From a481df0ec3572f769da653df67b39b9fd3c4df11 Mon Sep 17 00:00:00 2001
From: Konrad Firley <konrad.firley@student.reutlingen-university.de>
Date: Wed, 12 Jun 2024 17:14:36 +0000
Subject: [PATCH] Improvement insurance fraud detection
---
Insurance/Insurance Fraud detection/README.md | 2 +-
.../notebook_1.ipynb | 192 +-
.../notebook_2.ipynb | 4 +-
.../notebook_3.ipynb | 6397 +++++++++++++++++
4 files changed, 6578 insertions(+), 17 deletions(-)
create mode 100644 Insurance/Insurance Fraud detection/notebook_3.ipynb
diff --git a/Insurance/Insurance Fraud detection/README.md b/Insurance/Insurance Fraud detection/README.md
index 1f35d99..478d50d 100644
--- a/Insurance/Insurance Fraud detection/README.md
+++ b/Insurance/Insurance Fraud detection/README.md
@@ -1,4 +1,4 @@
-# Insurance Fraud detection
+ # Insurance Fraud detection
>see __German Version__ [below](#German_version)
diff --git a/Insurance/Insurance Fraud detection/notebook_1.ipynb b/Insurance/Insurance Fraud detection/notebook_1.ipynb
index 1a33a8f..6d9ac59 100644
--- a/Insurance/Insurance Fraud detection/notebook_1.ipynb
+++ b/Insurance/Insurance Fraud detection/notebook_1.ipynb
@@ -15,13 +15,29 @@
"# 1. Business Understanding"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a526e65c-4cd2-42af-b98f-f711b45e0274",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Title",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Insurance Fraud detection - Versicherungs Betrugserkennung "
+ ]
+ },
{
"attachments": {},
"cell_type": "markdown",
"id": "135c407f",
"metadata": {
"editable": true,
- "include": true,
+ "include": false,
"paragraph": "BusinessUnderstanding",
"slideshow": {
"slide_type": ""
@@ -29,14 +45,36 @@
"tags": []
},
"source": [
- "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?"
+ "Versicherungen verfügen über eine Vielzahl von Daten und kreiren täglich neue. Unter diesen Daten sind auch sehr sensible Informationen wie Name, Geburtsdaten, Adressen 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?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ca514ca-c655-462d-9b0c-3b36d1e0f95f",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Business",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Für jedes Versicherungs-Unternehmen ist wichtig nur begründete und gerechtfertigte Summen aus zuzahlen. Für Versicherungen ist daher eine Einschätzung der Legitimität von Schadensansprüchen, essentiell. Problematisch sind hierbei die gezielten Betrugsversuche (= Fraud). Soweit sich das Risiko Betrugs erkennen lässt, können Gegenmaßnahmen eingeleitet werden. Die Abschätzung der Wahrscheinlichkeit, mit der ein Kunde einen Betrug versucht, ist hierbei essentiell. Darüber hinaus stellt sich die Frage, anhand welcher Merkmale Betrugsversuche zu erkennen sind. Mit dieser Demo kann erkannt werden ob ein Versicherungsvorfall ein Betrug ist oder nicht. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "93491ec8",
- "metadata": {},
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
"source": [
"# 2. Datenverständnis"
]
@@ -47,7 +85,7 @@
"id": "a20002bf",
"metadata": {
"editable": true,
- "include": true,
+ "include": false,
"paragraph": "DataUnderstanding",
"slideshow": {
"slide_type": ""
@@ -58,11 +96,33 @@
"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)."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "0eda98ab-e58f-4df2-93ee-8cc621dcb4a1",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "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. Sie zeigt Informationen zu den Versicherungsnehmern, zu den Versicherungsdaten der Versicherten und ihrer Kraftfahrzeuge sowie zu Unfällen und Schadenshöhen. Die Zielvariable zeigt an, ob ein Versicherungsbetrug vorliegt (\"fraud_reported\"). Der Datensatz ist aufgrund der binären Zielvariable (später auch Ziel genannt) ein gutes Beispiel für Klassifikationsmodelle. "
+ ]
+ },
{
"attachments": {},
"cell_type": "markdown",
"id": "0c9b55e3",
- "metadata": {},
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
"source": [
"## 2.1 Import von relevanten Modulen"
]
@@ -137,16 +197,44 @@
"attachments": {},
"cell_type": "markdown",
"id": "70f940c9",
- "metadata": {},
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
"source": [
"--> 1000 samples and 40 columns"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "ff4755b1-f8db-4f01-a8fa-575d8f23bdd1",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenvorbereitung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Zunächst werden die Daten eingelesen und auf ihre Vollständigkeit überprüft. Danach werden die einzelnen Kundenmerkmale einer deskriptiven Analyse unterzogen. Damit lässt sich der Zusammenhang zur Zielvariable Betrug_Erkannt darstellen. In den Daten werden Untypische Daten ersetzt wie zum Beispiel „?“ mit „NaN“. Auf der Grundlage einer Korrelationsanalyse werden die Zusammenhänge zwischen Vorfalls-/Kundendaten und der Zielvariable untersucht. Merkmale die keinen Mehrwert bieten werden entfernt ( Alter, Adresse, usw.). Die Ausgewogenheit des Datensatzes in Bezug auf die Zielvariable wird grafisch dargestellt. Die Fälle in denen kein Betrug herscht machen etwa 75 % des gesamten Datensatzes aus, während Betrugsfälle etwa 25% ausmachen. Somit liegt ein unausgewogener Datensatz vor. Anschließend werden die kategorialen Werte umgewandelt (= Bildung von Dummy Variablen). Schließlich werden alle Kundenmerkmale auf ein gemeinsames Messniveau gebracht (= Standardisierung). Durch ein Undersampling wird die Unausgewogenheit des Datensatzes ausgeglichen. Abschließend werden Trainings- und Testdaten gebildet."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 4,
"id": "bb87fd51",
- "metadata": {},
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -4684,14 +4772,90 @@
]
},
{
- "cell_type": "code",
- "execution_count": 52,
- "id": "d2d84524",
+ "cell_type": "markdown",
+ "id": "f562caaf-a351-4b63-8155-a49aee7bc7e3",
"metadata": {},
- "outputs": [],
"source": [
"data_preprocessed.to_csv('dataset_dummies', index=False)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13b5c9f4-f6d9-479d-be00-a00b9623a0c3",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Teaser",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Versicherungs Unternehmen werden häufig zu Zielen von Betrügern, weshalb es sehr wichtig ist solche Betrugsversuche frühzeitig zu erkennen. Die Zeilen des Datensatzes stellen jeweils einen Kunden und Seine Vorfall dar. Die Spalten beschreiben die Merkmale der Kunden und die des Vorfalls für welchen sie ihre Versicherung in anspruch nehmen. Daten wie diese, werden von den Versicherungsunternehmen zunehmend automatisiert verarbeitet, ausgewertet und für weitere Versicherungsprozesse genutzt. Ziel ist es für bestehende Versicherungsprodukte das aktuelle Risiko zu berechnen und darauf aufbauend die Prämie und die mögliche Schadenshöhe zu ermitteln. Anhand dieses Datensatz soll mit „Machine-Learning“ ermittelt werden ob sich bei dem jeweiligen Fall um Betrug oder einen legitiemen Anspruch handelt. Logistische Regression, Entscheidungsbäume, Random Forest und Support Vector Machines werden hierbei genutzt um eine Vorhersage zu Fällen zu treffen. Das Finale Modell erreicht eine Genauigkeit von 95 % und einen Recall von 75 %. Die Mehrheit der Betrugsversuche wird mit diesem Modell erkannt. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd6e4d06-d1a3-4b65-b333-22d35816872a",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Klassifizierungsmodelle sind vielfältig und umfassen zum Beispiel logistische Regression, Entscheidungsbaum, Random Forest und Support Vector Machines. Alle oben genannten Modelle wurden mit dem Datensatz getestet und anschließend wird das mit der Höchste Präzision genutzt. In diesem Fall ist das die Support Vector Machines "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25702f9d-9b4c-44d8-94a8-0b3d5c02b0ea",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Für die Bewertung der Qualität einer Klassifikation werden Metriken wir Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Präzision der Vorhersage der Kundenabwanderung) und Recall (= Menge der abwanderungswilligen Kunden die korrekt klassifiziert wurden) genutzt. In einer ersten Modellstufe wird eine Accuracy von 92%, ein Recall von 75% sowie eine Precision von 95% erreicht. Schlussendlich konnten 85% der Betrugsfälle korrekt erkannt werden. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0edf9cce-4d87-4831-983b-cafd8f113f4f",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Umsetzung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Die Umsetzung bzw. Einbindung des Datenmodells bietet sich in CRM-Systemen an. Auf Basis von Vorfalls Merkmalen kann automatisiert eine Vorhersage über eine potenziellen Betrugsversuch erstellt werden. Auf diese Weise lassen sich Betrugsfälle identifizieren, in Form von Dashboards visualisieren sowie teil-automatisiert bearbeiten."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02d0d7b8-549e-43cf-9fba-44a72a67b2e5",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -4711,10 +4875,10 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.11.2"
},
- "skipNotebookInDeployment": false,
- "title": "Insurance Fraud detection"
+ "skipNotebookInDeployment": true,
+ "title": "Insurance Fraud detection - Versicherungs Betrugserkennung "
},
"nbformat": 4,
"nbformat_minor": 5
diff --git a/Insurance/Insurance Fraud detection/notebook_2.ipynb b/Insurance/Insurance Fraud detection/notebook_2.ipynb
index e9c63ce..b6b7d82 100644
--- a/Insurance/Insurance Fraud detection/notebook_2.ipynb
+++ b/Insurance/Insurance Fraud detection/notebook_2.ipynb
@@ -1493,9 +1493,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.11.2"
},
- "skipNotebookInDeployment": false,
+ "skipNotebookInDeployment": true,
"title": "Insurance Fraud detection"
},
"nbformat": 4,
diff --git a/Insurance/Insurance Fraud detection/notebook_3.ipynb b/Insurance/Insurance Fraud detection/notebook_3.ipynb
new file mode 100644
index 0000000..fff7f1f
--- /dev/null
+++ b/Insurance/Insurance Fraud detection/notebook_3.ipynb
@@ -0,0 +1,6397 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "6e39bc3c",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# 1. Business Understanding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a526e65c-4cd2-42af-b98f-f711b45e0274",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Title",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Insurance Fraud detection - Versicherungs Betrugserkennung "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "135c407f",
+ "metadata": {
+ "editable": true,
+ "include": false,
+ "paragraph": "BusinessUnderstanding",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Versicherungen verfügen über eine Vielzahl von Daten und kreiren täglich neue. Unter diesen Daten sind auch sehr sensible Informationen wie Name, Geburtsdaten, Adressen 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?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ca514ca-c655-462d-9b0c-3b36d1e0f95f",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Business",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Für jedes Versicherungs-Unternehmen ist wichtig nur begründete und gerechtfertigte Summen aus zuzahlen. Für Versicherungen ist daher eine Einschätzung der Legitimität von Schadensansprüchen, essentiell. Problematisch sind hierbei die gezielten Betrugsversuche (= Fraud). Soweit sich das Risiko Betrugs erkennen lässt, können Gegenmaßnahmen eingeleitet werden. Die Abschätzung der Wahrscheinlichkeit, mit der ein Kunde einen Betrug versucht, ist hierbei essentiell. Darüber hinaus stellt sich die Frage, anhand welcher Merkmale Betrugsversuche zu erkennen sind. Mit dieser Demo kann erkannt werden ob ein Versicherungsvorfall ein Betrug ist oder nicht. "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "93491ec8",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# 2. Datenverständnis"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a20002bf",
+ "metadata": {
+ "editable": true,
+ "include": false,
+ "paragraph": "DataUnderstanding",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "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)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0eda98ab-e58f-4df2-93ee-8cc621dcb4a1",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "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. Sie zeigt Informationen zu den Versicherungsnehmern, zu den Versicherungsdaten der Versicherten und ihrer Kraftfahrzeuge sowie zu Unfällen und Schadenshöhen. Die Zielvariable zeigt an, ob ein Versicherungsbetrug vorliegt (\"fraud_reported\"). Der Datensatz ist aufgrund der binären Zielvariable (später auch Ziel genannt) ein gutes Beispiel für Klassifikationsmodelle. "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0c9b55e3",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 2.1 Import von relevanten Modulen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "cde07ec9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "sns.set()\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e6ea7872",
+ "metadata": {},
+ "source": [
+ "## 2.2 Daten einlesen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "a564973b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Insurance_Fraud_detection_dataset.csv')"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "576e2df5",
+ "metadata": {},
+ "source": [
+ "## 2.3 Deskriptive Analyse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "91fb0caa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1000, 40)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "raw_data.shape"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "70f940c9",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "--> 1000 samples and 40 columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff4755b1-f8db-4f01-a8fa-575d8f23bdd1",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenvorbereitung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Zunächst werden die Daten eingelesen und auf ihre Vollständigkeit überprüft. Danach werden die einzelnen Kundenmerkmale einer deskriptiven Analyse unterzogen. Damit lässt sich der Zusammenhang zur Zielvariable Betrug_Erkannt darstellen. In den Daten werden Untypische Daten ersetzt wie zum Beispiel „?“ mit „NaN“. Auf der Grundlage einer Korrelationsanalyse werden die Zusammenhänge zwischen Vorfalls-/Kundendaten und der Zielvariable untersucht. Merkmale die keinen Mehrwert bieten werden entfernt ( Alter, Adresse, usw.). Die Ausgewogenheit des Datensatzes in Bezug auf die Zielvariable wird grafisch dargestellt. Die Fälle in denen kein Betrug herscht machen etwa 75 % des gesamten Datensatzes aus, während Betrugsfälle etwa 25% ausmachen. Somit liegt ein unausgewogener Datensatz vor. Anschließend werden die kategorialen Werte umgewandelt (= Bildung von Dummy Variablen). Schließlich werden alle Kundenmerkmale auf ein gemeinsames Messniveau gebracht (= Standardisierung). Durch ein Undersampling wird die Unausgewogenheit des Datensatzes ausgeglichen. Abschließend werden Trainings- und Testdaten gebildet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "bb87fd51",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>age</th>\n",
+ " <th>policy_number</th>\n",
+ " <th>policy_bind_date</th>\n",
+ " <th>policy_state</th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>insured_zip</th>\n",
+ " <th>...</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>total_claim_amount</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>auto_make</th>\n",
+ " <th>auto_model</th>\n",
+ " <th>auto_year</th>\n",
+ " <th>fraud_reported</th>\n",
+ " <th>_c39</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>328</td>\n",
+ " <td>48</td>\n",
+ " <td>521585</td>\n",
+ " <td>2014-10-17</td>\n",
+ " <td>OH</td>\n",
+ " <td>250/500</td>\n",
+ " <td>1000</td>\n",
+ " <td>1406.91</td>\n",
+ " <td>0</td>\n",
+ " <td>466132</td>\n",
+ " <td>...</td>\n",
+ " <td>YES</td>\n",
+ " <td>71610</td>\n",
+ " <td>6510</td>\n",
+ " <td>13020</td>\n",
+ " <td>52080</td>\n",
+ " <td>Saab</td>\n",
+ " <td>92x</td>\n",
+ " <td>2004</td>\n",
+ " <td>Y</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>228</td>\n",
+ " <td>42</td>\n",
