKvdf7Eo2KdG0IPY264/WcgAsAhq7B1DXUbBcw0tyGhYhis20AvfLKK3HllVd2/NzrX//6kR/QbtLvFg3tRLrAFtw+RQF0m0md/WjtBboOMIBSn6Rdhra4r6/bCisLSF+1xZmDhbaJCyqS2713lVbreNb5iwPddT5j4OSptoqnmfQAWoVWKEILL1LV14HCYD8z0bCk5wCGhZrt9ZyAG8qmdVUBLbAFl4ji039fFI2c7HeT+jYinWcLbp+ixzfYS3AnRPvJJVGf1DZLfV5gCqqJiV7vGOinvb3ueFivuSjutAJqZhL9mEinCgRrQAFwuyaaLNeBMKy+KqCA2gu0ZnuqAsoWXCKKCQNojFgBHS/pVAHdVK3LO6QxgNKApN+MBtT0I7xQMg2DiPppby+tqZPbMxYGuwAUrgGVUk1JF0bCA6hdhUjn2i5S8f2ZJke6NqCnUXP6r4DWwn1Amx7nGhBRLDi6NkbSrgApE0Lvf72QSOfhry+N8ahmh3Sq8I0s3vnRf8Z83sLBYg5nF/M4WMzjYDGHQrb/YMqTSxpUqwW8zzWgYQV0CgYRSXv79valVRVAh6mANn2JutNUQ1WsLJDQFlzpNYBmQ03BTav3CE7LponyGq0KaLq/CujKuq2m4AJAwwYGuAhORDQKDKAxGqg9L8AK6ACcKhpaBivrDgpZE9/+4TL+6bsnok/P58yNoXR/Dmfty8E0Ulvvy8wCIsX2OurbwEPGrKACmuBqX6ifFtywArp/YfAACgAV21UB1MwmtircqgTnIAxLbRnDAEoTJF0bIrd3oAroY0tuFEClaw98HkJEtFMMoDEaZIuGkLDyQKMO6XsQGv/7epFOFY5Qv2Tf8V+ehVxax+lqA0dLFRxdquJYqYKjpSr+3/uPwfV8AIAQwP6FLA4Wc1Gl9GAxj+JCBiJT4Mkl9W3gbZbCtcrTUAHtowX35GodhazR17q0dvlsEEBrrpqga2YA34P0GiNppx+lzYPOuBcoTZoM1oA6jWZfr7VwCFE46IzrQIkoDkwwMVIV0P4HEAHte4FWIbLz4zismSGdCuqYQ0oT0f5oe/IW9uQtPONnW5NJfV/i5GoNx0pVFU5LVTy2VMG3HypBBrd5zpOLuDgzx/Y66tvgFdBpGkIUhOQeA75Ka/WBt2AB2iqgwSAiEZ4oN+rJDaBBEBcZdqjQhHkOmkK9LvqpgObSBuxGEzIVLP3hVixEFAMG0BhJuwJtwHH90XYgdgVgAO1J2lVUxSLmcia0HvsqaprAmftyOHNfDs99yv7o406jieMrVfzt136ER0+WIc6d4xpQ6tugFVAYQdBKaLtpO2lXADMLoXWfY7e0Wsf55wz+HlUIAmg1CqBByG3UE/eet7kVWaTnIKun4jwk2mWka8MT6jXTbwUUAByY0dcTEU0ap+DGaKgW3KgCyiDUi5QS0qmi3DQxnxuuamKZKfzsmXM490AB5ZobrL9lBZT6M3AFVNOAhG85EpJOtef6T9fzcapsD1UBzQUBtLwpgCbycbFba0ABNS2bLbg0KVJKwHXgBgE029cQInWbuq/+ZAAlojgwgMZE+k2gURuiBTesgDKA9uQ1AN/DumsMHUBDhawBx23Ct7gGlPon7fLA2wCpgTvTsQa0V7BePl2HlIMPIAIQtMu3WnCjtWoJDKDtQ4iAcA1oOdpChmismi4AiYZUYbK/CqgKq2EABQMoEcWAATQmreEVw1ZAKyM/plkStsadclKYz+8sgM4FAdbRsoDXgHSdHR8fzT7pDLHG28pNRwuuU+lvAu6ewfYABQBNCOTSRmsNaDQdOHnDUqRTBUQKCCaKikwBkOriItG4hdXLhgwqoH2tAVW3qXpq2nsSX1dENPsYQGMy8PqwAFtw+xMG/FNOCnO5/vdZ7WQu2C+0rgWVGFZBqQ9DbbNkZqcivGzXghvtATpEBRRQXQeVKWjBVRcZchDBGvPWfsF8j6AJCC6G2mEFtI8W3LACWgkDKCugRBQDBtCYROvDemxj0IlIGYCRnoo2vTiFj0/Vt3bcghtWQCt+EEC5DpT6oNZ4z2YFFPb2AdQyU5gLtlQZVC5joFJrAGhNwU1iMN8cxEVaBVBOy6ZJkJ4KoHVfhcmBKqANACmdLbhEFAsG0Ji0KqCDbwAt0nlWQLcRBvyaHEEADSqgZT/YuJsnl9SHYSqgMLOQjWRfXJLSh2xsE0CDLVhEj+nTvRQyBip1T/3DSAMQiWwVlE4VaA+grIDSJAXhsR5UM9NmatsvCQNozXYhjAwroEQUCwbQmLQmZA5WIQm/hmtAe2tVQM0RrAFVVZxVV90PqxvUD2mXB+9wsLLJr4A26oCUPX+2YfcADeUyBir1oAIqNMBMJzOAbqoEiwyHxNHkhPMIqs0ULCMFPbX9KZ2hp2DoGqq2p7qpEvi6IqLZxwAaE1ZAxysMoKOogBp6ChkrhVOOCqLcC5S2I30PaNSHaMHNAp6jvj6hWgPUOldAfV+qADrk+k8AyAcV0HCarDCzyV0D2hbEoynlvEhFExC24NY8DRlr++pnKJvWVQXUTLMFl4hiwQAaE2lXAN0aaIuGkLDyrIBux6nCFzpcpKI1nDtRyJpYtaW6YsyTS9qGtIeccm2qUJfkKujmrUc2Wy078Jpy6AFEgGrB9Zo+HLepvpeZARL4mGxZA6ql1Psz3yNoEoLwWHa1vrZgCeXSBqq2xxZcIooNA2hMhlofFlAtuKzC9SKdChpaGpapI232/4u5m7msiXLNDfb548kl9TZsi3245UgSw1YoGoDWpQV3KdqCZWctuAA2TMJNWqug9D3AtbdUgvkeQZMStuBWXA3ZPibghlQFNGjBZQAlohgwgMZETcgcNoDmAdeGbCa3TS9u0q7Cxs7bb0NzORPr1YY6ueQUXNrGsC32rT0vkzuIqDXBu3MFdGlVheedtOAWNgVQmJnkBdDgIsHmx0FkeIGQJiRowS03xGAVUEtH1XYhuAaUiGLCABoTVQEdfAAR0LYXqMM23G6kUx3J+s/QXNbAeq0BLV1gdYO2NfQ+vzPQgru0VkdKE9hbSA/9PTpXQJP1mHTbSosVUJqUsAJ62hF9bcESyqYN1GwPwuAaUCKKBwNoTHZWAeWkxe1Ip4KKb2I+b43k/gpZE5WaC8kASn1oteAO+BoPK6AJ3ud3uwBaWq1jcU8GmjbcFiwAUAj2D63UwgCaSV4A7fI4iPQcJ2XTREjXBlIGao4/YADV1RRck2tAiSgeDKAxGUkFlIOIupJOFeuuMdIWXAnA09UEYin9kdwvzaaoAtpjr8xOwtsnLWy1k04VMNIQqc4nvEurdZyxg/ZboHMFFI16NBU3EZzOrcgiMwc4VS6RoPHzHAjdQs3xBhxCpKPueIBuqeU8/H1GRBPGABoDNbyizgroGEm7inVvtAEUAGwtC0iZ6AoVxS8KaQNOuRZmWAFNcgCtdA3WUkosrdVR3MEAIkCdIAu0rwHNAtIHvMaO7neUWtvRbG7B5fszTYZ0HUjDgteUAw4hUhd4XBG8PwWtvEREk8IAGoNhJ2SGWAHtTXoNoNkY+RpQAKhCnVizDZd6GbbFXugmkNKBJFdA7WrXAFquu7AbzR1NwAWAlKamerYqoMHrLkGPS7TVzpYK6Lz6PN8jaNxcGzKllpkMWgEFgAaCva3ZhktEE8YAGoOh14cFWgGUV9g7CSsTNWliPj/aCmjFV7/seXJJveyoxd7MJrvC7nQPoEurwRYsO2zBBYB8xkh2AHUqAARgbvxZRWZOfZ7vzzRm0nPgaypEDhJAw2qpDfV7TbqchEtEk8UAGoPW+rAhA6imA0aGFdAuwpP3qrQwnxvdECIAWGuqyZ6yzpNL6m5HQ8asXKKC1mayRwAtjS2ABvujJmjLCOlUASsLITS4XhNOowkA0MIWXF6konFzHTQ19TtukCFEuaAF1/aDr2mwAkpEk8UAGoOdtuCqr80zgHYRVUB9M6pc7lQurSOlCaw2givGPLmkHqRdGfoCE8zsFKwB7fyzLa3VIQAszo84gEb7oybncVFBXD0ON//jD3Hd578LoK0CyvcIGjPp2fB2UAGtN9WfbMElokljAI3BsJvUtxNpbnbeTbg/al1a0XYOOyWEQCFrYNlJARCQNk8uqbtZrYDKYACXSHdvwd07Z8HQd/6rpT2Ahm2uSQrm7ZXgk6dqeGwpuCBoZABNZwClsZNuA26wjnOQIURhBbTaTAX3k5zOAiLaHRhAY7DTNaDh17IC2kUwHATpHPTU6J7ic1kT5ZqnHnueXFIXsukBrj2ba0A9B/CbPSqgtR1PwA3ls1tbcGXCWnDD9/CK7aFqe3DcJoQQEJk5+GzTp3Fz7VYAHaICWvFUAGULLhFNGgNoDKRdCfbR61yd85o+bvziv+LRx7ufwLAC2l148m5khm9x7mQuZ2K91oDIzHENKHUVVuCHr4AmN4BGW4/0WAM6ivWfgKqANlwfDbeZzABqt7ajCYPyalltZyEyBXZJ0NhJz4EjVZgcpAXX1DXoKYGKG1ZAGUCJaLIYQGOg2vO6h6PHliq476ES7vthqettJlEBPb5cRc2evs3UpVOFD4FMrvNJ8rAKWRPrVTcIoDy5pM5aLfbDV0DRqEFKOcrDGonoPadDC27d8bBec7F/ITuS75XPqAt0lboL6CYgtERtTxO24EopUQ0C6Kl1dSLP9wgaNykl4KoAKgRgmam+v1YIgWzawHqDAZSI4sEAGgPpVHpWR46W1EneieXuVRCRzgOeo/a8HIOm7+N9N/0L7vi/j47l/sdJOhXUpYW5fHqk9zuXM1QFNF2Az+oGddFPi33d8aJq2WbCygFSAgk8KWxVQLf+bKW1YALuqFpw2wKoEGq7k6SsjZW+DzRqEFYOdqOJpq8uFpxaDyqgaQZQGrNmA4CE7evImDo0IQb68lxaR9mRgBCJmi5NRLsDA2gM1B6B3U9Oj5XUSd7xlV4BNBj1P6ZWvaXVOpxGE8unk3cSvB1pV1HxrZHtARqay5lwPR9Nk2tAqbt+KqC3fOWHuPZv7u/8yQROfA1F7cUdWnDDPUBHtga0vQKKYG1sUk6Ug/8bkc6jHA5KAnCqHFZAC5D1ciKr2DQbpKsudtSaqYHab0PZtI6q0wSMNCugRDRxDKAxkHa55xYNx4IK6NJqHV7T73ibMMCOax1oGILXulRpksyrV1D1TcyPaAuW0FywF2gjlQMadcimu81X0G7UTwX0+z9dxeOnah1f39F6xwSuA5V29zWgUQV0hGtAgc0BNBmhvD2IV9sCaFjV1jJzqkLlTd/7J02JIIDWm6mBJuCGcmkDNduDMDIMoEQ0cQygMVAV0O7VkaOlKtJmCk1f4uRq5yv+UQV0TOtAw+rrWmX6TqCadgU1aY0+gAb3VxdBQOAgIuogqoB2uch0at3GqXUHUqJjG24Y7pK05Uio1xCik6t1FLLGUNWYTrYG0ExiWgXbH4fo+ERbCy73AqUxk54KjVVPG74CarsQZjqR7f5ENNsYQCdMNt1gi4bOJ6flWgOnuZJsrgAAIABJREFUqw08+/xFAN3XgY67Anp8OQygjalrI5N2BTU5vgpoRaaD78OTS9pKTbnOQKQ6nxQ+fPR09Pflta2BSkQtuAmsgDoVIGVC6FtfW6W1+sjWfwJALgygtVYATUwF1N4aQA/szbZacNMMoDRmQQW04qYG2oIllLNUBZQtuEQUBwbQCWu153WugIatr8+9YD8EWkFws1YFdLwB1HGbsBvNsXyPcRFuDTVpYS5vjfR+C1l1Qrzuq/vlySV1st0a7x8dO41wXEinNdbCDKqLCayAwqlCdJiAC6glA8URtd8CgJ7SkLFSUcBDgtaAtlpw89Hx/cwZhQ4VUHZJ0HjIKIAOXwGtO0EATcjrioh2DwbQCdtufdixIPg94cw5FPdkug4iitr0xtCC2/R9PH6qhoWCClrT1IYrfQ+ppoOqP74W3DU3DKA8uaSttttm6UdHT+P8g/MQoksADSugCQyg4dYjm7mej1Pr9kgroIBqw41aXK0krQEN3pfTag2oAHCwmEPd8VB3PIhgD2JOy6ZxCVtw1xtiuApoWocE4KfYgktEk8cAOmGtCZmdA+jRUgW5tI49eRNnLea6V0C1lKoIjCGAquFHEk97wgKA6RpEFJ6027CQG2IwQy96SkPW0nGqoYKorJ/e5itoN+pVAbUbHh5bquDJP7OAvQULy6c7VB7MDACRmLDVrlsAXT5dh8ToBhCFNgRQMwM0bEjZeTDbJEVrQM0synUX2bSOfXOqNX+17LQ6VNglQeMSVEDXGwKZIX7XZdOqo8fTTLbgEtHEMYBO2HYtuEdLFRws5iGEwJn7snj8VA1Nv9sk3MJYAmgYep/2hL0AgLXqePYaHYewNQ5WVu0dOGKFnImVOoCUAZ8nl9SBqoB2DqA/Ob4OX0qcf3Aei/OZzhVQoQFmOrFTcHvvAZod6ffLZ8xomxNhZgAkY39U6VQBMwOhpVCtu8hnDOwNAuipsq3WyBoZBlAam7AF1/H1oSugAOAKBlAimjwG0AnrVQGVUuJYqYqzi6rCcNZiDl5TorTW+ZeDSOfHsgY0bAN+2rlBBXSKWnARDAfReqzB24n5rIFyzYXIzLEFlzrqNeX64WD953lnzWFxPt11n11h5RJaAa10nYALjKMCqre2OQm3p0nAejVpV6IgXgkDaLBkYbVtHSjfI2hsghZcR+rIWKmBvzzcuqUBQ20rNmXDBoloujGATlg0vKJDQFpZt2E3mjhYVJ87a1Gd6HUfRJQfWwV0cT6N+bwFy0xhrTxNFVD1WOmZ7mvwdqKQM7Fea6iTS67vok2kp/Z+7FYB/dGx0zirmEM2bWBxTwZrZQeu13kv0ERWQJ0q0GkP0NU6LDMVDeoalXzG3LAPKIBEBPP2VuRK3UUuY2BPwYIAcKocBtAC3yNobMIKaAN61E47iFzwNY40AOkD3NeaiCaIAXTCpF0JWre2tswcDSbghhXQA3vVCVevSbjjqIAeX65G4XdP3pqqCmh40m7kxhNA57Im1qsN9dizvY42idYGdqiA+lLix8fWcf7Z8wCAxfk0JBBt3dFOWLnE7HkZkl4DaLodp+AuBVuwjLrtPZ/RYTea8Jp+0IKbjOFM7QE0bMHVUxrmciZOrav/Ty0zx/cIGhvpOpCaAQltRxVQWxrB/bENl4gmhwF0wnpNyDxWUtXMsxdV9SRj6dg3Z3WfhDuGCmg4AffsMIDmzKkKoH7weKTzc2O5/7mciartAekC2+toi14t9sdLVdQdD0862AqgALDcocU+iRXQKFx3WQM66vZbAMiHe+/W3agCmoRgvrEC6iEf7Fm6d85qVUDTDKA0Rp4DP6VeH1lr+Apo3Q8uhjOAEtEEMYBOWPvaoc2OlarYN2dFVyYB4Mxek3DTeaDZgPRGFxDDCbhRBbQwXRVQp7IOXwK5uXFVQNUvbVfPQdbXuW6GNug1ZOxHx9TU5CdFFVAV2DpNwhVWEgNouPflxgpo05cqgI54CxYAUbCr1NxWBTQBLbhqP9Q8XK8Jx20iFwbQQhqrYQDNzkHaZcguQ+SIdkK6NpqaCqDDVEBNQ0NKE6h66muTsLaaiHaPvgLoDTfcgIsuuggXXXQRPvShDwEAjhw5gsOHD+PQoUP4yEc+MtaDnCW9JmQeLVVxdnHj587al8OJlRp8f2vQiUb9j7AKGobdVguuibVKY2qCllNdR12amM+nx3L/4V6gtpYFfA9w+UubWqIKaIeLTA8fPY25nIliENQWChZSmug8iMhMzp6XIWmHFdCNAXTltLpoVRxHBTQMoHU3MUOIpPSjYUyVugegdZwLBStqwRXpOUDK1mRuolFyHXhaWAEdfAquEAK5tI5qU30tW3CJaJK2DaBHjhzB3XffjS9+8Yu49dZb8b3vfQ9f/vKXccUVV+CjH/0obr/9djz44IP4xje+MYnjnXrdJmR6TR8nVloTcENnLebgej6W1zu06QVBdhwB9Mx96mRvT96C6/moO97Ivsc4ebUyatLCfM4ay/0XgpbAGoJqDFvsqE2vFtwfHVvD+WfPR+skNU1g75zVeSsWKwd4Dchmcl53rRbcje9RjwdLBM4YZwW0nqAKaKMOSBkEUDW4pRC14KZhN5qo2R5ERi0DYKs+jYP0HHhQ4TE75J7X2bSBshtUT3kxlYgmaNsAWiwW8a53vQumacIwDJx33nl45JFHcO655+Kcc86Brus4fPgw7rzzzkkc79Trtkn9yVM1NH0ZTcAN9ZqE26qAju4E51gwATdt6vBra1jIqqfIamU6JuFKu4qqtDCXN8dy/2EFtOKrk2GfJ5fUJqoSbhrUc7rioLRm47yg/Ta0OJ/B8lrnFlwgAWGrTbcJ3ieC96ZxV0CFbgKaHvsa0PYgHgbQXNsaUABYLdsQmfD9mRepdiO/sjLWziHp2mgIA3pKwNAHb8EF1F6g5Yb6HS8brIDS7uGXl+M+hF1v2wB6/vnn41nPehYA4JFHHsEdd9wBIQSKxWJ0m/379+PkyZPjO8oZ0dqiYWsFNNx7Mxz+EzorqESe6BhAx1MBPWsxB+k3UfvclTh76W4AU7QXaKOKmm9iPjumABrc75qnTjRZAaV20i4DZnbLlOtw/ef5BzcG0H1d9gKNBu4kaR1olwroieUqUprA3sLo297DAFoOt2Kx4m9Nbh/GFO5Rmm9bAwqorVhaFVC+R+w2/voSqp/5fTSPPji+b+I5aEgDmSHab0PZtIHTYQBlCy7tEs2TP1Kvz9IjcR/Krtb3O9fDDz+MSy65BJdffjlSqRQeeeSR6HNSyoHH7+/b13kdZJIUi6MdZOOtr6ACYK64iLlN933qX45C0wSe+ZQztlzN3DuXxkqlseV4mrkz8SiAnO5ifgTH2mz6ePxUHc97+pnYI9ZRcSrY01wGsB++ECN/PMbhpFeHoy3g4Nl7xnL/UkroKQ22oU7C83pjy//lOE3D/8FudhI2ZG5uy//TsW8+CkPX8JxnnLnh9f2Es+Zx9wMnMLcnC8tofbx2ehGPA5jPAumE/J+fSrlwNB3FMxc3vN8/vlLDgX05nHHGeCZPZ6wUfKj3HzuTgyncWF8HtbKPGoCFA0XgqPo/O/fgHuybz0AG/7euBIoHz1Lvz6nGSN6faTBxPkeqp76PqpTINlfH9n9f9114ooBC1hz6Z937/7P3JsGRpOl55vP7Hh4LgIhAZgKZlVmVVdVrFZu9aEjJOKSWIUeabrJHkplMMpnmKB1k0kkX0dqMJ2n6oMvITDM2B17GOGMjmUbsoShqSJpakmnhTJHVZKuqu9ndWVWZWQkggQgAsXlE+PrP4XcPAIklA4gNFfDn0l2ZgLtnLO7/+7/f934rBR63lAAtWpLV/HM6c/Jn+OLpbh/SBwreU1Y+9/aiL+dasIjP5VgC9N133+Xv/t2/yy//8i/z1a9+lXfeeYdGozH6+0ajwa1bty514v393pnBOteF9fUyjcZ0yyvj5g4AvdDAf+HYP3p8wO21Aq3D07v7d6oFPtpqnboemUhA0G02CaZwrTv7HlGcsFY0af74++ocHfU+P91uT/31mAV61CfWN2d6rWXX5Nmh+ux2Gnun3stZMYvPZM50GbYOkWbx1Pv03o8bvHqnfOr7XTDU4u+HHzTYqB05i/FACbzD3QaGtTHjqx6P4cEhwnZpNk9WXOw0VXr3rD6brm2yt+/RaHSJdYdht7PQ70G4q+6JrYFgZ09dh9/3aQQRSZIgBDzZarP/cA2EoNvYm8r9OWd8Fn2vDLaeAtDd253Zex8P+3jxOpahXfnfqgvJfl+CC73DFmH+OZ0pi/5c5ij8tGKz/fhHBK/+7IKvZvHM8nOpaeJcw/GlJbg7Ozv87b/9t/lH/+gf8dWvfhWAL3zhC3z00Uc8efKEOI75zd/8TX72Z/M38WVcNKJhq9k71f+ZsVkrst3sn+onEZoGtju1Etztplocb9aLxM0n6pp7TQq2TusT0AMqkwRb+kjTnel5KkWL1iBRSaV5eV3OMc7q8Q7CmMfPu6P5n8epr6azQF8sw03LXKV/vXpAX0z3lVKys++Nkn1nQalg4g3TElyrsPgU3GOlyN4gxDK1kautaxqrJZvDro8QGiKfF3wjyfrLkn5rZueQUcAg0a8cQASqBNcbJmDYeQluzo1Bpt/LJF3n5iyGl965fvVXfxXf9/nmN785+rO/+lf/Kt/85jf5O3/n7+D7Pj/3cz/Hn//zf36mF7oMnJeQOQwiGq0hP/P22U7HZr2IH8YcdHxqKyf7rIRTnloI0XbqbGzUXJI/SL+Yvsft4iekBzTrDTtnzM20qLgWnX6AKFTyxWXOCeSwi1a7d+LPHj/vEidyNP/zOKNZoC8EEV3PECLvVP9ndxAy8CNuzSCAKKPkmnT7mQB1R4uHRXEyhGh7lICbsVa2Oeimo1gKlTyE6AaSpJVDst+eyfGlTCD0GaBP1ANadAwkgOlALkBzbghyoL6XyeE2MgpUwF3O3Hnpnesb3/gG3/jGN878u9/4jd+Y+gUtM0cpkicd0Mx5fHEGaMYoCXffO0OAlqY2Zy5LwLVNjV7zaSqwOtxzfXY+AQI0ex30WQvQosmzRg+tWskd0JwTSP/0mKUsgOgsAbpSsjD007NAsxCi6+WAemil2ok/2ztUwvnWjB3QRnoeYRUW/ppI3wPDRugmvUE4SsDNqJZtnjVSkVqokOT3iBuHTB3QWQlQohCQeJE2YQiR+t1EtxdeWZCTMy/U91KAjEkOt9DXX1v0Jd1IXlqCmzM9RiW4L7gIzxrqz++9MAM046JRLNpUHdC+SsDtNCAcYLz6ZQDu2B7tT0AJbuCp18EszraZuuJadPsBOOXc3cgZISMfouBUhcOjZ23uVN3RDNnjaEJQqzg0XhSghgW6MXLbrgPS90alwRmZMJypA1owRym4WO7CF8pqk0G9x94gHCXgZlQrDgfdoQrnc/IqiZuGlJKkmzmgs3HrZaQ2hL1Ix53IAVWf3UTPS3Bzbg5Jv4V2S4nOOC/DXRi5AJ0jctgFu4jQTqbcPmv0sEyN+jkuQqlgUnHNc2aBlqbSAxonCc8P1AiWeF99IY3XvgLAuu7R6vkznWk2Dfpt9bC3i7NJ48wouxZRLEmsUr64zBlxVo+3lJJHW+0z3c+M+orDfvuMWaBW8ais/BpwVgnuXmuAEEelxLOgVDAZ+BFRnCCsAkQ+Molndr6XIYdHr0PvLAFatgnChL4fIQrlvErihiGHXYh8sIvIYReZRNM/SSoWJxeg6ncjzcpLcHNuBFJKZL+FfvtNsAp5H+gCyQXoHJHD0yEeAFsNj7v1ItoFo2w260W2989wQ+ySeshNKA4brSFRLNmsFdUXUujoG58Cs8AqXaJY4g1n8CCdIv2uWui5lfMX+9NgpaicLF8vIoe9hS6Gc64PRwL06Dv+/KBPbxCeGUCUUV8tnD0L1HavjQMq4wjC4WkBetinvlrANGb3KMkEnjeMjuajLtIF9U8K0BdLcNcq6SzQTjoLNByoGdA5N4Ks/Fa/9br67/70NyBkqBzQQBoTzwEFiISVO6A5N4NwCFGA5q6i1x7kDugCyQXoHDkrIRNgq9E7t/8zY6N+ThKuU4Y4ggkXOFtpz9LddZWAq1U3EbqJVqlTSlQfS6t7vftA/Z560BdXZjMDNKNcVA/tgSgAcmopxDmfbI5Cxo4c0EfP1HfnzYsE6IpDtx8yDF7Y4LGL1yaE6Kh//eR9aq81ODE+ZhZkArQ3CJUDymLDmTInOEkk/WFEyTntgAIcdIZKgMLU2iRyrj9Z+a1++w3gKPBkqqQluD7GRCm4mQMa5AI054aQlcULdwWt/oDk4OPcRFgQuQCdI3LYPbWA63gBnX7IvfrFi7jNWpGBH50ah6Klx5t0gZMl4N6pqpIErfaqOn75FnagvrDXPQk37KvXoLI2Wwe0kvby9WS6GM77QHM43uN99B3/8VabomNwu3r+aKCsfHX/jCCiRQfuZBxPfj1O43DAnVkLUDd1QAchZOFMC3RAs3E03jBEHru+jGrqgB52fTQnFaB5qf6N4ZQAnUEfaCYWJ3dA1e/6mIutKsjJmRNJGgwm3FX0+gOIQ5LWzoKv6maSC9A5ohzQkwE5W2kA0d1bFzugx5Nwj5Mdb1IXbnu/T63iYIdd5LCrvpiAKNcxBgeA5PCaC9Bk0GOQmJRLzst/eAIqaQluN1FOxyxKrHI+eZw1ZumDtP/zovL6eppsfSqI6DqV4J4hQIdBRKcfcqc227m7mcPY7S/eAZVSKgfUKdJLg5Fe7AFdKVpoQnDQHSIK6f057wO9MchOE2mX+B//72fA0YJ3qudIS3B9aU7UA2qbOromGCZm7oDm3AhedEAhnwe6KHIBOkfOKsHN4vrvvaQE97wkXDElB3Sr4XF3vUiSBhBlX0ytvI6IA8pieMp9vW5I32OAg67N9mOdLThbkRIOuQOaA9kmkDjRH7iz37+w/xOOBOiZDug1KcFlmAnQo/tUs6Wud9YOaDlzQIfhsfmoC3JrIh+SGGEX8QaqZPpFAappgtWyddQDSn6PuEkk3QahvcbTjtp0mkkSbioW/QkdUCEErmMwSAyIw7wUMWfpyUYjae4q2soG6FbeB7ogcgE6J2TkQxycdkCbvVHK7UVUXJOiY7BzSoBmDujVBahKwO2zWSumX0SBXnsFAK1SB+BuYUD7mjugIuwTaLN1PwEMXaNUMNn31XuWuxs5kApQ2x2lXGf9nxcl4IJy1E1Do/lCEq6wVQqulMlsLvgSjHpAjzmgjZa63lk7oFnIT7cfHAshWpADmjnS9vkOKEC17JzsAc3vETeGpNukb64RoxNohdmU4EbHQogm6AEFFUTUj9Nj5C5ozpKT9FugG2C5CE1Dq70yMl5y5ksuQOfEWQmZoBzQe+tFxAUleqB2KjfrxQsc0KuX4KoE3ITNukrA1VbvIEwl5ER5HYB7hcG1d0CNqE9kzG4cxHHKrsn+UAOh5/1dOUDW430sgGirja4JXtu4eCyQEIL6ijNyFEd/brsgJYSL3/gZleA6pwXorEOIbFPHMjTlOI5KcBfjgB7v880E6IspuADVis1h1wfDBt0iyQXojUAmCbLXpKOp77wniiPHZapMqQQXVBCRF6tNs0XP2M3JmTWy30IUVkZrbr2uknCvw0bvTSMXoHPirP6wRMq09PXi8tuMzXqRraZ3MgnXdkGIkUNxFU4l4NYejP5OKysH9I7Vv/YhRGYyJDFm68ZkVFxL9aTlc/5yUqR/ssT+0bMW92+XsUz9gt9S1FfOGMWSBe5cgz5Q6XsgNDCPNngarSGubVBKQ7lmSbFg0h0Ei+8BPdYLO3JAndMCdK1sc5Cmhqt7RL5JdROQ/UNIYvYTdR/oyMKMekDTECJ0CvbL7y8X4ToGvVA/cdycnGVFDjoI92hSglZ/AOEQ2Wks8KpuJrkAnRNnDanfbw/xw5h76+M5CJu1It4wotsPR38mhIawSxM5oFmw0Z1ijPQORgFEAMKwEYUKNb13rQWolBJHDsGerRuTUSlatFMBmrsbOZA6oGmPZBQnfPS8e+H4lePUV5yzS3BZ7MiRjGz0yPFKjb3WgPXVOVUcFEy8QYTQDDCsxTmgx5zg3iBE18SZAqBadgijRI2OKVTyHtAbQpIuYvcC9d09CJ2ZleDGwsAyzYkzD4qOSSdMj5EL0JwlR/ZbaMcEaLbezftA508uQF8gThL+zbvP8MPpNuOf5YA+SxNwXxZAlHFRENEkPaDbTY9axcHsbAFHAUSj45fXWZFd2r2A5IU5pNcFbxjgigDNmZMAdS26XoBw8sVljuJ4yvWT3S5hlLy0/zOjvurgDSP6w6NZoOI6OaDD3qnNnUZrwPrq7Huu4cgBhfR1WdB4miMHVJXgFgvmme0T1YpKyD7sqiCivEriZiDTESxbgzRYLLCQg/ap+d0TE/qEwppoBmiGaxt0gtwBzbkZJP0WkVXmf/t//piBH6Gt3QVNz/tAF0AuQF/gsOvzv//uj/h373481eOe5YBmCbibL5kBmnHRKJaJHNCmx2a9ONoB0mv3T/y9Vl6nGLeJE0nvmPt6nei0umhCYrjll//wFCgXTfp+BE5eXpeTjuc4lnI9CiAa2wFVTuJxF/Q6OqAZiZQ023N0QF2TXpo6K6zCAktwj8KYvEF4ZgARHM0CPej4aLkAvTEk3SYgeOpZWKZGJ3EhiSdqkTkLGQ4JMSdKwM1wHYO2nyb25j2gOUuMjEPwPXaHJv/uj7b5wZNDhG6ird3NHdAFkAvQF6hVHKoVm3f/eG+qxx2NaLCOFnFbjR71FWfsh8hqyaJg61N1QJNEsrPf524aQCTK9VNBSVq5jh200UiubRlur6XKnKzixYEv0yKbBRoYxXxxmQNRAHE42mB6tNWmvuKwWrLH+vWzRrGMEl8X5PYd50UB2ur6RLGcmwAtFkx6/TQEzSosMITIA91EGBa9QUjpHAdqrazedzULVAnQqbtgOdeOpNtAFNc49GIeblToyHRU17T7QKOAAGPiACJQJbiDJE/BzVl+su/hQai+lzupmaPXH5A0n+T36DmTC9AXEELw1ms1vvvjBlE8vVQs1R9WRBzr19hqeNwd0/3Mrm2zdnYS7lUd0L3WYJSAG+8/Qa89OPUzorKOIGFVu75BRP2uEqBOaU4CNA1e8TUXIn80GDznZpJtAGlOCSklj561x+7/hCMBejyIaDTz8hoK0CwBd14CtOSY9IcRSSLT+agLcmqOvQ69YXhmAi6oDSpdE2oWqFOBJF7Y6Jic+SG7TWK3Bqjqh06Sfoen3Acqw+HEM0AzXMdgKM3RcXNylhU5UAJ0b6g+7zv76p6s1R8gh12kd7iwa7uJ5AL0DN5+WKU/jPhwe3rOlhz24JizGMVq9ua9W+P1f2Zs1Its759cyKgQou6Vdm8yMXt3RUO2d0/1f4IqwQWoar1rO4rF76r3qrgy/qJ/EjIB6pGmcuZ9oDea4yX2jfaQtheM3f8Japakbeo0jgcRWQVAIINr0AP6QsJvIx0ZM68e0JJrIgFvGKYCdHE9oFnQVK9/fgmuJgRrZZvD7hBRSGc156X6S0/SbTC0VMDJw40VOkn6fJiyAyojn2FiTJyAC2oMi58J0LwEN2eJSdKNoGc9tXEzckBT4yXJy3DnSi5Az+CzD6pomuC9D/endkw1I/BoAfd8v0+cSO6OmYCbsVkr0vGC0QgASPtKk/hK5TNbqQC9LdS/Va/fP/Uz2SiWmta9tg5o4KnFXaE8rxJc9cDuZSVW+eLyRjMqgXdKfDDq/1y94DdOIoSgvuqcLMEVmio3XXAIkUxiCAanHFAhjnodZ00m9HqDUI1iWWAPqHCKSClVCa57tgCFdBRLR4UQAST5JtVSI+MQ6bXo6WrjaX3VQbjq/yfTTsINfYaJjnvGCKDL4jomMTpS6HkJbs5Sk20EPWkr6bOz30dKiVa7DwjiPIhoruQC9Axcx+Czr1anLEB7aCcCiNIE3PrlHNCzknAzYXuVMtydpketYmO0nwGg1V899TOiVAWhsWEPrq0DGg8yB+pyr+dVKacOaDtWvV55H+jN5ngJ7o+32hRs/VLl9QD1ijNyFjOE7S68BDdzG08I0PaAWsXB0OfzCCkfE6AssARXDlUJ7jCIiRN5rgMKSpxnPaCQ3yOWHdndByQtqd7vtbJDeaVMgDmTEtxBPD0HFCDR7bwEN2epkf0WCMHe0ODWWoFhEKukctNGW93IHdA5kwvQc/jyZ27xdLdHe0qO34slbFtND10T3Km5lzrOZl39/PEk3Cz45CpBRFtNj816iXj/CaKwcmI+0uj4moEoVbltebS619MBTYZH6ZTzwLF0LEPjMFBCNF9c3myO5kOWefSsxcPNFTTt9HiOi6ivFtjvDE6U0guruPgU3GE2euSkAzqv/k9g1Gs5ckDjUCUazhnp90YJuKB6U8+jWrY57PrI7P6c3yOWmiQdwdKMitimTsHWqVUcerIw/RLcUJXgTiOEKBvlEul2XoKbs9TIfovYKiHR+Mk3VGXfzkHWB3o/T8KdM7kAPYcvffoWAO9/dDDxsdSIhu7JESx7Pe7U3Es7CNWKg23qU3FAX0zAPav/M0Mrr6c9oNdTgIqgT4iJ0CcvSRrrfEJQdi2aaTN7Xl53s5HDLghBP7HYani8eYn+z4z6isPAj9V4nxRhL27mZcbR6JGTPaDz6v+EYw5oPzyaj7qAxbL0PbCL9IapAH2JAxrFkn6SVUnkZfrLTCZAd/wCa2UbIQS1isNhXCCZQQ9oIKeXggsQCSsvwc1ZapJ+m0BXz7EvvpkK0OZREq70DkjyjcK5kQvQc3htc4VK0ZqKACXyIY5OOKDPLpmAm6EJwUbNHX1p4OoOaCNLwK2aJIfb6BcK0DorsnNtBagW9Qn1+TkyoPpAW0PAsPPF5Q1HDnsIu8RHz7tIxp//eZxREm7r5CiWRYcQHbm76n7lBzEdL1iMAzoxI1jxAAAgAElEQVRMHVCYex+ozEbt2KVRD/55KbhwbBSLF4Gdj2tadmS3CZrBlmeO3vtqxaEdO8RTTNeUMkFEPj7TScF1LB1NCAJh5SW4OUuN7LfxRBEh4PW7KxRs4ygJNwsi2n+6yEu8UeQC9Bw0TfDWa1W+99EBSTLZbKBMGGZCceBH7HeG3Fu/Wr/i5gtJuFd1QLMAovtWB2SSNmKfjSivU0g8+l5/4tdj2sRJghUPiI05C1DXouMFozl/OTeXLGTsx8/aCAGvbVw+DKu+oj6/zWNJuMIuLr4H1D9Zgpsl9c5TgDqWjqELVYJrL8YBPf46ZAL0Ygc0FaAdH61QyZOyl5yk20CUaxx6AdVUgNZWHDrSnW4JbqRyGHxpjMpnJ0EIgesYBNLMBWjOUiP7LVqxw/pKAUPX2Ky5J2aBAsTNxwu8wptFLkAv4K2HVXqDkI+eT7ZwGI1oSEvYMuF32QTcjM16kcOuT3+YlupZLghtVCo3LlkZby3aA0A/I4AoI0vCXRM9uv3rFUTU7Ye4wkdal+unnZRy0aLTDxCFci5Abzhy2FP9n1ttXrlVupIzUU9LWk8EEV2HEKKR8FL3r8bh/AWoEIJiwaTXVyFE6rrm7IBmpchOUV0HXJiCWy2r9/Ow6+ebVDeApNtEK9VpdQPW0s2HWsWhkxTQYn9q4i6bOR1MaQ4oZLNADch7QHOWFJkkyEGHZmBzq6qeXRu1IzNH2EVEeZ2kmTug8yIXoBfw+VerCOD9Dycrwz1yQNUCbpSAe1UHtKaEa7ZzI4RAOKVLl+Bupwm4evtjsFxEKjLPIpsFWtOv3yzQdi/A1YITPWrzoOJadPshwsndjZuOHHbBLvLhdudS8z+PU3RMCrZxchSL5UIcLCRwJ2NUWZEKv0Zr/gIUVB/oKIQI5h7OJIdHQnxUgnuBA1VyTQxdcNAZIpxyXqa/5CSdPcJCjURK1konBShMcRZopASoL82pCdCiYzBIcgc0Z3mRwy7IhOcDg9tr6lm2UXfpeAFe2tOv1/IgonmSC9ALKLsWr21WeH/CcSzHh9QDbDU8bEuntnK1EI9REu4LQURXKcHdrJeIm0/Q6w8Q4vzUTlFRArSq9Ti8Zn2gbS/AFT56Yc4CtGgRJ1KlquWLyxuNHPbwpIMfxlfq/8yorzijElfgqNx0gS6o9D21QaWpx0WjNaRgGxeKr1lQekGAztutOV6C6w0iXNtA185/hGpCqFmguQO69MhgAL5H31Ap8mup++06Br6hNoynNQs0E4n+lEKIQM0C7SdGLkBzlhY5UBtAB4HD7bXUAa1mZk6WhPsA2dnN06DnRC5AX8Jbr1X5cKcz2vG+CkcCNC3BbfS4Wy+iXSD4LqK+UsA0tFOjWC7jgI4ScGs2ycHHFybgAojCClI3qV3DJNx2b4grAqxi+eU/PEUqafldoLvIQRcpk7meP+d6oFKuexz4ajH45t3To4zGpb7inHRAs9EnCxzFIn3vRIBaoz1gfdW5cMNqFhwJ0EX1gB7dx3vD8ML+z4xq2VEOaKGC9HvIJJ71ZeYsgCwBt6NlM0Dt0d9lo82m5oBmJbhTCiEC5YD2Ix1CP3+O5Swl2Szetixwu3rkgMLJJFyAOA8imgu5AH0Jbz+sISV8//HVy3CzEQ3YLlJKnjU87l2x/xNUQNJG1WW7eSyIyL6cA5ol4L5W7EMcoV8QQASqzFcr1VUJ7jWbBdrr9jBEgl26fPDLJFSKagboQLgg44WPy8hZEOEQkojnnsZa2R6Fz1yF+kqBZns4mgU6Elv+4pJws9mXGfOeAZqRCVDMxZTg8kII0UUJuBnVij3qAYWrzWrOuf5kAvQgVhs1a8fuAWa5ChwtgCdFRlkPqIlj6VM5puuYdCMdkCOBm5OzTGQbQJ2kMHJAszCi4w4oQJIHEc2FXIC+hNc2KhQdg/cmKMPNRjQIodHxAnqDkLtX7P/M2KwXXyjBvZwDmv3uplD/Lu2CAKIMrbLOuuFdux7QYVeVthnuvB1QJUA91M0snx91M8k2fp624Y27KxM5g/UVVcbbTSsuMuE3d7F1DDn0RteRSJnOAF2AAHVNvEGEFAJMZ/49oL4Hmg6GTW8wngO6VnY47PqQjcrKS/WXEtlpArAbuhi6GM2tBSitrhJJbXoCNC2TFaY9tSqEomPQDfQTx8/JWSayEvg+7qj9TdMEd6qFUTWh5q4i3FXiPIhoLuQC9CVomuDzr1V5/8ODkStxWbIRDaDmfwLcu8IM0ONs1Fz2O0OGgUrCVT2g3tjXmCXxrgS7YFhoK3de+jtauU71Gpbg+r005GnOIUTl1AHtxOpmlvd43UyyjZ+9vj5R/yccJeGOynAXlPh6HOl7o+9WuxcQxcliBKhjkkjJwI/S+ahzLsFNhbgQgl5/zBLcik2cSPrpJlV+j1hOkm4DTIc9T2O1dFIY1lYKdBOHsDcdAZo5lJp1tQyJsxil4IKq6MjJWTJkv0UgbFZXyyd69zdqxVGgJ4BWu0+SBxHNhVyAjsFbr9VoewEf710u5CcjG9EAqv8T4O6tyR1QOGqeFk5JlYGG4y3KsgRc7fApWu3+KGDkIrTyOg4+w+712sUPB5kAnUzUX5ZywUQAh6ESonkS7s0kc0A9aV85ATcjmwWaJc0ehRBdjxLcowTc6S1+xyUbeTLqA11AD2gmxC/TAwrQiVVJZn6PWE6SbgOtvM5B72gGaEat4tCRBcLuZGn6GVkJrmZNbxOo6JgMpfo85w5ozjIi+2268qj8NmOj5tJsDQkj1Z+v1x+QtLaR0fWq9FtGcgE6Bm89VD0cVy3DlX7vhANacc1R+eZVyQRoVkqbCdxx+0C3mx4bNZd4/yl67eIAooxsTIvoNy97uTMlGR7N55snmiYoueaRAM3djRtJ5oAGWoFXJtxYqq+cdECPAncW44BKmYDfH323FjWCBRgJvl5fJeEuogRX2EWiOMEPYkqFlwfAZGE0+366uM/vEUuJ7DbRynVaXZ+1ysnNmdqKGsWSTC2ESN0bDHuKDqht4GcCNE8AzVlCkn6bw8gZBRBlbNSKSOD5gfrca/UHIBOSg2cLuMqbRS5Ax2C1ZHP/VunK80CVA3o0A3TS/k9QC0BdE6Pa9ez44/SBJolk56DPmysBhEO0+sUBRBla5RYAjn9InFyjpDz/aD7fvKm4Fs2hAYi8v+uGkqWj1m/XMfTJbqnZeJNGJkANC3RzcQ5oMADkCQdUCOXqzJtSQW309AYhLEiAkgYQqesZrwQXoNnXQNPze8QSIqUk6TYQ5XUOuv5oBmiGmgXqIobTEaAyLcE17Wk6oEbugOYsNXHvkFZ8tgMKjMpwR0m4eRDRzMkF6Ji89bDGo602Az+61O+pEQ1dhFMmkZLtpse9KQhQQ9e4U3XZaWYluJkD+vIFTqM9IIwSXrUOAdDHCCAC1QMKUNV7dLyrj6WZJn4YY8ZZueJ8HVBQSbjtfqR6cHN340YSeh1iKbh/79ZUjqeScI/NArXchY1hkS9s7jRaA6plZ2KhfRUyxzErwZ3/GBYP4RwJ0HFScEsFE9PQOOjls0CXFTnsQhQQOmtEcXJiBAvASsmiKwuYUR8ZX279cOb5Ip9AGrhjfP7GxXXMkQOa94DmLBtSSuSgTScpcOsFB/RO1UVwrJqwVAe7SJIHEc2cXICOydsPq8SJ5AdPDi/3i+EQkhjhlGi0BgRRwt0JRrAcZ+NYEu6RA/ryEtztNAhpXTZA09HW7o51PmEXiY3CtZoF2vECXBGQCEO5RXOm7Jp0+gGiUM4XlzeUzsEBfWnzxr21qRyvvnp6FuiiQohGM4xHDuhwIf2fcKwEdxCq3tg5vyZZGJN3CQdUCEG1nI5icSp5UvYSItMRLD1d9X+/KEA1IYjtLAV5Ci5oOMSf4gxQyB3QnCUnHKAlIZ0zekAtU6e+6hzlqQiBXrtPvJ8HEc2aXICOyet3V3Asnfcv2QeaOZLCLrGVJeBOwQEF2Ky5StSG8aVKcLME3FJ/B23tHkIf/0Em3ZoSoNdkFmjbC3CFT2K5L//hGVBxLTpegHAq+Yy/G8qg06KX2LxxdzpzaOsrzolZoNju4npAj82+hMXNAAVVnqwJkTqgBWQwuHIy+WWRcQThEGEX6fbHF6CgBMlBx1ebVHkI0dKRdJQAPZRKZK6dMQdYc1eBo1mEE50v9PGT6QpQ5YCq4+U9oDnLRjaCxcOlekb7iErCPXrGavUHJPsfI5PJKxZyzicXoGNi6Bqfe7XKex/uX2rRM3IQnDLP0gTczfp0xNJmPWue7qvh7EJHDl/eK7a971GtWHDwdFTvPi5aZZ2qfn0c0HbPx9UCsOZffguqBHcYxEi7nLsbN5TQ6xAZLq4znZK4+kqBMEroeCqFT1juwnpAR+d1ivhBTNsLuLW2GAEqhKBUMEY9oMgY4vkkFR4X4r3h5QRoteJw0B3mJbhLStJVoXzNSD2DsuTj4xglVR0xFQHqD1UJ7hQFqGPrhMJAIvIS3JylI/ve6cVVtDNm527UXJ4f9EkStbbX669CEpEc7szzMm8cuQC9BG89rLLf8U/slLyMkQPqlHjW8FhfdXCs6Tw4jifhCiHSWaAvd+G2Gx5vViXS740dQJRhrd2iqvXUcPVrQOaA6oX5BxCBEqAAoVHMF5c3