+ " <td>342868</td>\n",
+ " <td>2006-06-27</td>\n",
+ " <td>IN</td>\n",
+ " <td>250/500</td>\n",
+ " <td>2000</td>\n",
+ " <td>1197.22</td>\n",
+ " <td>5000000</td>\n",
+ " <td>468176</td>\n",
+ " <td>...</td>\n",
+ " <td>?</td>\n",
+ " <td>5070</td>\n",
+ " <td>780</td>\n",
+ " <td>780</td>\n",
+ " <td>3510</td>\n",
+ " <td>Mercedes</td>\n",
+ " <td>E400</td>\n",
+ " <td>2007</td>\n",
+ " <td>Y</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>134</td>\n",
+ " <td>29</td>\n",
+ " <td>687698</td>\n",
+ " <td>2000-09-06</td>\n",
+ " <td>OH</td>\n",
+ " <td>100/300</td>\n",
+ " <td>2000</td>\n",
+ " <td>1413.14</td>\n",
+ " <td>5000000</td>\n",
+ " <td>430632</td>\n",
+ " <td>...</td>\n",
+ " <td>NO</td>\n",
+ " <td>34650</td>\n",
+ " <td>7700</td>\n",
+ " <td>3850</td>\n",
+ " <td>23100</td>\n",
+ " <td>Dodge</td>\n",
+ " <td>RAM</td>\n",
+ " <td>2007</td>\n",
+ " <td>N</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>256</td>\n",
+ " <td>41</td>\n",
+ " <td>227811</td>\n",
+ " <td>1990-05-25</td>\n",
+ " <td>IL</td>\n",
+ " <td>250/500</td>\n",
+ " <td>2000</td>\n",
+ " <td>1415.74</td>\n",
+ " <td>6000000</td>\n",
+ " <td>608117</td>\n",
+ " <td>...</td>\n",
+ " <td>NO</td>\n",
+ " <td>63400</td>\n",
+ " <td>6340</td>\n",
+ " <td>6340</td>\n",
+ " <td>50720</td>\n",
+ " <td>Chevrolet</td>\n",
+ " <td>Tahoe</td>\n",
+ " <td>2014</td>\n",
+ " <td>Y</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>228</td>\n",
+ " <td>44</td>\n",
+ " <td>367455</td>\n",
+ " <td>2014-06-06</td>\n",
+ " <td>IL</td>\n",
+ " <td>500/1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1583.91</td>\n",
+ " <td>6000000</td>\n",
+ " <td>610706</td>\n",
+ " <td>...</td>\n",
+ " <td>NO</td>\n",
+ " <td>6500</td>\n",
+ " <td>1300</td>\n",
+ " <td>650</td>\n",
+ " <td>4550</td>\n",
+ " <td>Accura</td>\n",
+ " <td>RSX</td>\n",
+ " <td>2009</td>\n",
+ " <td>N</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 40 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer age policy_number policy_bind_date policy_state \\\n",
+ "0 328 48 521585 2014-10-17 OH \n",
+ "1 228 42 342868 2006-06-27 IN \n",
+ "2 134 29 687698 2000-09-06 OH \n",
+ "3 256 41 227811 1990-05-25 IL \n",
+ "4 228 44 367455 2014-06-06 IL \n",
+ "\n",
+ " policy_csl policy_deductable policy_annual_premium umbrella_limit \\\n",
+ "0 250/500 1000 1406.91 0 \n",
+ "1 250/500 2000 1197.22 5000000 \n",
+ "2 100/300 2000 1413.14 5000000 \n",
+ "3 250/500 2000 1415.74 6000000 \n",
+ "4 500/1000 1000 1583.91 6000000 \n",
+ "\n",
+ " insured_zip ... police_report_available total_claim_amount injury_claim \\\n",
+ "0 466132 ... YES 71610 6510 \n",
+ "1 468176 ... ? 5070 780 \n",
+ "2 430632 ... NO 34650 7700 \n",
+ "3 608117 ... NO 63400 6340 \n",
+ "4 610706 ... NO 6500 1300 \n",
+ "\n",
+ " property_claim vehicle_claim auto_make auto_model auto_year \\\n",
+ "0 13020 52080 Saab 92x 2004 \n",
+ "1 780 3510 Mercedes E400 2007 \n",
+ "2 3850 23100 Dodge RAM 2007 \n",
+ "3 6340 50720 Chevrolet Tahoe 2014 \n",
+ "4 650 4550 Accura RSX 2009 \n",
+ "\n",
+ " fraud_reported _c39 \n",
+ "0 Y NaN \n",
+ "1 Y NaN \n",
+ "2 N NaN \n",
+ "3 Y NaN \n",
+ "4 N NaN \n",
+ "\n",
+ "[5 rows x 40 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "raw_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "09bf37ac",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 1000 entries, 0 to 999\n",
+ "Data columns (total 40 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 months_as_customer 1000 non-null int64 \n",
+ " 1 age 1000 non-null int64 \n",
+ " 2 policy_number 1000 non-null int64 \n",
+ " 3 policy_bind_date 1000 non-null object \n",
+ " 4 policy_state 1000 non-null object \n",
+ " 5 policy_csl 1000 non-null object \n",
+ " 6 policy_deductable 1000 non-null int64 \n",
+ " 7 policy_annual_premium 1000 non-null float64\n",
+ " 8 umbrella_limit 1000 non-null int64 \n",
+ " 9 insured_zip 1000 non-null int64 \n",
+ " 10 insured_sex 1000 non-null object \n",
+ " 11 insured_education_level 1000 non-null object \n",
+ " 12 insured_occupation 1000 non-null object \n",
+ " 13 insured_hobbies 1000 non-null object \n",
+ " 14 insured_relationship 1000 non-null object \n",
+ " 15 capital-gains 1000 non-null int64 \n",
+ " 16 capital-loss 1000 non-null int64 \n",
+ " 17 incident_date 1000 non-null object \n",
+ " 18 incident_type 1000 non-null object \n",
+ " 19 collision_type 1000 non-null object \n",
+ " 20 incident_severity 1000 non-null object \n",
+ " 21 authorities_contacted 1000 non-null object \n",
+ " 22 incident_state 1000 non-null object \n",
+ " 23 incident_city 1000 non-null object \n",
+ " 24 incident_location 1000 non-null object \n",
+ " 25 incident_hour_of_the_day 1000 non-null int64 \n",
+ " 26 number_of_vehicles_involved 1000 non-null int64 \n",
+ " 27 property_damage 1000 non-null object \n",
+ " 28 bodily_injuries 1000 non-null int64 \n",
+ " 29 witnesses 1000 non-null int64 \n",
+ " 30 police_report_available 1000 non-null object \n",
+ " 31 total_claim_amount 1000 non-null int64 \n",
+ " 32 injury_claim 1000 non-null int64 \n",
+ " 33 property_claim 1000 non-null int64 \n",
+ " 34 vehicle_claim 1000 non-null int64 \n",
+ " 35 auto_make 1000 non-null object \n",
+ " 36 auto_model 1000 non-null object \n",
+ " 37 auto_year 1000 non-null int64 \n",
+ " 38 fraud_reported 1000 non-null object \n",
+ " 39 _c39 0 non-null float64\n",
+ "dtypes: float64(2), int64(17), object(21)\n",
+ "memory usage: 312.6+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "raw_data.info()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "5c7fd07d",
+ "metadata": {},
+ "source": [
+ "# 3. Datenaufbereitung"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "05609119",
+ "metadata": {},
+ "source": [
+ "## 3.1 Datenbereinigung"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "66d09246",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# replace \"?\" with \"NaN\" in the dataset\n",
+ "raw_data.replace('?', np.nan, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "edcc3b6f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "months_as_customer 0\n",
+ "age 0\n",
+ "policy_number 0\n",
+ "policy_bind_date 0\n",
+ "policy_state 0\n",
+ "policy_csl 0\n",
+ "policy_deductable 0\n",
+ "policy_annual_premium 0\n",
+ "umbrella_limit 0\n",
+ "insured_zip 0\n",
+ "insured_sex 0\n",
+ "insured_education_level 0\n",
+ "insured_occupation 0\n",
+ "insured_hobbies 0\n",
+ "insured_relationship 0\n",
+ "capital-gains 0\n",
+ "capital-loss 0\n",
+ "incident_date 0\n",
+ "incident_type 0\n",
+ "collision_type 178\n",
+ "incident_severity 0\n",
+ "authorities_contacted 0\n",
+ "incident_state 0\n",
+ "incident_city 0\n",
+ "incident_location 0\n",
+ "incident_hour_of_the_day 0\n",
+ "number_of_vehicles_involved 0\n",
+ "property_damage 360\n",
+ "bodily_injuries 0\n",
+ "witnesses 0\n",
+ "police_report_available 343\n",
+ "total_claim_amount 0\n",
+ "injury_claim 0\n",
+ "property_claim 0\n",
+ "vehicle_claim 0\n",
+ "auto_make 0\n",
+ "auto_model 0\n",
+ "auto_year 0\n",
+ "fraud_reported 0\n",
+ "_c39 1000\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# checking missing values\n",
+ "raw_data.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "b71a2875",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1000, 39)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# delete column _c39, no relevant feature\n",
+ "data_no_mv = raw_data.drop('_c39', axis=1)\n",
+ "data_no_mv.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "22813b8d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rear Collision\n",
+ "NO\n",
+ "NO\n"
+ ]
+ }
+ ],
+ "source": [
+ "# since there are relatively few records anyway, the zero values are replaced by the mean value of the respective column\n",
+ "print(data_no_mv['collision_type'].mode()[0])\n",
+ "print(data_no_mv['property_damage'].mode()[0])\n",
+ "print(data_no_mv['police_report_available'].mode()[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "7f41969b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_no_mv['collision_type'] = data_no_mv['collision_type'].fillna(data_no_mv['collision_type'].mode()[0])\n",
+ "data_no_mv['property_damage'] = data_no_mv['property_damage'].fillna(data_no_mv['property_damage'].mode()[0])\n",
+ "data_no_mv['police_report_available'] = data_no_mv['police_report_available'].fillna(data_no_mv['police_report_available'].mode()[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "3c714633",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "months_as_customer 0\n",
+ "age 0\n",
+ "policy_number 0\n",
+ "policy_bind_date 0\n",
+ "policy_state 0\n",
+ "policy_csl 0\n",
+ "policy_deductable 0\n",
+ "policy_annual_premium 0\n",
+ "umbrella_limit 0\n",
+ "insured_zip 0\n",
+ "insured_sex 0\n",
+ "insured_education_level 0\n",
+ "insured_occupation 0\n",
+ "insured_hobbies 0\n",
+ "insured_relationship 0\n",
+ "capital-gains 0\n",
+ "capital-loss 0\n",
+ "incident_date 0\n",
+ "incident_type 0\n",
+ "collision_type 0\n",
+ "incident_severity 0\n",
+ "authorities_contacted 0\n",
+ "incident_state 0\n",
+ "incident_city 0\n",
+ "incident_location 0\n",
+ "incident_hour_of_the_day 0\n",
+ "number_of_vehicles_involved 0\n",
+ "property_damage 0\n",
+ "bodily_injuries 0\n",
+ "witnesses 0\n",
+ "police_report_available 0\n",
+ "total_claim_amount 0\n",
+ "injury_claim 0\n",
+ "property_claim 0\n",
+ "vehicle_claim 0\n",
+ "auto_make 0\n",
+ "auto_model 0\n",
+ "auto_year 0\n",
+ "fraud_reported 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# checking missing values\n",
+ "data_no_mv.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "8cc2f1b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# checking duplicates\n",
+ "data_no_dup = data_no_mv.copy()\n",
+ "data_no_dup['policy_number'] = data_no_dup['policy_number'].duplicated(keep=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "e666f85b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1000, 39)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_no_dup.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "005fa322",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
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+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>age</th>\n",
+ " <th>policy_number</th>\n",
+ " <th>policy_bind_date</th>\n",
+ " <th>policy_state</th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>insured_zip</th>\n",
+ " <th>...</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>total_claim_amount</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>auto_make</th>\n",
+ " <th>auto_model</th>\n",
+ " <th>auto_year</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
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+ " <td>1000.000000</td>\n",
+ " <td>1.000000e+03</td>\n",
+ " <td>1000.000000</td>\n",
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+ " <td>1000.000000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000.00000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1</td>\n",
+ " <td>951</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>14</td>\n",
+ " <td>39</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>2006-01-01</td>\n",
+ " <td>OH</td>\n",
+ " <td>250/500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NO</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Saab</td>\n",
+ " <td>RAM</td>\n",
+ " <td>NaN</td>\n",
+ " <td>N</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1000</td>\n",
+ " <td>3</td>\n",
+ " <td>352</td>\n",
+ " <td>351</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>686</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80</td>\n",
+ " <td>43</td>\n",
+ " <td>NaN</td>\n",
+ " <td>753</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>203.954000</td>\n",
+ " <td>38.948000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1136.000000</td>\n",
+ " <td>1256.406150</td>\n",
+ " <td>1.101000e+06</td>\n",
+ " <td>501214.488000</td>\n",
+ " <td>...</td>\n",
+ " <td>1.487000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>52761.94000</td>\n",
+ " <td>7433.420000</td>\n",
+ " <td>7399.570000</td>\n",
+ " <td>37928.950000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2005.103000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>115.113174</td>\n",
+ " <td>9.140287</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>611.864673</td>\n",
+ " <td>244.167395</td>\n",
+ " <td>2.297407e+06</td>\n",
+ " <td>71701.610941</td>\n",
+ " <td>...</td>\n",
+ " <td>1.111335</td>\n",
+ " <td>NaN</td>\n",
+ " <td>26401.53319</td>\n",
+ " <td>4880.951853</td>\n",
+ " <td>4824.726179</td>\n",
+ " <td>18886.252893</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.015861</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>19.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>433.330000</td>\n",
+ " <td>-1.000000e+06</td>\n",
+ " <td>430104.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>100.00000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1995.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>115.750000</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>1089.607500</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>448404.500000</td>\n",
+ " <td>...</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>41812.50000</td>\n",
+ " <td>4295.000000</td>\n",
+ " <td>4445.000000</td>\n",
+ " <td>30292.500000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2000.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>199.500000</td>\n",
+ " <td>38.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1257.200000</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>466445.500000</td>\n",
+ " <td>...</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>58055.00000</td>\n",
+ " <td>6775.000000</td>\n",
+ " <td>6750.000000</td>\n",
+ " <td>42100.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2005.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>276.250000</td>\n",
+ " <td>44.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2000.000000</td>\n",
+ " <td>1415.695000</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>603251.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>70592.50000</td>\n",
+ " <td>11305.000000</td>\n",
+ " <td>10885.000000</td>\n",
+ " <td>50822.500000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2010.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>479.000000</td>\n",
+ " <td>64.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2000.000000</td>\n",
+ " <td>2047.590000</td>\n",
+ " <td>1.000000e+07</td>\n",
+ " <td>620962.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>114920.00000</td>\n",
+ " <td>21450.000000</td>\n",
+ " <td>23670.000000</td>\n",
+ " <td>79560.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2015.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>11 rows × 39 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer age policy_number policy_bind_date \\\n",
+ "count 1000.000000 1000.000000 1000 1000 \n",
+ "unique NaN NaN 1 951 \n",
+ "top NaN NaN False 2006-01-01 \n",
+ "freq NaN NaN 1000 3 \n",
+ "mean 203.954000 38.948000 NaN NaN \n",
+ "std 115.113174 9.140287 NaN NaN \n",
+ "min 0.000000 19.000000 NaN NaN \n",
+ "25% 115.750000 32.000000 NaN NaN \n",
+ "50% 199.500000 38.000000 NaN NaN \n",
+ "75% 276.250000 44.000000 NaN NaN \n",
+ "max 479.000000 64.000000 NaN NaN \n",
+ "\n",
+ " policy_state policy_csl policy_deductable policy_annual_premium \\\n",
+ "count 1000 1000 1000.000000 1000.000000 \n",
+ "unique 3 3 NaN NaN \n",
+ "top OH 250/500 NaN NaN \n",
+ "freq 352 351 NaN NaN \n",
+ "mean NaN NaN 1136.000000 1256.406150 \n",
+ "std NaN NaN 611.864673 244.167395 \n",
+ "min NaN NaN 500.000000 433.330000 \n",
+ "25% NaN NaN 500.000000 1089.607500 \n",
+ "50% NaN NaN 1000.000000 1257.200000 \n",
+ "75% NaN NaN 2000.000000 1415.695000 \n",
+ "max NaN NaN 2000.000000 2047.590000 \n",
+ "\n",
+ " umbrella_limit insured_zip ... witnesses \\\n",
+ "count 1.000000e+03 1000.000000 ... 1000.000000 \n",
+ "unique NaN NaN ... NaN \n",
+ "top NaN NaN ... NaN \n",
+ "freq NaN NaN ... NaN \n",
+ "mean 1.101000e+06 501214.488000 ... 1.487000 \n",
+ "std 2.297407e+06 71701.610941 ... 1.111335 \n",
+ "min -1.000000e+06 430104.000000 ... 0.000000 \n",
+ "25% 0.000000e+00 448404.500000 ... 1.000000 \n",
+ "50% 0.000000e+00 466445.500000 ... 1.000000 \n",
+ "75% 0.000000e+00 603251.000000 ... 2.000000 \n",
+ "max 1.000000e+07 620962.000000 ... 3.000000 \n",
+ "\n",
+ " police_report_available total_claim_amount injury_claim \\\n",
+ "count 1000 1000.00000 1000.000000 \n",
+ "unique 2 NaN NaN \n",
+ "top NO NaN NaN \n",
+ "freq 686 NaN NaN \n",
+ "mean NaN 52761.94000 7433.420000 \n",
+ "std NaN 26401.53319 4880.951853 \n",
+ "min NaN 100.00000 0.000000 \n",
+ "25% NaN 41812.50000 4295.000000 \n",
+ "50% NaN 58055.00000 6775.000000 \n",
+ "75% NaN 70592.50000 11305.000000 \n",
+ "max NaN 114920.00000 21450.000000 \n",
+ "\n",
+ " property_claim vehicle_claim auto_make auto_model auto_year \\\n",
+ "count 1000.000000 1000.000000 1000 1000 1000.000000 \n",
+ "unique NaN NaN 14 39 NaN \n",
+ "top NaN NaN Saab RAM NaN \n",
+ "freq NaN NaN 80 43 NaN \n",
+ "mean 7399.570000 37928.950000 NaN NaN 2005.103000 \n",
+ "std 4824.726179 18886.252893 NaN NaN 6.015861 \n",
+ "min 0.000000 70.000000 NaN NaN 1995.000000 \n",
+ "25% 4445.000000 30292.500000 NaN NaN 2000.000000 \n",
+ "50% 6750.000000 42100.000000 NaN NaN 2005.000000 \n",
+ "75% 10885.000000 50822.500000 NaN NaN 2010.000000 \n",
+ "max 23670.000000 79560.000000 NaN NaN 2015.000000 \n",
+ "\n",
+ " fraud_reported \n",
+ "count 1000 \n",
+ "unique 2 \n",
+ "top N \n",
+ "freq 753 \n",
+ "mean NaN \n",
+ "std NaN \n",
+ "min NaN \n",
+ "25% NaN \n",
+ "50% NaN \n",
+ "75% NaN \n",
+ "max NaN \n",
+ "\n",
+ "[11 rows x 39 columns]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_no_dup.describe(include='all')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "87b9ba9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# map the target from \"no\" and \"yes\" to 0 and 1\n",
+ "data_no_dup.fraud_reported = data_no_dup.fraud_reported.map({'N': 0, 'Y': 1})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "786725ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>age</th>\n",
+ " <th>policy_number</th>\n",
+ " <th>policy_bind_date</th>\n",
+ " <th>policy_state</th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>insured_zip</th>\n",
+ " <th>...</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>total_claim_amount</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>auto_make</th>\n",
+ " <th>auto_model</th>\n",
+ " <th>auto_year</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>328</td>\n",
+ " <td>48</td>\n",
+ " <td>False</td>\n",
+ " <td>2014-10-17</td>\n",
+ " <td>OH</td>\n",
+ " <td>250/500</td>\n",
+ " <td>1000</td>\n",
+ " <td>1406.91</td>\n",
+ " <td>0</td>\n",
+ " <td>466132</td>\n",
+ " <td>...</td>\n",
+ " <td>2</td>\n",
+ " <td>YES</td>\n",
+ " <td>71610</td>\n",
+ " <td>6510</td>\n",
+ " <td>13020</td>\n",
+ " <td>52080</td>\n",
+ " <td>Saab</td>\n",
+ " <td>92x</td>\n",
+ " <td>2004</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>228</td>\n",
+ " <td>42</td>\n",
+ " <td>False</td>\n",
+ " <td>2006-06-27</td>\n",
+ " <td>IN</td>\n",
+ " <td>250/500</td>\n",
+ " <td>2000</td>\n",
+ " <td>1197.22</td>\n",
+ " <td>5000000</td>\n",
+ " <td>468176</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>NO</td>\n",
+ " <td>5070</td>\n",
+ " <td>780</td>\n",
+ " <td>780</td>\n",
+ " <td>3510</td>\n",
+ " <td>Mercedes</td>\n",
+ " <td>E400</td>\n",
+ " <td>2007</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>134</td>\n",
+ " <td>29</td>\n",
+ " <td>False</td>\n",
+ " <td>2000-09-06</td>\n",
+ " <td>OH</td>\n",
+ " <td>100/300</td>\n",
+ " <td>2000</td>\n",
+ " <td>1413.14</td>\n",
+ " <td>5000000</td>\n",
+ " <td>430632</td>\n",
+ " <td>...</td>\n",
+ " <td>3</td>\n",
+ " <td>NO</td>\n",
+ " <td>34650</td>\n",
+ " <td>7700</td>\n",
+ " <td>3850</td>\n",
+ " <td>23100</td>\n",
+ " <td>Dodge</td>\n",
+ " <td>RAM</td>\n",
+ " <td>2007</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>256</td>\n",
+ " <td>41</td>\n",
+ " <td>False</td>\n",
+ " <td>1990-05-25</td>\n",
+ " <td>IL</td>\n",
+ " <td>250/500</td>\n",
+ " <td>2000</td>\n",
+ " <td>1415.74</td>\n",
+ " <td>6000000</td>\n",
+ " <td>608117</td>\n",
+ " <td>...</td>\n",
+ " <td>2</td>\n",
+ " <td>NO</td>\n",
+ " <td>63400</td>\n",
+ " <td>6340</td>\n",
+ " <td>6340</td>\n",
+ " <td>50720</td>\n",
+ " <td>Chevrolet</td>\n",
+ " <td>Tahoe</td>\n",
+ " <td>2014</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>228</td>\n",
+ " <td>44</td>\n",
+ " <td>False</td>\n",
+ " <td>2014-06-06</td>\n",
+ " <td>IL</td>\n",
+ " <td>500/1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1583.91</td>\n",
+ " <td>6000000</td>\n",
+ " <td>610706</td>\n",
+ " <td>...</td>\n",
+ " <td>1</td>\n",
+ " <td>NO</td>\n",
+ " <td>6500</td>\n",
+ " <td>1300</td>\n",
+ " <td>650</td>\n",
+ " <td>4550</td>\n",
+ " <td>Accura</td>\n",
+ " <td>RSX</td>\n",
+ " <td>2009</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 39 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer age policy_number policy_bind_date policy_state \\\n",
+ "0 328 48 False 2014-10-17 OH \n",
+ "1 228 42 False 2006-06-27 IN \n",
+ "2 134 29 False 2000-09-06 OH \n",
+ "3 256 41 False 1990-05-25 IL \n",
+ "4 228 44 False 2014-06-06 IL \n",
+ "\n",
+ " policy_csl policy_deductable policy_annual_premium umbrella_limit \\\n",
+ "0 250/500 1000 1406.91 0 \n",
+ "1 250/500 2000 1197.22 5000000 \n",
+ "2 100/300 2000 1413.14 5000000 \n",
+ "3 250/500 2000 1415.74 6000000 \n",
+ "4 500/1000 1000 1583.91 6000000 \n",
+ "\n",
+ " insured_zip ... witnesses police_report_available total_claim_amount \\\n",
+ "0 466132 ... 2 YES 71610 \n",
+ "1 468176 ... 0 NO 5070 \n",
+ "2 430632 ... 3 NO 34650 \n",
+ "3 608117 ... 2 NO 63400 \n",
+ "4 610706 ... 1 NO 6500 \n",
+ "\n",
+ " injury_claim property_claim vehicle_claim auto_make auto_model auto_year \\\n",
+ "0 6510 13020 52080 Saab 92x 2004 \n",
+ "1 780 780 3510 Mercedes E400 2007 \n",
+ "2 7700 3850 23100 Dodge RAM 2007 \n",
+ "3 6340 6340 50720 Chevrolet Tahoe 2014 \n",
+ "4 1300 650 4550 Accura RSX 2009 \n",
+ "\n",
+ " fraud_reported \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 0 \n",
+ "3 1 \n",
+ "4 0 \n",
+ "\n",
+ "[5 rows x 39 columns]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_no_dup.head()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7a3ad030",
+ "metadata": {},
+ "source": [
+ "## 3.2 Test auf Korrelation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "a8491fbe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1296x864 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# checking correlation\n",
+ "plt.figure(figsize=(18, 12))\n",
+ "\n",
+ "feature_corr = data_no_dup.corr()\n",
+ "mask = np.triu(np.ones_like(feature_corr, dtype = bool))\n",
+ "sns.heatmap(feature_corr, mask=mask, annot=True, cmap='coolwarm')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "410675dc",
+ "metadata": {},
+ "source": [
+ "--> hohe Korrelation zwischen *Alter* und *Monate_als_Kunde*, auch zwischen *Gesamtschadenshöhe* und *Verletzungsschaden, Sachschaden und Fahrzeugschaden*. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "7826e0db",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1000, 29)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# drop features because of the correlation \n",
+ "cols_to_drop = ['age', 'total_claim_amount', 'policy_number', 'policy_bind_date', 'policy_state', \n",
+ " 'incident_state', 'incident_city', 'incident_location', 'incident_hour_of_the_day', \n",
+ " 'insured_zip'] \n",
+ "data01 = data_no_dup.drop(cols_to_drop, axis=1)\n",
+ "data01.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "6faa309f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# seperate numeric and categorical feature\n",
+ "data_cat = data01.select_dtypes(include='object')\n",
+ "data_num = data01.select_dtypes(exclude='object')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f9c6db85",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>insured_sex</th>\n",
+ " <th>insured_education_level</th>\n",
+ " <th>insured_occupation</th>\n",
+ " <th>insured_hobbies</th>\n",
+ " <th>insured_relationship</th>\n",
+ " <th>incident_date</th>\n",
+ " <th>incident_type</th>\n",
+ " <th>collision_type</th>\n",
+ " <th>incident_severity</th>\n",
+ " <th>authorities_contacted</th>\n",
+ " <th>property_damage</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>auto_make</th>\n",
+ " <th>auto_model</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>250/500</td>\n",
+ " <td>MALE</td>\n",
+ " <td>MD</td>\n",
+ " <td>craft-repair</td>\n",
+ " <td>sleeping</td>\n",
+ " <td>husband</td>\n",
+ " <td>2015-01-25</td>\n",
+ " <td>Single Vehicle Collision</td>\n",
+ " <td>Side Collision</td>\n",
+ " <td>Major Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>YES</td>\n",
+ " <td>YES</td>\n",
+ " <td>Saab</td>\n",
+ " <td>92x</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>250/500</td>\n",
+ " <td>MALE</td>\n",
+ " <td>MD</td>\n",
+ " <td>machine-op-inspct</td>\n",
+ " <td>reading</td>\n",
+ " <td>other-relative</td>\n",
+ " <td>2015-01-21</td>\n",
+ " <td>Vehicle Theft</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Mercedes</td>\n",
+ " <td>E400</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>100/300</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>PhD</td>\n",
+ " <td>sales</td>\n",
+ " <td>board-games</td>\n",
+ " <td>own-child</td>\n",
+ " <td>2015-02-22</td>\n",
+ " <td>Multi-vehicle Collision</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Dodge</td>\n",
+ " <td>RAM</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>250/500</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>PhD</td>\n",
+ " <td>armed-forces</td>\n",
+ " <td>board-games</td>\n",
+ " <td>unmarried</td>\n",
+ " <td>2015-01-10</td>\n",
+ " <td>Single Vehicle Collision</td>\n",
+ " <td>Front Collision</td>\n",
+ " <td>Major Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Chevrolet</td>\n",
+ " <td>Tahoe</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>500/1000</td>\n",
+ " <td>MALE</td>\n",
+ " <td>Associate</td>\n",
+ " <td>sales</td>\n",
+ " <td>board-games</td>\n",
+ " <td>unmarried</td>\n",
+ " <td>2015-02-17</td>\n",
+ " <td>Vehicle Theft</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>None</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Accura</td>\n",
+ " <td>RSX</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl insured_sex insured_education_level insured_occupation \\\n",
+ "0 250/500 MALE MD craft-repair \n",
+ "1 250/500 MALE MD machine-op-inspct \n",
+ "2 100/300 FEMALE PhD sales \n",
+ "3 250/500 FEMALE PhD armed-forces \n",
+ "4 500/1000 MALE Associate sales \n",
+ "\n",
+ " insured_hobbies insured_relationship incident_date \\\n",
+ "0 sleeping husband 2015-01-25 \n",
+ "1 reading other-relative 2015-01-21 \n",
+ "2 board-games own-child 2015-02-22 \n",
+ "3 board-games unmarried 2015-01-10 \n",
+ "4 board-games unmarried 2015-02-17 \n",
+ "\n",
+ " incident_type collision_type incident_severity \\\n",
+ "0 Single Vehicle Collision Side Collision Major Damage \n",
+ "1 Vehicle Theft Rear Collision Minor Damage \n",
+ "2 Multi-vehicle Collision Rear Collision Minor Damage \n",
+ "3 Single Vehicle Collision Front Collision Major Damage \n",
+ "4 Vehicle Theft Rear Collision Minor Damage \n",
+ "\n",
+ " authorities_contacted property_damage police_report_available auto_make \\\n",
+ "0 Police YES YES Saab \n",
+ "1 Police NO NO Mercedes \n",
+ "2 Police NO NO Dodge \n",
+ "3 Police NO NO Chevrolet \n",
+ "4 None NO NO Accura \n",
+ "\n",
+ " auto_model \n",
+ "0 92x \n",
+ "1 E400 \n",
+ "2 RAM \n",
+ "3 Tahoe \n",
+ "4 RSX "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat.head()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "78a33f86",
+ "metadata": {},
+ "source": [
+ "## 3.2 Überprüfung der Merkmale in Bezug auf das Ziel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "4190f23f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>insured_sex</th>\n",
+ " <th>insured_education_level</th>\n",
+ " <th>insured_occupation</th>\n",
+ " <th>insured_hobbies</th>\n",
+ " <th>insured_relationship</th>\n",
+ " <th>incident_date</th>\n",
+ " <th>incident_type</th>\n",
+ " <th>collision_type</th>\n",
+ " <th>incident_severity</th>\n",
+ " <th>authorities_contacted</th>\n",
+ " <th>property_damage</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>auto_make</th>\n",
+ " <th>auto_model</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>250/500</td>\n",
+ " <td>MALE</td>\n",
+ " <td>MD</td>\n",
+ " <td>craft-repair</td>\n",
+ " <td>sleeping</td>\n",
+ " <td>husband</td>\n",
+ " <td>2015-01-25</td>\n",
+ " <td>Single Vehicle Collision</td>\n",
+ " <td>Side Collision</td>\n",
+ " <td>Major Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>YES</td>\n",
+ " <td>YES</td>\n",
+ " <td>Saab</td>\n",
+ " <td>92x</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>250/500</td>\n",
+ " <td>MALE</td>\n",
+ " <td>MD</td>\n",
+ " <td>machine-op-inspct</td>\n",
+ " <td>reading</td>\n",
+ " <td>other-relative</td>\n",
+ " <td>2015-01-21</td>\n",
+ " <td>Vehicle Theft</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Mercedes</td>\n",
+ " <td>E400</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>100/300</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>PhD</td>\n",
+ " <td>sales</td>\n",
+ " <td>board-games</td>\n",
+ " <td>own-child</td>\n",
+ " <td>2015-02-22</td>\n",
+ " <td>Multi-vehicle Collision</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Dodge</td>\n",
+ " <td>RAM</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>250/500</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>PhD</td>\n",
+ " <td>armed-forces</td>\n",
+ " <td>board-games</td>\n",
+ " <td>unmarried</td>\n",
+ " <td>2015-01-10</td>\n",
+ " <td>Single Vehicle Collision</td>\n",
+ " <td>Front Collision</td>\n",
+ " <td>Major Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Chevrolet</td>\n",
+ " <td>Tahoe</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>500/1000</td>\n",
+ " <td>MALE</td>\n",
+ " <td>Associate</td>\n",
+ " <td>sales</td>\n",
+ " <td>board-games</td>\n",
+ " <td>unmarried</td>\n",
+ " <td>2015-02-17</td>\n",
+ " <td>Vehicle Theft</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>None</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>Accura</td>\n",
+ " <td>RSX</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl insured_sex insured_education_level insured_occupation \\\n",
+ "0 250/500 MALE MD craft-repair \n",
+ "1 250/500 MALE MD machine-op-inspct \n",
+ "2 100/300 FEMALE PhD sales \n",
+ "3 250/500 FEMALE PhD armed-forces \n",
+ "4 500/1000 MALE Associate sales \n",
+ "\n",
+ " insured_hobbies insured_relationship incident_date \\\n",
+ "0 sleeping husband 2015-01-25 \n",
+ "1 reading other-relative 2015-01-21 \n",
+ "2 board-games own-child 2015-02-22 \n",
+ "3 board-games unmarried 2015-01-10 \n",
+ "4 board-games unmarried 2015-02-17 \n",
+ "\n",
+ " incident_type collision_type incident_severity \\\n",
+ "0 Single Vehicle Collision Side Collision Major Damage \n",
+ "1 Vehicle Theft Rear Collision Minor Damage \n",
+ "2 Multi-vehicle Collision Rear Collision Minor Damage \n",
+ "3 Single Vehicle Collision Front Collision Major Damage \n",
+ "4 Vehicle Theft Rear Collision Minor Damage \n",
+ "\n",
+ " authorities_contacted property_damage police_report_available auto_make \\\n",
+ "0 Police YES YES Saab \n",
+ "1 Police NO NO Mercedes \n",
+ "2 Police NO NO Dodge \n",
+ "3 Police NO NO Chevrolet \n",
+ "4 None NO NO Accura \n",
+ "\n",
+ " auto_model fraud_reported \n",
+ "0 92x 1 \n",
+ "1 E400 1 \n",
+ "2 RAM 0 \n",
+ "3 Tahoe 1 \n",
+ "4 RSX 0 "
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat = pd.concat([data_cat, data_num.fraud_reported], axis=1)\n",
+ "data_cat.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "c4059c89",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 2015-01-25\n",
+ "1 2015-01-21\n",
+ "3 2015-01-10\n",
+ "5 2015-01-02\n",
+ "14 2015-01-15\n",
+ " ... \n",
+ "974 2015-02-08\n",
+ "977 2015-02-21\n",
+ "982 2015-01-01\n",
+ "986 2015-02-19\n",
+ "987 2015-01-13\n",