kERKtNBDK5SndswsCXcURLTIElw/K8Et0WgvLgE3o+RaaQpulg48H7fmuAC9TAkuqCCiVjcAJy/TX0Zkt4FwyjT7IARUiqc/F85KDYDIu2QLzxnEwQBfmrjO9ASoJgSubRIJKy/BzVk6MgFqV6pn/v1GTaWbZ9kL2bo4yYOIZkouQC/BW6+pD+9lynCPO6Bbjd7Uym8Bbq+5aEKwvX8URPSyHtAsAfdTbrojNGYAUYZeWccSMX5nOjPNJqXdCygKH2NRAjQdpzPUVVDMNEImcj45bDc9Cvg45cnmfx6nngq87GGoAncWXYLrLnQES0bJMY7mgMLchPnodXBK9AYhlqFhmfpYv7tWdkikxNeKEAWjFNOc5SDpNhHldQ67Q1ZL9okh9xmlqhKgg9bVRrmdOF/oE0y5BxTULNBAWMggF6A5y0XiqRLcUm39zL/frKVmTrqW1ip3wLCJ9/MgolmSC9BLUF8psFFzee+j8cVXJggjo8juwWBqAUQApqFxa63AzrEgopc5oFkC7j39AIRAq75yqXNqZfUFzsqOFk3bCyjqAZqzGAFaTne7+6hFed4HerN49HGLkhhSqZ69s3oV6pUXZoHa6eaGnP/oIzn0wHQQmkGjpa5n4Q7oUPWAAvNLB/aPwph6g3CsBNyMahpK40n1vuYu6HKRdPbQKutqBmj5dP8nQG21SC+x8duTb9zKcIg/5RJcANcxCDAhzHtAc5aLfqtJKDVqtdUz/36jfnIUi9A0tNorJM08iGiW5AL0krz9sMYPn7bww3isn5fDLgiN592ERMqpOqCgynCPzwJ9mQOaJeCuRXtoKxsI8+wH5nmIVIDq/cl3cqdBloLLAkawwJED2ssXlzeSJ88a6EJSXJlOAi6AbemUXXMk+ITtgpQL6c1Sya9HAUQFW6c4xdK/y1IqGPT6ISy0BDcau/wWGIVetBN1r82DiJYHmSTI3gFaNgP0HAFardh0kgJx6sRMROjjS3MGDqiBL828BDdn6fDbB3QSl9vVs9eJRcekUrRGYw1BzQON958uZOP3ppAL0Evy1sMqUZzww6fjPUjksIdwSmylH+y7UxegLrsHA6I4SUtwL3bgMrFqd7fQLhlABEezQG3/epTg9noeJtFoTuG8yQRoO0oXl7kAvVHsbO8CTLUHFFS1xf6oBFc9NBfRByr93okZoOsrBcQZIQ7zolSwVAiRmVYczEuADo8EaHcQXFKAqnvDYaDuFfk9YnmQ3gHIGFGuc9j1WSud44BWHNqyAMPJQ4hEHKgQoilvBLmOyUAauQDNWToir0UnKXC7en4A6GbNZefgKOxPr78K4RDZ3pvDFd5McgF6ST79yiqWofHemH2gcthNA4h66Jo4NYNoUjZrRRIp2T3oKwfU7124Y7PV9LhXSaB/eOkEXABhWPhGiVLSIYoXvzMU9LMxN4txQG1LxzZ1DsNcgN402l6A31ULSuFM9/NXX3FGIUTYWb/j/JNwpe+N/m2LHMGSUSqYxIkkEKmYm1MJrvR7KmHGKtC7pAPq2gaWqdEYqt/J07KXhySdARo6VYZBfOYIFlCzBgdaCSOYvEVDxD6+nEUPqEE/NiDvAc1ZMrRhhx4uq6XzZ8XfqRXZafZHUy60mgoiivMgopmRC9BLYho6n3mwNnYQkfR7KoCo6bFRK2Lo033JR0m4+0qAIiVc4ApsNz3eqqgy3as4oKAetlWtR7s3nxEI5yGlJO5nIU+LEaAAZddk30+HeOfldTeGR8/aFDUVKCOcKTugqw777SGJlKPNlYUEEaUluImUNNvDayFAAbw4FYBzE6AewioihIY3CC8lQIUQVMsOu31175eDvE98WZBpFkJbqBnA55XgAkRmGSf2JirpkzJBT0J8DFx7vBCscXEdk35sIPMe0Jwlw4q6RFb5wuqdjZpL349G48+0tbugGSR5ENHMyAXoFXjrtSq7hwP2Dl+++JFDVcL2rNHj3hQDiDLuVF0E6SiWdBF8Xhlukkh29vu85qQJuLXLjWDJkMUaNW3xs0C9YYRD1ie3mBJcgJWixf4A0Ix8cXmDeLTVoqynszqnLUBXCsSJpNX15574epysB7TdCwijhPXV0zMO50km/HqhBkLMsQS3B44S4t7wciFEoMpwG70ETCevklgikm4DhOAwPn8G6IjCChrJS3MaLiRS95sIC9OYrgAtOgaDxJjbdyonZx7IKMCWPqJwcVL9i0m4QjfQqneJ8yCimZEL0Cvw9kMVqf7+GGm4ctglNoscdPypJuBmWKbO+mohFaCl9JxnP+CyBNzbNBDl9SuXrRqVW6xqHq3uYh9UbS+gKFIBsKASXICya9HpR4hCJS+vu0E8etbmlfSZJqacwpzNAm22hyqECGDOJbhSSuTQUzNAr8EIFoCSmwrQYQRzHE+TCfH+MELK8WeAZqyVbQ67PqJQyaskloik00AUqxx4ajbsRQ6oUVIJnEn/6kFEWX+mNM4vJbwq7rEQoqwMMSfnk06UBn+Z5YuDAjdqJ5NwIQ0iaj7Ovw8zYmwB2uv1+NrXvsazZ88A+Pt//+/zC7/wC3z961/n61//Or/7u787s4u8btxaK7C+6vD+hxcLULWA69FN0w+nnYCbkSXhvswB3U7HtZSHz6/U/5lh126jC0n/YLHN2Z2ejyvSEsgFCtBK0aLTDxCFfND8TSGMYh4/77JZSkDoo1CcaZEJ0P328MgBnXcJbjhUASt28foI0MwB7atRLPOcAyrsEt4gTK/jcv131bJDq+eroLi8SmJpkN0mWhpABLB6TggRgFVRo5oGrQkC/LIZssbl0uvHoeiYDKWJkAnE4dSPn5OzCFoN1aftrNYu/Lm1so1t6ezsHz1TtPqr4HsqbCxn6oz1FP3ud7/LN77xDR4/fjz6s/fff59f+7Vf49atW7O6tmuLEIK3Htb4z+89J4wSTOMcHR/0QSYcBmrRNAsHFNQMo/c+3CfJ0jLPcUC3mx6OCDD6TbT6z135fG7tDkMgai1WgLa9AFe7DgLUpNsPEE4lnwN6Q/hop0ucSOpOjHBKU0+GzQRooz0A6xYg5h5CdHz0SKMxQAC1lWtSgjtIBegcx7BoqxsqgZfLO6DVio2UEJkljEG+mFkWkm4D/d5bHHZ9yq55/loAKK6pBPnefpPy61c7n4zS590lx6eNQ+aAgnJaxQxc1pycedNuNHCASm39wp8TQrBRdU86oMeCiLTSxQI25/KM5YD+s3/2z/iVX/mVkdgcDAZsb2/zy7/8y/ziL/4i//gf/2OSZPGJqPPk7Yc1/DDm0bPzy2kyIbg30HAsnVplNou3zVqROJE0B3p63vMd0M+U1TXptas7oHpFfZFlr3HlY0yDdjYDVOhgLm5hXHYtpITYKiH7k8fs51x/Hm2p97liBFPv/wQVdrZSslQJrtDAKszdAR0JXkc5oNWKPfUQtcviOgZCQHcQKmd4ToEpahxNke5IgF5ucZ7NAh1q7syqJKInf8jw3/9qPrduTsgoQPZbaOV1NYLlgvJbgEpdCdBh++oztGXqgGrW9J93mQMKLGTmcM7s6T76Q/b+9f+66MuYK/2WCgqr3bn90p/dqBVPOqC1V0AIkhn0gcphj8Fv/0/Eh9tTP/YnhbEc0H/wD/7Bif9uNpv89E//NL/yK79CuVzmb/2tv8U//+f/nL/yV/7K2Ceu1RYXGjMu6+vnLyx/plLgf/7193n0vMfP/omzxdww2MED9gYGr25UuHWrMpPr/PybEfADPGlS0g0cLaB2xrXvtob8yRUPPFj/9OcxSldbOMuaywcILL914Ws0a8IESnqAVijN7LUdh1fuqEZAUVyBYZd6ffqOWMYiX++cIz5ueNxdL1JgCOWVmbwvG7UinX7I+nqZgVvCFuFc3/9BL6EPrN1ep/X/HrC5Xj73/PO8rrJrEQN2qULU3pv5uWUS0w0GFKtVtDT45f7dVdbr41ddvB6pHqLIUrOa63WVqDtNnn/7PxI+epe1z/8Upc/+yake+5PIrD8Xwf4WPWDl7j263wu5UytdeE6rYLElTfA7V762fkcwAJzixee6CrGm4Uu1JFwtatj5s2YmLPIZ/sH/+Tvc6vyAkv4/UKjejOrFpN8mkYKHn7qPpl8sed58sMbvfe85xbKD65hAGb9+D6OzNfX3bf/b3yJ68oeU73+KtU99eqrHvgqL+FxeaZDUK6+8wj/5J/9k9N9/42/8Db71rW9dSoDu7/dIkuvb2Lu+XqbRuLic8s17K7zzvR1+8afPTpONnqsh9R/tx9z5VOGlx7sqTrqO+cFHB9yxS/QPDkheOFeSSD7e7fKXN5sId5XDgQ4T9CJ5WgVzuD+zf9M47DR6/IQRguUu9DpkHANw6JusxiGN7QbCmn6v3DifyZzZI6Xkex/u84U3agS9Ntrq5kzel9WixaOtNo1Gl0QvMOi05/r+h3uqwqE90Nhu9PiJ12tnnn/en0vXNmgc9AlWTOJ+b+bnTtKKkkFssrOn/n8w8Gk0LuE0Ruoe0RwYVGRC49nuVIOrpJQMnv1InePb/wf96ucQ2s3NGJzHZzL6+DEAPco0Dnd5cPvic0op6coCYfvgytcW7quKq0SYU//3+cNw5IAe7O1j6PWpHj9nsc9wKSXFrhop8uF3/oBbX/ivF3Id8ybsHNAXBfYPXl4tU3GUJHrvh3s83FSmhly5x2D7B1N935J+C++d3wKg8/j7RJ9d7Lpulp9LTRPnGo5XekL98Ic/5Ld/+7dH/y2lxDCmOxT5k8DbD2tsNTwOOmeXq2QluM2hMbMAIgDHMqhVHHbSUSxnleA20wTcWrx75fmfxxlaq5SixZabdjyfshHCAvs/ASppMmcfJTrzIKLlZvdwQG8Q8sbdFTVmacoJuBm1FYeDjk+cJAjbnX8PaHr/CnWHthcsPIAoo+SaePPsAR0e9cL2BiGaEBTsyz3vXMfAsXQOQ3WvmHZatuzsIYdd9HtvkbS2iR793lSPn3OaJJ0BGjtVeoPwpSW4QggGWhE9mOC9T0twDWf630XHNgjyEtylRXoHFKW6l/lbP1rw1cwPLegSGOM9o89Own0V2W+RTLG9KvijfwVJhL75WeLdD25s28SVBKiUkn/4D/8h7XabMAz5p//0n/LzP//z0762a89bD1Wq3XnjWDIh2Ets7l6iXOsqbNaLo1EsZ4UQbTU9TCKcQWOiBNyMyKmyKrqE0eK+OKoH1F9oABGoFFyAdqz6cnIButw8fq7e39fulFIBOpvSlfqKQyKlGt9hF1Wo2RzJBO9+2lt+bQSoY9LtH/WAzvrhLX11P81ScIsF40ol9mtlm31f3SumfY+Idx8BsPPgL6DV7uO/+y1kEk31HDknSToN0A3aibrvV18iQAFCs4wVXn0OaOyrDRezMP3voiYEIu0tlbkAXTqGqej0Egvt4KMFX818iOKEQtwjscdr0VpfLaBr4oUkXFXhmOxPpw806e0Tfv/fYn7qZzDf/FMQ9Elaz6dy7E8aVxKgn/nMZ/ibf/Nv8tf+2l/jq1/9Kp/97Gf52te+Nu1ru/bcrRdZK9u8/+HZoQJy2CMROj4m927Ntud1s+6yc9AHu3SmA7rd9NjUDxFItAkCiEaU6lS0Ie3W4koH2l5AAR9hL7afuFgw0YSgHavFZZLP+Vtqnu72MHTBRkUDmczMAa2ngi8bxTKvkSMZ0vfAsGh0VfnotRGgBRNvmApQKY9GU8yIURqwoxzQyybgZlQrDs9HQXFTFqB7HxAKi2/+5nPCz/8Sstsg/OF/nOo5ck4iuw1Eqc5h7+UzQDMSu4Irr17JEPpKGFozcEABNDut4plTunTO/PA+/iGB1Pn94HWK/R3kDRi102gNqGgDtOLqWD9v6Bq31grnJOFOR4AG3/mXAFhf/jrabRWHnex9MJVjf9K4lAD99re/zb179wD463/9r/Nbv/Vb/M7v/A5/7+/9vZlc3HVHCMHbD6t87/Eh8RkpwHLYxRcOKyX7youWcdmsFQmjBF9zznRAt5senyqpRc80HFBjVTWwdxuL2bmJ4oReP8SSw4U7oJoQlF2TAz+NsM/n/C01T3e73K2X0MNMmMzOAQVotIZgL0CADtXsy6MZoIsdwZJRcpUDipUtlmf7uhwfRzORAC3bbPdSATp1B/QDPk7WiaXg3+2tod16neA7v4GMgqmeJ+eIpNtAq6xzkM4AHUeAasVVbBHh96/mgoZD9Vl3ZuCAAmi2m51oJsfPWRzJ3gc8jersaJvoxDNJdr1u7O57lMUQO53BOw6btSLbxxxQYRcRlVtTeb2Szh7hD/8D5md/Dq1UQ1u5A3ZxVPYWKV0AACAASURBVMFy07i5KQVT4q3Xagz8iA+2Ti8o5LCHl9jcm3H5LagSXIBOZCMDD/mCIN5qerzutMEuIqYwz8ipqkjr4cFiBGi3H6IRYyTBzByoy1B2LRrDTIDmDuiyIqXk6W6P+7ePSt1n5cDXKg4C1b8tLBfiYL671unokUZrgGPpM99EG5dSwSSKEyItLRecsVtzJEBLEzugu54AIaa6SSVDn/jgY348rKJrgn//3R2ML/9FpHdA+Mf/fmrnyTlJ0m2ORrAArJZeLkCt8hoArUbzSueMhgN8aeA6s5nRmTmreQnuciGjAKe3xZO4TvH+pwC1abXsHDQaaEJSrI4fqLVRd2kcDojiozW0Xrs/FQfUf/dboGlYX/xFAITQ0G89vBHvxVnkAnRCPvfqGpoQvP/R6TLcZNijFVncnWEAUcZGTQnQw9BUZWnHXIEkkezs99nQmuj1B1MZEVJe3wAgbO9NfKyr0MlmgMLCHVCAStGkPUjUvMZcgC4th12f3iDk/u2jsK9ZbYAYusZq2VazQNPP+DyDiKTvjQTo+mphZqOFLksmAIek/ZSzFqBZRYnl0huEFK8oQNfKNgka0ipN9R4RNz5EyITH0Tq/9DOv0fYCvtuto298huAP/+VodmTO9JBBH3wPrVznsONTsI2xgqncVbX5292/2gztOBgSyPHOdRUKBZsAIxegS0ay/xSNhEP7LvU7GxzERfydHy/6smZOd19t9Dgr4zugG7UiiZTsHhzvA30V2W1M9PyND7eIfvx7mJ//c2juUUmwfusNksOtuc/5vg7kAnRCXMfk9bsV3vvwdBBR5HXoJjZ312cvkFzHYK1ss9fPeoyOdtib7QFxFFEJGtPp/wSKazUCqYN3tZ3cSWl7Pq5QC6vrIUAt2l6AKFRyAbrEPNlV36sHt8tHDuiMSnBBleEqAapK4+b5kBoJ0Pbw2vR/wpEA7SepEAxmK8ql74HlghD0BtEEDqhyyCJzygI07R/a1e7wF37qPuurDt/+zhbWn/jLyEGH4Hv/ZmrnylEkHSUgRXmdw54/VgARQLmmnJh+6+zciJeeNximDuhsBGjRMfClCXkP6NT54dND/uN3txZy7qzEM1p7jTtVl8fR+o0o+xy01br8uOB7GZupmXM8iEhPg4ji/adXvpbg3W+BaWP95FdP/Ll++3VAEu/djGCo4+QCdAq89bDGk+ddOt7Jfptk2FUluHNwQAE2ay7bPZGe+6jHZLvZ547eQpPxVPo/ATRNo00Zo392AvCsafcCXC11QK9BCW7FtVRZsFM5MwQqZzl4uttDAPduFY8J0Nl9/uorBfbbA4SVbrLMsQ9U+h6MHNDr0f8JRwLUi9UifB4luMIuEoQJUZxM0AOqXkNfd6d6j0h2P2CfFe7eu42ha/yZL97jR8/a7Ggb6K/8BMF3/1UeKjNlshEsqgR3OFb/J0ClrrITgs7Vnpsy9PGlOTMH1LUNhomZO6Az4Nd+90f8L//Xf1nIuaPdR+wnJVbW19mouTyO6ujDFol3uJDrmRdRT/37hLsy9u/cqZ4exZIZN1ftA42bT4g+/H2st38B7YUNa/3WQ0AQ7y3/hsCL5AJ0CvzEQ1VW871j41ikTNDDPp60R/2Zs2ajXuRZOqro+AJnq9njFV1d27QEKEBPX6EQtqZ2vMuQjWCB6+GAll0TP4xJ7HLugC4xT3e73K66OJahvmOaAebsxFl9xeGg6xMbab/jnASolBLp9wg0hzBKrqUD2o3Sao95CFBH9X8eP/9lyUTKgALJYDoz5aSUhM8f8civ8+n7apf/Z35iA9PQ+PZ3nmF/5S+B7xG899svOVLOZZBd5YBq5ToHXZ/VMQWo4ZaJpYbsX+25KSN/piW4rmMwlMZo3EvOdHjW6LHV8Oh4wSmjYh5Ezx/xOKyzUXWprxT4WKqNkGV2QYMwRvfVWkwUxhegtqVTq9gnR7G4K4ji2pX7QP0/+BdguVhv/7en/k5YLtra5o3sA80F6BR45XaJimvy3vE+UL+PQCKcMrapz+U6NuvF0aDz4wJ0u+nxeqENho1YuT218/nWGuWkjZRyasccl7YXsGqpOXfXQYBms0BDo5gL0CXm6W6X+7eV46lmgJZm2htZX3WQEjpR+r2ecbnpiDiAOMJL1Of6WglQV70WnexeN/MU3N4oAReuLkCzPsFu4kztHiG7DYTf5XG0zqdeWR1d30997ja/973n+OW7GK9+meC//PaZ6eg5VyPpNsAsEBkFOr1g7BJcIQR9zUVccQyPiHx8DAr2bNYURcdkKE2SXIBOlXd+cJSVsdWcXx8/QNI7QAxafBSts1EvommCoHyPGH1Uvr+M7KUjWGLdQRiXC+3aqBXZ3j/5Pmm1+1eaBRrvPiJ++l2sL/yFc9eq+u3Xifc+WMhaepHkAnQKaELw+ddqvP/hAUn6Acoe9oXy+Dsvk7JZK+Il9onzgyrBfWAdotfuI8T03vKoUMMmgDkGo2S0vYCqfY0EqKtucKq8rncqhTjnk09vELLf8XlwW5XQyGF35uXf9ZV0FuhAidy5OaBD9Z1upyLvOgnQYtr/1hkkoOkzL0vOSnAnFaCg+kBbkQ3BYCqJxpmDsc3t0ecS4M996R5BmPCf3nuO9ZW/COGQ4L/864nPl6NIuk20Sp1uP0Qy3giWDN8oY4ZXK8EWsU8oTHRtNks3N+0BTfKS7akhpeSd7+9yL80C2WrMdyMoK+18Eq2Pyktv18rsUF9qB3T3QAlQeQn3M2OjVuT5fn+0ngfQ66+StHaQ0eVC3fw/+HWEU8Z66+fP/Rnt1uvge8j2YqZKLIpcgE6Jtx9W6Q1CnjxXD5agp0qsSqtrc7uGzXqRAINYGCMBmkjJ8/0e9aSBNsXyWwBRVoEKw8PdqR53HDo9n1UzAiFG8wAXSeaA9ikAqnwxZ7l4mgYQ3R8J0N5MA4gA1rNZoP1MgM5nsyc7z4FvIFAjYa4LuqZRdAy8YYyw3Nn3Nw5PCtCrpuCC6gPdD6Y3Lzje/YAAk9Kd+xj60eP8wZ0yr29W+PZ3niHW7mK88VME7/8uyRVLP3NOIrsNtPLxGaDjfz9iq0wh8U4sbsdFiwMSbTYjWEA5oL7MU3CnyePnXfZaA/6br7xC2TXn7oDGux8QY9C1b402z+7UXB4NayTNx8g4muv1zIu9wz4VMcAoXX4NvlF3CaKEg/bR90Cr3wcpSfY/Hvs40fYPiLe+h/WTX0Nc0Kqj334DWO6S6LPIBeiU+PxrVQTw3oeqDPegqf53bX3ymZvjUiqYVIo2vlYYCdBme8hK0sKQ4VT7PwHMVdVH4DV3pnrccWh7ARUjRFjFqbq6VyVzQHsy7dXLy3CXjqe76js1KsH1ezN3QNcqNpoQNLoR6ObcUnCzDZTmQKNasTGNxX/HjlMsmHQHgRp7NEMBKmWCDKbngK6VbfYGaXjSFcswjxM+f8TjsMabD04/Z/7sl+6xezjg+48PsL/830McEfzRv5r4nDcdKSVJt6kScEcCdHwHVLirlMWAdu/yvYB6MlsBqnpALcgF6NR45we76Jrgy59e5/6dCluNOQvQvQ/Y1dZZr1VGf3an6vJhuA5xRDJBsut15vlBn1V9eCUBOkrCPTiehPsqwNh9oFJKgt//Fwh3FfNzf+bCn9VWN8AqLHVJ9Flcr1XFJ5iya/HqRoX303EsrX0lQNdvr8/1OjZrLl5ij3pAtxse94w0inrKArRQvQOAfzD/WaBtL6CoBeAsvvwW1BxQgHaUlkDnAnTpeLrbZa1sU043G+bhgOqaxtrxWaDzKsFNHdDdvrhW5bcZ5YKJNwhTB3SGr0kwACkRdgkvc0AnGIFRrdjsDTMHdLJ7hIx85OHHPI7W+fQrp8cMfOUztyi7Jt9+dwtt5Q7mp36G8Pv/lqR3tREgOQo56EAUpDNAlVC7jAA1S6uUNJ/91uWrZAwZIo3xz3VZimkIkRbns2OnQSIl7/xgj7cf1ig6Jg/ulNlq9ubW6yfjkKTxmEd+nc2aO/rzbBQLLK/rtnvQp6L1L5WAm3Enfa12jrnVolhF2KWx+0DjZ+8R7/4Y60u/9NIeVCE09Fuv37ggolyATpG3H1b5YLuNNwzxWqrUqX67Ptdr2KwXaUXWSIBuNXvc0w9AM9DWNqd6rsraCl5iEXfmK0D9IGYYxBSEfy36PwFMQ6dg6xyGqTjJBejS8WS3e9T/mSRzcUAB1lePZoHOzwFVD96tDtSvoQBVDmiIsAozfU2y10E4ygEt2PqJUtfLUi07dJOsSmKyEty48RghE54mt3ht4/RGiGlo/OwXNvnuoybN1gDrS78ESII//JcTnfemc5SAq0pwLUO71KaEvVIFoLN/uRnaMkkwiWCGAtR1THxpoiUhMolndp6bwqNnbQ67Pv/VZ1W12IONCgM/HjnnsyZpPoEk4sfDKndqR2ulOzWXtnTxzcrSCtB2q4VBjHYFAVpxLUoFk+1jSbhCCLT6g7EcUCkl/u//C0S5jvnpnx3rnPqt10kOn92okVm5AJ0ibz2sISV8//EhfrdFhI4+5/7EzXqRTmQRp4ub7Waf1+xDtOo9hDbd6PbVks1BUoLe5R6kk9Luq9IlRw4R9uJngGaUXeuovyufBbpU+GHM84P+qPyWoK+csRk7oAC1FYdmawCWO78e0DSEaLd3/R1QZlmCmwlQu0hvGE5UfgvKAe0l0ynTzxaO2vpDTOPsVNQ//ZN3QcC//aMttHId8zN/mvCP/wPJnDcNl4lsBqgor9Pq+ayV7UslYZeqalPaO7jkczMLPzFnKEBtFUIE5GW4U+CdH+xiGRo/+aZ6zx/cUWWwz+ZUhpuVdD6O1PzPjKJjUnFN9oyNpSz7HAYRsp+OYHFPV4eMw0bNPTELFNQYw+Tg2Uv7ZqMn3yFpPsb+0tcR+njrbv32GyAlceOjK13vJ5FcgE6R1zbKFB2D9z7YJx50CbTCTEc0nMVmrYgnbZJMgDZ6bOoHU+//BFWucyDLmMP5DjPupL0zZjK8Ng4oqF2z5sAAIXIHdMl4ttdDyuMBROr7NY/P3/pKgVYvUAJ0Tg4ofg+pGQQYrK9enwCijMwBnXkPaCb47RK9/uQCdK1s42OQaAbJhPeIcOcRe3GZ+/c3zv2Z2orDF99c5z98d4cwirG++DXQNPx3/++Jzn2TSV6YAXqZ8lsAZ0X16w47By/5yZNk6ZsXhZlMiqaJo5nDuQCdiDhJ+IM/3uMn3qjjWEqE3L+jnh9bzfmEFMa7H+Bbq3Sky0bVPfF3WRmu7O0vXTjZ3qFKwIVJBGjxxCxQSNvYkpjkcOvc35NJQvD7v462cgfjzT819vn0Ww+B5S2JPotcgE4RXdP43KtV/vDHDcy4j7TmL44260qAatGQOI4YHO7hyOHU+z9BlSR4+gqFsIWU8xs70uqpB7Ee9a+XAC1adPohwinnAnTJOErAVQ5okoZ8zcsBBQiEPb8xLL5HbBSAa+qAuiZBmCCNGZfgZu9zGkI0SQIuQLXiAIJAL04UQiSlJNp9pPo/71+8wPqzX7pLbxDyzg/20IprmJ//c0SP/jPx4faVz3+TkZ0GolBBmDaHncsL0GxBHPcut3ErAyUIdWu2G0IidViz8+VcjT9+2qLTD/mptPwWVJXUasmaWxBRvPuIfWsTy9Corpz83NypFfleb3X0c8vE7uGAFU09F67SAwoqT6U3COn2j8LC9JpaRycXlOFGH75DcvgM6yt/EaGNP69X2EW01c2ldKTPIxegU+ath1W8YURR+GiF2S9OX6TsmkR6EYFkv3HAHVSZzywcUIDAXkMnRvbbMzn+WbS9AEGCFg6ulwB1TTr9AFGo5AJ0yXiy26PoGEfjSOYoQDMB2JfWXMewBJpz4vzXiUwIBkIlds5q7m6WBpwJ0EkdUNvUKToGA1GYqAdUdpvoQZen8Tqvb168wPrsgzU2ai7f/s4zAKwv/Hdg2ATvfuvK57/JJN0GorxOImVagns5QSgKZSQgBpd7ZoZD5ejMWoCSOazhzelFmwX/3/d3cSydtx+eTKi+u16aiwBNvEOkd8DTWM3/1F6oxrtTdfmRtwKasXThN88P+iMHVLvCHFBg1DN73AUVK7fAdM7tA5VJjP/ur6NV72E8/BOXPqd++3WS3Q/mFlK1aHIBOmWym01R87FLV/vgT4IQAquk+gwauw3uGgdIBFr13kzOF7vq35v1xcyDUQIuzCUEZlzKrkWvH4JTmbi8Lud68XS3y/3b5VFJ/agEdw6fv3q6c+3FJgSDuVQbSN9jgI1t6ZQnFF2zoOSoa/JFmi44o8Xy8R5QbxiOzjsJa2WHnixMtEmV7ZIHa69iWxfvsgsh+DNfvMtHO10+2umgFSpYb/080YfvjD1SIOeIpNtEK6/T7YfEiby8A6oZBJqLHlyuDHPYVwth057thlCWW5GX4F6dMEr4zg8bfPHNdSzz5Pfzbr3I9r5HksxWZGSu5vd6a6NU1+PcqbnE6ASVeyRL5rrtHfRZtwPQTbBO/9vHIUsN3j7WByqEhl67T3xOEm70o/+EbO9ifeUvXWk8oHb7DaTfQ3Z2r3TNnzRyATplVks2r9wqUdJ87PL8BShAcVXNPTps7vOKvo9Y3UDMKDlPK6vm+iwZ8P9n782e40rT887fd5bcNyQSO0CQBFlksTbW0rV1d7XUY0utllqhUWsmwjPhmJmL8ZXlP0AX44hxKMZ3nhg7FHNhj8dzMRNe1JJlWW7ZrZaK1VXdXV1b18LiTgIkCCQSCWQi97N9c3HyJAACILZzEpms/EVUdDSWPIeJc05+z/c+7/N2g41ai5GY+/DuqQpoPIQEbD0xqIA+QVi2w4NCbTOAiO5acDOJMKoiKJs6IAMN3fGQrSo1O8xIuvt97AchGXOFYFO2U6cDsuHKZs3d8Uah0bKPXQEFN4iobIWP9Ywwlm7SkhrDp+YO9PNff26CcEjlxx+2q6DPfwdCMVof/MmRz+GriHRsZLXojmCpuAIte0gBCmCFksRkjXrz8WEmWzEa7jWuRYMVoErEXXh/ldI4/eaLu2vUWxavXRrb8b2pkTim5VAoBfv+2iu3QdW4Wo4zMbxzneT1hJYjU9iFu/sG6/QT+fUGIyEDEcsc+fMrm44Q0hWWVnf2gTrFhR2uG2mbtD769ygjZ9BmXzzSMdVR93n+pFWk92IgQAPgd74xS0wYXVmc7kYq68a855dWmNHX0UZOB3YsPeP2Nxjr3duxKVcNRqJuRHyvCVAAQ40NUnCfIJaLdSzb6QQQQbsCquqwz3wvP1AUwXAqwnrLfVx3I4hINmuUzd4MIIJNC27daQvQgHpjZavWrn66i7NEzA8BGmHN0JHNjSNbrZqLN1iwhnnq1PD+PwxEwxpvPjPOz79coVI3EOE4oRd+A3vhk69Uz9FxkbU1kA4iNcL6hptFkDmCAJXRNGmlwdrGwauMngANRYIVoLr3+oMK6JF5/8s88YjGpdNDO743PeJuZC6uBmvDdfK3sdKnsFC3JeB65DIRVEWwKMbANnHW7gd6Pt0kv14nozWP3P8JoAjBeHb3JFwsA2djedvXzWtXkNUi4a99/8iiVxmaBD36xPXk7sVAgAbAC6diCOSJ2UOHR9yqpF18QErUO43TQZBOxSk7UVql7sX6l2sGw+EeFKDtxWlDibl9adZgmPeTwHwngGirAK0iIsmuVQeH0xGKjbYA7UIQkWzVWGtpPdn/CXRswTXbTZcMqlojW1W3/7MdROFLBTQZZs0MgW0daZEvLQNtY5F71gjnpw++wPr2S1NYtsM7ny4BEHr2byMiSVq/+MGhz+Gritdq4s0AhaNVQNV4hpTSYPUQAtTrAY3Egv3M06Pu6w8suEejZdp8fHOVVy6O7joz2BODi4XgknClbWGv3qUcnWofc+c1oyoKo0NRbjTcTawnRfTUmxaVukmCOsoRE3A9JndLwt0liEhaBsbH/wF1/CnUqWeOfDwhFNTRs1+ZTcGBAA2AbvaH7cbY+AgA5zV3oRFEAq5HJhGmaCdwyt0VoJmQW5HopTmgXgW0RruH5piD5gf0Bgv5KiFN2RZjL5sVRKR7mx8jmQgrtXb/acAVUGmbYLWo2HrPClCvAlqx2jPWAhOgtU4A0dbjHoehZJiK4z0jDm/DtVfvoeBQS54iGj74bOepkQQXT2X4648WcRyJ0COELv8m9uIXWA+/PPR5fBWRG5sjWErVFqoiSMYP74IIp7IkRYO18sHvZavlCsJw7Gg9bQclHHVf32oOLLhH4dPbRVqmzatP77TfAkRCGrl0JNAKqFNcANvioRhHAGNDuz/Hx7Mx7pRVRHzoibF95tfdeypsVxFHDCDymBiOUdxo0jLszteUoQlQtW398+bVv0LWS4SOUf30UMfmcNbufyU2gAYCNAC8Ie4nZcHNZBIYUuOM5opCNXcquGMlwhSdJKJeDOwYW3GkZKNmkNbb/QpdFAH7kYy5C5GKT4PmB/QGC/kK06MJFGXzg0W2ql29v4fTUVY7FdBgrVve69dluGcFqKYqRMMqZdMN+AhMlHcEaNuC60MIUTYVOdYzwly6CUB86qlD/+63X5qmuNHk09vu81q/9G1ELIPxwZ98ZZIXj4NTKYAQiESWtY0WmUR4R7roQYiks6hCsrF28FEsluFVQIMVoNFYFEsqmI3uJG4/abx/NU86HuLCzN7Vt+mAk3C9aubNZpbhdGRHEJLHxHCclfUGyshZ7JUnowKaX6+jY6FajWNZcGGzcry8tiUJV9FQsjOdCqg0Ghif/AXq1DNoExeOdTwAdfQcSIlduHvs1+p1BgI0AE66AiqEoCmiqEJixXKII6aAHQRXgCbQW2WkE3wTe71pYTuSpNpOwQ3w33ZY4hENVRGUzPYctWPM+RvQG0gpWVipbrPfQtuC28Xqey4dodEO3CFgC663gVaXoZ7tAQWIR3TKrWAFqFsBTVBruhVQv0KIqtJ9X50jPCOqC9cp2ElOn5k69O9ePp8jkwjxV+2RLEILEXrpt7GXb2A/+PzQr/dVw6msIuJZhKKxXmkeOgHXQ4m74qRZXjv4sY0mjoRoPNhNoXhEoyl1rGZ3Zg4/STRaFr+8XeRrF0e3bVg+ytRInOU1N1sgCOyV24h4lrsldVf7rcd4NobtSBqpWWRlFadeCuR8ukl+rUHSG8FyTAvuxC5JuODOA7VX55FSYnz+X5DNCuGvff9Yx+q89pgXRPRkbAg8joEADYBNAXoyFVAAW3dvHH0kOPst4FYhSCKQyOrBP0yPSrnq9t3ElBaEooca9Bs0QgiSMZ2i6S5SBxbc/qdQbtJoWdsScMGz4Hbv/h5JR6l3El+DroC6vUl1J0wu3ZsVUHCTcDeDmfy3C0opkc0aIrJpwfVDgA4lwlsqoId7RkgpEcU73LNGeOoxFZa90FSFX7k8xRd31zq7+vqFtxCJYVof/GBQBd0Hp1JASbotLuuV1pEFqGgvjM1DfGZKo4WBRiQU7FikWESjJXWs1sCCe1g+ulHAsh1e3SX9ditTuTi2I7dV1vzEzt9CGZtjea2+awCRhzeeZUWfdH/vCeg9zK/XmUm0M0KOKUDH2vNTHw0iUnKzYNRxigsYn/4ntNkXUUfPHutYHiIcR8lMPDGW6McxEKABIDsjGk6uPzHWHsWij54J9DhCCIyIm7rrbAQ/iqVccyufEdnqqf5Pj1QsRLHpLhAGs0D7n4VlVyDMbg0gcmxo1bt6fw+nI7SkjkQEHkLkWXDVWBJd692PiHhUZ6PhgKoHUwE1myBtRDhBtWGiawoh/fjvR0hXO9fOYS24slokbFUpRaaOLIa/dXkSVRH89UeLAAhVI/zy7+AU7mLNf3Sk1/yqICuriOQIUspjCdBOZaZ+8L+/NJuY6IEHn8UjOi2p4Ri90YO2vFbnj/708219eL3K+1+uMJyKMDeZeuzPTXlJuAHYcJ16CVkt0krOYljO4wVoO9dgwRgCRcV5AkRPfq3BVEeAHs+Cq6kKI0PRHaNY1HauSvPtfwFGg9Ar//WxjvMoyugczsrtJ35DsHdXF32MO6IhFNjszYMQT7sfcGqAAUQeMuamqDldmAXqCdCQ0+ipBFyPVDzEWkOCFhr0gD4BLKxUUIRgemTzWnMFmuxqBTSdCKGqKqYSDj4Fty1AYyc0x/igJKM61Ybp2vCDqIC23wc3BdckEfVv8Z9OxWiJw88CNZfd/k99/NzRj50I8/KFEX7y2VJnUa+dfxORHsf4xZ8gZTC2wH5HWgayXkJJ5ai3LAzLOVICLmwujDVj4+A2TKuFSbDVT3AroE2p98wc0L/+aJEPrq1wbeHg/bInQaVucPXeGq9eGt33OTHerqwtrvqfhOtVzgoht6r5OAtuIqqTjOkslQyU4dknogK6sl5nLOY6Vo4rQAEmh2MsPVKpVrLTIBSc4gLa2VdRh/3NWVHHziGbFeRG98I9T4KDR+h9RZBGg+bf/HMeOg1M82g7bk5p+USrn7CZDhtkAq6Hlsxi1xVkO6I+SMpVV4BqdvPE3+PdSMZCLBXriKHUQIA+ASzkq0zkYujaptX7JBwOihBuFZQwsaBTcNsW3ETmePaloIl7AjQbDaQC2gl7altw4z4EEHlkkxGqa1Hih3xGlO9dQ5UqY3Pnj3X8b780zftfrvDTq8v8yuUphKISfvl3aP74/8S6/T76udeP9fpPIk51cwTLcWaAAggtjK2GSSoN1iutA4V9CdvAEsEL0HhEpyl1hHXyFVApJfdu3OS/j3/AnfuTvHAud9KntCcf3ihgO5LX9ki/NW/9lPXrG3Dh19E1hbFsNJAKqJ2/BYrGvDEElDo2270Yz8ZYLtZRz85hfvk20rEQSn9Kg2rDpNa0GNabblhY5PGV6IMwPhzj09tFbMdBVdyandBCKEOTOOuLhF75nWMf41E6faArt1HSj7dz9zODCuhuCOH+x9H+UzIT6Jd+9STOvIM29xqhF76LeIbFDQAAIABJREFUEj3+Dbgf6WSUkox3pQK6UTPQNQVh1HqyApqOh9xB75FUpxd4QP8yn69wanR7pdMpuwOolWR3F0Mj6Qh1GQo8BdeqV7GlYGgo+GfHcUhGdZqGDfEsTgA7xZsV0ATVpkki6t+ibCgVZsMOH/oZYeVvsWDluHBq+FjHPz+dZmY0wY8/XOzYvLS5VxHpMcyrPz7Waz+pyA1XgIptM0CPHtLlhFOkRINi+WBCT7Fb2MrhR74clljY7QEVPTDHej5f4SXrI14N36F2//pJn85jef9qnvFsjJnRnRuT0rFp/exfs37lX+PU3EruVEBJuM7KbZTcLEvrLRJRnVTs8dfMeDbG8loddXQObAOn+MD3c+oW+XalMq00EdE0Qjm+xJkcdvt1V9a3OwJCz3+H8Gv/LWpm8tjHeBQlMwV65IkPIurPbY4AEaEo0b/99xkZSVIo9K+A0MbPo40fb5f8oGQSYVbtOMPlblhwW6Tjoc54hF4jGdcxLAcZTkCj/xPlvsqUawblqsHsIwFEzuo8CIGSnenq+eTSESprAfU7bqFRKdOSYUaGeidheje8HkgrPY1y/a+QtoVQ/ftI8yrBIhyn1lhnKuff8yabDFO2wzj18sHPxzKI1ZdY0Z/nxcTx2juEEHz7pSn+1Q+vc/NBmadmMgihoF94C+P9f4tTWkLJTBzrGE8a3garksyxvuzeg0ftAQW3DzRVLlPcOJgAVR0TRw8+lVpRBJYSQrGDDxXcj19eW+TN8D0AlNJ9LNtBU3uvblKqtri+UOJ7Xz+9q/3Wvv8Zsp0wa15/h/BLv810Ls6H11ZoGTbhkD9hitKxsAt30S99m4d36vtWP8G16L7z6RKtjLtetFduoY6c9uV8uo0X6hSTNV/st7BpYV4q1rfZmfWnvuHL6++GUBTU0bNPfBBR793JA/qOTCJE0U50rQc0HdORPVoB9XYbTS0+sOD2OQt5dwPq0REs9uo8SnoCoXe3x3s4HaFqazjNYCugRq3SHsHSuwm4AIn2vdZMTIJj46wv+vr6nXnOYdeC60cCrkc2FaHqRHAOEUJjrdxDxYGcP2mLr18aJxrW+PFHmxUP/amvg1Awr7/jyzGeJJxKAVQNEUuzXmkhcHuzj4qeHCKlNA4uQKWBo3bnmWOrYTTn5CugjRs/JyIsHKEyKVaZz/dmUeAX11aQwKt72W+vX0FEU0Rmnsa8/g5SOkyNxJHsHPFxHJzifbBN1LE5los1JrL7C1AviCjfjCBimb4WPfn1BkKAblaOnYDr4YU4PZqEGzTq6BzO2n2kefL3YVAMBOiAY5NJhFlzkiitSuA3S7lmkItJkLI3U3Dj7oKkpcSRjcoTn2L2JLMpQB+pgBYXutJb/SgjmSh1JxS4ALUbVepOuPcFaMStdlbCbqXOGwzuFx2rcyhGrWGRiPnZAxqmIiNg1NxU5QOwdvcqAEOnn/blHMIhlW88N8GH1wuU2uOtlFgG7dQLmDd+0pW5zv2E3CigJHIIobBeaZFKhI5VjVMTQ6SVBsXSwcJ+dGmCFrwFF0CqYTRpnOjn18PVGk+bX9AI52DyWaa1NW49OLhjoJu8/2WemdEEk7u4JJx6CWv+l2jnv07ypV9DVgrYS9cDScL1LJvN1CwbdfOxAUQeXpV0eb2BOjrX17bPlfU6uXQE6mWUqD8V0GhYYygZZqnY3bm46tgcSAe7cLerx+0mAwE64NhkkmGKjvswdQIOIipXDYajbmqgiPRuBbQuoiBtCNguOSA45vNVcukIsS3hM05jA1lb60q69KMMpyM0ZAjM4MewNEWYpI+CKwi8CmhJpN1+Gd8FaBXUEE1bwZGShI8hREPtCqhAdkKt9qOxeINVO8G5c/5Zv7/90hS2I7nyycPO1/QLbyEbG1gLn/p2nCcBp7KKSI0C7Rmgx7RBK7E0IWFR2di/qudISQgTtOAtuADoEQTACfaBfvnZVc7qBUIX3yIyfpZRZYN794N3WR2W1VKD24sbvPr06K7fN2+8B9JGv/hN4hdeg1AU89oVRjNRNFXh4aqPAnTlNiI+RL7lXiePG8HikUtHUBXh9oGOzSErhb4dIZdfazCeiSCbG75ZcMF9H/2ugDqO5O1PFmm0dt/oU0fdpHN7pX83BPZjIEAHHJt03LXgAsgAbbiW7VBtmGTD7RlPPWjB9RbtVekNmu/PB/kAtwI6+4j91quynUgFNB2lIcMojoW0jMCOo1p1HD0W+LzB4+JZYqtNG3X4FHbRXwFKq4aIxKk03Ej/uI8W3KFEmIrjVphlc/9nhJSS6MYCS2KcbMo/ETKWjfHsmSx/88liZxyIeup5RCyDee1t347zJOBUCp3gsePMAPUQ7QpNq7L/eJFmyyYkrK7Z/oXevjbNk0vCVW6/i4Mg9dxbqLlTCAG1pbs95yp6/5obgLab/VZKiXn9CurYedTMJIoeRj/3BtbdXyDMOpPDMR74OIrFzt9GHZ1jqS1qDyJANVVhdCjKUrGOMuaKnn6cByqlJL9eZyaF65DzyYILMJGNs1Ss+3rt/eSzJf7VD6/z9pbNv62ISAKRHu/Lv8VBGQjQAccmGtaoau6HaZB9oJW6uxBM6+7/0sMW3LLtLhT6dSfxq06jZbGy3thhv/VEjt9zvw5CMqZjCPe6CjKIKOQ0e3Jz51G8VNpqw0TJzeIUF5COfzMsZTvorNoWoH72gOqagtN+fsnGASpg1SIxp4qZ8X/j49svTVOqGnx8s53yqqjoT30d+/6nncTOrzqyVQOjjpIcAVwBepwEXKCzQHZqpX0Xto2GQUjYqKHuVEBFuH0c42QEaHG9xgXrGuupCyixDEruNABD5gqFA1qWu8X7V/OcnUzt2rJg528iy8voF9/qfE2/+BbYFuatnzI1EvfNguvUy8hKAXXsHEtrdTRVkEsfrI2ik4SbOw1C7cuq20bdpGnYTCS8GaA+CtBcjKZhs17xxxHQMm3+9J07AFyd3zvsSx2dw1653XObLn4xEKADfEGLp7HQcDaCE6Dlmnvzp9T2A6YHF8maqhALa5TMtlAYCNC+5P6Kuyv9aACRszqPSI6cyLUnhECNuseVrWAEqGObhDFQo8n9f/iE0TWVsK5SbZjuhoBldEbk+IEnQGsBCFAANe5u2h3kGVG87fZ/xmcu+HoOAM/PDZNLR/jxh1vCiC58E6TEvPGu78frR7zWEpHM0TQs6i2LodQxK6DtBXJM1jqbq3tRr7siRQ13R4BqYbdyJs2TEXt3P/gJSaVJ/Fl3nJ2IZXDCSaa1Ijd7qA90qVhjYaW69+zPa1dAj6Cd/Vrna2ruNMrwKcxr7zA1kmC90qLefPzf/yB4olEdnWO5WGcsG0NRDuZiGc/GWFmv4ygaSu5UX/aBeiNYRsPue6n4asHdTML1gx99cJ9S1eDsZIob90uY1u4bp+rYHLKxEaiz8CQZCNABvjCUCFMWqUBvlHLVtR3GVVeI9qIABUjGQxRb7mJ1MAu0P5nfMwF34UT6Pz0iifb5BFQBray7i7twordngHokolq7AnoaAMdHG65s1twZoAEJ0HDi4AJ0Y/4ahlSZuehPANFWFEXwqy9Ocf1+iQcFd+NFSY+jTlzAvH7lid19PwydESypkU4VxI8eUIC02D8Jt1V373ct3J1gMLV9HLN5MhkG4YWfUiVG7tIrgLv5po/Mckpb59Zi7wjQ979cQQCvXNzZ/ymNBtad99HnXkM8Mj5Hv/gWTnGeM2HXYbDoQx+ok78NiurOAC3WDhRA5DE+HMOyJavlplt1K9w9cDhar+AJ0CHd3TTxswd0sm1l9iOxuNow+YufLXD5XI7ffGMWw3S4vcc1rbYt0f24IXAQBgJ0gC9kEm4QUZAhROWaK0Cj9LYATcd0Cg13rtegAtqfLOQrpGI6mS1jFqRRR27kT6T/0yPSFoadhFafWVt17UCxlH8f3kGSiIZcATo0AarmaxCRbFXbFlw3JMLPFFyARDqDLcWBnhHq2l0eyhFGs8G0HXzj+Qk0VeHHH22OstEvvIXcWMFeuhbIMfsJ2ZkBukWAHrMHlHAcqWgklQbF8j4CtOEurvVIdwRoKOouuJu17o6eANgo5DllzbM6/CJC2ZyPqeZOM66WuHv/5OeTgttz+P6XeS6cyux6LZi3fw6Wsc1+66GfewNUjYn1jwB/knDtldsouVksNFZKjQONYPGYyLprqeVi3RU9loGz9mCf3+ot8usNVEWQkO69InxKwQW3tSoW1lj2oQL6H396j6Zh8bvfOsuFmSEUIfa04SpD06CFsVeezD7QgQAd4AuZRJi8EcepFALbMfcEaFg23ZQ+H4fO+0kyHqLUsN0FxkCA9iUL+SqnxpLbgnjs1QUA1OGTE6DJtPuh2qwGU1kvr7s78slMvwhQtwIqFA0lO+PrKBbZqkG7B1QIt9fdT7KpCFUZwaw9vqLjWAYZI089MRNYMFQyFuK1p0f56efL1Juu4NbOvgK6m9j5VcfZWIVQFBGObwrQ41pwhUBEU6SUBmv7VECNepcFaMwVJK169wXo0i9+hCIk2Rf/1ravK7lTKDg4pUVqPlhWj8v9lSpLxfresz+vXUEZmkIZ2Tm3V4TjaGdeQV34BfGQPLYAlY6FvXIXdXSOlfU6Uh4sgMijM4plrY46Ogf0X9Utv14nl4lCowzhOMLHkUVCCF+ScFfLDf7qwwd8/dkJpkcSxCIaZydTfHF39157oSioo2f7ejbr4xgI0AG+kEmEWLXiYDYhoOrMRtUgFtYQZr1nq5/g7pZt1AyUaGogQPsQ03J4uFrb2f9ZPLkEXI/UkNs3VisHY0OrlUoApLNDgby+3yRioY5FVh2exV6d92UDTFoG2CYi4grQeERH8Vn8DaXCVJwIRrX02J9bu3cDTTiEJs/7evxH+fbL07RMm59+4fbRCi2Mfu51rLsfBFZx7xfcBNzNACI4vgUXQIlnyKhNVvcToC3XVhiOHlxUHIdIW4Caje7+3aV0SDx8n3tygskzp7d9T23b7Ke1tT0ti93k/S9XUITg5QsjO75nrz3AKdxBv/DWnptG+oW3wGjwraFlFo+ZhOusPQDbcAOI2lW6w1hwE1GdRFRnea2OSOYQ0VTfVd3yaw3GhqLIetnX/k+PieE4D49ZAf3Td+4Cgt/55pnO1y6dHuLe8saemyrq6BxO8T7yBEciBcVAgA7whe2zQIPpAy3XWqQToXZvVg8L0FiIWtOCSHIgQPuQh6s1bEcyO/5o/+c8IpYJ5MPtoAwNuwK0HlAFtFFxr1c91ic9oBGdajvARcnNglFH+tAG4M3m9HpA/e7/BMgm3QqoU3/8M2K1HUA0dv5Z389hK2cmUkzl4nx4faXzNTex03TthF9hZGV1mwCNRzRCurrPb+2PEsswpDVY23j84tJqtgVorDsCNBxPbDtut6jPXyXllCmPv7pDuInkCOhRZtS1Ew8i8uy3l84MkYztrLSZ166AoqI99eaer6FOXkQkR3hRuc6DQu1YG2detdJLwAU3WOgwjA/HWCrW3bC7sXN9VXWTUrJSqjM2FMNplH1NwPWYyMXYqBlHrr7fX6ny08+X+VuvTG8bpXXpdBYp4dr87huR6tg5kDZ24d6RjtvLDAToAF9Ix0NdEKAG6XjInc/X0wLUXaxaoYEA7Uc2A4i299s5qwsnWv0EyGVTGFLFqAZzXRk193V7+f7aSiKmU29Z2I7TCYfyYx6oV/HzUnADEaCpMFUnAvsElTn526w7CSamJ3w/h0e5fD7HjfvlTlVZyZ1Gyc58pW24Ukqcyipi2wxQf9JoRSxDUuzfA2q33O9rXbLgxuMxHCmwA0rb3ou1j39E3dEZf/EbO74nhEDNnWIuWuLWCQvQOw83WC03d02/lbaJefNdtNMvoUT2ThMXQkG/8E1GWwuEW2ts7JOE/Djs/G1ELIOIZ1kq1hhOhQmHDrdB4o1iAVBG55Ab+b4ZI1eqGhimw1g2iqyXfO3/9Ogk4a4e7Z7447dvEw1r/OYb29cQZydThEMqV+/t0Qc66lq4+2lD4KAMBOgAX8gkwxTttgDdCCaIqFwzSMVDnfEIvYo3C9RQYziDFNy+Yz5fIRJSt811k1YLp7R4ogm4APGIRkOGA7PG2c0aEiDUnUrLcfGEYa1hoWSnQSi+9IHKllcBjQdWAc0kwlScKKr5ePtdqvGAUmQqsP7PrVw+n8ORks9uF4F2+ujFt3BW7/ka8NRPyEYZbKNTAV2rNMkes//TQ8TSRGST0sbj72fbaCd7at0ZwxKL6rSk1hG+3UC2aiQKn/O5PMfZmdyuP6PkTjNKkfmlEpbt38zfw/LzL/NoqsKL53fab617H0Or5lps90G/8E0kgtfDt1gsHN2Ga6/cRh2dQwjBUrHO+CHstx4TWbfCV2+anfRVZ+XOkc+pm3gJuGOZtgANwKXkJeEepQ/02vw6n94u8ptvzhKPbP8s0VSFCzOZvQVoNIVIjeH04WzW/RgI0AG+kImHaRHCVKOBjWJxK6DhdjplMGmQfuBZcpoiBq0a0rZO+IwGHIaFfIVTo4ltPX/O2gOQEuUEA4jAFQSmEg6kJ8+0bBSzhqVGEEp/fDR4wrDaMBFaCCUz6YtQ2loBDUqAaqqCqcfRHGPP/p615SXSooZoB4MEzZmJFOlEiI9vbj7DvcRO8/pXswrqWbqVlCuKSpXW8RNw23iVGqW1QcvYe+yFY7SvD92f4+5HLKLRQu/qHNDm9ffQsKhNvb5nv7U6fApVWmRkqTOruds4juQX11Z4fm6YWGRnMJl5/QoinkWdembf11LiQzD5DK+Gb7G4crRqo9PYQG6soI6dQ0rJcrF+qARcDy+IaGmtjjpyGoTaN0FEy+ttAZqQYFsoAVhwc+komqocehaolJJ/+ze3GUqG+a9emt71Z545nSW/3mC1vPv9po7NYedvPXEjsfpjlTGg5wmHVKJhjZqaDsSC2zQsWoZNOq67PaCR3q+A1nAraINZoP2D40jur1R3mf/pipqTroAC2FoUYfpvjVstN4kJA0fv3XvrUbYKUHD7QP2pgLYFaCQRmAUXgLZFTzZ2f0YsXf8cgOxZ/+d/7oYiBJfP5fjs7lpnOLqIJNBOv4x562duONNXDGfD7YkVyRFMy2GjbvomQL2Fcmq/WaCW+z3RJQGqKgoGuhsq2CWqn/8ND6wh5p5/bs+f8eb9Tp9gH+iN+yXKVYNXn945+9OprGI/+AL9wjcPvIkXvfQtMkoD+8HnRzofp23NVMbOsV5p0TJtJnKHf4Z7PaPLxTpCC6MMz/RNENHKWgNNVch4M+IDEKCKIhjPRg89C/TD6wXuLm3wO988s2ff+KXTbujf1Xu7p+Gqo3PIxgayGtyYw5NgIEAH+EYmEaJEKpBZoBvtESxDUUDavW3BbVdAN2zXLjXoA+0f8ut1DNPZmYC7Ou9GuyeGT+jMtqDH0Oym77uhhVKDmGih9PC99SiPClA1N4tslHHqj0+W3Q/ZdBcZhhLBsBzi0WBGPqltq9hez4jm4g1MqTI+dzGQ4+/Gi+dztAybawubiyH9wlvQqmHd+6hr59EreJ9nSjJHqepfAi5sLpT3HcVitlxrvOrfaIn9MEUI0aXkTXt1nkh1kY+cC1yY2Vs8KJlxUEOcj5W59eB49/hRef/LPGFd5YW5nTZh8/o7gGutPSja7IvUiTK2drR7y165DUJFzc1uJuAeoQI6komiKqLTB6qOzmGv3EE6J2d1Pij59TqjQ1FoupsSQVhwwe0DPYwF17Id/vjt20zl4nz92b17+CdzcdKJ0J42XM8S/aT1gR5IgFarVX7rt36LBw/cwbTvvfce3/ve9/i1X/s1/sk/+SeBnuCA/iGTCFN04sjKKlL6+9DyZoBm9Hajfg8vkqNhFU0VlG13sTCogPYPewUQ2cUF1NxsV/rw9kOJxIlguEnLPrKy3iCmGGixvYMzeo3dKqDA8augrSooKnVT2XYcv9ET7kJpr7CPWHWBNW0MVQ+oArsLT88OEdZVPr65uZGoTj2NSOa+kjZcWSkgoimEFvZtBqiHt1BOKY3Hj2KxDSyhd/X5YylhFLs7ArT15duYUsU+9SqauveyVCgqyvA0Z8Ilbi6Wu25JtGyHD64XuHw+tyPkRzoO5o2foE5dQknu3sO6G0LVWEw9zxn7Lnbt8KLazt9CyZ1CaKGOODrMDFAPTVXIZaKbAnRsDqwWzvqDQ79Wt8mveyNY3PcvOAEaY7XUxDD3tstv5Z1Pl8ivN/j+r8yhKHvfu0IILs1muXpvHWeXa1rJToMW7htL9EHZV4D+8pe/5O/8nb/DvXv3AGg2m/zBH/wBf/RHf8Rf/MVf8Pnnn/P2228HfZ4D+oBMIsxyKw6OhTzCg/RxlKuuAE1p7kKzl3tAhRCk4iHWjLYAHVRA+4aFfBVNFUxusTBJx8Ip3j/x/k8PPZYgKow9+0WOSqHUJKG00GO9e289SiL2SAV0+BTAsftAvaCzalvkByVAY+ksAK3KzudluVxlXBYws6cDOfZe6JrKs2eyfHKz0FkMeYmd9uLVjiX1q4JTWUWkXLtlR4D6lYIbTQGCtNJ8bAVUsVtYonvVTwBbCaF2QYBKy8C8+VN+aZziuadn9v15NXeaYWeFjWqL1X3Sg/3my/l1qg1zV/ut/fAqslp0RxcdktapN1CFZOPzw23wSMfGLtzpVMiW1upEw1qnDeiwTGRjLBc9AepV3Xpb9DhSsrLeYGwohqy7FdAgekDBrVRK6Ij0x9E0LP79T+7y1HSaF+b2d05dOj1EtWHyYJfeZqGoqCNn+sYSfVD2FaD/5t/8G/7hP/yHjI66N9ynn37K7OwsMzMzaJrG9773PX74wx8GfqIDep9MMsRiw/1g9rsP1KuAJjoCtHcroOAGEa023UXrQID2Dwv5ClO5xLZdeGf9IThWT/R/AoTjSSLCYHXd3z7QQqlBXDF6enPnUcK6iq4pnVmgIhR1EwN9EaCJjrANSoAmhtzen3ppZ+/PwvVraMIheap79luPy+dzlKoG88ub7g39qW8AomMz/KrgVAqdipYnQLN+hRApKiKaJBduPXYUi+oYOF203wJILYIug+/5te59iGI1+NB+imdOZ/f9eWX4FJrdIqtUuz6O5edX80TDGs+e2SkozGtXIBxHO/3SoV93eOY0d8wRnJvvHKqq66w9AMtAbYeULRfrTA7HjlwpHx+OkV9v4DgSkRxBRJI9b/tc22hi2Q6j2ajbeqGGQA9mXJE3iuUgAvS//OI+GzWD3/vVcwf6e1xqX/t79oGOzeGsLjxRffj7Nrb84R/+4bb/v7KywsjIZvT06Ogo+Xz+0AceHu79Rc7ISP9Y0XqB6bEUH1ju3zVBlaSP758pQREwmhSsAsMTo4R6+O+Ty0QpV5qgaEREk2GfznVwTQaHlJL7KzVef3Z82/tceZinDuSeukRo+OTffzkxQvUGGK2mr9dDqdIgQot4dojsIV/3JK/LdDyEJTfPwZmao/Xw1rHO6aHTRCZSKLr7ETkzmQnk33jm9DjNdzUwqjtev7l0E4CnX3+NSKa77++3XwvzL//TNW4sbvDq81PuF0eSLJ29jHHrPXLf+bsI5XBzBruNH38v6dhUqmsknp8iO5KkaTlEwyqnpod8OEOXVjJLzmzxYcPa9Zwt20GTJuiRrt5naiSG3jQCP+bDv3yXdZlk6Pxlpib3r1y1rEssvgNz0RIPivWuvSeGafPxzVW+/vwkkxPbLZ52fYP5ex+RevnXyY0/XkTvdr7ReJj//d+e47+r/5RU6yGRmYNtOm0sPKAOjDz9AnomSX69zosXRo/8npyfzfLDny8gNZWx4TjLMxcxi3d7et3xYM11Al04M0zo8xoyOcToaCqQY6UyMYSA8h73qke52uKH79/njecmeOPy7sm3jzIykmRmLMnNxTJ/d5fXrp1/jvwn/5GUtUJkwv9QupP4Gx86WcFxnG1qXkp5pN2WYrGK4/RupPDISJJCYdC7dxg0AWtOHImg/PA+zQn/3r+llQrJWIjKqjufbr0uUHr47xPRFe5stBCZJLViEceHcx1ck8GyttGkUjcYTUe2vc/Nu9dBj1CyE4geeP8t3ErI8mKeQuG0L68ppWR9rYRIQsPWD3WdnfR1GY/oLK9WO+dgJqewyu+xcn8JETnaRqdRLSPiwyzlXfeC2TQC+Tcqjk3ZiSDWiztff+UWFZGkYoapnMD7e34qzbu/XOTXX9lcQMmzb2Lf+Zjlj3+Gdur5rp/TQfHrmnQ2CiAdGmqKQqHC4kqFTCLs67Vgh5MkxArLq7VdX7dSNwgLC0eEunqf2SJECDPQYzobKzTnP+e9xmWeOTN0oGNJMQRC5VKqyo9uFbr2nnx4vUCjZfH82Z3naXz2n8GxsGZff+z5PO66vBe+gCk+YOVnPyQamTrQOTVuf4GIplk3IjTur7O20WIofvTrJK67zp8vbq6gOjmsoVnMm7841rM0aG7cc9eEYQHN9SIyHOznUS4d4dbC+mOP8f/+lxu0DJvfev3Uoc7lwnSaK798yMOlErq2fYPPiUwCULz+KaHIwUTtQQnyM1xRxJ4Fx0On4I6Pj1MobNorC4VCx5474KtNJhHCRsUOpwKx4KbjoW3z+XqZVCxEpW4gIklkc2DB7Qe8AKLZRxNwiwuo2RmE6JHQ8JAbMFHb8O+62qib6HZ72H0fWXDB7ctZXN1MJvSs0nZx4civ6Y56SlBpW3DjAVlw04kQVRnZ8YyoNkxGrWUayVOBHPcgXD6f40GhRqG02Wuszb6IiCS/MmFE3ueYknRdX+s+zgD1ENEMcVlnvdLadVO+3rIICQu07oxg6ZxXKIImHEwjuD5Q8/o7SAQfWud2TZXd9bxUHSU7ySltjcVCjXrTDOz8tvL+l3mSMZ2nZ7dXv6WUmNfeQRk5g5rdv4d1L0ZyGb7kHNadnyONg/X32/nbqGNzCLGZXnuUBFxiryXyAAAgAElEQVQPL7yo0wfatvbaK73bB5pfaxDSFTLJMLJeCqz/02O/JNyVUoO//niRb74w0bHsHpRLZ7IYlrOrtVyJphDJkZ63RB+GQ6+oXnjhBe7evcv8/Dy2bfPnf/7nvPXW4ZuuBzx5ZNrR9M1QpjO82y/KNYNUIoRsVUENIbTu9sMclmQshGVLnHBy0APaJyzkqwhgenRLAJF0sFfnO+mqvYAIu4uERtW/68odweL2lvTyjN3dmBlNsF5pbSbhtoOIjtMH2gkhaphEQupjkzmPg6ooNJU4amt78MSdW/Nk1RrhyfOBHPcgvHjeFQRb03CFqqGdfxPr3sd7Jvc+SXRDgCqxNGGrhpROZ8zLVhotizBW12aAeqhht4+uXtkZiuIHXmrsLTnN5KkZouGDG/KU4dNkjDwSye2HwV+HTcPil7dWeeXiKOoj8z2dwl2c9QfoF791rGNMjcT568oZsAzM2z/f9+edZgW5kUcZbQcQeQm4R5gB6pGMhYhHtM0k3JGzIJSeFj359TqjmRiKEDj1UmAJuB6Tw3GW1xp7Ojj/9ModVEXw218/c+jXvjCTQRGCq/N79YGew1653fX056A49KdqOBzmH//jf8zv//7v893vfpezZ8/yne98J4hzG9BnZBKuKKyqade65CMb7QoorVpfLJDT7RQ6U4sPBGifsJCvMJaNEQltLoRkOQ9Wq2cCiABEuwJq1Ku+fRAVSg1iSnuId59VQL0Ng/vt9EAlmkLEs9jFowlQaZtgtRDhOLWGGVgAkYelxwnZ23fU1+58CcDIuWcCPfbjGB2KMZWL88nN7c9y/eJbIG2sm++e0Jl1D1lZBaEgEllsx6FcNfyvgMbSCBzioklxlyTcRtMiJEyUkD/JuwdFjXgbXcEIUPvBZ8jaOleqZ3n5wsj+v7AFNXcK1aySURrc7EIQ0Uc3ChiWw2tPj+34nnntCmgh9LnXjnWMqVyCO8YwTmriQA4Dpy0K1TG3SrlUrKMqglz6eNfJ+HCsI0CFHkbJzvR0+mp+vcFYNuqG8xiNzmzdoJgYjmHZzq4p9PPLFX52Nc/f/trMkZ4T0bDG2anUY+aBziHrJWS1eOjX7kUOLEB//OMfMz3t+o7feOMN/uzP/oy//Mu/5A/+4A96YjbegJNH11TiEY2STCJr60jbnzmFjpRtARruVCZ6nWTcXbS21DiyUXlidqyeZBbylZ3zP9tVtN6qgLrXf8huUqn7Yz8rlBrEhSdAe//+2srMqGuZ3hpfr+Zmj1wB7dj8IwmqDSsw+23neOEkEdnYNjtZFm5joxAaOx3osffj8vkcN+6XO9VlAHVoCmV0DvPa4RI7+xGnUkAksghFZaNm4kjp2wgWD2/BnFIauybh1lsWYWGhhIJJ9twLvS1Am/W97YbHwbx2BUONcdWa5vK5g8/NBFBypwG4PFzn1gN/R77txrufLZNLRzg3vb26Js0W5u2foZ39GuKYf5+pkTggWM29jLNyB3tt8bE/b+dvgVBRR04DbgV0dCh6bLfGRDbOUnEz5VUdm8NeuYN0/J3t7ge247Ba8kawuNdB4BbcdoX5YXFnEu6/e/s2iajOb7x29PXCM6ez3FuqbHvmeqjtancvbwgchh5pahrwpJBJhlm1EoD0bZem1jCxHdnpAe2HBXIq5lZAG0TBNsDqzkDvAUej2jApbrR29H/aq/OgaChDkyd0ZjvxLLhRxfBtDl6h1CAXbS8w+uD+2ko6HiIV07lf2BSgSm4Wp7SMNA///mztM692oQKqRFOoOMime9xGyyJrLFKJTiLUYI+9H5fP53Ck5LPb25/l+sW3cEoPcZ6QhdBeOJXVjv12reJeS/5XQLcI0F0qoF4PqBbubgU0FHWfM60ABKhTL2PNf8InznnmpocPPbdSHZ4BBE8nK9xZ2sCygxNHaxtNrs2v8+az4yiPFFusO++D2US/cPw2tMnhOAK4oV0ERd23Cmqv3EYZnkG0e4OX1+qH7jncjfHhGOWaQaPlFhDU0Tkwmzilxwvik6BYbmI7krGhaGcGqIgGa8H1+mQf7QP94t4aX9xd47fePE0scuh81w6XTg8hgWu72HCV4WlQQz0/m/WgDAToAF/JxEMsGe4N6lcQkTcDNJ0IueEgfWAR9D5Qq9JdNAxsuL3NQjuA6NSOAKJ5lOw0Qjn6B4rv6BEkgqgwdrUBHYVCqclwxAb6rwIKbh/o/a0V0OFZQOIU7x/6tbYK0FrDJBmwANUT7oKpVnYXHDcXisyoxU4AyElyZiJFOhHi40dtuGdfBS3s2g+fYOTG5gzQks8zQD2Uds/aaNiguLFLD2jTICRs9Eh3K6DhmPscMBr+zhsGsG6+B9LmR+uzvHRI+y2A0CMo6TEmlSKG6Wy79/3mvc+XkcCbz03s+J55/R1Eehx1/KljHyccUhnJRLlXcsO+rJvvue0AuyAdB7twt2O/tWyHlfVGRxwdh/F2iFGnD3SsXXXrwT7Q5fYIlrFszJ0BCoH3gMYjOql4iKXVzfvCkZJ/99e3GU5F+NUXD5ZgvBdnJlJEQuqufaBC0VBHz/Tk3+IoDAToAF/JJMI8qLuiy/EpiKgjQONuCFE/LJC9qsmGPRCg/cBC3l3AbLXgSimxV+d7qv8TQAgFEY6RVE1++PMFWqZ97NcslBoMhSzQoz0/33E3pkcTPFytYbdtYp5l2j6KDbfpCdAE1YYZuAU3mnJTNStFt8qYv3UdXTgMnb0U6HEPgiIEl8/l+OzuGqa1WWUSoSj63KuYtw+e2NlvSMtANsqITgXUFYdBVUBHIwZru1RAW3V3oetZYrtFNO4+Cy2fBaiUEvP6FcqxGfJOhpfOH16AgmvDTTaXAXZNDfUDKSXvfr7MU9NpRjPbNwCc0hL28g30C2/51oY2NeImeusX30I2K1jzH+/6c876AzCbHXFYKDWwHemvAG1bTEVqFBFJ9mTVLb/unuO2CmjAFlyAyeHYtgroL75cYT5f4XffOouuHU9WaarCxVNDXL27Rx/o6BxOcd7tee1zBgJ0gK9kkmEe1DRQVKRPFdCNqlcBdXtA+8EiqKkKiajOuulWQmXj5OdHDtibhXyFoWSYZGzTCiarRWjVeqr/00OEYjw3E2V+ucK//Isvj9WLZ1o2pUqLlGb2RcDXbkyPJDAttwoAIOJDiEgS5whBRLKdSOuEotRbVuAW3NiQO7i+VnYXHK3lmwCEJ04uAXcrL57P0TJsri1s35HXL7wFVgvzzvsndGbB4m2gehXQ9Uqr81z3E6GFIBRlONTatQfUbLpf63YKbiTRFqAtfwWok7+FU1rifeM8p8eTDB8xNEfNnULU15hKSW4uBiNA7zzcIL9W37X6aVy7AkJBf+pN3443mYuTX6vjjF1CxLN7Ogy8CpjnkvB6Nv2w4I4ORVGEYGmtvREnBMro2Z6026+sNYiEVFLxkNsDKhRENLn/Lx6TieE4D