+ "Name: incident_date, Length: 247, dtype: object"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat.incident_date.loc[data_cat.fraud_reported == 1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "6fe725f2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 2160x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# data_cat.incident_date.values\n",
+ "chart = sns.catplot(x=\"incident_date\", col=\"fraud_reported\", data=data_cat, kind=\"count\", aspect=3)\n",
+ "chart.set_xticklabels(rotation=90)\n",
+ "# plt.savefig('./Daten/VglIncidentVSFraud')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "9496b3f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 1728x1728 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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SJKnVRpUQR8T2wzy3c++rI0mSJE2sEW/dHBFbNA+/EBF7ArOanweAzwH36V/VJEmSpP4bMSEGPg7s0zxe0vH8KuDCvtRIkiRJmkAjJsSZuS9ARJyVmc+amCpJkiRJE2d9LcQAZOazIuIewBbcOWyCzPxhvyomSZIkTYRRJcQR8VbgeOBPwNrm6bXAvfpUL0mSJGlCjCohBg4DdszMG/pZGUmSJGmijXYd4t+bDEuSJGkmGm0L8WUR8XZqqbW/DT7pGGJJkiRNd6NNiI9q/j+k4znHEEuSJGnaG+0qE/fsd0UkSZKkyTDaVSZeMtzzmXlqb6sjSZIkTazRDpl4QMfjecDuwNd7Xx1JkiRpYo12yMQzO3+OiK2Aj/alRpIkSdIEGu2ya/8gM/8M7NDbqkiSJEkTbzxjiGcBD6XuWidJkiRNa+MZQ7wW+B3w8t5XR5IkSZpYYxpDHBH3AAYy89d9rZUkSZI0QUY7ZGJH6i512wGzI+LPwP6Z+Yt+Vk6SJEnqt9FOqnsv8PbM3DwzNwPeBJzev2pJkiRJE2O0CfE2mXnO4A+Z+WFgUX+qJEmSJE2c0SbEcyNii8EfmnWI1/anSpIkSdLEGe0qE+8BvhcR5zc/Hwa8sz9VkiRJkibOaFuIv0C1CM8DdgK2By7sV6UkSZKkiTLahPhs4PTMfCXwH8CJwFn9qpQkSZI0UUY7ZGKrzHw3QGYuB06LiCPX96aIOAU4mGpd/lBmnhoRewOnAhsC52fmSeOruiRJktS9sUyq227wh4jYhrqF8zpFxKOBxwAPpG71fFxE7EK1LB8E3A94WETsN56KS5IkSb0w2oT4VOBHEfGRiDgH+CHw9pHekJnfAPbKzFXA1lRr9ELgV5l5bfP8ucAh4628JEmS1K1RJcSZeRawN3AV8ANg38w8bxTvWxkRJwNXA5dRd7q7seMlNwJ3HWulJUmSpF4Z7RhiMvMnwE/GuoHMfH1EvA24CLjPMC9ZM5byttxyk7FWYUSLFi3oaXmWO33LnU51tVzLHa9ex1BJmglGnRCPVUTsBMzPzB9l5u0RcQE1wW51x8u2BW4YS7lLltza04C+ePGyvz/u5YnKcqdfuZ1lWq7l9nPfncykeMmSW1mzxvsqSZreeh1H+5YQA/cCTo6IR1GrTBwEnAm8IyJ2BK4FDsfl2yRJkjSJRjupbswy8wvUDT2uAv4fcEVmfgI4CvgMNa74GuDT/aqDJEmStD79bCEmM18PvH7Ic5cBu/Rzu5IkSdJo9a2FWJIkSZoOTIglSZLUaibEkiRJajUTYkmSJLWaCbEkSZJazYRYkiRJrWZCLEmSpFYzIZYkSVKrmRBLkiSp1UyIJUmS1GomxJIkSWo1E2JJkiS1mgmxJEmSWs2EWJIkSa1mQixJkqRWMyGWJElSq5kQS5IkqdVMiCVJktRqJsSSJElqNRNiSZIktZoJsSRJklrNhFiSJEmtZkIsSZKkVjMhliRJUquZEEuSJKnVTIglSZLUaibEkiRJajUTYkmSJLWaCbEkSZJabe5kV0CSNHNssdkGzJk3r6syVq9Ywc233NGjGmmmWbBwQ+YPdJ++LF+5imVL/9aDGmkmMCGWJPXMnHnzuP70Y7sqY/tjTwdMiDW8+QNzOejTX+q6nM8dvC/LelAfzQwOmZAkSVKrmRBLkiSp1UyIJUmS1GqOIZYktdbChQMMDMzvqoyVK5ezdOnKHtVo4i1YOJ/5AwNdl7N85UqWLV3egxpJE6+vCXFEvB44tPnxksx8RUTsDZwKbAicn5kn9bMOkiSty8DAfD5+9r5dlfG0o74ETN+EeP7AAE+44LSuy7nkyS9iGSbEmp76NmSiSXwfB/wr8CDgIRHxNOAs4CDgfsDDImK/ftVBkiRJWp9+jiG+EXhpZq7IzJXAL4D7Ar/KzGszcxVwLnBIH+sgSZIkjahvQyYy8+eDjyPiPsBhwLupRHnQjcBdx1Lulltu0pP6DVq0aEFPy7Pc6VvudKqr5VruePU6hvbL0M9pzaoVzJ7b3Q0/elHGukz29zpVTLfPYSLqu3r1WubMmTVlytHw+j6pLiLuD1wCvIwaZBVDXrJmLOUtWXJrTwP64sV3LsvdywPDcqdfuZ1lWq7l9nPfncykYcmSW1mzZm3fyu/n9/qtD+zfVZm7P/fiCdsPp5N+xeZ+mY71PeeCxV2Xc+STF03r/azXeh1H+7rsWkQ8ErgMeFVmngNcD9yl4yXbAjf0sw6SJEnSSPrWQhwRdwM+CxyWmV9rnv5+/Sp2BK4FDqcm2UmSJEmTop9DJl4GzAdOjfj7KIn3AUcBn2l+9wXg032sgyRJ0notWLgR8wfmdF3O8pWrWbb09h7UaGbZYrMNmTOvu7Rz9YpV3HzL33pUo3/Uz0l1JwAnrOPXu/Rru5IkSWM1f2AOh37mmq7L+eRTdsKRvv9szry53PTub3ZVxjbH79Gj2vwzb90sSZKkVjMhliRJUquZEEuSJKnV+r4OsSRp6tlis/nMmTfQVRmrV6zk5luW96hGkjR5TIglqYXmzBtg8RnndlXGomOeDpgQS5r+HDIhSZKkVjMhliRJUquZEEuSJKnVTIglSZLUaibEkiRJajUTYkmSJLWaCbEkSZJazYRYkiRJrWZCLEmSpFYzIZYkSVKrmRBLkiSp1UyIJUmS1GomxJIkSWo1E2JJkiS1mgmxJEmSWm3uZFdAkiSt34KF85g/sEHX5SxfeQfLlq7oQY00E22x2UbMmTen63JWr1jNzbfc3oMaTQwTYkmSpoH5Axuw3+eO7LqcSw86h2WYEGt4c+bN4Y/v+G3X5dzl5ffoQW0mjkMmJEmS1GomxJIkSWo1h0xIktRDmy0cYN7A/K7LWbFyObcsXdmDGk2OBQvnM39goOtylq9cybKly3tQI2ndTIglSeqheQPzedsn9u26nFc+9UvA9E2I5w8MsP+nP9Z1ORcffATLMCFWfzlkQpIkSa1mQixJkqRWMyGWJElSq5kQS5IkqdVMiCVJktRqJsSSJElqNRNiSZIktZrrEEuSJE0zmy/cmLkD3bdrrlq5hr8sva0HNZre+p4QR8SmwBXA/pl5XUTsDZwKbAicn5kn9bsOkiRJM8ncgdl87+w/dV3Orkdt3YPaTH99HTIREY8Avg3ct/l5Q+As4CDgfsDDImK/ftZBkiRJGkm/xxA/FzgWuKH5+eHArzLz2sxcBZwLHNLnOkiSJEnr1NchE5n5HICIGHxqO+DGjpfcCNx1LGVuueUmPanboEWLFvS0PMudvuVOp7paruWOlzHUci3Xcqdzuf2q60RPqps1zHNrxlLAkiW39jSgL1687O+Pe/khW+70K7ezTMu13H7uu5OZFC9Zcitr1qydVp//dCt3qsc6y7Xc6VzuYJm9jqMTveza9cBdOn7eljuHU0iSJEkTbqJbiL8PRETsCFwLHE5NspMkSZImxYS2EGfmcuAo4DPA1cA1wKcnsg6SJElSpwlpIc7MHToeXwbsMhHblSRJktbHWzdLkiSp1UyIJUmS1GomxJIkSWo1E2JJkiS1mgmxJEmSWs2EWJIkSa1mQixJkqRWMyGWJElSq5kQS5IkqdVMiCVJktRqJsSSJElqNRNiSZIktZoJsSRJklrNhFiSJEmtZkIsSZKkVjMhliRJUquZEEuSJKnVTIglSZLUaibEkiRJajUTYkmSJLWaCbEkSZJazYRYkiRJrWZCLEmSpFYzIZYkSVKrmRBLkiSp1UyIJUmS1GomxJIkSWo1E2JJkiS1mgmxJEmSWs2EWJIkSa1mQixJkqRWMyGWJElSq5kQS5IkqdVMiCVJktRqcydjoxFxOHASMA94Z2aePhn1kCRJkia8hTgitgfeDDwK2AU4OiJ2nuh6SJIkSTA5Qyb2Br6WmTdn5m3Ap4GDJ6EekiRJ0qQMmdgOuLHj5xuBh4/ifXMAZs+eBcDsBRv3pDKD5f19Iwu26Eu5G2yydV/K3XiTbfpS7qYb96fcrTfcqj/lbrRpz8uscrvfz4Yvd35fyl200UBfyt14o+6vnYcrd4NN+lPunE3n9Lzc2Qs26LrMjnJ3AP4ArOpJoaPzDzEUehNHh/38exBHh99fuo+jw+7fPYijxtB1ldufc3UvYuhw5fYihg5Xbi9i6HDl9iKGDlduL2LocOX2Io52lLkDPYyjs9auXduLckYtIl4DbJSZJzU/Pwd4aGY+fz1vfRTwrX7XT5ImyD2B6yZwe8ZQSTNNz+LoZLQQXw/s3vHztsANo3jf/zbvuxFY3Yd6SdJE+sMEb88YKmmm6VkcnYwW4u2Bb1PDJG4DrgCOzswrJ7QikiRJEpMwqS4zrwdOBL4O/Ag4z2RYkiRJk2XCW4glSZKkqcQ71UmSJKnVTIglSZLUaibEkiRJajUTYkmSJLWaCbE0xUXEP99aS5I0KsZQjYYJsVolIu4bETtOdj3GaCFARPTteO31CSMi/i0i9uplmZImnzF03XoZR42hE8+EuI+GOzim8pVqRGzZgzKm8t83ABwBLI2IBZNdn9GIiLsBP4qIx2Xmmn59vpn5D+svdrOdiJgD7AY8IyK26LZuE2Uq77ttZQydWoyhI+tVHDWGTo4ZnxD3+4pwhO3OGjw4IuKREfGvEbHR0ANmqoiIzYD/jojHRMSu4yyj82/evlcBsxcHWUTsATw5M18P3B04JSIe2HXlht9WT4JC83n+HjgFeE9E7JaZa/sVdCLi6RFxMVRgH+92MnM18H0qvuzQlD2nV/XshyH77ibNiX/Ctt38v2lEbNw8nlKf12TEUWOoMbQX5UxkDG222XUcNYaOfdvN/13F0BmdEEfEvMxc0zy+Z0TERG27Y8d4AfAe4N+Bu3TUbb1fVseX/K8R8YCIeESfqktm3gKcB1wIXDK4U42xjMG/+QTgEurkcFg39RpykB0dEW9oyh+rDYF3RcThwA3A1sDBEfGAbuo31GB9mxP4/t18Zx0n/huAG4FzI2LPXgX0Ycr4JPCgiDhvcPtj2U5EPCwiDmre+23gl8CZETEnM1d30VoyeBxEROwWEQsiYu46/oZxld+xjx0PfBZ4Wz+Pt07N53xQs92vNift1VPlBDhZcdQYagyd6jF0sL5Dnhp3HDWGjk+vYuiMvVNdc5D+a2Z+JCKOA54GbE7dMvqczPz+BNRhN+BdwN7APakrvf2AkzPzhlGW8QTgjcA5zf8HZ+aXe1jHweAzG5gPfJjqqnlWZn5lHOU9CzgKeDnwKmAD4OOZ+dEu67kv8Dbg4039rsvM9Qb1IQfqCcCrm/pdCfw3FSg/npk/66Z+nduKiMcD/0MdnLsCJ2bmZeMs81jgSOBNwCOo/fi5mXlZRMweTFS6rPf9geWZ+ZuI2AD4OfCDzHxq5981wvtnUWP0fgVsApwB/FdmXh8RbwX+BpzSTctecxy8E7gJ+AtwKfDRzLx1vGUOs41jqM/3v4DjgD8Dpzcnpr6JiPtR+8spwL8CbwX2zMwrBk+E/dz+euo2qXHUGGoMZRrE0GY7446jxtCut9uTGDqTW4h3Aw6MiFcDBwF7AY8CVgCHRh+a84e50roBuBw4E3gd8GTqqvq0GEUXZERsD7yWqv9i4GrgNxGxS6/q23GA7UIdiM8HDgHeHxGHNq+bO1IZHY83AR4MvBD4Y1Pnr1CtCM/vop7PoMatPTsz3wa8Adg2Ik5d33uHBPJ/A74JvB94LPBSYBvgORGxcxf1mzW4rYi4d1O/fYAvA2uB10XE48ZSVvN4DtUi9trM/HxmnkjtR/8TEXuNN5AP2caewEnA4yPi7pl5B/AAYO+IOGfw7xqprMxcm5l/AY4BzgLuAbw6It4M/IDaD7Ybuu0x1HcnKrg+OTN3p06SDwD+ZaxlDSl354h4ZkTMbk5guwNPok5KNzX/Py8iHtPNdtZThwBOBDIzv56ZpwIvBr4SEXtMZjLcmNA4agw1hk6HGDrMdvZknHHUGNqdXsbQGZcQDwbJzDyT6nLavfnVhpm5hGoh2BfoqhtqmO12XkU/PKqrYAHwC+A31AH5DOB91I4yGkuBnwCPpgLkU4FNgbf3os4d9X0xdaX/GuATwM+AlwBvj4gPAW8e7iAc8je/gDp53gjcjTohfBL4ArAlFdBHNeFkmBPdSuDx1OcA9Zm8Fdi5uXoerozBLqJZEXEf4FnASzPzUODg5v27NX/zABVwxqz5m45sHs8D5gHXAX+iWjZOoQLamRHxtPWV1/F5Pgs4nGoRO6LjJV9oyj41IjYcT3Ds2MYxwOOok87DqWB+j8z8G3W1/eCIWLSe4nbqePxj6nM8iUpgbgP+E3g68IzObY9W1LjM5wP3AbZqnj6bajU7Yh1vG025c6kT+qOpz3kF1WryUCrp+iDwbeCBwGuixsT1o4v1euB3wD0jYt+ImJuZp1Of4eURsXA0iV+vTUYcNYYaQ6dLDB2ynW7jqDF07NvuSwydUQlxE1wGx7ptkJkfprrb7gAeFhGbN8H8M8DyXm674+B4GRUoXgy8BfhFc1V6v4h4L/Bm4P3DXZl2BKB/iYjXZuZt1NXtmcABmXkddUX3127qGhELOx7vCTwxMx9DnSgWAwOZeSF1sCyhukb/6SDs+JsPbF7748x8MzUZ4DnAT6mr2j8Dhzef/frq1vkd7h8RjwI+Tx1wx0XEQc0V34+BV1JjC/9JR31nUyeYPzf/k5lXAh8ALgAeBByXmeMK5sAa4JkRcWlTZgLnU8FndWZeSp3Mr2CUJ4yI2A94WdNFegywa0S8pwlAj6c+1wMy82/j7UJrvveXAe9sjpOLqaTnmRFxMrWfPW6kzyUiNgI+GRGfjIidM/OXwC3A2zPzp5n5FqoFaTFwSBOY1hsQO46Dgaxxme8Cvth8Djs3+8eFwKoYxzjbqG7SVZn5HqoV7kDgAOBF1In3KZl5OTAX+BHw9My8tZvuys6/rWkFe0xEHAkcSrVYXdXU4dFRXXzvBO6ZmUt71aU7xjpOeBw1hhpDmUYxtNnOnnQRR42hY9fPGDpjEuIhV9ovBT4VEZ8EvkWN6XoG8NrmSv5w6gq5F9td2PF4N2CfJjD+ijpZXBM1Dm8bKjA+PTN/Okw5swe/ZOoAODiqi+pEqoXmQxHxPOAV1NXdeOu7E/Cm+McujCujBsJvBzyP6v46PjO/A7wmM68eobz5VFC9DRicRLKEOgjOpg7E12Xmn0ZTv47v8HjgNKpV4oPA/wLHAu+IiEMzc3Vm/jgzrx+hbnsB52eNkfozcFHHr/9E7RfXjjfhaL6zv1AtGA8DtszMNZn5GeApwB5xZ1fVOzPzq+tqJep4vC3V6nZbROzU1H0fqqvyI9S4wv/JzD+Mp87NNjaguuT+RrUckJnnU61Ra6ku29dn5o0jlLEncHRTr5uAl0TEidTJ9edRXexk5uebbezdBKYRA2JHsDsQ+HBEfJ46qVxAjV19Y0ScRCVKl+U4hhR0JAvHUt/bdtSYyCOoAP6niDifSsr+KzP/ONZtjLDtwfGRp1ItG++j9o+3Uq0rzwAGj80/NPWcsKWMJiOOGkONoUyzGNpsp6s4agwdn37G0Bk3qS6q2+kw6qrtDOBmakzLI6muhyuAd2Xmr7vczizqYPgKNUD/8qhB9UdSO8T9qaum1wJk5knrKGdeZq5oHj+SmvDwfGBHammbvwHvBo4HbgWuyswvd564xljvLakWluXUVfj1wKeoFo0HN6/5EPDLzHzbSNuJiCdRV/ffpQL394AzM/OmqHFgD6UmFfxmjHXcC3g91Q31MuAJVAvB8cCewMnAI3PIZIChdW2+j9dQ3aZvoFqJtqdO7k8A9mtajMasI+jMBbZt/p1G/b3HR8R2wOlU1+ebm9aidZbTPN4gM+9o6v2Cpt7nZebPm6v4+dT3tHQ8dW628VQqgJ1Mzdo/ELgyM9/d8ZqBzFy5rvpSXW2XU92DL8jM90V1bx9OBfcPUfvvm5rWibHWcS9q8s+TqUkgCzNzn6hu21dSx93HMvPsLo6DAD7a1HcWNRHkEVTS8Gtq3/1iZuZYyx5hm3OoruVzqSTtXs3/z6K6TS+huvLPysyf92q74zERcdQYagydjjG02c6446gxdPz6HUNnVEIcERtSB+yJVGvCkdQkh7tTV5uPooLhOq+Ix7CtwYP5eOrLeAEVGP+HOugOzcwlEfEqarmaN8A/jv+JGld0DHBxZv4wanzUAzLzNU2rwQOocTBfBs4Y71X4YH0Ht98E9JOosVoXATtT3VN/Ba6lguZBmfmr4f7m5vFcajzeC6nA+1MqoH+TCuij7j4bJgg/DdiDOukcS50knkvNwv0P4KbMvH2E8nbOzKubvzmoE8Ia6rPen2qF+X/jPVA7vvvHUmO7/kid0L5Gndy/Sn2ufwGWZeYf1xd0mpasRwL3psZnbkp9/38DPp2Z42qJG+azvXtTx09QJ59/p8aCXp2Zox5X2fztJwOrge9m5qs6/o69qZPlSzLztHHU+fnUsTSXas05gvruPtFs7+nN784arqVwHWUO/Rx2pLoMD8nMa6ImM51GnYTe07RQ9VREbJKZt0bEh6kWoUcAR2fmryLiaur7npWZq3q97THWc0LiqDHUGDodYmhnfTt+7jqOGkPHrt8xdFoPmRimGXwVFaC2o8YJXUI1pz+AmlV5aS+S4SGupnasi4BFVHfJX6luxbdR48A+kTWLdOjBvAM1vu3g5srtt83jXTJzeWb+L3WF+xiqO2Jc3aeDO3ITgO5KDX5/JdXCsTvVrfApYDNqZz50aCCHf+iK27jZ4S6kui1eQw2cfyZ1pXxkjHJc0pATxE7NQXUx1cqzK/CbzPwc8H/UZ83QQB4R86MZNN8Eqm9FxLFNub+irpDvR83e/WJmntfNVWtHID+Tusq/jppA80IqKO7a1H/bbLqKhn73EbFnRDyoefwf1NX1i6nusidT+9TnqNaRA2Ocs/k7PtuHRsSOmfk7Ktg+kQqUlzR/w70iYvP1lRd3Tk74BfBDqjt3q4h4V7O9d1Enz+cDXxpNHYfZp1dTycYLgKdl5rXUMb1lZv6YGg95G6OcWDVkH3t4cwz8iepCPCQi7tu0lF1FHYPfGU25o9128/8DgAujunO/TXXbv7QJ5HsAt1PH34SvKjEF4qgx1Bh6HVM0hnZuuxdx1Bg6NhMZQ6dtC/GQL+goYCPgp5n5rYjYBvhwZj4+atmbh1Fjhq7tcR2eTQXr51CtKHs2j7ekWgy2Ac7NGii/rjKOog7e66hukscAD6HGfP2Fai25nFrf8I1d1vc4agD6htQakh+mWgtWAp9tPrt/ugof8lnvCbwXeHxm/i5q8fkDgROoVo6fUF1Svxtj3V5AnQwuoFpylkaNQfojNSv47cD+WXcd6nzfXlSwvhj4UGZeGxH/RrW0nJo1S56I+C9quaZX5SjXLx2mjp2fw+uA32bmOc0Bex/qOzuKCjKb5zrGqEUtH/Q+4IjM/G5T1uLMPKP5/eOpE8W/Ud2T12XmaGfVD1fXe1AtEb8Dzs7M/4tajuon1D7wNuCOzFznRKPm5DUP+MbgyTQiXknNMv9vqvXh+sx82RjrObjg/GOBu1L75keoff6n1Any36jP9tDM/EHzvvmZud4JXUM+h2Op/fQv1P6xilrbdl+qVe4J1D62zuN1PKIm+BxCJTw3Uq2HhzZ/20VUS95rssYKTqjJjqPGUGPoVI2hw9S3qzhqDB2/iYqh0zYhHhTV3fZcqitgcCmYr1JX7GdR3Tv/npm/6MO23wL8KZsujYh4ETWZ44jM/OYo3v8Eqmvnm1TryxXUYt4LqJPD7dSg+wdSV7/PpA648Yz3eSIVbA+gWl/OpCZuvJYaXH8rtazR8iHv6zwY5mXmioj4b2rs0+GZ+fuI2Jpal3I+Nbv0tjHW7UCqe/aJ1NX8XOC+1BXo06gJAc/I4SfSPJQaZ5bUEjCfoE4Iq6i7Rr2D6iZ8bFPf8c6EHtzeo6nJJXtQrWdPy+rCmU2NJ3xH1gzsdb3/cdQ++rLM/FpEbEp9L2TmSzted15T1lXjqONwJ+TdqO7ZP1GL6P8mao3L3anZ1uscpxYRd6E+342oE/nvsmbwEjXx6kc0M6OBK3IdYz2HlLkgM5c1jw+gjoOXUovFv5RqbfsosIwaW/imzLwkxrmQfrON/2j+PZ6KC1cC/49qVdi+qXtXcwuG2e59qe76p1HH2O7Ufv506kSyKfDXzPz+cN/bRJmsOGoMNYYyBWNo8/6exVFj6PhNZAxd52LhU1XUjOQ7qPFMd6c+mAdTLQQ3UuOlbqYC4AHUjOWur1bW8UEvpta+G8jMlZl5WkQ8HTgxIq5khMAbtdzK4cDLs+6Y8whqJuyDgdMy8wNR6yjuTXXJPHk0V3PD1TciFlDdCOcNXnE39fwFdRX5GmDecOV3lPESYPeoMTz7RE0a+XhEPJcK7DdRdxNabyAf5rNcTi3hdDQ1SH57YE5Tt+cAm44QhK+nun4+QLUEPZya8PEO6ur4cdT+8rJuA3njX6nJPmdQLVjPiYgzqEkK9222NazmCv58ajLL1RFxL+qzvwQ4PSL+Sl3ZP4pq4RrVrPIh27g39Xn8sWnNejDVVfYyKsHZG3hpRNxELZf09PUkw5tnjd97JdWCsQx4UkQ8kGp12grYrtmHB5cHWl8dN6XWZ/1hZr6fOsEMrhd6JRX8HtzsZxsBm2UzU3ucgXxrKojfK2vB/AujuqMPoAL5B3Kcd8EahbnUJKHvNnX5NbUPnQW8KDvu9DaRyfBkxFFjqDG0MaVjaLOdnsVRY2jXJiyGTqsW4qY14BhgC2rW8B+pcUcnUK0bX6BmQj8LeHFmnt2j7XYGxmdRO8Aq4BvAx6grsS9SXT4HAq/OzP8bRbkfprp5XtH8vB81nugi6op3F+oq7CPjbZmJ6uLYnDrIXgHcfzBoN60zV2Xmp9ZTxsHUeKTHAffJWkqIqPF996cOwsNzFJMWOq9Oo7rqBruX/oPq7nk31crzbGDjrLvOrK/MZ1KTDx4QNcv8QmrCwzZUF98jB6+kx2roiSciHkztb8dS3btPpb73WcBbM/OCEcp6PNXish+1VuM3gQsy8x1NAP4ANf5qB+CFOYZZslFdjhtSY8O+DfyS6k56DjWhYlfqxLGSurp/ONXF9LMRyjyAGpO3lprQ9ECqleidVPc5zeewgJpY8Y1R1nVL7pyRfBm1n99OncSekzUm7GfAo3MU664OU/5wLXKPoCYYXZ2Zxze/exrVvf6KrKWfuja47YjYihq7Opf6rj+QNTaQqCXLHksdkyfkCENV+mEy4qgx1Bja8fOUjKGDdaWHcdQYOnaTGUOnTULcBPE3UTvPapoxQc3BNYcaj3Vyc4W1LbVwe6/HsRxD7YQvor6gJ1ETDl5JHcz3BJ4/3EHY8SXfnwowi6nZsLtSO9hHo+7HfRo1UPxnzftGfR/uYbb5aOqKf4+s2dpnUneVOZrqQnsB1WryyyHvG6zr7MxcExEvB7bJzJc1V4VbUF1sz2vesjbHuIxNU+ZBVPA6dvD9zUH3SOpq9xk5wvqdHWVtRXWh3Uy1cB2XtZg7EbF9dj8bfi/qJH16Zv66+Rz/mpkvj+rm2xG4PTP/sI5WsM6y9qe6zOZTJ6Dzou6ssypqLOEK6iS2dIx1HPyuHkh1325KnShOa37/IirZeVhm3h4dS1Wto7x9qBaiY6nWkYMz89FRXb1bUOPHrqBO8IdQLVuj/pyjVgc4kGpdeTR1Qtw1M38cNUHinVSr5M1jKHNwvx38/7nU2Lll1Cz7P1AntiWZ+ZLmPZvkkKWnxqtju0+g9scrqNakm7hzbOhlVNfmf1LH/qtG+h56bbLjqDHUGDpVY2hTds/iqDF07CY7hk6LhDiqyf8saimP70TdivCR1BJAN1JB9TpqYPubgX1znGsjrmP7s6kD4wyq6+0J1I74XGCvzPxY87rNcuTu54OoL/V31FiYDanxNw+llrG5F3WAX7S+oDCKOj+U+lz2Bd472MrTtGjcpfl7Xjc0WA65MrwHdQAcQV15njx4wEbEZ4GTRmphHFLursCKrKWR7k99l4+JiIdQJ8GdqWB8f+pq+T/HcnUfEf9JnVQfn5lfjBHW0h2rqG65d1BjvAaoiTtHMM6r4qaV44PU2quXNCdIxnvSHlL2Iqr14qNUwnBcNl2wEfEJqotpxEXSI2JvqtXucU1w3YFqifkGFRgfRnUVn5u1nusGWd1oY63rNlTr3UOoZYvuoILdnlQL4UVjLO9e2bQqRk3WOo5KWPanjrU/N+WfAvxvZp7Y7XE2TB0GJyi9hkos7k61tn2Zal28gxp7ugUVq540nhaccdZt0uKoMdQYyjSJoU35XcVRY+j4TWYMnS4J8WAXxgepq5T/oVo3NqGWJTmQGiu0MdUyMKp19dazzeEG1L+H6jrbkGrlgLr95a4jBfHmvdtRkxWOzlqz71+pgLCMmi17P2pyydU9COT3p1owTqOuPO8FfCmbhc2juoXm5Ahr9TUtOc+iuo2+QnWx/oCasDKXmm376PUlV01Z96YmevySGpe1AfU9fp5qZbmRmlV+cWb+T4xh0H/HFeUGTXln5ghdbqMob2tgftbs7z2pMW1zqMC4GRUgHk51VR2fmR8a53YOoL6f12fmueOt75Ay7wV8mgoSg13RX6XGEe5ITf7ZIzP/PEIZs6h95yXU+LA/AF+nPtsfUH/3DtTxtyU1dm6d65mOos53oQL5famZyz8AbsvMK0d7HMSd3ZxLqJtFvCoizqKWB/tUkwgeSI2TfRHV8rk4u7xTVef2OxKgN1IJ2uBSZddQidAnM/NjEXEYlbgcRnWRdh2rxlDPCY2jxlBj6HSLoU25XcVRY+i4/oYpEUOnRUIMEDWB4Q3Ule8XqeWALo/q6nlbZj57vFdZw2yr88s5lLr6uhTYC3gVNbD8+qi71RxLXQX+bUgZ96GC4TxqofZLqS6SZ2Z1Gc2j1hm8X2Ye022dh2x7c+oq7n+oIPRSqkXjisz8xCjevz/VXXEwtcj1L6Jm1x5EtYpsQ82mHrH1Ie68I89VVDA4i1pb8jDq5PUwqjvqqqhxhftRM0dXjOVkNnhyoj7fP1BXxuOZRX4/qgXrKCrwnUZ1ax5JreF5YtaSRDtQXTXPpG4T+adxbu8J1B13npeZnxzr+4cpb2PqMzyGGkf5fWpM3d2pk+d7RtNiFHVDg6dS3bnbU3/3R5vfPYQ6Dp8EbJLd3TVv8EQ8DziHujXrOmeXj6KcB1AnsLdQ4yq3opYJW9q87hvAs7PHs6Cbsveiumq3bv6tpVrDrqAm+SygWjuWUhO/fpg9XgZylPWckDhqDDWGMg1jaFNm13HUGDp2UyGGzu5lYf3UXAHuDTwmM4/MzMubX+0L7Bg1a7on40g6AvkLqYkQf6Fmab6eGtD/yYi4gBosf+wwgTyoZWugurBeSQX2q4G9ImLbrDEv1wJbRMTGcedi3eMWtWj4A7K6oJ5PDTrfnOoCXQY8JGrR9qHvmz9k+5sAl2TmNcD/Ne/ZFfhOc+I5cjSJVdZC9sup7pbHUSeV86mA+dvMfC21AP3JVDfq6zLzjrEGxmY7q6hxRR8cZ2AN6rs9Laub+DiqO/OMzNyVmsV9RrO966jv90/AmvG2RGXmJVQr2Q/HUd+FTYsfEXFgRNw7q0vvC1QQfyk10eJZVKvSG0fznTX1Wk6ddM+iWqM6JzftSLUObNZNIG/cP2oJpX2oWcPj0gTyjZqWgqdQSxc9j9r3HxUR2zcnzk2oANsP2wP/BXwva93WZ1JjY1c1/56emb/LzL9m5mcmIxmGiYujxlBjKFM8hjZ17kscNYaOy6TH0GnTQjwoatzbPtRyQVtQzfdPyx4Pk2i6j86h1u4bnJyxgBqrtpI6uG/Kf17kfGfqoP9QZn6keW5bqqXhUVQ3wAZUl8ALqLFJl/SgvltTJ54DqK643zaPP5mZ34vqbrwjh4y1iX9clP2DmXldRDyMusPPoZn57eZ151HrLo5rbF7TDXUh1Z1zNRXgX0m1lNyHOnnkeD6HbkXETk3d7gPsnJm/jIgLqa6jy5vXbEiNYTo4axLSbtT+sVf2qNtoDPUdXCVgSypwPa2p+5Ob1pdNqH3gSc3rvpLjW2pnA6q16T+AV1PB8WTgWT063nakxondi7oBwOfH+P49gB06jrN7Ui05V1AnsI2o4+4hVCB/YdZdmnomajLa/2XdBOFE4CfNMXIO9Xndm0oK/t7VPt6Tfy/1K44aQ42hTIMY2tSn73HUGDqqOkyZGDodE+LNqS/q8dTV5Vt6tGN1LmXzTGoA/JupyRuvypp5ujvwOmoB7n9abzJqnM1PgSsz82nNc/Mzc3nUIP3Lm99/muru+lZWd+V4gmNnIH8otZbkfGoHegF1FfdMarzak3Ld4506F2V/DDVG71tUt94J1Iz0zaluzUNGc1XWXLEeQU0a+CY1nunHUWsnXk7dr/56Kkg8s9cH2FhEdd2dT93ucy01WeAAqivyFOARWWtI7krNbN4vMxdHdattMfRkPgH17VwlYC1wTWb+JWps5v2p7qxrI+Jo6kT5kcz8bRfb24CarX4KlTz19OYMTavahpl521iPg6ibEXyI6p69lNpvP5W1lu0OVFD/cmYeFRGLsjdrqHZuf1OqG3tRU4cDqfVEnx11+9vdqQX4h7172WTqRxw1hhpDmQYxtKnzhMVRY+iI259SMXTaJcTw9/FOc6mxWT1dsqjZQV5BXRU+mDqwf5y1yPsB1GD5w3MdazJGxJOpk8Bb8s4xQ4Pr+B1NLZb9/Bjn3WKG2d5LqJaCH1HLrxxBtbxsRM3G3ooaXzXsjty0vHysee0q6krwVOqEswO1Run/UQFhvWtkNmU+iVokHqrb6alUd9NV1KSKw4FPUvc7v7SbhK1bzRXxAwavrKOW1TmWGuu3D9UC8zFqTNlrMvPz0cUyTl3WdbhVAh5Fff/XUrezvBr4NdW9+thefLZR4+EOA76f1QU8ZcSdSzBtQE0E61yC6X7UONCds/uuyaHb3bg5AR1DjQd8K9WC8nLgDTmKtV8nW7/iqDHUGMoUjaFN/SY8jhpDh93ulIuh02YMcaes8U4rexzE5zQH9rnU1eJiqkvuh8BBEfElqqvjpHUF8qZuF1Ang5OjJrBAzSaFOvkMjpWb1YM67wYcmJmPoXakG6hAvnlmXp+ZR1OtB+u8qsu6e81HqYHz36VOCjdTV7K3U1fHrxhtIG/KvJAKhNdQV5j3pBY3Xwr8nmpJ2Ru4aDIDOUBmXtsRyGdlrTf5Hqq782JqbOWXqBaDzzevmZRATrVkbAncNWo5p/OocV47N7/7EjWb/WqqBa4nn23TkveRqRbIATLzYqo1b3ACBsDaJoH6BdUduLSX24xageCUiHhGZp5BzUC/W/P/EuDfmxbYKa3XcdQYagydBjEUJiGOGkP/0VSNodOyhbhXhmthiLo15PupoP3xqPUN51MB6abRdhk0LSHvotadPKcJvO+j1sj8ajf1jTtnhD6GWgblOupK9ilU68vszPzvMZQ70qLsd81xju9qPoP3UBM9PtLx/AAwN4dMpJlKom7XeSLVVfrdya7PoFj3KgHbNs+fkGO4Pe1MEetYgqlX3Wwdx9xdqATn4dRwg8XAZ5vHx1Nj+bbIccz0no6MoX8v1xg6xFSNoWAcHY4xtOUJ8aCou7E8kFrw+cPU7S/fB7w8Mz/dRbkHUIP1L6bu9vKfOc7JH0PKvWvWHX22pcZubZqZD2p+9z7g+sx84xjL7Mui7Os6yKaDiDiB6uq9fLLr0qkZ27Vxdsx2jogjqDsIPSXHcGeimST6sATTkPL/HXgb1Rr4qcz8bdStd+9GLa91dPbodvHTjTHUGDqcqRpDwTg6nLbH0FYmxFGzhf+ambdGxPHUFf2rqaDzw8w8JmpSw6eopT7GdLeXIdt6IrWG3jMy87PjLONhwAaZ+e2mvs+h1kP8FDWxZCeqe+4X1FI3B2Tmr0ZZdk8XZV/HNp5AjSE7uh8HWb/16gq5l6KPq61MZ03A/XX2eJ3MiHgQ1bV6BDXUbDU1PvTzVPfr8cB3M/MLvdzuVGUM/YeyjaHrMRVjKBhHh9PmGNq6hDhqiY/LqYHbF1BjZ95JzdZ9HLVI+HFUq8T+1BIgXe0YEbFVZv55vEGhuco+nBqTtRs1eeN51Nqe/4+atPEkauzbuTmG23U25fdkUfb1bKMvB1lbRZ9WW9GdOhKdOcADqGPsD9R6snejVhG4IjNfMvQ9k1LhCWIMHbZ8Y+g0ZBztr+kWQ9uYEG9P3Ynlp8BFVDP9DlSgOTBqBurnqeVx/tLjbY91SZSHAAupsTZLqavXczPzjRGxJbX+4CzgC5n51W53pIgIYLUBd3qIPq620mYRsUlm3to8fiyV5J1NzZwPao3cr1O3YN0jM18zSVWdFMbQEbdnDJ1mjKO9N11j6LRcZWK8mh3/z9QyIttT9/7+MnXF8u3mZU+kBtoP9Hr7Ywzk+1E70F7UkjA/pbrM/iMids9aHP4U6v7jj4+IDbu9qspiIJ8msg+rrbRd1M0DvhV3rm4wH1iZNdv65Zm5D3dOwHoTtexVaxhD11s/Y+g0YxztrekcQ1vRQjzY3dbx892p5X+WUUH7aurKJamWjiOyubPSZIiIRwAfpJapuTJq4e21TV2PpmZCvyEzvxG1sPX8zPzTZNVXmgma2c+zqLVo302NiV1FxYaDqeW+1lAL2a8BLshx3nVsujGGSlqf6R5DZ3xCHBH7ULcivIC6T/uazLwlIl5IjR27C/AE6p7ZXwau63U331hF3eXpbpl5SjQL0jfPb0m1dmxG7WAvzMwrJrGq0owQtYzVCcAe1G1a70ctHfY96lbDf6XGmX6XGmv4oexYvmtyaj0xjKGS1mcmxNA2DJnYgJo9ehzwEuBlEbEz1X12MrWcz3epNTJ/M5mBvOmOhNqRtmsedy7bs4Za63I+cCZ1605JXcpaHuuLwJXUskDXUJPD9qJWIjiWWg92O+Cn2ay9O1UCeZ8ZQyWNaCbE0BmfEGfdheUg6raXW1MtGp+lZjeuoRYN/xDwysz862TVE/5hx/gysFczzm1tRMyOupXiX6gd7orMPDMn+Q5F0kzQdKfTdPF/jIoV/0klS08FDgHumZlfom648L3JqutkMIZKGslMiaEzfsjEoGaCxXuppXauo76gZ1P3K99vKl2lNLO0Twa2Bd6fmd9snj+EGpNzUGb+fhKrKM0oEfFQ6mYSN1DjTHelxsG9GvgXajzqw4Ebp1KsmEjGUEnrMhNiaGsSYoCIOIhaOP5Fmfm5ZlHurTLzN5Nbs3/WLG10LHXS+Q41/mZPatHwMa2RKekfRcSOwGGZ+eZmfdf3Al+glgG6iEr4tmt+PgH4W7MqQasZQyXBzIyhrUqIASJif+rWhC/IzPMmuz4jiYh51GD0PYAbgW9PxROPNN0068V+GXg+NS72Tc2KAw8FDqOC+VeAp1MzoX80SVWdcoyhkmZiDG1dQgze8Udqs+auSWuBN1PjYXejxsR+JzNXR8STqbuw7Q7Mzczlk1XXqcoYKrXXTI2hcye7ApMhM7842XWQNDkyczVARFwD/BfwM+DfgP+l1sn8DXAzsGFmLpusek5lxlCpvWZqDG1lC7GkdomIHaiA/fPM/EnH829oHj4O+D9gCTXO9OTMvGBiaylJU1MbYuiMX3ZNkoAtqXFt50TESyPiPs3zv6HW2d0XuBD4OTU29oKONW0lqe1mfAy1hVhSKzR3UnoE8CrqTknLgDdQ69K+OTM/P3m1k6SpbabHUBNiSa0SEQuBragJIX8BHkxNDDkuM1dNYtUkacqbqTHUhFhSa0XELsCjgeume+uGJE20mRRDTYgltU5EzBp6t6ThnpMk/bOZGENNiCVJktRqrjIhSZKkVjMhliRJUquZEEuSJKnVTIglSZLUaibEkiRJajUTYkmSJLWaCbFmlIh4TkS8YJLrcHZEvGwy6yBJ42UcVRuZEGumeRSw0WRXQpKmMeOoWmfuZFdAWpeImA28E9gVWADMAp4DPBf4WWb+V/O6s4GfAb8BDgT2iYi/Ae8HTgUeC6wGvg+8ODOXjbDNPYH/BG4A7g/cDrweOB4I4DOZ+eJ11S0zvzOkvFOBXYCDgBXA26jbXM4BrgKOz8y/jvczkqSRGEel0bGFWFPZI4DtgH/LzJ2Bc4BXrevFmXkh8HngnZl5OnBS8/5dmn+zgXeMYrsPA96UmTsBNwGvBp4APBg4NiK2G0XdZkXE6cAOwOMz89bm96uAh2TmLtTJ4q2jqI8kjZdxVBoFW4g1ZWXmdyPiJOB5EXFvYE9gGbBklEXsB5yYmSsBIuI9wGdH8b5rM/Oq5vFvgFsycwXw54j4K7DFCHUb9BJga+BBmXlH89z+wEKq5QVgHvCnUf4tkjRmxlFpdGwh1pQVEU8ALml+/BzwPqpLbW3z/6B56yhi6P49GxgYxabvGPLzyjHUbdA3gBcBZ0fE4DbnACdk5oMy80HAw4GDR1EfSRoX46g0OibEmsr2AS7KzDOA/wWeSAXDxcBDASJiK2D3jves4s5g/SXg+REx0IxVOxb4Sp/rNugHwHuBpcAbOurzwoiY19TnA9Q4O0nqF+OoNAomxJrK3gc8OiJ+AnyX6na7J3A6sG1EJPAx4PKO91wKHB8RrwbeBPwR+BHwCyrAn9DPujUBGoDMXAs8C3hBROwGvBG4jpoEcjXVEvLSHtVHkoZjHJVGYdbatWsnuw6SJEnSpHFSnVonIs6nlv4ZzmGZmRNZH0maboyjmmlsIZYkSVKrOYZYkiRJrWZCLEmSpFYzIZYkSVKrmRBLkiSp1UyIJUmS1Gr/H+IgYppKUBukAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 720x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(24, 24))\n",
+ "chart = sns.catplot(x=\"auto_make\", col=\"fraud_reported\", data=data_cat, kind=\"count\")\n",
+ "chart.set_xticklabels(rotation=45)\n",
+ "# plt.savefig('./Daten/VglIncidentVSFraud')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "2987fc69",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 2304x2304 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(32, 32))\n",
+ "chart = sns.catplot(x=\"auto_model\", col=\"fraud_reported\", data=data_cat, kind=\"count\", aspect=1.5)\n",
+ "chart.set_xticklabels(rotation=90)\n",
+ "# plt.savefig('./Daten/VglIncidentVSFraud')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "2c3a012f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 2304x2304 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(32, 32))\n",
+ "chart = sns.catplot(x=\"policy_csl\", col=\"fraud_reported\", data=data_cat, kind=\"count\", aspect=1.5)\n",
+ "chart.set_xticklabels(rotation=90)\n",
+ "# plt.savefig('./Daten/VglIncidentVSFraud')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "49932ce2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>250/500</td>\n",
+ " <td>0.262108</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>100/300</td>\n",
+ " <td>0.257880</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>500/1000</td>\n",
+ " <td>0.216667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl fraud_reported\n",
+ "1 250/500 0.262108\n",
+ "0 100/300 0.257880\n",
+ "2 500/1000 0.216667"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat[['policy_csl', 'fraud_reported']].groupby(['policy_csl'], as_index=False).mean().sort_values(by='fraud_reported', ascending=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "7e4a21c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>insured_hobbies</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>chess</td>\n",
+ " <td>0.826087</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>cross-fit</td>\n",
+ " <td>0.742857</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>yachting</td>\n",
+ " <td>0.301887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>board-games</td>\n",
+ " <td>0.291667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>polo</td>\n",
+ " <td>0.276596</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>reading</td>\n",
+ " <td>0.265625</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>base-jumping</td>\n",
+ " <td>0.265306</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>hiking</td>\n",
+ " <td>0.230769</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>paintball</td>\n",
+ " <td>0.228070</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>skydiving</td>\n",
+ " <td>0.224490</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>video-games</td>\n",
+ " <td>0.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>sleeping</td>\n",
+ " <td>0.195122</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>exercise</td>\n",
+ " <td>0.192982</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>basketball</td>\n",
+ " <td>0.176471</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>movies</td>\n",
+ " <td>0.163636</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>bungie-jumping</td>\n",
+ " <td>0.160714</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>dancing</td>\n",
+ " <td>0.116279</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>golf</td>\n",
+ " <td>0.109091</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>kayaking</td>\n",
+ " <td>0.092593</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>camping</td>\n",
+ " <td>0.090909</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " insured_hobbies fraud_reported\n",
+ "5 chess 0.826087\n",
+ "6 cross-fit 0.742857\n",
+ "19 yachting 0.301887\n",
+ "2 board-games 0.291667\n",
+ "14 polo 0.276596\n",
+ "15 reading 0.265625\n",
+ "0 base-jumping 0.265306\n",
+ "10 hiking 0.230769\n",
+ "13 paintball 0.228070\n",
+ "16 skydiving 0.224490\n",
+ "18 video-games 0.200000\n",
+ "17 sleeping 0.195122\n",
+ "8 exercise 0.192982\n",
+ "1 basketball 0.176471\n",
+ "12 movies 0.163636\n",
+ "3 bungie-jumping 0.160714\n",
+ "7 dancing 0.116279\n",
+ "9 golf 0.109091\n",
+ "11 kayaking 0.092593\n",
+ "4 camping 0.090909"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat[['insured_hobbies', 'fraud_reported']].groupby(['insured_hobbies'], as_index=False).mean().sort_values(by='fraud_reported', ascending=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "e2cbb5c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>insured_occupation</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>exec-managerial</td>\n",
+ " <td>0.368421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>farming-fishing</td>\n",
+ " <td>0.301887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>craft-repair</td>\n",
+ " <td>0.297297</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>transport-moving</td>\n",
+ " <td>0.291667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>tech-support</td>\n",
+ " <td>0.282051</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>sales</td>\n",
+ " <td>0.276316</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>armed-forces</td>\n",
+ " <td>0.246377</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>machine-op-inspct</td>\n",
+ " <td>0.236559</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>protective-serv</td>\n",
+ " <td>0.222222</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>prof-specialty</td>\n",
+ " <td>0.211765</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>handlers-cleaners</td>\n",
+ " <td>0.203704</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>adm-clerical</td>\n",
+ " <td>0.169231</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>other-service</td>\n",
+ " <td>0.169014</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>priv-house-serv</td>\n",
+ " <td>0.169014</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " insured_occupation fraud_reported\n",
+ "3 exec-managerial 0.368421\n",
+ "4 farming-fishing 0.301887\n",
+ "2 craft-repair 0.297297\n",
+ "13 transport-moving 0.291667\n",
+ "12 tech-support 0.282051\n",
+ "11 sales 0.276316\n",
+ "1 armed-forces 0.246377\n",
+ "6 machine-op-inspct 0.236559\n",
+ "10 protective-serv 0.222222\n",
+ "9 prof-specialty 0.211765\n",
+ "5 handlers-cleaners 0.203704\n",
+ "0 adm-clerical 0.169231\n",
+ "7 other-service 0.169014\n",
+ "8 priv-house-serv 0.169014"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat[['insured_occupation', 'fraud_reported']].groupby(['insured_occupation'], as_index=False).mean().sort_values(by='fraud_reported', ascending=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "267dec9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>insured_education_level</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>PhD</td>\n",
+ " <td>0.264000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>MD</td>\n",
+ " <td>0.263889</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>College</td>\n",
+ " <td>0.262295</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>JD</td>\n",
+ " <td>0.260870</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Associate</td>\n",
+ " <td>0.234483</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>High School</td>\n",
+ " <td>0.225000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>Masters</td>\n",
+ " <td>0.223776</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " insured_education_level fraud_reported\n",
+ "6 PhD 0.264000\n",
+ "4 MD 0.263889\n",
+ "1 College 0.262295\n",
+ "3 JD 0.260870\n",
+ "0 Associate 0.234483\n",
+ "2 High School 0.225000\n",
+ "5 Masters 0.223776"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat[['insured_education_level', 'fraud_reported']].groupby(['insured_education_level'], as_index=False).mean().sort_values(by='fraud_reported', ascending=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "29500b25",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop not relevant features\n",
+ "data_cat.drop(['incident_date', 'auto_make', 'auto_model'], axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "035e1ac9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>insured_sex</th>\n",
+ " <th>insured_education_level</th>\n",
+ " <th>insured_occupation</th>\n",
+ " <th>insured_hobbies</th>\n",
+ " <th>insured_relationship</th>\n",
+ " <th>incident_type</th>\n",
+ " <th>collision_type</th>\n",
+ " <th>incident_severity</th>\n",
+ " <th>authorities_contacted</th>\n",
+ " <th>property_damage</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>250/500</td>\n",
+ " <td>MALE</td>\n",
+ " <td>MD</td>\n",
+ " <td>craft-repair</td>\n",
+ " <td>sleeping</td>\n",
+ " <td>husband</td>\n",
+ " <td>Single Vehicle Collision</td>\n",
+ " <td>Side Collision</td>\n",
+ " <td>Major Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>YES</td>\n",
+ " <td>YES</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>250/500</td>\n",
+ " <td>MALE</td>\n",
+ " <td>MD</td>\n",
+ " <td>machine-op-inspct</td>\n",
+ " <td>reading</td>\n",
+ " <td>other-relative</td>\n",
+ " <td>Vehicle Theft</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>100/300</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>PhD</td>\n",
+ " <td>sales</td>\n",
+ " <td>board-games</td>\n",
+ " <td>own-child</td>\n",
+ " <td>Multi-vehicle Collision</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>250/500</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>PhD</td>\n",
+ " <td>armed-forces</td>\n",
+ " <td>board-games</td>\n",
+ " <td>unmarried</td>\n",
+ " <td>Single Vehicle Collision</td>\n",
+ " <td>Front Collision</td>\n",
+ " <td>Major Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>500/1000</td>\n",
+ " <td>MALE</td>\n",
+ " <td>Associate</td>\n",
+ " <td>sales</td>\n",
+ " <td>board-games</td>\n",
+ " <td>unmarried</td>\n",
+ " <td>Vehicle Theft</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>None</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl insured_sex insured_education_level insured_occupation \\\n",
+ "0 250/500 MALE MD craft-repair \n",
+ "1 250/500 MALE MD machine-op-inspct \n",
+ "2 100/300 FEMALE PhD sales \n",
+ "3 250/500 FEMALE PhD armed-forces \n",
+ "4 500/1000 MALE Associate sales \n",
+ "\n",
+ " insured_hobbies insured_relationship incident_type \\\n",
+ "0 sleeping husband Single Vehicle Collision \n",
+ "1 reading other-relative Vehicle Theft \n",
+ "2 board-games own-child Multi-vehicle Collision \n",
+ "3 board-games unmarried Single Vehicle Collision \n",
+ "4 board-games unmarried Vehicle Theft \n",
+ "\n",
+ " collision_type incident_severity authorities_contacted property_damage \\\n",
+ "0 Side Collision Major Damage Police YES \n",
+ "1 Rear Collision Minor Damage Police NO \n",
+ "2 Rear Collision Minor Damage Police NO \n",
+ "3 Front Collision Major Damage Police NO \n",
+ "4 Rear Collision Minor Damage None NO \n",
+ "\n",
+ " police_report_available fraud_reported \n",
+ "0 YES 1 \n",
+ "1 NO 1 \n",
+ "2 NO 0 \n",
+ "3 NO 1 \n",
+ "4 NO 0 "
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "7d855ead",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>auto_year</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>328</td>\n",
+ " <td>1000</td>\n",
+ " <td>1406.91</td>\n",
+ " <td>0</td>\n",
+ " <td>53300</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6510</td>\n",
+ " <td>13020</td>\n",
+ " <td>52080</td>\n",
+ " <td>2004</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>228</td>\n",
+ " <td>2000</td>\n",
+ " <td>1197.22</td>\n",
+ " <td>5000000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>780</td>\n",
+ " <td>780</td>\n",
+ " <td>3510</td>\n",
+ " <td>2007</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>134</td>\n",
+ " <td>2000</td>\n",
+ " <td>1413.14</td>\n",
+ " <td>5000000</td>\n",
+ " <td>35100</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>7700</td>\n",
+ " <td>3850</td>\n",
+ " <td>23100</td>\n",
+ " <td>2007</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>256</td>\n",
+ " <td>2000</td>\n",
+ " <td>1415.74</td>\n",
+ " <td>6000000</td>\n",
+ " <td>48900</td>\n",
+ " <td>-62400</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6340</td>\n",
+ " <td>6340</td>\n",
+ " <td>50720</td>\n",
+ " <td>2014</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>228</td>\n",
+ " <td>1000</td>\n",
+ " <td>1583.91</td>\n",
+ " <td>6000000</td>\n",
+ " <td>66000</td>\n",
+ " <td>-46000</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1300</td>\n",
+ " <td>650</td>\n",
+ " <td>4550</td>\n",
+ " <td>2009</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer policy_deductable policy_annual_premium \\\n",
+ "0 328 1000 1406.91 \n",
+ "1 228 2000 1197.22 \n",
+ "2 134 2000 1413.14 \n",
+ "3 256 2000 1415.74 \n",
+ "4 228 1000 1583.91 \n",
+ "\n",
+ " umbrella_limit capital-gains capital-loss number_of_vehicles_involved \\\n",
+ "0 0 53300 0 1 \n",