4t1pJRYtsMPrtxmZjTBa8/sDKk6Ck+fHmKl1Ng2AstDGZsDxz5WynuvMBCgA3wlkwhjOQIZy/qWhOtVQFNhwO6fRXIyplM03MWK0wg+qW/A0ZnPV3b2f7bFS69VQMG1h2ZCNt//lTne/3KF//DuvSO/1mq5iQRiitEX7oLdmBl1F8ueFU8IgZKbPVIF1LPgNhx3wR+0AE1nXYHT2ijRMm2Stfs0tBRKIhvocQ/K07NDhHV12zgWAGXsHEpmAvP6Oyd0ZsEiKyvAoyNYQoGELirRNBmlyepGc8dmktncTCTtJp7l12n5W+E2rl1BamH+88rYodNvt+IFEb2ca3DrQSmQQKx3P18mpCl87eL2WffSsbBuvot26gVfQ2+mRuLYjmSl1ES/8A3sB1/g7JKlYa/cQrTnQsJmP+L4MWaAemiqwkgm0qmAgmvDdUpLyGZwVuejkF+vMzYUQwiBUy+770kX5nVPDMdotCw2agZvf/KQQqnJf/Mrczt6hI/KM6fdZ/9uabibQUS9V5E+LAMBOsBXvFEsVjTrYw9oi5CmEJbtneA+6AEFN4io0HB7BwcW3N6lZdosr9V3JOA6q/MgFJRjDBcPChGOIY06v/HaKd58dpw//cldfnFt5Uiv5e2yRmSzb+6tR5kYjqMqYnsfaG4WZ21xzz6qvZCtGgiFquXeu/FosP2/qewQjgSrVub2YplZtYCdPfwMuaDQNZVnz2T55GYBZ8siXwjhhhHlb2Gv915AyXFxNlzBLVJbBWgwQUAiliEh6rQMm3pre3q8bbSronp3Q4i84zk+Wqyl0cC68z6F9LMY6Lz01NEFqNqe93s+XqFUNXatHh8H07J5/2qel54a2TGj1Fr4JbKx4Y4k8pHpnPv8fbBaRb/wTfc8rv9kx8/Z+duoo3OdzZDlYp2hZPhQs1Qfx3h2cxQLbOkDXbnjy+v7RX69zljW3SiRjXLg/Z8eXqX5ztIGf/buXZ6eHeKZM/5tGE4Mx8gkQly9t0sQUSyNSI48EX2gAwE6wFcyCXeXtqFn3BlqPlCuGW6oj7EZDtIPpOIh1hrSDY1pDiy4vcqDlSpS7gwgslfnUTITrkWuxxChmDsTVwj+h+9c5NxUmn/x51e5t3z4jY5CyV246U6jb+6tR9E1hfHh2DYBqgzPgrRxDimOvKTtatMVAkFXQFVNo0EEp7HBvTv3GVZrpE5dCPSYh+Xy+RylqsH88vbnmHb+6yDUJzKMyKkUQNU7qZrrlabvAUQeIpYhYrvX7qNCym61N1617lZAhaJiosERkqT3wrzzPlgtflI/y2QufizLqIgkEIlhxqS70e23DfeTW0XqLYs3nxvf8T3z2hVELIM687yvxxwfjqEqgsVCDSU5gjp1CfP6le0jmppVZHnZtWK2eVis+1L99JgYjrO81sBx3A0ndeQMCNFTVTd3FmeT0SH33y3rpa70f8JmEu7/96ObVOomv/crc746I4QQXDqd5cv59W2bfh7q6NwTMYplIEAH+IonQKtKGtmsHGkMwqOUq0YnARf6SIDGQlRqBiKaGlRAe5jNBNydFdBe7P+EdgW03Zulawp//3efIxnT+ad//BnrlcON/CmUGoQ0BcWo9829tRszowkeFLZXQOHwQUSyWYV2Ai5AMhr8BkRTiaG0KlTv3wAgMnX8VE0/eeFcDkWIHWm4SjSFNnu5ndjpz9znXkG2R7AIIZBSsl5pkQlMgKbRzSogd4xicYyT6QEFMEUI4eMIMfPaFUhPcOVhhJeeOtzsz91Qc6eJ1BYJh1Ru+SxA3/1siaFkmEuz2ytbTm0d+/6n6E99w/fANk1VGMvGWCy4ax39wjeR1SL24tXOz3jCw7NiSilZXqv5EkDkMT4cw7KdzrUo9AhKdrqnqm6FUgMp3QAicAWo0qUK6FAyTCSkslpu8rWLo5yZSPl+jGdOZ6k2TO7nd9qe1bE5ZG19V3t2PzEQoAN8Jd224K5Lt5rkhw13o2aQjoe3jUfoB5JxnXrLQkQGArSXmc9XiUc0hlObFjenXkbWSz3Z/wlAKI40NlP4UvEQ/+D3XqDetPhnP/gU4xDJuIVSg5F0pG9m7O7FzEiCtY0WtaYrHEVqBPToocMaOhXQtgAN2oILYGpxNKtGZGMeG7XnNj4SUZ3z02k+ubnT1bJfYme/4lQKiHYAUaVhYtnS9wRcDyWWQdgGYcwdFdDOJm63LbiAJUII258KqL22iLNym8XMi0gpePmp0f1/aR+U3ClkOc/FibCvSbjlmsHnd9Z4/ZkxFGV7Zcu88ROQsmOR9ZupXJzFVVd0aKdfgnB8m8PAzt8CobhVyfa5Nlq2LwFEHo+OYgFX8Nord7ZVY0+S/JYRLNKxkY1K1yqgQggm2tXq3/3W2UCO8fRpNx191z7QHh6NcxgGAnSAr3gpgQXLfRjKjePbcMs1ozOCBVzrTT/gzQK19PggBbeHWchXODWW3Gah8dJTleHeEgIeIhwD29oWxT4zmuDvfe8S95Yq/F+HSMYtlBpMZlSQTt8EfO2GF0T0oBNEpKDmTh2+AvqIAA3aggvghJNEnDqnlBXM5BRCDf6Yh+Xy+RwPCrUdyYzq9HOI+NATF0bkVAqbAUQbwcwA9fB617J6i7VHZoHKdgWy2xZcAFsNodr+jHswr18BReXt0gzDqcgOx8lR8DYIX8g2eFCo0mj5U4X/2RfLOFLy9We3p99K6WBefwd14gJK2p/E00eZGolTKDVpGTZCC6GfewPr3kedACB75TZKdqZTEd9MwPWxAvrIKBZwq26YDZz1h74d5zhsG8HS2ABk1wQowHdfP83f/fULjA0FMx4pkwgzNRLni10EqJKdAVXvexvuQIAO8J1MIszDlntTHrcCatkO1YZJOh6CPquAptojPQwtgWwOKqC9iGU7PCjUdiyGPNGi5k6dxGnti3cPSGP7iIQXnxrhd7911k3Gfe/evq8jpaRQajKR8F63PzZ3dmP6kSRccDcQnOJ9pHPwXfutAlRTBWE9+LmoIpoirTSY0Yo9Z7/1ePG8Ww18NA1XKAr6U9/AfvBZ31vCPGSrBkYDJdUewVL1ZoAGF0IEMJ2wWN1iwZVSongW2BOw4DpqBN05vgVX2hbWzfcQM5f5cKHJyxdGfOmZ85Jwz0TKSAm3H/pTBX33s2XOTCSZzG1fa9hL15EbK+gXv+XLcXZjqh1E9LCdbKtf/BY4FuatnyIdB3vljisG23gJuH5WQJMxnXhEY+mRCijQM6Inv94gHtFIRHVke8qA16/dDV6+MMJbL0wGeoxLs1lu3C/vcDQJVUMdOdOTs1kPw0CADvCdTDJEvqaAFj62AN3wRrAkQu6iQFHhBHaCj4JXAW0qUbcf9hCL4AHdYblYx7KdHQFEzuq8O4A71N3h7wfFOy+5y4y+774+yxvPjPOn79zlg32ScSt1k5ZpMxpvX5t9srmzG+l4iERU39kHahs45aUDv45sVhGRBNWGSTyqBzJ241H0eJqIMAkJu2cF6OhQjKlcnE9u7nym6xe+CVK69sQnAO9zS2wZwQIEZsH1KqDjMZO1LQLUsBx02pbyE7DgilAEHZMfXLmNZR/988ua/xjZrDCfeAHLlsdKv92KEssgoimGrRWEwBcb7kK+woNClTef3Tn707x2BUJRtDOvHPs4ezE94j6DveeYOjyDMnIG89rbOKVFMJsdCya4FdBwSO1MIPADIYSbhFvcbPMQ6TEIx3F6RPTk1+qMtkewyHoJoGs9oN3i0ukhLNvZtb9ZGZ3DWZ0/dMp7LzEQoAN8J5MIU6oZKMmRYyfhejNA03E3hEiE411ZEPpBKuba6GoyClJ2LMQDeof5TgDRzgTcnu3/pG3BhY4rYNv3hOB//I0LzE2m+Od/fnVHculWPDtlLmK3X7d/BagQgpnRxPYKaPtveNA+UOlYYDY6FdBu2G8BwqlN69jW6kavcfl8jhv3yx17soeSGkWdfBrz+js90yN2HLwZ1pszQJsoQrhOnADwZknmQtvHidSbFiFhIRFwArbs4eEMmbDDn783zz/6Vx9su7cOg3n9CiKe5Z2VDKmYzrkp/4SCkjuNWL/PzEiCmz4I0Hc/W0ZVBK9d2m6xla0a1t0P0M+9EWgy+kgmiq4pnSAicDd4nLUHmJ//CHBTUD2WizUmsjHf10WPjmIRQrjpqz3Sd7iyZQSLU29XQLtowe0GF05lUBWxqw1XHZsDxzp0xkEvMRCgA3wnkwhTrhmIZO7YFdBNARpGtqp9ZRFMti24FcfduR70gfYeC/kqIU1hYkuEvWzVkJVCzwXBbKVTATV2VkDBnd3497//PImYzv/xx59Squ5uo/MEaCbk9k710/21GzOjCRYLtc74ACUz4fbKHFSAtivKop2Cm+ySAE0Pu1ZPO5xGxP2bJ+c3l8/ncKTks9s7rbb6xbeQlVXsh9dO4Mz8xds4VdohROsbLdKJ0I5AGt8IxUDVyGpNyjUD03JFfKNlERYWjho6kY1XLRwjFXL4/e8/R7lm8L/+37/gP/70HvYh3DxOtYh9/3OU81/nl3fXefGpEV/fR3X4FM76Ik9NxrnzcONQ5/Yolu3ws6vLXD6X27H5ZN76Gdgm+gV/Z38+iqIIJofjPFzdIkDPvQ5qCPPa24hIEpHaDHBaWqv7ar/1GB+OUaoa2/pq1bFzOKWHnUDIk8IwbdY2Wp3+S68C2q05oN0iEtKYm0ztOg/0SQgiGgjQAb4zlAghJZiRLE5l9cBhKLuxsbUC2mcpnZGQSkhTKFmuEB30gfYeC/kK06OJbQsiu7gAgNqjAUSwpQf0MQuBdDzEP/j+89SbFv/0jz/bNRnXE6BJtW3z6+MQIoDpkQSG5XQCKoSiomRnDr5LvKXPvNq0iHdJgEaS7s59ePJ8Tzs8zkykSCdCO8axAGinX96R2NmvOJUChGKd+2y92gosgAjc6pKIpkkI97pdq7hV0EbLIoSJVE+o7SQUQRpNLs8N84/+p1d4+fwwP3j7Fv/b//MBS6tuW8l+/5nXfwJI7kSfpWXYvOyT/dZDyc2CdLg0VKdl2jxYObo4+vzOGpW6ufvsz+tXUIZnurIxOTUSZ3GLABWhGNrZrwGu9dJ7RjQNi7WNlq8BRB7jWffa35aE64melTu+H+8wrJQaSLaOYClDON6T4W3H5dLpLAvLlZ2uk1gGkRjuqdmshyX4fPkBXzm8WaCNUIa42XQHBMeHjvRa5XblJhUPYbSqiPiwb+cZNEIIkrEQa4a7zyNPMKBDmi1qP/hfCL3wXUIBBig89hykpP5nf4g2dYnwK797Iufw6PksrFR3WK08sdLLFVCvV1PWH285OzWW5H/+3iX+2Q8+41/+p2v8ve9d2iZwCqUmmUQIxXJ3WHu15/WgdJJwC7VOVUDNzWLe/hlSyn3F3eaopwTVepnEdHd21L2d+1623wIoQnD53P/P3nvHN3Lfd97vmcEARGHvy76s23vXUs1qltXdYlmOk1zi3J198bW0e3x58jx3yb2c+PFdEse5i9Mc1ySSXGRbliXLorS70mpXW7VLLrm77Fz2BoAABjPz/DEESC4blgUgyN/79dIfAgfAb0lgZj6/7/f7+eTw9pVetLCBapvaw7YcOw+jNb6BGZpAsjsTuNLlYYz0RNtvwZoBLcpZ3c0ZyZWBK2x9/oZGA+RnuvBPVkBZxZbPBddkd4Eewvu1XwXgWeDZLCAEvACxNuQqRds43W7gdNioK1vavcC8rz1pRFSmjgAKzZ0jlBWkLvic+ThxuYdUl8qOzTPvM/SBNoyBNhxHPxmXDaKiHDcnL9/CF9Bwp1iiSq2rJ9x8Ysb8Z0QcrooAzZ6KYonkXCq5FSBJ6D1N2Ep2rPh7xsr0CBawroPrbf4zwtaKLL731k2utg1zoG5mdJGSXx117E9GhAAVrDjpkwJ00FWJGwi3vI191yNLeq1RXwh3ig3VJhMM+JCz16Yr6XykuVW6QylWTMH106g1dyVkHeGb72KO9qJd/ClqbX1Cqiz6rWsYvS2Ehrux7/7Qqs7RxEL/aICJYHhOB1zJnYXsXPlw6ZVCSklFzixCu/Huot+tvTW5PHP3Zp5/4wabctw8drQ8+rO+kQlyM5yYgQ6w2RP+N1kum3JcyJJER583erGWc8rg6uuY4/0zWtfmIjqn7XDhCwzGbQZUTssj5b7PYCvbE5f3Ww57qnN443w3V9uG2Vk580bdVrob7f3X0PtvYivamqAVLg/DN4ze04h916OAtVE1NBZkW8XqtkbLrgzsQ1bERcQJdyJozYAmwoAIJs2lJAlum+sNBHXONfdza2iCnPQU9tbkLpCXKyGX7+Pc16+zqyobm7KyjXdSag7YXTh9XWSmVtPSNcoH9pfc8et4JzQutAxwz56iWWvUGhtAsVmtsHGgaNKIqKvfR02J1R2hFNRY54iSndHjIhEsBavQgpuX4USSZkaxSHYnyqataC2nsB94GklKTBNl37QIFgDDP7Lu5j8jVBSm4nQoXGkdmiVAHQc/jDG2sNHgWkYIUMGKE3Fj6zczKM+vQmtqQN358JJEz6gvFHWTtVpwk2tGLc1lZ3g8iLrjLkLnX8LwDiF74j/jpTU2gCRhjHRj9F2fsYsa7zUQ8hNuPYtadSTua5hO+6Q5T9ntDriDbWt+o0OSJNTaeoJvfxt9qBMlq3jB4z94uIzuAR8vNtxgU7aLfbXWhax/ZIItZZlJ+d2aC9WmUJDtimaBwlRWoD7QhryYAA1YFaig5EQ3zGj1IR4k+vsQK1vKMnGoCudbBmYJUCXPCmXXe1uSVoBq106AaVriC5gI6gQ1naxVimCJILnSkXsakSCaBeoPhkmVwsj2xHw3ZXcmjj2PzXrcAdx92OStSz18+9Vmnn8HPn5fFfW7Ns15nb/aOoQvEF7x9luYNMfJKUMfaKO6eP+SjYhOX+0lrM+R/RkOobWcwla+P24Z5JEolq6BKQEqSdKsc0TPoB9ZkqJCbCVRbTK5Gc4ZUSxgVWIDr30VvesKtuLtK/6+sdA77CfVpeKaPD+b/hHkgrXpHr5cFFmmrjST92/OkQeamhOdU09GxAyoYMVJc9uRgBFv0HJvG+lZsnX3qDdkzX/qGoSDSTUDCpDqtjPmDy0rpsA0TV5ouM4ffu3tJVnhGyO30G9ds3b0bY6EzGiZIT/hG+9a1dfU3DUxJ9beN44sSVHbe7BC342RnjXtgBvBVnMUZCWm36XljFtH5aY0/nrSGVcL64yMB8nNcEKSzVcvxCwn3MwikJSY5kAjLbg+09r0ilcFNJlQbQrbK7I439yPcdt8v+RwI2dsWjNZgXeKaRpoTQ0ohbXI6VZr/vDkPOZqRbBEkFwZEPSR6VGiTrgTky64cgIyQBdDkiSO79zE//trh9hcmMY/vNzEl//5QjSyZjrvXRvAbpPZXrE6IzRydinGUAfVmzwMjwdnOAnHyolLtyjOdc/qiAm3noWQH7Vudc2HppOV5iDFrtDVv3CTc8+gj9xM54pXla7Yx9QAACAASURBVCNYUSwzBaitfG/CZ71vDU1MGRCZJqZ/dN1WQMGaAx0YDdA36dmwXhACVLDi2BSZVLedEW8ItfIQqCmElnCy+unpdlq6RinJS502m5VcN8npbjvjfg0pNXdJMQWmafLtV5t56WQbZ6728uqZzjteg9bUAJKMuv0DqJUH0W6cxtTu/AK9HLSWd0APodbdjVp7HL37asJbR9p7vRTmuFBtSvQxY7ADTHNtz39OIqekYivfS7j5ZExZYKpN4bNP78DjtJxxr3eNYQK5GSlJZ/C1EMW5bgbHAvgDk8ZKNjty5ib0GGZlogJUt4SnxyUE6Fzsrs5hxBuaM+JHya/E6L2+LPO5RKH3NGGO9aFOm5Mf9q5uBmiEyBxwaarO4GQLrj8YJkXSkB1rd542Oz2F//jx3Tz7QA3XOkb4wtfe4dTlW9G/v2GavNfcz/bN2TjsyiKvtjSsvN8wtenW36q5a+SOnt8z6ONmzxhHtxfOquBqjQ2T1++6FVvvYkiSZBkR9S9sqHRr0D/DwX2lKchy0Tvsn7HRJCkqavVRwq3vYQbiHy13prGPax0j0cowQR8Y4WiU0Xpka7k1N31ljjiWZEYIUMGqkOGxM+INIqkpqJsPEr5xGjMU2+6NaZr80+stfPfnLeyvzeXD91QmrQBNddnRDRNfIDwZU9Afc0yBaZp862fNvHq2kwcPlLB/Sz7fP3Fzzh3meV/DCKNdO4GtdBeyK8OykNcChK+fXuo/aUloTQ3IWcXIuRXWHKwkoTW9Gdc13E5b7zilebPzP4GkqIACqLX1mEEv4dZzMR2f7nHw757ZiS+g8ZUXLwFW7pwVcZRc3635KJn8m3ZOu3mTc8qs0O5FRJEZ9ILdhTdgOQaLCujc7KrKQZakOd1w5fwqzKAXc6w3AStbHlpjA9id2Cr2Rx8bHouPAI2YqBS6wjMEqEMKI63BCuh0ZEni/n3F/OGvHGRTjpu/fukKf/niZcZ8IW72jDE8HmRvzeq1CsqTRkS5Zh8OVaHlDttwT1y6hSxJHNl2myHdWB9691XU2uNxn3csyvHQNeCb95ylG5bb92oYEEUozHahhQ2Gbqsoq3X1YITRmk+u2nvPRXvvOF/70RUqi9J44q4KAIyJSAbo+jQhAmsjIDPVwZU52nCTGSFABatChscRzR5U6+ohHES7sbjo0Q2Dv/3xVV5+p5179xTxm09sR7XJUwI0TjMYK0XaZAVl3B+yYgrsrphaVwzT5BuvXOO19zp56GAJH7uvis88tQPDMPnOa80xv3+4/SLmxGg0u0zOr0LOKCTUFL/2GX2wA6P/ZtT8SPZkoRTvQLv2FuYyMtuWw6gvxKg3RNlt7VbGQJuVs7aGsxinoxRtQ3JnWVXuGCnNT+XXP7QVX8DKd7NMiHxJH8ESIeKE23HbHKg5MRbNi5uPSCU4YnkvBOjceJwq1cXpnGsemPUzJc9y8k22fDoz6CN88wxq1ZEZZlyRDb+Iu/tqEWkhzEsJMTQWwDDNaA6oZFvbAjRCfpaL3312Lx+5t5IL1wf4wt+8w4sNN1BkiV1VqyhA0wvAZsccbGfzprQ7EqCGYXLq/Vts35wVNVCMoDW9CZKUEPPAolzrPBSJorudgdEAYd2MutWuBgVZU06401GySpBzK9AaG+LW6TDmD/Hnz1/CnaLy2ad2RB24I07w61mASpLE1vJMrrYNRzOu1wNCgApWBUuAWidOOa8SOWPTosIrqOn8xfOXOHHpFk/cVcEnH6yZymcMJGcFNGKgNOYLTcYUHCHcembB/MaI+Hz9XBcPHyrlo/dWIUkSBdluHj1cxruNfbwfYyuG1tiA5ExHKbWc8yLmNUZvC/pw9/L/gbGsoakBZBtq9dHoY2rtcUzfMHrnpbis4Xbae63WwdLbDIj0SQOitZzFOB1Jlq2W5s73McZni4H52Febx8fvq6KyKI00l7puTIjA6r5wp9hmzoFOVrQXmwM1gz6kFA9evxCgi7G7Ooeuft+suSQ5cxOoTvQlzv0nCu36O6Br0c26CEPjQdJc6ozImdUgIkCz1CBh3WTcF4rOgCbKBXcpyLLEI4fK+INPHyArNYUrrcPUlWWuqqGXJMvWHOhAG9XF6XT0e5kIhmN67tW2YYbHgxzdPjP70zQMtGtvoRTvSIhxYPFk7E/nwNz3Cj0DlijctAoOuBEi7rq3GxGB1X1jDHdi9N9ctfePENYNq6LuD/HZp3fM2CiIbCqu5xZcsOZAfYEw7X2zxx6SFSFABatChsfOuC9EWDcs0VN3HKPvOvpQ15zH+wIaX/rueS5eH+S5B2t44q6KGSIgEo+QbDfJaa5JATp5Q6vW1YMeRms+Nefxhmny9Zeb+MW5Lj54uIyP3FM54/fwyOFS8jKcfOOVa2jhhauHhn8EveMias0xJHlq9sZWcwwk5Y6qZkvF1DW05pPYyvfOqF7byvYgpaQmzMhgSoBOrcnUwxhDnUnTfhtBrbV25+/U4OrBg6X8l+f2I+kaGGFIsu/WfEiSREmeh85pBh5KVgkgLToHagamKqAS4HIIo/j52FNtVbTO31YFlSQZJW9z0hkRaY1vIGeXzJr/HvEGyVxlB1wAKSUNJIk0yRL0A2MBJgIhbOiwxltw56Io18N/+dQ+PvVQLR+7d/Vd15XsUvTBdqqKUjFNuNE9FtPzTlzuweWwRT/PEfTOS5i+4agbcrwpyp10wp1nDrRnyHp8NSugaS4Vp8M2qwIKoFYdAsUel2v4t15t5lrHCL/ySF00kzRCRICuZxMisAQoMKcbbrIiBKhgVcjwODAh2j5iqz5mOXbOIXqGx4P8j2+8R2vPGP/6ye3cu3d2pMRUC25yVUBTp1VAwWoFlHPK5vw9GKbJP/ykkYYL3Tx6pIxn7t48qxKn2hSefbCG3iE/Pz3dvuB7a9feAtOYtaMvO9Owle0mfO0Eph7bLvFSCbe+B0GfNf9qmnzlhUu80HADSbFhqzlGuO08xkRsNworSVuvl5z0lKiNO4Ax3AWGnhQGRNORU3NRirZaBldLaGmObu4k2XdrIYonBWjEPEOyO5HS82