+ "1 5000000 0 0 1 \n",
+ "2 5000000 35100 0 3 \n",
+ "3 6000000 48900 -62400 1 \n",
+ "4 6000000 66000 -46000 1 \n",
+ "\n",
+ " bodily_injuries witnesses injury_claim property_claim vehicle_claim \\\n",
+ "0 1 2 6510 13020 52080 \n",
+ "1 0 0 780 780 3510 \n",
+ "2 2 3 7700 3850 23100 \n",
+ "3 1 2 6340 6340 50720 \n",
+ "4 0 1 1300 650 4550 \n",
+ "\n",
+ " auto_year fraud_reported \n",
+ "0 2004 1 \n",
+ "1 2007 1 \n",
+ "2 2007 0 \n",
+ "3 2014 1 \n",
+ "4 2009 0 "
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_num.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "dfdf6abc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "chart = sns.catplot(x=\"auto_year\", col=\"fraud_reported\", data=data_num, kind=\"count\", aspect=1.5)\n",
+ "chart.set_xticklabels(rotation=45)\n",
+ "# plt.savefig('./Daten/VglIncidentVSFraud')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "567eda9a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(data_num.policy_annual_premium, data_num.fraud_reported)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "a3405af2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(data_num.umbrella_limit, data_num.fraud_reported)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f6069417",
+ "metadata": {},
+ "source": [
+ "## 3.3 Merkmal Technik\n",
+ "### neues Merkmal mit prozentualem Anteil des bezahlten Schadens (ohne Selbstbeteiligung)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "8d204f0b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\LOMIKU~1\\AppData\\Local\\Temp/ipykernel_1952/644168615.py:3: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " data_num['total_claims'] = data_num.loc[:, 'injury_claim':'vehicle_claim'].apply(sum, axis=1)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>auto_year</th>\n",
+ " <th>fraud_reported</th>\n",
+ " <th>total_claims</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>328</td>\n",
+ " <td>1000</td>\n",
+ " <td>1406.91</td>\n",
+ " <td>0</td>\n",
+ " <td>53300</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6510</td>\n",
+ " <td>13020</td>\n",
+ " <td>52080</td>\n",
+ " <td>2004</td>\n",
+ " <td>1</td>\n",
+ " <td>71610</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>228</td>\n",
+ " <td>2000</td>\n",
+ " <td>1197.22</td>\n",
+ " <td>5000000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>780</td>\n",
+ " <td>780</td>\n",
+ " <td>3510</td>\n",
+ " <td>2007</td>\n",
+ " <td>1</td>\n",
+ " <td>5070</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>134</td>\n",
+ " <td>2000</td>\n",
+ " <td>1413.14</td>\n",
+ " <td>5000000</td>\n",
+ " <td>35100</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>7700</td>\n",
+ " <td>3850</td>\n",
+ " <td>23100</td>\n",
+ " <td>2007</td>\n",
+ " <td>0</td>\n",
+ " <td>34650</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>256</td>\n",
+ " <td>2000</td>\n",
+ " <td>1415.74</td>\n",
+ " <td>6000000</td>\n",
+ " <td>48900</td>\n",
+ " <td>-62400</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6340</td>\n",
+ " <td>6340</td>\n",
+ " <td>50720</td>\n",
+ " <td>2014</td>\n",
+ " <td>1</td>\n",
+ " <td>63400</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>228</td>\n",
+ " <td>1000</td>\n",
+ " <td>1583.91</td>\n",
+ " <td>6000000</td>\n",
+ " <td>66000</td>\n",
+ " <td>-46000</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1300</td>\n",
+ " <td>650</td>\n",
+ " <td>4550</td>\n",
+ " <td>2009</td>\n",
+ " <td>0</td>\n",
+ " <td>6500</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer policy_deductable policy_annual_premium \\\n",
+ "0 328 1000 1406.91 \n",
+ "1 228 2000 1197.22 \n",
+ "2 134 2000 1413.14 \n",
+ "3 256 2000 1415.74 \n",
+ "4 228 1000 1583.91 \n",
+ "\n",
+ " umbrella_limit capital-gains capital-loss number_of_vehicles_involved \\\n",
+ "0 0 53300 0 1 \n",
+ "1 5000000 0 0 1 \n",
+ "2 5000000 35100 0 3 \n",
+ "3 6000000 48900 -62400 1 \n",
+ "4 6000000 66000 -46000 1 \n",
+ "\n",
+ " bodily_injuries witnesses injury_claim property_claim vehicle_claim \\\n",
+ "0 1 2 6510 13020 52080 \n",
+ "1 0 0 780 780 3510 \n",
+ "2 2 3 7700 3850 23100 \n",
+ "3 1 2 6340 6340 50720 \n",
+ "4 0 1 1300 650 4550 \n",
+ "\n",
+ " auto_year fraud_reported total_claims \n",
+ "0 2004 1 71610 \n",
+ "1 2007 1 5070 \n",
+ "2 2007 0 34650 \n",
+ "3 2014 1 63400 \n",
+ "4 2009 0 6500 "
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Preparation\n",
+ "# data_num.loc[:, 'injury_claim':'vehicle_claim'].apply(sum, axis=1)\n",
+ "data_num['total_claims'] = data_num.loc[:, 'injury_claim':'vehicle_claim'].apply(sum, axis=1)\n",
+ "data_num.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "a0213331",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\LOMIKU~1\\AppData\\Local\\Temp/ipykernel_1952/4156420314.py:2: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " data_num['pct_paid_insurance'] = (data_num.total_claims - data_num.policy_deductable) / data_num.total_claims\n"
+ ]
+ }
+ ],
+ "source": [
+ "# add new feature\n",
+ "data_num['pct_paid_insurance'] = (data_num.total_claims - data_num.policy_deductable) / data_num.total_claims "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "26aa3f21",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 0.986035\n",
+ "1 0.605523\n",
+ "2 0.942280\n",
+ "3 0.968454\n",
+ "4 0.846154\n",
+ " ... \n",
+ "995 0.988532\n",
+ "996 0.990782\n",
+ "997 0.992593\n",
+ "998 0.957429\n",
+ "999 0.802372\n",
+ "Name: pct_paid_insurance, Length: 1000, dtype: float64"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_num.pct_paid_insurance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "d6b1864e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 540x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "chart = sns.catplot(x=\"pct_paid_insurance\", col=\"fraud_reported\", data=data_num, kind=\"bar\", aspect=0.75)\n",
+ "chart.set_xticklabels(rotation=45)\n",
+ "# plt.savefig('./Daten/VglIncidentVSFraud')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "2e349d53",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1296x864 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# checking correlation with the new feature\n",
+ "plt.figure(figsize=(18, 12))\n",
+ "\n",
+ "feature_corr = data_num.corr()\n",
+ "mask = np.triu(np.ones_like(feature_corr, dtype = bool))\n",
+ "sns.heatmap(feature_corr, mask=mask, annot=True, cmap='coolwarm')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "b4800eb6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\users\\lomikukus\\coding\\ml_projekt\\insurance_ueberarbeitung\\machine-learning-services\\venv\\lib\\site-packages\\pandas\\core\\frame.py:4906: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " return super().drop(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# drop not relevant features\n",
+ "data_num.drop(['auto_year', 'total_claims'], axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "dc6f52a4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>fraud_reported</th>\n",
+ " <th>pct_paid_insurance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>328</td>\n",
+ " <td>1000</td>\n",
+ " <td>1406.91</td>\n",
+ " <td>0</td>\n",
+ " <td>53300</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6510</td>\n",
+ " <td>13020</td>\n",
+ " <td>52080</td>\n",
+ " <td>1</td>\n",
+ " <td>0.986035</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>228</td>\n",
+ " <td>2000</td>\n",
+ " <td>1197.22</td>\n",
+ " <td>5000000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>780</td>\n",
+ " <td>780</td>\n",
+ " <td>3510</td>\n",
+ " <td>1</td>\n",
+ " <td>0.605523</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>134</td>\n",
+ " <td>2000</td>\n",
+ " <td>1413.14</td>\n",
+ " <td>5000000</td>\n",
+ " <td>35100</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>7700</td>\n",
+ " <td>3850</td>\n",
+ " <td>23100</td>\n",
+ " <td>0</td>\n",
+ " <td>0.942280</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>256</td>\n",
+ " <td>2000</td>\n",
+ " <td>1415.74</td>\n",
+ " <td>6000000</td>\n",
+ " <td>48900</td>\n",
+ " <td>-62400</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6340</td>\n",
+ " <td>6340</td>\n",
+ " <td>50720</td>\n",
+ " <td>1</td>\n",
+ " <td>0.968454</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>228</td>\n",
+ " <td>1000</td>\n",
+ " <td>1583.91</td>\n",
+ " <td>6000000</td>\n",
+ " <td>66000</td>\n",
+ " <td>-46000</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1300</td>\n",
+ " <td>650</td>\n",
+ " <td>4550</td>\n",
+ " <td>0</td>\n",
+ " <td>0.846154</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer policy_deductable policy_annual_premium \\\n",
+ "0 328 1000 1406.91 \n",
+ "1 228 2000 1197.22 \n",
+ "2 134 2000 1413.14 \n",
+ "3 256 2000 1415.74 \n",
+ "4 228 1000 1583.91 \n",
+ "\n",
+ " umbrella_limit capital-gains capital-loss number_of_vehicles_involved \\\n",
+ "0 0 53300 0 1 \n",
+ "1 5000000 0 0 1 \n",
+ "2 5000000 35100 0 3 \n",
+ "3 6000000 48900 -62400 1 \n",
+ "4 6000000 66000 -46000 1 \n",
+ "\n",
+ " bodily_injuries witnesses injury_claim property_claim vehicle_claim \\\n",
+ "0 1 2 6510 13020 52080 \n",
+ "1 0 0 780 780 3510 \n",
+ "2 2 3 7700 3850 23100 \n",
+ "3 1 2 6340 6340 50720 \n",
+ "4 0 1 1300 650 4550 \n",
+ "\n",
+ " fraud_reported pct_paid_insurance \n",
+ "0 1 0.986035 \n",
+ "1 1 0.605523 \n",
+ "2 0 0.942280 \n",
+ "3 1 0.968454 \n",
+ "4 0 0.846154 "
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_num.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "37ce51ac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl</th>\n",
+ " <th>insured_sex</th>\n",
+ " <th>insured_education_level</th>\n",
+ " <th>insured_occupation</th>\n",
+ " <th>insured_hobbies</th>\n",
+ " <th>insured_relationship</th>\n",
+ " <th>incident_type</th>\n",
+ " <th>collision_type</th>\n",
+ " <th>incident_severity</th>\n",
+ " <th>authorities_contacted</th>\n",
+ " <th>property_damage</th>\n",
+ " <th>police_report_available</th>\n",
+ " <th>fraud_reported</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000</td>\n",
+ " <td>1000.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>7</td>\n",
+ " <td>14</td>\n",
+ " <td>20</td>\n",
+ " <td>6</td>\n",
+ " <td>4</td>\n",
+ " <td>3</td>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>250/500</td>\n",
+ " <td>FEMALE</td>\n",
+ " <td>JD</td>\n",
+ " <td>machine-op-inspct</td>\n",
+ " <td>reading</td>\n",
+ " <td>own-child</td>\n",
+ " <td>Multi-vehicle Collision</td>\n",
+ " <td>Rear Collision</td>\n",
+ " <td>Minor Damage</td>\n",
+ " <td>Police</td>\n",
+ " <td>NO</td>\n",
+ " <td>NO</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>351</td>\n",
+ " <td>537</td>\n",
+ " <td>161</td>\n",
+ " <td>93</td>\n",
+ " <td>64</td>\n",
+ " <td>183</td>\n",
+ " <td>419</td>\n",
+ " <td>470</td>\n",
+ " <td>354</td>\n",
+ " <td>292</td>\n",
+ " <td>698</td>\n",
+ " <td>686</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.247000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.431483</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl insured_sex insured_education_level insured_occupation \\\n",
+ "count 1000 1000 1000 1000 \n",
+ "unique 3 2 7 14 \n",
+ "top 250/500 FEMALE JD machine-op-inspct \n",
+ "freq 351 537 161 93 \n",
+ "mean NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN \n",
+ "\n",
+ " insured_hobbies insured_relationship incident_type \\\n",
+ "count 1000 1000 1000 \n",
+ "unique 20 6 4 \n",
+ "top reading own-child Multi-vehicle Collision \n",
+ "freq 64 183 419 \n",
+ "mean NaN NaN NaN \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN NaN \n",
+ "25% NaN NaN NaN \n",
+ "50% NaN NaN NaN \n",
+ "75% NaN NaN NaN \n",
+ "max NaN NaN NaN \n",
+ "\n",
+ " collision_type incident_severity authorities_contacted \\\n",
+ "count 1000 1000 1000 \n",
+ "unique 3 4 5 \n",
+ "top Rear Collision Minor Damage Police \n",
+ "freq 470 354 292 \n",
+ "mean NaN NaN NaN \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN NaN \n",
+ "25% NaN NaN NaN \n",
+ "50% NaN NaN NaN \n",
+ "75% NaN NaN NaN \n",
+ "max NaN NaN NaN \n",
+ "\n",
+ " property_damage police_report_available fraud_reported \n",
+ "count 1000 1000 1000.000000 \n",
+ "unique 2 2 NaN \n",
+ "top NO NO NaN \n",
+ "freq 698 686 NaN \n",
+ "mean NaN NaN 0.247000 \n",
+ "std NaN NaN 0.431483 \n",
+ "min NaN NaN 0.000000 \n",
+ "25% NaN NaN 0.000000 \n",
+ "50% NaN NaN 0.000000 \n",
+ "75% NaN NaN 0.000000 \n",
+ "max NaN NaN 1.000000 "
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_cat.describe(include='all')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "0f6823ba",
+ "metadata": {},
+ "outputs": [
+ {
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+ "<div>\n",
+ "<style scoped>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>months_as_customer</th>\n",
+ " <th>policy_deductable</th>\n",
+ " <th>policy_annual_premium</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>fraud_reported</th>\n",
+ " <th>pct_paid_insurance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1.000000e+03</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.00000</td>\n",
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+ " <td>1000.000000</td>\n",
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+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1000.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>203.954000</td>\n",
+ " <td>1136.000000</td>\n",
+ " <td>1256.406150</td>\n",
+ " <td>1.101000e+06</td>\n",
+ " <td>25126.100000</td>\n",
+ " <td>-26793.700000</td>\n",
+ " <td>1.83900</td>\n",
+ " <td>0.992000</td>\n",
+ " <td>1.487000</td>\n",
+ " <td>7433.420000</td>\n",
+ " <td>7399.570000</td>\n",
+ " <td>37928.950000</td>\n",
+ " <td>0.247000</td>\n",
+ " <td>0.923527</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>115.113174</td>\n",
+ " <td>611.864673</td>\n",
+ " <td>244.167395</td>\n",
+ " <td>2.297407e+06</td>\n",
+ " <td>27872.187708</td>\n",
+ " <td>28104.096686</td>\n",
+ " <td>1.01888</td>\n",
+ " <td>0.820127</td>\n",
+ " <td>1.111335</td>\n",
+ " <td>4880.951853</td>\n",
+ " <td>4824.726179</td>\n",
+ " <td>18886.252893</td>\n",
+ " <td>0.431483</td>\n",
+ " <td>0.639068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
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+ " <td>1.00000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-19.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>115.750000</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>1089.607500</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-51500.000000</td>\n",
+ " <td>1.00000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>4295.000000</td>\n",
+ " <td>4445.000000</td>\n",
+ " <td>30292.500000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.963948</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>199.500000</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>1257.200000</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-23250.000000</td>\n",
+ " <td>1.00000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>6775.000000</td>\n",
+ " <td>6750.000000</td>\n",
+ " <td>42100.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.980558</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>276.250000</td>\n",
+ " <td>2000.000000</td>\n",
+ " <td>1415.695000</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>51025.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>3.00000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>11305.000000</td>\n",
+ " <td>10885.000000</td>\n",
+ " <td>50822.500000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.988895</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>479.000000</td>\n",
+ " <td>2000.000000</td>\n",
+ " <td>2047.590000</td>\n",
+ " <td>1.000000e+07</td>\n",
+ " <td>100500.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>4.00000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>21450.000000</td>\n",
+ " <td>23670.000000</td>\n",
+ " <td>79560.000000</td>\n",
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+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " months_as_customer policy_deductable policy_annual_premium \\\n",
+ "count 1000.000000 1000.000000 1000.000000 \n",
+ "mean 203.954000 1136.000000 1256.406150 \n",
+ "std 115.113174 611.864673 244.167395 \n",
+ "min 0.000000 500.000000 433.330000 \n",
+ "25% 115.750000 500.000000 1089.607500 \n",
+ "50% 199.500000 1000.000000 1257.200000 \n",
+ "75% 276.250000 2000.000000 1415.695000 \n",
+ "max 479.000000 2000.000000 2047.590000 \n",
+ "\n",
+ " umbrella_limit capital-gains capital-loss \\\n",
+ "count 1.000000e+03 1000.000000 1000.000000 \n",
+ "mean 1.101000e+06 25126.100000 -26793.700000 \n",
+ "std 2.297407e+06 27872.187708 28104.096686 \n",
+ "min -1.000000e+06 0.000000 -111100.000000 \n",
+ "25% 0.000000e+00 0.000000 -51500.000000 \n",
+ "50% 0.000000e+00 0.000000 -23250.000000 \n",
+ "75% 0.000000e+00 51025.000000 0.000000 \n",
+ "max 1.000000e+07 100500.000000 0.000000 \n",
+ "\n",
+ " number_of_vehicles_involved bodily_injuries witnesses \\\n",
+ "count 1000.00000 1000.000000 1000.000000 \n",
+ "mean 1.83900 0.992000 1.487000 \n",
+ "std 1.01888 0.820127 1.111335 \n",
+ "min 1.00000 0.000000 0.000000 \n",
+ "25% 1.00000 0.000000 1.000000 \n",
+ "50% 1.00000 1.000000 1.000000 \n",
+ "75% 3.00000 2.000000 2.000000 \n",
+ "max 4.00000 2.000000 3.000000 \n",
+ "\n",
+ " injury_claim property_claim vehicle_claim fraud_reported \\\n",
+ "count 1000.000000 1000.000000 1000.000000 1000.000000 \n",
+ "mean 7433.420000 7399.570000 37928.950000 0.247000 \n",
+ "std 4880.951853 4824.726179 18886.252893 0.431483 \n",
+ "min 0.000000 0.000000 70.000000 0.000000 \n",
+ "25% 4295.000000 4445.000000 30292.500000 0.000000 \n",
+ "50% 6775.000000 6750.000000 42100.000000 0.000000 \n",
+ "75% 11305.000000 10885.000000 50822.500000 0.000000 \n",
+ "max 21450.000000 23670.000000 79560.000000 1.000000 \n",
+ "\n",
+ " pct_paid_insurance \n",
+ "count 1000.000000 \n",
+ "mean 0.923527 \n",
+ "std 0.639068 \n",
+ "min -19.000000 \n",
+ "25% 0.963948 \n",
+ "50% 0.980558 \n",
+ "75% 0.988895 \n",
+ "max 0.995548 "
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_num.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "0ba21ca3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 1800x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
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+ ]
+ },
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+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
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+ "text/plain": [
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+ "text/plain": [
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+ ]
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+ "text/plain": [
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+ ]
+ },
+ "metadata": {
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+ ]
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+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
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+ },
+ "output_type": "display_data"
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+ "text/plain": [
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+ "metadata": {
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+ "output_type": "display_data"
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+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize = (25, 20))\n",
+ "plotnumber = 1\n",
+ "\n",
+ "for col in data_num.columns:\n",
+ " if plotnumber <= data_num.shape[1]:\n",
+ " ax = plt.subplot(5, 5, plotnumber)\n",
+ " sns.displot(data_num[col])\n",
+ " plt.xlabel(col, fontsize = 15)\n",
+ " \n",
+ " plotnumber += 1\n",
+ " \n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "610ae82c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 753\n",
+ "1 247\n",
+ "Name: fraud_reported, dtype: int64\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "([<matplotlib.patches.Wedge at 0x1b7850dce50>,\n",
+ " <matplotlib.patches.Wedge at 0x1b7852c6550>],\n",
+ " [Text(-0.7704522141128092, -0.7851136132870644, 'No Fraud'),\n",
+ " Text(0.8054727308753049, 0.8208006334161048, 'Fraud')],\n",
+ " [Text(-0.4202466622433504, -0.42824378906567145, '75.3'),\n",
+ " Text(0.45526719571212887, 0.463930792800407, '24.7')])"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "print(data_num.fraud_reported.value_counts())\n",
+ "plt.pie(data_num.fraud_reported.value_counts(), labels=['No Fraud', 'Fraud'], autopct='%.1f', \n",
+ " startangle=90, explode=[0, 0.05], colors=['#7ed6df', '#ffbe76'], textprops={'fontsize': 12})"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "ffc37012",
+ "metadata": {},
+ "source": [
+ "--> Es handelt sich um einen unausgewogenen Datensatz."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0537926c-8b97-4924-9b2d-ebc7aad4ad11",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "## 3.4 Dummy-Variablen erstellen"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "da842b3c",
+ "metadata": {},
+ "source": [
+ "## 3.4 Dummy-Variablen erstellen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "024c1724",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create dummy variables\n",
+ "data_cat.drop('fraud_reported', axis=1, inplace=True)\n",
+ "dummies = pd.get_dummies(data_cat, drop_first=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "74de27cf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl_250/500</th>\n",
+ " <th>policy_csl_500/1000</th>\n",
+ " <th>insured_sex_MALE</th>\n",
+ " <th>insured_education_level_College</th>\n",
+ " <th>insured_education_level_High School</th>\n",
+ " <th>insured_education_level_JD</th>\n",
+ " <th>insured_education_level_MD</th>\n",
+ " <th>insured_education_level_Masters</th>\n",
+ " <th>insured_education_level_PhD</th>\n",
+ " <th>insured_occupation_armed-forces</th>\n",
+ " <th>...</th>\n",
+ " <th>collision_type_Side Collision</th>\n",
+ " <th>incident_severity_Minor Damage</th>\n",
+ " <th>incident_severity_Total Loss</th>\n",
+ " <th>incident_severity_Trivial Damage</th>\n",
+ " <th>authorities_contacted_Fire</th>\n",
+ " <th>authorities_contacted_None</th>\n",
+ " <th>authorities_contacted_Other</th>\n",
+ " <th>authorities_contacted_Police</th>\n",
+ " <th>property_damage_YES</th>\n",
+ " <th>police_report_available_YES</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 60 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl_250/500 policy_csl_500/1000 insured_sex_MALE \\\n",
+ "0 1 0 1 \n",
+ "1 1 0 1 \n",
+ "2 0 0 0 \n",
+ "3 1 0 0 \n",
+ "4 0 1 1 \n",
+ "\n",
+ " insured_education_level_College insured_education_level_High School \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_education_level_JD insured_education_level_MD \\\n",
+ "0 0 1 \n",
+ "1 0 1 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_education_level_Masters insured_education_level_PhD \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 1 \n",
+ "3 0 1 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_occupation_armed-forces ... collision_type_Side Collision \\\n",
+ "0 0 ... 1 \n",
+ "1 0 ... 0 \n",
+ "2 0 ... 0 \n",
+ "3 1 ... 0 \n",
+ "4 0 ... 0 \n",
+ "\n",
+ " incident_severity_Minor Damage incident_severity_Total Loss \\\n",
+ "0 0 0 \n",
+ "1 1 0 \n",
+ "2 1 0 \n",
+ "3 0 0 \n",
+ "4 1 0 \n",
+ "\n",
+ " incident_severity_Trivial Damage authorities_contacted_Fire \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " authorities_contacted_None authorities_contacted_Other \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 1 0 \n",
+ "\n",
+ " authorities_contacted_Police property_damage_YES \\\n",
+ "0 1 1 \n",
+ "1 1 0 \n",
+ "2 1 0 \n",
+ "3 1 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " police_report_available_YES \n",
+ "0 1 \n",
+ "1 0 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 \n",
+ "\n",
+ "[5 rows x 60 columns]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dummies.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "07d760ae",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1000, 74)"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_preprocessed = pd.concat([dummies, data_num], axis=1)\n",
+ "data_preprocessed.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "220ba08b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
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+ " <th>policy_csl_500/1000</th>\n",
+ " <th>insured_sex_MALE</th>\n",
+ " <th>insured_education_level_College</th>\n",
+ " <th>insured_education_level_High School</th>\n",
+ " <th>insured_education_level_JD</th>\n",
+ " <th>insured_education_level_MD</th>\n",
+ " <th>insured_education_level_Masters</th>\n",
+ " <th>insured_education_level_PhD</th>\n",
+ " <th>insured_occupation_armed-forces</th>\n",
+ " <th>...</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>fraud_reported</th>\n",
+ " <th>pct_paid_insurance</th>\n",
+ " </tr>\n",
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+ " <td>0.968454</td>\n",
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+ " <th>4</th>\n",
+ " <td>0</td>\n",
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+ " <td>0</td>\n",
+ " <td>0.846154</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 74 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl_250/500 policy_csl_500/1000 insured_sex_MALE \\\n",
+ "0 1 0 1 \n",
+ "1 1 0 1 \n",
+ "2 0 0 0 \n",
+ "3 1 0 0 \n",
+ "4 0 1 1 \n",
+ "\n",
+ " insured_education_level_College insured_education_level_High School \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_education_level_JD insured_education_level_MD \\\n",
+ "0 0 1 \n",
+ "1 0 1 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_education_level_Masters insured_education_level_PhD \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 1 \n",
+ "3 0 1 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_occupation_armed-forces ... capital-gains capital-loss \\\n",
+ "0 0 ... 53300 0 \n",
+ "1 0 ... 0 0 \n",
+ "2 0 ... 35100 0 \n",
+ "3 1 ... 48900 -62400 \n",
+ "4 0 ... 66000 -46000 \n",
+ "\n",
+ " number_of_vehicles_involved bodily_injuries witnesses injury_claim \\\n",
+ "0 1 1 2 6510 \n",
+ "1 1 0 0 780 \n",
+ "2 3 2 3 7700 \n",
+ "3 1 1 2 6340 \n",
+ "4 1 0 1 1300 \n",
+ "\n",
+ " property_claim vehicle_claim fraud_reported pct_paid_insurance \n",
+ "0 13020 52080 1 0.986035 \n",
+ "1 780 3510 1 0.605523 \n",
+ "2 3850 23100 0 0.942280 \n",
+ "3 6340 50720 1 0.968454 \n",
+ "4 650 4550 0 0.846154 \n",
+ "\n",
+ "[5 rows x 74 columns]"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_preprocessed.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05ac93ec-c954-453c-ba1a-abeb75e52069",
+ "metadata": {},
+ "source": [
+ "data_preprocessed.to_csv('dataset_dummies', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d72c779-3834-4752-90ac-aa896efce82e",
+ "metadata": {},
+ "source": [
+ "# 4.0 Modellierung"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bf81707-c75f-4951-829d-4e6f8866f7b6",
+ "metadata": {},
+ "source": [
+ "## 4.1 Import von relevanten Modulen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "ac4007f2-6737-4173-bae1-0ed4c021cac8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, precision_score, recall_score\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.svm import SVC"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd9dcc23-1e34-4f9e-a51f-59e63e053af8",
+ "metadata": {},
+ "source": [
+ "## 4.2 Daten einlesen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "1463800b-f1ea-4e95-ba15-b2fd40e2677c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = data_preprocessed #pd.read_csv('dataset_dummies.csv') # file is generated in notebook_1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "f804e2fc-0527-4829-b351-7ecc5ca15cd5",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl_250/500</th>\n",
+ " <th>policy_csl_500/1000</th>\n",
+ " <th>insured_sex_MALE</th>\n",
+ " <th>insured_education_level_College</th>\n",
+ " <th>insured_education_level_High School</th>\n",
+ " <th>insured_education_level_JD</th>\n",
+ " <th>insured_education_level_MD</th>\n",
+ " <th>insured_education_level_Masters</th>\n",
+ " <th>insured_education_level_PhD</th>\n",
+ " <th>insured_occupation_armed-forces</th>\n",
+ " <th>...</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>fraud_reported</th>\n",
+ " <th>pct_paid_insurance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
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+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>53300</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6510</td>\n",
+ " <td>13020</td>\n",
+ " <td>52080</td>\n",
+ " <td>1</td>\n",
+ " <td>0.986035</td>\n",
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+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>780</td>\n",
+ " <td>780</td>\n",
+ " <td>3510</td>\n",
+ " <td>1</td>\n",
+ " <td>0.605523</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>35100</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>7700</td>\n",
+ " <td>3850</td>\n",
+ " <td>23100</td>\n",
+ " <td>0</td>\n",
+ " <td>0.942280</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>48900</td>\n",
+ " <td>-62400</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>6340</td>\n",
+ " <td>6340</td>\n",
+ " <td>50720</td>\n",
+ " <td>1</td>\n",
+ " <td>0.968454</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>66000</td>\n",
+ " <td>-46000</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1300</td>\n",
+ " <td>650</td>\n",
+ " <td>4550</td>\n",
+ " <td>0</td>\n",
+ " <td>0.846154</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 74 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl_250/500 policy_csl_500/1000 insured_sex_MALE \\\n",
+ "0 1 0 1 \n",
+ "1 1 0 1 \n",
+ "2 0 0 0 \n",
+ "3 1 0 0 \n",
+ "4 0 1 1 \n",
+ "\n",
+ " insured_education_level_College insured_education_level_High School \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_education_level_JD insured_education_level_MD \\\n",
+ "0 0 1 \n",
+ "1 0 1 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_education_level_Masters insured_education_level_PhD \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 1 \n",
+ "3 0 1 \n",
+ "4 0 0 \n",
+ "\n",
+ " insured_occupation_armed-forces ... capital-gains capital-loss \\\n",
+ "0 0 ... 53300 0 \n",
+ "1 0 ... 0 0 \n",
+ "2 0 ... 35100 0 \n",
+ "3 1 ... 48900 -62400 \n",
+ "4 0 ... 66000 -46000 \n",
+ "\n",
+ " number_of_vehicles_involved bodily_injuries witnesses injury_claim \\\n",
+ "0 1 1 2 6510 \n",
+ "1 1 0 0 780 \n",
+ "2 3 2 3 7700 \n",
+ "3 1 1 2 6340 \n",
+ "4 1 0 1 1300 \n",
+ "\n",
+ " property_claim vehicle_claim fraud_reported pct_paid_insurance \n",
+ "0 13020 52080 1 0.986035 \n",
+ "1 780 3510 1 0.605523 \n",
+ "2 3850 23100 0 0.942280 \n",
+ "3 6340 50720 1 0.968454 \n",
+ "4 650 4550 0 0.846154 \n",
+ "\n",
+ "[5 rows x 74 columns]"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d03f82d0-ca39-42ac-a893-d9ca3a40c7fb",
+ "metadata": {},
+ "source": [
+ "## 4.3 Datenvorbereitung für die Modellierung"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "2992b50d-a038-4d64-8208-3fbd3633d159",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target = data.fraud_reported\n",
+ "features = data.drop('fraud_reported', axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "1d1788c5-2347-4978-a317-f13d97f947e8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Split data in training and test datasets\n",
+ "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=365)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "e595224c-824c-4e8e-8b99-6179a2b2a0a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_csl_250/500</th>\n",
+ " <th>policy_csl_500/1000</th>\n",
+ " <th>insured_sex_MALE</th>\n",
+ " <th>insured_education_level_College</th>\n",
+ " <th>insured_education_level_High School</th>\n",
+ " <th>insured_education_level_JD</th>\n",
+ " <th>insured_education_level_MD</th>\n",
+ " <th>insured_education_level_Masters</th>\n",
+ " <th>insured_education_level_PhD</th>\n",
+ " <th>insured_occupation_armed-forces</th>\n",
+ " <th>...</th>\n",
+ " <th>umbrella_limit</th>\n",
+ " <th>capital-gains</th>\n",
+ " <th>capital-loss</th>\n",
+ " <th>number_of_vehicles_involved</th>\n",
+ " <th>bodily_injuries</th>\n",
+ " <th>witnesses</th>\n",
+ " <th>injury_claim</th>\n",
+ " <th>property_claim</th>\n",
+ " <th>vehicle_claim</th>\n",