OrgDo8eAMarhTbVAeGYBZ5mS6Kctycn2MOVMmvxBjqiLvZ2VLRB9owBtpQa++edc4dGguu+vwnWFU8KSUVN/7o+4aDlhhNlhbc27EpMvfsKaI4b/U3t+ScMtACVHhCSEBz5+JGRBPBMO819XNwS94MMzqY7B5KSU1YNm+a206qS53XCbdn0E+a2766lWVJojB7thMugGR3Yas8gHb9bUwtdl+KO+X1c1384lwXjxwu5fC2glk/N/yjYLNDEnUJLIV0t53iXDdXWocTvZQVQwhQwaoQmZeJtuE607CV7Zl07JwSPT2DPv7oH88wOBbg339kF/vr5s7pM4M+kGRQ164b4FykOlUkmDHHodbWYwy2ow+0Rh8zTJO//3Ejb17s4UNHy3m6frb4jLBjczb7anJ56WQrA/PYcpumidb4phUnkDH7pK3W1WMGxgm3xWZes1S0xgYkTzZK0VYa20c4e62fl0620nCh2xLGpk742olVXcNctPeOz87/TDIDoumodfWY3kH07it3/NxkNfhaiJJcDyHNoH946vsRyQpciIgZk29Cw+OyL3iswGrDvdYxGp2ZjaDkVYFposehPW8l0JoaQLGhVs/OYh0eD8RFgIJVxXGEre6MwdEARmhSwK/zm+uVQJk0IrKPd1GU6+F61+JzoGca+wiFDY7umJn9aUyMEW47j63mGJKSuC6Iohw33fO04K62A26EgizXnBVQYMrU8Oa7q/LeTe3DfOtn19hZmc0z9ZVzHmP6R5Gc6UkzNrMctpZn0dw5SkjTE72UFUEIUMGqkJFq3byNeqd2xtTamaLnZs8Yf/yN99DCBr/zib1sKZ9/ziJiDpJsJxlZlvC4VMb90wRo1WFQ1GjrimGY/N2Pr/LWpR4eP1bOU8crFv13/tIHqkGCb89jSGTFCfTOqn5GUIp3ILkzV9WJ1hjvR+96P+og+OqZDjxOla3lmfzjT5u46XMj51ehNcXPyACsXe++4YlZeW/6QBuoKUhpc2+CrGWWk822LgVo/jxGRN7BeaMDTNOAoB8pxWrB9ThF++1i7K7OwTBNLl6f2Yar5CePEZEZDqE1n8JWvn/WdyCk6fgC4bgKUDk4htOhMDgWQJ8UoGvdBXctIGcWgaxgDLRSXZzO9e6xRQ1bTly+RX6mk8pNM9s6w80nwNTnvX7Gi6JcD51zOOGapknPoI/CnNU/ZxdkuRgeD845U6sU1CClF6xKG+7AyARfefEyeZlOfuOxbfN2o5j+kXU//xlha3kWYd2g+Q5dntcqQoAKVoWpCuiUAFWKt0cdOy/fHOSL3zqH06Hwe8/to6wgdb6XArBuGpP0BjnNZY/OgIJ1o2+r2I/Wcgo9FORvfnQ1arz05PH5K5/TyUpL4fFjFZxrHuBCy2zzGa2pAVQnts3753j2pHlNzV3onZcwvKszU6A1vQVIqLXH6R+Z4HzzAHfv3sS/fnI72ekp/MWLlwiVHsEY6cGIo2FJRJTMZUCkZJfG3W5/JVhONlvk+GSbr16ITdluJGmmAJWzrcr2vFXQ0ARgRmdAPavY2rZeqChMI91jnz0H6nAjZxQmhRFRuPUshPzWfP5txCsDNILsSsf0j5KVlmJ1t0RaG5O0BTeeSIoNObMYfaCNquJ0AiF9xhz47fSPTHCtY4SjO2Zmf1rdQw3I+VUomZvisfR5Kcp1EwxN5cJGGPdr+ALhuFRAIzEvvcNztOFK1vVdv3UNY6Rnxd4zEArzZ89fwjBMPvfMTlwp828Gmv6Rde2AO53akgwUWVo3eaDJd6clSArSXHYkCYa9U5U/S/QcI9xxmX/4l1PkZTr5/U/uIz9z8ZNopAKajKS57bOs1NW6eghN8NqL3+fU+7d48nhFNNcqVh48UEJhtotv/uzajJYMM+QnfOMMatWhBWeH1NrjYJp3bF4TC6ZhoDW9iVK8DdmTzc/f60SSJO7dU4Q7ReVzz+wkqOn81fkUsDniYogUoW0OB1zTMDAG25Nu/nM6au3SstnWYwXUrioUZLlmGhFFnHDnMSKa+j14JiugQoAuhixJ7K7K4dLNoVmmaHJeJUbf9bh2NywFrbEBKTUXZVPdrJ9FMkCz4lgBNSfGyEm10zXgwy5ZVadkcsFNJEpOKcZgO9WTFc2FKkUnL99CAo7eNldo9LZgjPQkzHxoOkWTFc7bjYh6Bq3/X80M0AjRKJY55kAB1JpjIMkr1k1lmCZ/86OrdA14+c0nt0Xff97j/aPr3oAogsOuUFWUHnMKwlpHCFDBqiDLEulu+4wKKMA7oWokTB7O6eB3PrF3Vu7WfCSzAE11qYz5b8vyyq9mXMkgZ+AsT9Vv5vFjdyY+wTJ4+OQDNQyMBvjx21M31VrL26CHFm0fktPyUDZtmWyBXdk8Tr3rfUzfEGptPYFQmIYLPeyvyyUrzbqRKspx8xuPbaW5N8h1tQbt+mnM0NzzrCtN+61x0lwqGZ6pGT9j7BaEQ9E5omREyV5aNluyGnwtRkmeZ0YFVErxIHmy562ATlWCrQqoWwjQmNhTnUMwpHO1baY5hpJfhRkYxxyfbVK0VjDG+tC7r0bHBG4nmgEaLwHqTAfToNBjMjAawCFZnTOiBTc25JwyzMA4mTY/GR47LfPMgZqmycnLPdSVZZKdPlPcW91DKaiVh+Kx5AWJCtDb5kAjsSir6YAbIS/ThSTNzgKNILsysJXumsz1Xr6p4UsnWjnb1M9H761ie0X2gsea4SBoExtGgAJsLc+kvdc7Y6wrWRECVLBqWFmg1gXcNE2ef+M6f//mAN1qKYcc13E6Yv/4JbMATXPbZ5wsdMPgay818sZ4BdVqL49uW7qx0pbyLA5uyePHb7fTN9kiozW9iTwZFL0Yal095vgAenfjktcwF1pTA5LDg618D6cu32IiGOYD+0pmHLOnOpenjlfwg95iCAfRbpxe0TXMxVsXezj1fi91ZZkz2q4iBkRyTumqr2E1WVI2W9AHig2U9WW6U5zrYWA0MGN2aSEjoogQ121OQpohKqAxsqUsE4eqzHLDnZoDXbttuFrTmyBJqDV3zfnzoXGr9TF+M6BWK2G+07pe2Jn87IoW3JiIbCCagx1UFWfQMo8TbnPnKP0jAY7tuC37MzSBdv006uaDa6Lq7EpRyUx1zHLC7RnwY1fl6IbuaqLaZHLSU+YVoDB5HzExRrj94rLe62xTH9976yZHtxfw4IGSRY83/dYGg7xBWnBhKo7l9g2/ZEQIUMGqkeFxMDIeQjcM/uHlRn50qo27d2+i4q5HML0D6F1XY34tM+idkSOZTKS57EwEdbSwTlg3+N8/uMLpq33k7bsPJGnZrSsfu68amyLxzZ81Ex5ow+i/iVpXH9Msqa18H9hdK2oiYEyMEW59D1v1UUzZxqtnOykvSKWyKG3WsR86Wk7O5i3c0tMZu/DzFVvD7ZimyY9OtfK3P77KlrIMfvnhme12+kAbKCpyRmJnfpbLUrLZLOdXT9IZfC1GyWT0w/Q2XDmnDHO0d85qe0SA+rFu6jwuIUBjQbUpbK/I4nzLQDT2BkDOKAI1Zc0KUNMw0K69hVK8A9kztwHe8HgQl8NGij0+hlQRM5Vs1dq4dURbcIUAjQU5kvc70Ep1UTqDY0GGxmZHAZ241IPDrrCvZqbhnHbjNISDc84DJ4qiXPfsFtwhHwVZLuQ4nbMLs930zNOCC6CU7ERyZaA1vrHk9+js8/K1l66yeVMav/xwbUzXI2NSgG6UGVCA8sJUnA7bupgDFQJUsGpkeOwMjwf4yxcv03Chh8eOlvOph2pRK/ZZjp0xzv2Zhg6hiaSugIJ1M/N/fvA+Zxr7+Oi9Vdx/fAdKyc7J1pWl22pnpjp48q4KLt0YpPudV0C2oVbNjhOYC8lmR606Qrj1TPQGfLmEm0+BoaPW1XOldYieQT8P7C+Z84IiSRK/+qGtXLVtwzHaRl/ryrtmGobJt15t5vk3bnB4az6/9ZFdOB0zbyiNgTbkrGIkWZnnVZIDye7CtvnOstnMQPJ2FyxEVIBOd8LNLgNM9KGOWcdH8lB9hvV9FSZEsbO7OocRb4i2W+PRxyRZRsnbjN63Np1w9c5LmL7hBWf9hseDZKbFT/xFWgkzFEs02SdbcEUMS2xIqsMyv5o0IgJmteEGNZ13G/vYX5uLwz47+1PO2IScN3fkRyIozvHQPehHn5bxfGvQT2F2/M7ZBVkueof8MzaYpiPJCmrNMfSOixi+O6/MjftD/NnzF3E6FD779I5ZmazzYfqtCvdGasFVZJm60gzevzm85ufrF0MIUMGqkeFx4AuEOd88wLMP1PDUZLalJXoOE755NibHTjNk7bwl601y2mSe4F++eJkzTf187L4qHj5ktXqqtfWY/hH0jkvLeo/79xdTluMgpfMMcumeO6oWq3X1oIfRmk8taw0w6SDY1ICcuxklq5hXz3SS5rbPm+8KkGK3ceSxJwibMpde+f6cdu9LRQsb/NUP3ue1s508dLCEf/XYVmzKzNOeaZroA21Jmf85F2rdnWWzJXN7+0JkpjpwOWwznXAjRkRztOFGNmDGw5bwFC24sbOrKgdZkjh3extuXiXGYMeqBtUvFa2xASklFVvZnnmPGR4PkhmjT8FKEKnkeLCuedEKqGjBjRl50oioJM+DXZVnGRGdu9ZPIKRzbPvM7E99uAuj7zpq3fE11Q1SlOsmrBv0TWYaBzWdwdFAXBxwIxRkuQiFjagp11xMmRreWa53WDf46vcuM+IN8blndkYTFGJhIwpQgG0VWQyOBeibJwc+WRACVLBqFOW6sSkSn3liG/fvK57xM7XubsuxsyUG0RNIbpfOVLd1I9ve5+Xj91fz0MGpOUNb2S4kZ9qyXWAVWeZXdgRxSkFOazV39tycMuScMrSmN5a9o2b038AY7kKtq6d3yM/F64Pcu6cI1bbwqSa3IJ9QwXa2Gk38zQ8uzbvTeif4A2G+/E/noxXnj91XPWfLkjk+ACE/chIbEE3HymbLj7kNd70KUEmSLCOiaS24kisDyZk25xyoGfCBmoI3aFUahACNHY9Tpbo4nXPNc+SBmgb6ncwkxwFjYoxw23lsNceQlPnba4fHg3Gb/wSrIwW7E4fuRZEl7FIYU5JAEZ/FWFFyyjB9Q8ghL5sL02i5TYCeuHyL7LQUakpnihatsQFkBVv1sXgud1GKcmc64fYO+TEhLhmgESJuuwvNgcrpBSiFtWhNb97RfcS3X2umsX2EX3mkjorC2WM6C2H6R0GSk3Y8a6lE5kCvtCb3HKgQoIJVY19tHn/x+XoObsmf9TMluxQ5pzwmx85Ia1yy5hTmZ7rITHXw7AM1swbrJdmGrfoY4bYLGP65DRNiJWfgLD4ljW+/r0Rt2mNFra3HGOyYszJ0J2iNDWCzo1Ye4tWznSiyxD27Y5urzN7zATxyEL39PN9/c3k3rMPjQf7HN9+juXOUX39sa7TiPBf6ZCyHkp3cBkQRrGy2+piz2cygD5L0u7UYxXkeOvt80Q0NSZKQc8rmrYBGHHAB4YJ7h+yuzqGr3zdjV17JqwJA71tbc6Dh5hNg6gs6hYd1gzFfKK4CFCbnQCdGyUx14JDCSLaUNVWRW+tENhKNgTaqijPo6PMSCFmV5OHxIFdahzi6vWDGZqSphwk3n8RWtgfZeWciaLUpzHYjMeWEG5nFjHcF1Hrvhe8r1Np6zLFe9J6mmF73F+e7eP29Lh4+VMqR7QWLP+E2DP8IkjMtKbO7l0N+ppOsNEfSz4FurL+aIO7Y1fl7+dW6eoyhDoyB1gVfI9ljIjxOlS/922OzqsAR1LrjYOpo1+4sv3E6xlg/etf7uLbdg1218Y1Xrt3RLqRadRgUdVmVWFMLoF1/B9vmgwRMlbcu9XBwS37MUTtK8XYkdyYfzGnnhydbOdPYt6R19Az6+KN/PEv/6ASf/8gujmxb+MJmDLSBJCNnzf33SUbuJJvNMvhKzu/WYpTkeQhqOgPTRVF2GcZwN2Z4po19xIwpIkBFBfTO2FOdA8D5aVVQKcWDlF6A0bt25kBN07Rm/fKrUDLn3xwb9YYwIS5Oo9ORXBkY/hGy01JIkcPCgOgOiWwk6oNtVBenY5gmN7rHADj1/i1ME47e5n4bbjuHGRhfNLosEThUhdxMZ9QJt2fQhwTkZy3dPf9OSXPbcTqUBSugALbN+0F1xmRG1NQ+zDdfucaOzdl8+O6lzdyaExsnA3Q6kiSxtTyLxrZhDCN550CFABUkDLXykCV6FmkVnAqIX583yUrGJpT86sk8zqWdTLRrbwISnu138/Tdm7naNszpq7ELOMnhxlaxH63llJWttQTCN94FLYBaV89bF3sIhnQ+sD92USfJMmrtcQqCrewqlPjaj67MmN+Lhevdo/zxN95DC+v8zif2sK1ibnfL6egDbciZm6z2t3VCrNlsZjgE4dC6/W5FjIg6+qZ27uWcMjB1jOGuGceaQR9SihvfhIbDrizaNi6YSV6mi6Ic9+w4lrxK9L7ra8Yww+htwRjpWdB8CKYyQONdAZVc6Zj+UfIynXhsBggBekdIDjdSai7GQBuVm9KRgJbOUUzT5MSlHqqK08nPnFk91JoakNxZKMXbE7PoRSjKcUcroLeG/ORkpMRs1LMSSJJEQZZrUQEq2RyT/h4LmxoOjE7wlRcvk5vh5DOPb0WWl1bhN/0jG8oBdzpbyzPxBcK09Y4vfvAaRVxhBQljSvS8vaDomRKg67NNECZztEZvofc23/FzTcNAa3oLpXgbsiebe3YXUVaQynd+3nxHhj5qXT2EJgjfOHPHa4BJB8H0AqTcKl4720lVUfodz3SoNccBk0/XDONOUfnz5y/GHLh8oWWAP/nWOVwOG7//3D7KC2J7b2OgLWpOs55QaxfPZkt2g6/F2JTjRpKgo2/qIh0xm5o1BxrwRltwhQPu0thdncO1jtFo/jNYc6DmxBjmeP8Cz4wfWlMD2Byomw8ueFw0AzSOJkRgVUBN/yhPH6+gttCJZBMOuHeKkl2KPtCOK8VGUa6b5q5RWm+N0zPo59htrZ6GdxC94zJqzTEkeW3eEhfleugdmkALG3QPxNcBN0JB1sJRLBEsU0MN7fo7c/58Ihjmz5+/hG6YfO6ZHbiWca41/aPR6KKNxtYya3P9/ZvJ24a7Nr9tgg2DWnc3aAuLnqhTrj1+Mw/xxrb5AKgpS8rj1LsuY/qGotllsizx3IO1jHlDfP+t2GcplcI6pLS8JbXh6iPd6L3N2GrruXRziL6RiTuqfkaQ03JRiraitJ7ks09tY8Qb4qvfu0xYNxZ83psXu/nz5y9RmOPm957bR15mbJ8Vwz+COTEaDTBfTyilkWy2+f+eZmB9b+44VIX8TBed03L0pNRcsDtnzYFOnwEV7bdL4/jOQkxMfnZmKuZGyY/MgSa+DdcMTaBdP41aeQjJvnALY7QCGscYFgDZlQ56iDSHQYqiixbcJSDnlGGO9WKG/FQVZ3C9a5S3Lvag2mQO1M30pNCuvQWYa7L9NkJxrhvDNOke8NE77I+aAsWTgmwXw+NBgqGFI+PknHLk7JJZ1x3TNHm3sY//62vv0Nnv5TOPb1uWkDYNHXNifEO24ILVFl2S50nqOVAhQAUJRSmsRUrLX1D0mEEv2F1rdndyJZDUFNTKQ4RvnMYM3Zm19lxxAps3pVG/exOvnumckYO44BokCbX2OHpPE8Zo7x2u4U2QFNSao7x6poPMVAd7a3Lv6DUiqLX1mOMDlBhdfPqRWhrbR/juz+c2MTFNk5dOtvJ3P25kS1kGv/1Le0h3x95KGxEh8joxIJrOVDbbhXmz2aYMvtZnBRQsI6LpFVBJklCyy6LmU2B9jiwB6pkUoPM7owrmJy/Txf7aPH5xrivafSFnFoPNgd6beCMi7cZp4Lv+hAAAIABJREFUCAejm3ULMTwexK7KuBzx/SxITqul0PCPYGoB0YK7BCIbivpAO9VF6QRCOg0XutlTnYMrZervaZoGWtObKEVbkdOWdr2KB0WTjrcXbwyihY2EVEAjpkeLtuFOmuAZA63RLpOuAR9/+p3zfPV7l/E4VX732b3srMxe1nrMiTHA3LAtuADHdhQu6LOy1lm/d/SCpGCm6Lk15zHrNSbidtS6egiH5m1dmQsrTuActuqjSLdZ9T9zdyWuFBvfeKUp5vkrteYukKSYzGsimEaYcPMJbGW76PGrXGkd5t49RbPyNmPFVr4XHG60pgaObi/koYMlvHa2k4YL3TOOMwyTb/2smRcabnB4az6/9ZFdOO/wZjFygVwvDri3s1g2W1SArlMTIoCSXDf9I4EZ7ehyTpmVT2lM7uZrATCNaAVUOOAunYcPlTIR1HnjvPV9lWQZJW8z+howItIaG5AzNiHnLW56YkWwxN+BNlLRMf2joAVFBugSkHOs87kx2EZVsSVQdMPk2I7bsj+7GzHHB9Z09RMgP8uFIkucnTTmK4ijA26EghiiWCKoVUdAsTFx5Q2+81oz//ffnqbt1jiffLCGP/j0AaqLl1+1NP1WvM5GFqAPHijh8x/ZlehlLBkhQAUJx3LsnF/0WOYg67NFcDpy7mbkzKI7asMNN58EY+44AY9T5cP3VHKtc5STl+cW97PW4M5EKdk5aV6zcKtNdA1tFzAnxlBr63ntTAc2RebuGKNX5kKy2VGrjhBuPYsZ8PLheyrZVpHFP/60KZrppoV1/ur7l3ntvU4eOljCv3ps65IErzHQhpSev2g7XrKyaDZbkmfsxkJJXiowFWMAk3OguhaNqZleCfaJFtxlUVGYxpayTF55tz3aOq/kVVqCf4kGZyuBPtyF0Xcdte54TKJyeDxIpif+xmTTBagZDooK6BKQXRlIrgz0gTZy0lNI99hJ99jZVj7TlE5rfAMcbmvTcw1jU2QKsly0T3YzbYpjBmiE/EwnEotHsQDgcDOSuQ3f1RO8/m4rx3YU8kefOcx9e4uXbDh0O+ZkbN1GnQFdDwgBKkg4UdHTNLfoMQMbowIabV3pv4E+1Lno8aZpojU1IOdtRskqmvOYu3YWUrkpjX9+vQV/QItpHWpdPaZ/BL1jfvOa6WhNDUiuDAK5dZy8fIvD2/JJdS3vxs0yMgijtZxCkWV+84ltZKen8BcvXqKr38v/990LnGnq56P3VvGx+6pnZLrdCfpgG0r2+jMgms5C2WwbweCrOM86d0x3VI6YTkVasCO/B9Puxh8ICwG6TB45XMqIN8Sp962NLyW/Ckwdvb81YWvSGhtAUrBVH4vp+OHxAJmp8TcAkicrOuZkC64wIVoacnYpxkA7kiTxS/dX86mHameIHzPgJdx6FrXqcFI4oBflWucxj1NNyPlJtSlkp6csWgFt7x3nf3zzPb55IxeXFOS/3m/j04/UkbbMe4LbieSmb9QZ0PWAEKCCNYFadzfmxCj6HI6dG6UFF8BWcxRkJaYqqNF3HWO4e8H2IVmS+OSDtYxPaLzQcCO2NZTuQnKmxbYG3zB6x0XUmrt481IvobDBA/tLYnqfhVCyS5FzytEarWgad4rK557ZSVDT+a9/e5qWrlF+/bGtPHxo6a2zZsCLOT4QDS5fr0Sz2eaYszaDPpBkUNfvTW52WgpOh23GLLScXgiKPdqCHTFjCkoOTEQG6HLZVp5FSZ6Hl99pxzBN5LzNAAlrwzX1MOHmk9jK9yA7F3fHNkyTEW+IrDgbEAGW2Z6iWjfYWlCYEC0RJacMY6QLMxzk4JZ89lTPnPHUWk6BHraMEJOAyBxoIgyIIhRkzx/F4gtofOOVJv7w79+lZ9DP0Q/cg+TJIbPv3VVZS7QFN4bvs2BtsiwB+txzz/Hoo4/yxBNP8MQTT3DhwoWVWpdgg2Er3WmJnjlvkr0bRoDKKanYyvcSbj6JqS9csYzGCVQeWvC4soJU7ttTzOvnurjZM7boGiTZhq36GOH2C9FdxnnXcO0tME3k6mP8/GwndaUZ0ezF5aLW1WMMdWAMtALWBfgzj28jL9PF5z+yiyPbChZ+gUXQB9sBUHLW5/xnhGg22413Z2WzRb5b8Z5ziyeSJFGS66ajf0qASrKMnF2CMTizAuo3rZt9IUCXhyRJPHKolJ5BPxdbBpGdaUhp+Rh9iTEiCredwwyMxzzrN+4LoRtm3DNAwfrdSa50TO8QGGHRgrtE5JxyME2MObqJot1DOeVJM/9flGtdVxMpQAuz3Nwa8mNMG+cwTJOGC9383v9+m9fPdXHfnmL++DOHqd9djFp3HL3rCsZY7JnksWL6R8DhnuV9IUgelixATdOktbWV73//+9H/du1K3mFYQWKRZBtqzV2zRI9pGhD0bxgBCpMtk0Ev4dZz8x5jagG066exbT4Y0/ziU/UVZHgc/K9/vkD3wOIzHPa6ejANtGsn51+DaaA1NqAU1nGxT2FwLMj9+5Zf/YygVh4CxT6jEru7Koc//o3DbKvIWuCZsRF1wF2HGaC3M18220bpLijO89DZ551x46TklKEPtGOaRnQG1GcIAbpSHNiSR3ZaCj9+Z9LoK78Svfd6zIZoK4nW1IDkzkIp3h7T8UORCJY4Z4BGkFwZGGOWE7lowV0akY3FWXm/WOd+Y7AjJjfktULx5MbuppzEjUsUZLsIaQYjk9+Pmz1j/Pevn+Hvf9JIYbaLP/j0AZ59sAb3ZLanWnMXcGemhrFiTmzcDND1wpIF6I0bVjvfr/7qr/L444/zjW98Y8UWJdiYWI6dxkzHztAEYK7rGbXbUYq2IbmzFoymCV8/DVog5guoK0XlP318N5Ik8SffPrfoHIecUYhSUIPW1DDvDaPe04Q53o9aV8+rZzrJTkthT3VOTOuJBcnhxrZ5P1rL26tiXqIPtiF5spFTUlf8tdcack45ctYc2WxBH2wAg6+SPA+BkM7gaCD6mJxTBtoE5lh/tAI6FrZclIUL7vJRZJmHDpbQ0jlKc+cISl4l5sQopncgruswvIPoHZdRa47FHOWVqAzQCLIzHWN0smokKqBLQvLkgMM9K+8XJruHFHXR7qG1RF6Gk889s4PjOwsXP3iViLjvNneO8vc/ucp/+4czDI0F+fUPbeV3n91Laf7Ma6nsyUYp2Y527QSmsXCW951i+EfE/GeSs+SAq7GxMY4cOcIXvvAFNE3jU5/6FBUVFRw7FtuAf3b22r/pyc1d/zema4rcVLpLtqC3vEXOBz6GJElowz68QFpuDqkb6O8xtOd+Rt76FzLsE6jpedHHI5/Jrh+fQM3eRMH2PTG3T+bmpvJH/+YYv//VE3zpu+f5439zF4ULuOmN73+A/pe+QmqgE2fp1lk/7zt5CtnhIlC0l6aOk/zKh7aRn7+y8xgThx6mp/kkzoHLpO64Z0Vfu2O4A+emzRvmez66/wEGX/lb0vQBHAUVAHSGJ7ClZi37d7DWf4c7avLg5SZGAzpbJ9caDG+lqwHcWh9BWUNTHUgOq9pUWpRBbgKy9tYbT91Xww9PtvLzc9385w/upOsEuCe68GzevOrvHflMDjf+BB8m+UceRs2M7XOqNfUDUFWenRAjooHsXMZarTzo9KwMPGv8+7VWCRduxhztnHF+MrQgbdffxrPlCHnFyxvjWArLOVc+mODPgeKwNub+9w/eR5Elnri7kl96sBZXyvwbdt4DD9H3wp/iGb+Oq2rl3IYnAmOk5Jes+WtPspCI3+OSBeiePXvYs2cq+P7DH/4wb7zxRswCdHDQi2HEvxUnVnJzU+nvH1/8QMHKUnkM7Rdf49als9gKa9H7rDYkb0ghsIH+HkbJQeBf6D31Uxz7ngSmPpP6cDfBziYchz7KwIB34Re6Daci8R8+upsvfus9fu8rb/I7z+4lJ33uFl4zdyeoKfS/8zJO58zWWjPow3v1FGrtcb77i5vYVZm9VVkr/p0xnSVIafkMvfsKgYJ9K/e6WgBtsBup/OCG+Z6bBXtB+Tp9b/+ElGPPAaD5xjDSNi3rd5AM50q3TUYCrrT0U1VgbX6aUgbICiM3GjH8Y2B30zNpVBSaCNHfv7I79huVe/cU8YMTrTQdLsZjszPc8j4TebtX9T0jn0nTNPCdew2laCsjYTfE+Dnt6BlDkSXrcxCje/hKEpSm5vzGJ0wm1vj3a62ipxWhXX6Vvt5hJNm63dWuncAM+tHLj8b9vJUM58qFME2T4lwPqS6VTzxQQ1GOG994AN94YP7nZNYhpaQy8M7LONOrV2wdYe8IIdmd1L/PtcJqfi5lWZq34LjkFtwzZ85w6tSp6P+bponNtmQ9KxAAYKs4AGpKtFVwKiZiY1Uj5NRclKKtVn7jba0rWtOdxQncTkmeh//08T1MBHW++K1zDI3NffGQVAdq5aR5TWhi5hpa3gZdI1R2hLff7+Xo9sLo3MdKIkmSZWTQ04QxGluWaSwYgx2Aue4NiKYjpXiwle+bbGkOARtnBtRhV8jLdM40IlJU5Mwia0Zs0ozJO6GhyBIpdiWBq11f3LevGLtN5ifvdqHkVqD3xs+ISO+6ijk+ELP5EIB3QqN7wEdmqmPJ8U7LRZqMYgFEC+4yUHLKwQhjDHdHH9OaGpDS8lEKaxO3sCRFkiT+n187yH/+pT1RV95Fn6PYsFUfJdx2HmNicRPEmAj6wAgjOdMXP1awZlmyAB0fH+eLX/wiwWAQr9fLiy++yAMPPLCSaxNsQGaKHv+Ua2fK+r9Jvh21rh7TO4jefSX6mKmHCV87ga1sdzQvbimUFaTyHz++G19A40++fY4R79wzlmpdPYRDs8xrtMYG5OxS3uhQCesG9+8rXvJaFkOtuQskeUWNDPSoAVH5ir1mMqDW1kPQR7j1PUwjDFoAaYN8t4rzPDOyQGEyqmGgLSrEvRMaHqe6rl2B402ay85dOws5dfkWWkYFxkB7dANktdGaGsDhxlY+s/UvpOl09nk509jHSydb+ZuXrvDf//EMn/ufDfy7//Um51sGyMtc3NxttZhuriKt44ik1Uae3GCMzIEao1Yeslp7XHzH44haVw+mTrj5xOIHx4ARiWBZxj2QIPEsuWR57733cuHCBZ588kkMw+ATn/jEjJZcgWCpqHX1aI2/QGt5B0wdYEOZEEWwle8FhxutsQHbpHtjuP28FSdQd3zZr19RmMa//8huvvRP5/mTb5/jtz+xl3T3zLBoObcCObMYrfEN7FvuASzxZgy2oR55ltff6GRbeWbMu6FLQXZloJTsRGt6C/v+p5Hk5VenjME2JGfahjMxUIq2IKXmoDU1oBRZc70boQIKUJLr4b2mfgKhMCl269In55RhNr2JPmxgK6zD59WEA+4q8ODBUl4/18W5kTT2mTr6QBu2gpVpx5sP3T+OdvMs45sO8fb5XnqH/PQO+bk15GdoLMj0AaDMVAf5mU4ObMmnINNJfpaLquLE3dzOOC/ZRAV0qchpBWBzoA+0odYetzYxJQm1ZmndQ4KloWQWIedVojW+ibrj4WWLf3MyKWGjXb/XG8vqmf385z/P5z//+ZVai0AATBM9TQ3YyqxZIcmRuOyrRCEpKmr1UbQrr2MGvEAqWmMDkisDpXjHirxHVXE6n//wTr78zxf40++c47d/aQ+prikRGmmBDZ76NvpQB0rESVWxccmsYsR7k19+uG5F1rIQal09gfbz6O0XsZUvf6NLH2hDzi7dcLvgkiSj1hwndPZFjP5W67ENsrlTkufBBLoGfFRussSFkj0ZwRP0IaW4Ge/XhAPuKpCX4eRAXR4/bulknxuM3hZYJQHadmuc//b1s5QOv8NTzjB/9X4mXRebcToUCrJcVJdkUJDloiDLRX6mi/wsZ3RDYq0wvbIjiRbcJRPN+x1owzR0tGtvoZTsRHZnJnppGw61rp5gw99h9LagLPO7HxGgIoYluVlyC65AsFpYoqceo/8mencjqClRA4GNhlpbD0YYrfkk4bFB9M5LqDV3rUgVMEJtaSa/9cxO+oYn+NJ3zuOdmGm6Yas+CrKC1tiAGQ6htZzCVr6fVy4MkpfpZEdl9oqtZT5spTuRnGkLRtPEiqlrGENd1nzQBkSttbLZQhd/AmycCmgkR296G66cXQpYmxCSw4NvQlRAV4tHDpUxELQzYc9ctTnQUV+IP3v+IiPeAPent+J3F/Hcxz/Alz93F3/x+Xq+8MsH+I3HtvH4sQoObsmnrCB1zYlPACklDSY3x0QL7vJQcsrQB9sJt1/A9I+g1t2d6CVtSNTNB8HmWJFruGjBXR8IASpYk6jVR0G2oXdf3TA3yHOhZJcg51agNTYwfvF1MM1VCc/eUp7F557ZQfegjy999zz+ac6PckqqZV7TfJLw9Xcg5Gcwbz/Xu8a4f19xXIw6JNmGWnMX4fYLGJO7n0vFGO4CU4/OB200ItlsevdVYOMI0Jz0FFLsCp3TBKikOpAzJqMYojOga0+QrAfKClLZWp7JtYkswr0t8+YLL5WwbvDVFy/hndD4whMFpIX6yNxzPzUlGaS77UnV7SDJsiVCAWz2hQ8WLIiSXQbhIKF3X0BypmEr3ZnoJW1IJLsTtfIg2vXTs0wN7xTTP2J9L8TmTFIjBKhgTWI5dlrGERulRXA+1Np6jOFORt7+PsqmLchpeYs/aQlsr8jm3z61g84+L1/+pwtMBMNTa6izzGsCJ7+FlJrLT25aN/N37YhfKLZaWw+mgXZteUYGEQOijVoBBWa4gkopG+P7JUnSnEZEco7VhjtlQiRu+FeLRw6VcS2QDROjmL6hFX3t77zWzLXOUT79SB3pPe+AoqJWHlrR94gnkisDJBkUUZFfDpHvtzHcOdk9JDaYEoVaWw/hINqN08t6HXNiFMmVkVSbSoLZiG+iYM2i1tUTvnF6w7h0zodadYjgqW9jBv2otcs3H1qIXVU5/Osnt/PV713mf/7zBf79R3eRYrehFG1F8mRbrrxbHuB0Qz/37inC6YjfKUTOKEApqEFrbECZvKlYCnr7RbA7kVJzV3B1yYWtbA9SSipmYHzDVEDBMiJ6+8otTNOM3rwoOWWEW95GU5zohk+04K4iW8szeSO9FMKnCd9qxl61Mu37b17o5ufvdfHQwRIO12bi/eZb2Cr2J/VnW3Klw3ifuMleJnJmEcg2MMKrfv0ULIycX4WcUYh25efInqV/942RHjH/uQ4QAlSwZlGKtiKl5S/rRLUekOwu1KrD6B3nsVXsX/X321uTy288vo2/+v5l/uxfLvJbH9mFQ1VQ6+4m9N4POOnfjGEMcf/+1YtemQ91yz0EXv8/TPz4T5f1Okrx9g19YycpNtTa44Suvg72xMVNxJuSPA+vn9MZHAuQk279u5WCGgACtnTAh1u04K4akiSx7+BeQideZKTpMiVVh5f9mte7R/nHV5rYWp7Jh++pRGv8hbVZl+SzfnJGIaZ3MNHLSHokxYaStxlkBTkjfh07gtlIkoS65R6Cp7697Gu4TTgZJz3iSitYs0iSjPvJL4AiPqaOY58kK/VTDE/E53dxoC4PXd/KX//wCn/+/EV+68M7se9+FGnzUX769avsqMwmPzP+zsS2qiO4MjaBri1+8ALImZtWaEXJi/3AM6jbH0CSNs4kxnQjoqgAzavE/YkvMTCuAt2iArrK7N9awNUTeTi6m5f9WiPeIF954RIZHge/+cR2ZCPMxHs/wFFch1JYuwKrTRyOA0/DnscSvYx1gfOh37LamQUJR932AZT8ajD0Zb2OnF2yQisSJApxZy9Y02yU+bTFkGx2bJ5UmBiP23se3laAbpj87Y+u8hcvXOazT+/gTKfGmF/jgf2JOflLkoSSW56Q915vSLKCtMHiCCJ5tZ19XvZUT7Vgy55sfH1WtUkI0NVFkWXUgipybp3gWms/NeVLa4XXwgZfefES/mCY//LcfjxOldDFn2L6R8h6+j/gTfIOB8nmEBmgK0Qyt2KvNyRZsSrSgg2P2BISCATzcmxHIZ96uJZLNwb56vcu87N3OynMdrG1fGMJF8H6wOmwkZfhnGVEBETjh4QAXX1Kt+3EJhm8d+rskl/jW69e43rXGL/26FYr41ULEDr/EkrRNpxl21ZwtQKBQCBYaUQFVCAQLMjdu4vQDZNvvHINgOceqt3Q85OC5KY4z0NHv2/W40KAxg/HpmrCQKi3ha4BX7QyHSu/ONfFG+e7efRIGQfqLFfw0OWfYQbGcex/ahVWLBAIBIKVRFRABQLBoty3t5hnH6ihpjido9sKEr0cgWDJlOR56BvyE9RmziB5JzQkwJ0iBOhqI7sywJ3NZrWfn77TfkfPvdYxwjd/do0dm7N56rjVymcGfYQu/ASldBdKftVqLFkgEAgEK4gQoAKBICbu31fM735yHw67kuilCARLpjjXgwl03VYF9U2EcaXYkGVR3Y8HtoJqalKGOPX+LYbGAjE9Z2gswF9+7zI56Sl85vGt0b9V6NJPIeTHsf/p1VyyQCAQCFYIIUAFAoFAsGEoyZs0IuqfOQfqDWi4Rftt3FDyK3Hq46Th49UznYser4V1vvLiJYKazmef2YlrslJtBMYJXXoF2+YDy8oHFggEAkH8EAJUIBAIBBuGnAwnDrsyy4jI6w+J+c84ouRVAnB/eYhfnO/CH5g/Wsk0Tb7+chM3e8b59Q9tnTEzGjr/IwgHse8Ts58CgUCQLAgBKhAIBIINgyxJFOe6ZwvQibAQoHFEzi4FReVA1jiBkM7r57rmPfa1s52cuHyLx4+Vs7dmKrbF8I+gvf8atqojKCLbVyAQCJIGIUAFAoFAsKEoyfXQ2efFNM3oY94JTQjQOCIpNpTcClzedraVZ/LqmU608Oxw+sa2Yb7zWgu7q3J4/K6KGT8LnfshGAaOfU/Ga9kCgUAgWAGEABUIBALBhqIkz4M/GGZoLBh9zBsQAjTeyHmVGANtPHKwiFFfiFPv9874+eCoZTqUn+Xk1x/bijwt/skYH0C7+gvU2uPIaXnxXrpAIBAIloEQoAKBQCDYUBTneQDomDQiCusGwZAuTIjijJJfCUaYGtcYZfmp/OSddgzDqkoHNZ0/f+EiumHwuWd24nTMjC0PvfcDQMK+97EErFwgEAgEy0EIUIFAIBBsKIpzLQHaOTkH6p2wDHBEBTS+RDI7jb7rPHK4lN4hP+eaBzBNk394uZGOXi+/8dg2CrJcM55njN5Cu/YW6tZ7kT3ZiVi6QCAQCJaBbfFDBAKBQCBYPzgdNnLSU6JGRF6/EKCJQHZlIHmy0fuus+++B8jNSOEn77TRPzLB2+/38lT9ZnZV5cx6XvDs90GxYd/9aAJWLRAIBILlIiqgAoFAINhwlOR5olmg0QpoitiTjTdKXiV6bwuKLPPQwVJudI/xz6+3sK82lw8dmZ3rqQ91Em55G/u2DyC7MhKwYoFAIBAsFyFABQKBQLDhKMnzcGvIT0jTpwSoy57gVW08lPwqTN8Qhm+YYzsKSXPb2ZTj5tce3YI0zXQoQujMi6A6sO/6YAJWKxAIBIKVQGz3CgQCgWDDUZzrwTSha8CHNyBacBNFZA5U723BsfkAf/DpAzgdCin22bcnen8r4daz2Pc+gZTiifdSBQKBQLBCiAqoQCAQCDYcJXlTRkS+qAmR2JONN3J2KSg29L7rAGSmOuYUnwDBMy+Aw41950PxXKJAIBAIVhhxtRUIBALBhiM304ldleno86IoEnZVRrUpiV7WhkNSbMg55ei9LQsep99qRu+4iP3gR5DsrgWPFQgEAsHaRlRABQKBQLDhkCWJ4lzLiMjr10T7bQJR8qswBlox9fC8xwTPvIDkTMO+7QNxXJlAIBAIVgMhQAUCgUCwISnO9dDR58U7oeFJEQI0USh5laCHMQbb5/x5uOsKevdV7Ls/hKQ64rw6gUAgEKw0QoAKBAKBYENSkufBFwjT2e/F4xICNFFMNyK6HdM0Cb77PJI7C3XLPXFemUAgEAhWAyFABQKBQLAhiRgRDY4FRQtuApHdmUjurDkFqN5xAaPvOva9jyPZREyOQCAQrAeEABUIBALBhqQ4dyrKwy0EaEJR8quiTrgRTNMg+O6LSKm5qLV3JWhlAoFAIFhphAAVCAQCwYbElWIjOy0FQMyAJhglrxLTO4jhH4k+Fr55FmOwDce+J5FkYdovEAgE6wUhQAUCgUCwYYm04YoW3MSi5FcCU3OgpmEQOvMickYhtqojiVyaQCAQCFYYIUAFAoFAsGEpFgJ0TSDnlIFsQ++12nDD19/GGOnGvv8pJFncqggEAsF6QpzVBQKBQLBhiVRAxQxoYpEUFTmnDKPvOqYRJnj2e8jZJdgq9id6aQKBQCBYYYQAFQgEAsGGZefmbB49UkZtaUail7LhUfKr0Ptvol19A3OsD8f+p5EkcZsiEAgE6w1xZhcIBALBhsVhV3jm7kocqpLopWx4lPxK0DWC73wXOW8zSunuRC9JIBAIBKuAEKACgUAgEAgSjpJnGRERDuHY/wySJCV2QQKBQCBYFYSvuUAgEAgEgoQje7KRUnORPdkoRVsTvRyBQCAQrBJCgAoEAoFAIFgTuB77XSQ1RVQ/BQKBYB0jBKhAIBAIBII1gezJTvQSBAKBQLDKiBlQgUAgEAgEAoFAIBDEBSFABQKBQCAQCAQCgUAQF4QAFQgEAoFAIBAIBAJBXBACVCAQCAQCgUAgEAgEcUEIUIFAIBAIBAKBQCAQxIVlCdAf/vCHfPCDH+TBBx/km9/85kqtSSAQCAQCgUAgEAgE65Alx7D09vby5S9/mRdeeAG73c7HP/5xDh06RFVV1UquTyAQCAQCgUAgEAgE64QlV0BPnjzJ4cOHycjIwOVy8dBDD/Hyyy+v5NoEAoFAIBAIBAKBQLCOWHIFtK+vj9zc3Oj/5+XlcfHixZifn53t+f/bu4OQqNY+juO/SU0KjBBmNEJaRIsQrCgqJRRbjOE9OqeFAAAGKUlEQVRxUkRIgmbhKogEN2UltYrMBElqGUYkRC1ClBIhaJNDoZRuXEhlZVlNWtnUNDM5z90Ndl/e973XW+eZ63w/u+cwzPkvfpwzvzkPnKWe2jVeb57tEYCfkEmkI3KJdEMmkY7IJdKRjVwuuYAmk0l5PJ7U2hjz0/r/mZ2NKJk0Sz39b+f15ikc/mJ7DCCFTCIdkUukGzKJdEQukY5+Zy5XrPD81weOS96CW1hYqHA4nFqHw2H5fL6lfh0AAAAAYJlbcgEtKytTKBTS3NycotGohoaGVF5e/itnAwAAAAAsI0vegltQUKCWlhYFg0ElEgk1NDSopKTkV84GAAAAAFhGllxAJSkQCCgQCPyqWQAAAAAAy9iSt+ACAAAAAPB3UEABAAAAAK74R1tw/4kVK/76K1ts+TfMiMxCJpGOyCXSDZlEOiKXSEe/K5f/63s9xpj0fRknAAAAAGDZYAsuAAAAAMAVFFAAAAAAgCsooAAAAAAAV1BAAQAAAACuoIACAAAAAFxBAQUAAAAAuIICCgAAAABwBQUUAAAAAOAKCigAAAAAwBUUUAAAAACAKyigf9Lf36/q6mr5/X719vbaHgcZLBKJqKamRtPT05Kk4eFhBQIB+f1+dXV1WZ4OmejSpUtyHEeO46ijo0MSuYR9Fy9eVHV1tRzHUU9PjyRyifRw/vx5tba2SpImJiZUX1+vqqoqnTp1Sj9+/LA8HTLNoUOH5DiOamtrVVtbq7GxMXu9xyDl7du3prKy0nz8+NF8/frVBAIBMzk5aXssZKAnT56YmpoaU1xcbF69emWi0aipqKgwL1++NIlEwjQ1NZn79+/bHhMZ5MGDB+bAgQMmFouZeDxugsGg6e/vJ5ew6uHDh6axsdEkEgkTjUZNZWWlmZiYIJewbnh42OzatcscP37cGGOM4zjm8ePHxhhjTpw4YXp7e22OhwyTTCbNnj17TCKRSB2z2Xt4ArrI8PCwdu/erbVr12r16tWqqqrS4OCg7bGQgW7evKkzZ87I5/NJksbHx7VhwwYVFRUpOztbgUCAbMJVXq9Xra2tWrlypXJycrRx40ZNTU2RS1i1c+dOXbt2TdnZ2ZqdndXCwoLm5+fJJaz69OmTurq6dPjwYUnS69ev9f37d23dulWSVF9fTybhqmfPnkmSmpqatH//fl2/ft1q76GALvL+/Xt5vd7U2ufz6d27dxYnQqY6e/asduzYkVqTTdi2adOm1I+nqakp3b17Vx6Ph1zCupycHHV3d8txHJWWlnK9hHWnT59WS0uL1qxZI+k/7+Fer5dMwlXz8/MqLS3V5cuXdfXqVd24cUNv3ryxdq2kgC6STCbl8XhSa2PMT2vAFrKJdDE5OammpiYdO3ZMRUVF5BJpobm5WaFQSDMzM5qamiKXsObWrVtat26dSktLU8e4h8O2bdu2qaOjQ3l5ecrPz1dDQ4O6u7ut5TLblbP8SxQWFmpkZCS1DofDqS2QgE2FhYUKh8OpNdmEDaOjo2pubtbJkyflOI4ePXpELmHV06dPFY/HtXnzZq1atUp+v1+Dg4PKyspKfYZcwk137txROBxWbW2tPn/+rG/fvsnj8fx0rfzw4QOZhKtGRkaUSCRSf4wYY7R+/Xpr93CegC5SVlamUCikubk5RaNRDQ0Nqby83PZYgLZs2aLnz5/rxYsXWlhY0MDAANmEq2ZmZnTkyBF1dnbKcRxJ5BL2TU9Pq62tTfF4XPF4XPfu3VNjYyO5hDU9PT0aGBhQX1+fmpubtXfvXp07d065ubkaHR2VJPX19ZFJuOrLly/q6OhQLBZTJBLR7du3deHCBWu9hyegixQUFKilpUXBYFCJREINDQ0qKSmxPRag3Nxctbe36+jRo4rFYqqoqNC+fftsj4UMcuXKFcViMbW3t6eONTY2kktYVVFRofHxcdXV1SkrK0t+v1+O4yg/P59cIq10dnaqra1NkUhExcXFCgaDtkdCBqmsrNTY2Jjq6uqUTCZ18OBBbd++3Vrv8RhjjCtnAgAAAABkNLbgAgAAAABcQQEFAAAAALiCAgoAAAAAcAUFFAAAAADgCgooAAAAAMAVFFAAAAAAgCsooAAAAAAAV/wBbxghkedo2KcAAAAASUVORK5CYII=\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
@@ -2732,7 +2746,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
--
GitLab