+ " <th>pct_paid_insurance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>908</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>52600</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>500</td>\n",
+ " <td>500</td>\n",
+ " <td>4500</td>\n",
+ " <td>0.636364</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>591</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>7270</td>\n",
+ " <td>21810</td>\n",
+ " <td>50890</td>\n",
+ " <td>0.993748</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>836</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>52100</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>21330</td>\n",
+ " <td>7110</td>\n",
+ " <td>56880</td>\n",
+ " <td>0.988279</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>-57900</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>7640</td>\n",
+ " <td>15280</td>\n",
+ " <td>76400</td>\n",
+ " <td>0.994966</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>606</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>-66200</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>5750</td>\n",
+ " <td>5750</td>\n",
+ " <td>46000</td>\n",
+ " <td>0.982609</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 73 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_csl_250/500 policy_csl_500/1000 insured_sex_MALE \\\n",
+ "908 1 0 1 \n",
+ "591 0 1 0 \n",
+ "836 0 0 0 \n",
+ "145 0 0 0 \n",
+ "606 0 1 0 \n",
+ "\n",
+ " insured_education_level_College insured_education_level_High School \\\n",
+ "908 0 0 \n",
+ "591 0 0 \n",
+ "836 0 0 \n",
+ "145 0 0 \n",
+ "606 0 0 \n",
+ "\n",
+ " insured_education_level_JD insured_education_level_MD \\\n",
+ "908 0 1 \n",
+ "591 0 0 \n",
+ "836 1 0 \n",
+ "145 0 0 \n",
+ "606 0 0 \n",
+ "\n",
+ " insured_education_level_Masters insured_education_level_PhD \\\n",
+ "908 0 0 \n",
+ "591 0 0 \n",
+ "836 0 0 \n",
+ "145 0 0 \n",
+ "606 0 0 \n",
+ "\n",
+ " insured_occupation_armed-forces ... umbrella_limit capital-gains \\\n",
+ "908 0 ... 0 52600 \n",
+ "591 1 ... 0 0 \n",
+ "836 0 ... 0 52100 \n",
+ "145 0 ... 0 0 \n",
+ "606 0 ... 0 0 \n",
+ "\n",
+ " capital-loss number_of_vehicles_involved bodily_injuries witnesses \\\n",
+ "908 0 1 1 0 \n",
+ "591 0 1 2 1 \n",
+ "836 0 1 0 1 \n",
+ "145 -57900 1 2 1 \n",
+ "606 -66200 1 0 3 \n",
+ "\n",
+ " injury_claim property_claim vehicle_claim pct_paid_insurance \n",
+ "908 500 500 4500 0.636364 \n",
+ "591 7270 21810 50890 0.993748 \n",
+ "836 21330 7110 56880 0.988279 \n",
+ "145 7640 15280 76400 0.994966 \n",
+ "606 5750 5750 46000 0.982609 \n",
+ "\n",
+ "[5 rows x 73 columns]"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x_train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "7d2d9511-6a0b-40eb-a97a-e0469e01bd32",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Scale data\n",
+ "scaler = StandardScaler()\n",
+ "scaler.fit(x_train)\n",
+ "\n",
+ "x_train = scaler.transform(x_train)\n",
+ "x_test = scaler.transform(x_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "a458a1a3-8537-4526-ba94-cc1978d16ea1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 596\n",
+ "1 204\n",
+ "Name: fraud_reported, dtype: int64"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# distribution of target in train data\n",
+ "y_train.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "ca86184e-e92a-4f49-850d-5f056d89e908",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 157\n",
+ "1 43\n",
+ "Name: fraud_reported, dtype: int64"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# distribution of target in test datat\n",
+ "y_test.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "152bad6b-ae10-4c8d-8f4c-6291b7e1dc7f",
+ "metadata": {},
+ "source": [
+ "# 4.4 Modellierung und Evaluation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2326fd45-688d-4b42-8576-ab39fca9fcef",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Klassifizierungsmodelle sind vielfältig und umfassen zum Beispiel logistische Regression, Entscheidungsbaum, Random Forest und Support Vector Machines. Alle oben genannten Modelle wurden mit dem Datensatz getestet und anschließend wird das mit der Höchste Präzision genutzt. In diesem Fall ist das die Support Vector Machines "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8161aaf1-e868-4b71-81dc-e0cdb9f57e0e",
+ "metadata": {},
+ "source": [
+ "### 4.4.1 Logistische Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "2c52bb12-2ca2-4b50-8344-e41d6ae4a284",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "LogisticRegression()"
+ ]
+ },
+ "execution_count": 76,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "logreg = LogisticRegression()\n",
+ "logreg.fit(x_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "3724db64-a0ca-4663-935e-c48164b77d8b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.93 0.92 596\n",
+ " 1 0.79 0.75 0.77 204\n",
+ "\n",
+ " accuracy 0.89 800\n",
+ " macro avg 0.85 0.84 0.85 800\n",
+ "weighted avg 0.88 0.89 0.88 800\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print(classification_report(y_train, logreg.predict(x_train)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "6066f8eb-4385-49e1-b61f-dda310b4fb4b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 88.5\n",
+ "Precision: 78.8659793814433\n",
+ "Recall: 75.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print('Accuracy:', accuracy_score(y_train, logreg.predict(x_train))*100)\n",
+ "print('Precision:', precision_score(y_train, logreg.predict(x_train))*100)\n",
+ "print('Recall:', recall_score(y_train, logreg.predict(x_train))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "e9b654cc-0ede-4c34-af4f-1225fd98b652",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.91 0.91 0.91 157\n",
+ " 1 0.67 0.65 0.66 43\n",
+ "\n",
+ " accuracy 0.85 200\n",
+ " macro avg 0.79 0.78 0.78 200\n",
+ "weighted avg 0.85 0.85 0.85 200\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print(classification_report(y_test, logreg.predict(x_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "8c3fc54a-f6f6-4224-a04f-e21dc52c0519",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 85.5\n",
+ "Precision: 66.66666666666666\n",
+ "Recall: 65.11627906976744\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print('Accuracy:', accuracy_score(y_test, logreg.predict(x_test))*100)\n",
+ "print('Precision:', precision_score(y_test, logreg.predict(x_test))*100)\n",
+ "print('Recall:', recall_score(y_test, logreg.predict(x_test))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "cd1c4683-3fe3-4ad8-847d-086ba2437302",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "143 14 15 28\n"
+ ]
+ }
+ ],
+ "source": [
+ "tn, fp, fn, tp = confusion_matrix(y_test, logreg.predict(x_test)).ravel() \n",
+ "print(tn, fp, fn, tp)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "ae7fc2a6-fc73-43f4-8dda-9ef331e435f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm = confusion_matrix(y_test, logreg.predict(x_test))\n",
+ "sns.heatmap(cm, annot=True, cmap='terrain', fmt='g')\n",
+ "plt.xlabel('Real data')\n",
+ "plt.ylabel('Predicted data')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "755da63f-6d7a-4aa2-8ee2-58df383360d3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-1.87199801])"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "logreg.intercept_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "9ef9014f-572e-45dd-9972-700b03777270",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 5.80290586e-02, -2.37429629e-01, -3.18975936e-02,\n",
+ " 1.03264901e-01, 1.57395846e-02, 1.54412249e-01,\n",
+ " 9.98550038e-02, 1.01734974e-01, 1.65847423e-01,\n",
+ " 7.04430787e-02, 2.21983720e-01, 2.75034627e-01,\n",
+ " 6.08030524e-02, -8.57181314e-02, 6.39780307e-02,\n",
+ " -1.53624306e-01, -4.61802683e-02, 1.51000581e-01,\n",
+ " -5.51566389e-02, 5.90062519e-02, -8.83889618e-03,\n",
+ " -8.20083809e-03, -1.42216723e-01, 1.90073855e-02,\n",
+ " -2.16204990e-01, -4.62653155e-01, 7.95035036e-01,\n",
+ " 5.66640168e-01, -3.05200290e-01, -2.31887634e-01,\n",
+ " -8.43891138e-02, -9.43983125e-02, -3.03973994e-01,\n",
+ " -9.70096772e-02, -2.00177795e-01, -5.26322421e-02,\n",
+ " -1.90789105e-02, -1.19299001e-01, -2.22788639e-01,\n",
+ " 8.18201612e-03, 2.03631238e-01, 3.87350193e-01,\n",
+ " 4.07693675e-01, -2.79170918e-02, 2.25397475e-01,\n",
+ " 1.85538456e-01, -1.95996214e-01, -1.87436352e-01,\n",
+ " -2.87031385e-01, 1.41595895e-01, -1.11191352e-01,\n",
+ " -1.74200227e+00, -1.66185713e+00, -1.21492255e+00,\n",
+ " 1.24360304e-01, 1.64944689e-01, 2.58711282e-01,\n",
+ " 3.23758889e-01, 1.30618426e-03, -1.22778321e-01,\n",
+ " -1.01884582e-02, 5.54932851e-02, 5.82757827e-02,\n",
+ " 3.61968828e-01, -1.20848644e-01, -2.42543595e-01,\n",
+ " -1.93472781e-01, 8.04217694e-02, 5.86457472e-02,\n",
+ " -1.71694568e-01, -3.64878309e-02, 1.53029355e-01,\n",
+ " 3.50253316e-03]])"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "logreg.coef_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17f10bf0-4c51-4efc-8dac-bab253874113",
+ "metadata": {},
+ "source": [
+ "### 4.4.2 Entscheidungsbaum"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "7e6ee83c-9235-4c85-9cef-69c0e2d888c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 85,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tree = DecisionTreeClassifier()\n",
+ "tree.fit(x_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "id": "061d23a7-2058-4025-9808-d03a787ea5e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 596\n",
+ " 1 1.00 1.00 1.00 204\n",
+ "\n",
+ " accuracy 1.00 800\n",
+ " macro avg 1.00 1.00 1.00 800\n",
+ "weighted avg 1.00 1.00 1.00 800\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print(classification_report(y_train, tree.predict(x_train)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "33747cbb-55fa-450b-91c2-76c178bc78bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 100.0\n",
+ "Precision: 100.0\n",
+ "Recall: 100.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print('Accuracy:', accuracy_score(y_train, tree.predict(x_train))*100)\n",
+ "print('Precision:', precision_score(y_train, tree.predict(x_train))*100)\n",
+ "print('Recall:', recall_score(y_train, tree.predict(x_train))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "72e22e4a-1ac4-469c-99df-6e76503b8ba8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.84 0.86 0.85 157\n",
+ " 1 0.44 0.40 0.41 43\n",
+ "\n",
+ " accuracy 0.76 200\n",
+ " macro avg 0.64 0.63 0.63 200\n",
+ "weighted avg 0.75 0.76 0.76 200\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print(classification_report(y_test, tree.predict(x_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "e4e3b189-b435-4c4e-80bd-bda6465cf01f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 76.0\n",
+ "Precision: 43.58974358974359\n",
+ "Recall: 39.53488372093023\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print('Accuracy:', accuracy_score(y_test, tree.predict(x_test))*100)\n",
+ "print('Precision:', precision_score(y_test, tree.predict(x_test))*100)\n",
+ "print('Recall:', recall_score(y_test, tree.predict(x_test))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "id": "ba9c3ddb-c454-4d67-9dcf-d75c120ce38d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm = confusion_matrix(y_test, tree.predict(x_test))\n",
+ "sns.heatmap(cm, annot=True, cmap='terrain', fmt='g')\n",
+ "plt.xlabel('Real data')\n",
+ "plt.ylabel('Predicted data')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21e48874-4af1-4dc8-b1fd-2cf8d3f25344",
+ "metadata": {},
+ "source": [
+ "### 4.4.3 Random Forest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "id": "e0e9d203-627b-42d9-9d1c-aa3712bda046",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RandomForestClassifier()"
+ ]
+ },
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "forest = RandomForestClassifier()\n",
+ "forest.fit(x_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "id": "d813ae77-9ad1-4e1b-8e3a-f5bc04359e88",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 596\n",
+ " 1 1.00 1.00 1.00 204\n",
+ "\n",
+ " accuracy 1.00 800\n",
+ " macro avg 1.00 1.00 1.00 800\n",
+ "weighted avg 1.00 1.00 1.00 800\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print(classification_report(y_train, forest.predict(x_train)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "id": "8f8f83dc-bef3-412a-ae62-6114c047f07a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 100.0\n",
+ "Precision: 100.0\n",
+ "Recall: 100.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print('Accuracy:', accuracy_score(y_train, forest.predict(x_train))*100)\n",
+ "print('Precision:', precision_score(y_train, forest.predict(x_train))*100)\n",
+ "print('Recall:', recall_score(y_train, forest.predict(x_train))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "id": "9e1f39f4-a2fb-4a2d-a9d2-2e7332695340",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.83 0.96 0.89 157\n",
+ " 1 0.63 0.28 0.39 43\n",
+ "\n",
+ " accuracy 0.81 200\n",
+ " macro avg 0.73 0.62 0.64 200\n",
+ "weighted avg 0.79 0.81 0.78 200\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print(classification_report(y_test, forest.predict(x_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "0ef4142c-6ff4-403c-a67f-e740e1270d2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 81.0\n",
+ "Precision: 63.1578947368421\n",
+ "Recall: 27.906976744186046\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print('Accuracy:', accuracy_score(y_test, forest.predict(x_test))*100)\n",
+ "print('Precision:', precision_score(y_test, forest.predict(x_test))*100)\n",
+ "print('Recall:', recall_score(y_test, forest.predict(x_test))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "id": "4975d240-fc13-4f4f-8e74-bcd71ebc6919",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm = confusion_matrix(y_test, forest.predict(x_test))\n",
+ "sns.heatmap(cm, annot=True, cmap='terrain', fmt='g')\n",
+ "plt.xlabel('Real data')\n",
+ "plt.ylabel('Predicted data')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "539919b6-5c27-4a45-a9f5-ae85b392bd6b",
+ "metadata": {},
+ "source": [
+ "### 4.4.4 Support Vector Machine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "id": "8756f845-3bbb-4200-b293-a5f8b9e8c2ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SVC()"
+ ]
+ },
+ "execution_count": 97,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "svc = SVC()\n",
+ "svc.fit(x_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "id": "97743f75-4f09-409d-9aeb-36f76cc2fc01",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.99 0.95 596\n",
+ " 1 0.95 0.75 0.84 204\n",
+ "\n",
+ " accuracy 0.93 800\n",
+ " macro avg 0.94 0.87 0.90 800\n",
+ "weighted avg 0.93 0.93 0.92 800\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print(classification_report(y_train, svc.predict(x_train)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "id": "43426a60-fd2a-413b-a7d8-258acba57106",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 92.625\n",
+ "Precision: 95.03105590062113\n",
+ "Recall: 75.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train data\n",
+ "print('Accuracy:', accuracy_score(y_train, svc.predict(x_train))*100)\n",
+ "print('Precision:', precision_score(y_train, svc.predict(x_train))*100)\n",
+ "print('Recall:', recall_score(y_train, svc.predict(x_train))*100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05e2dca0-c71d-402e-86c1-cae197e580b7",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Für die Bewertung der Qualität einer Klassifikation werden Metriken wir Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Präzision der Vorhersage der Kundenabwanderung) und Recall (= Menge der abwanderungswilligen Kunden die korrekt klassifiziert wurden) genutzt. In einer ersten Modellstufe wird eine Accuracy von 92%, ein Recall von 75% sowie eine Precision von 95% erreicht. Schlussendlich konnten 85% der Betrugsfälle korrekt erkannt werden. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "id": "548c9d85-4862-4a94-a9f5-8ec86f0b603c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.85 0.99 0.91 157\n",
+ " 1 0.89 0.37 0.52 43\n",
+ "\n",
+ " accuracy 0.85 200\n",
+ " macro avg 0.87 0.68 0.72 200\n",
+ "weighted avg 0.86 0.85 0.83 200\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print(classification_report(y_test, svc.predict(x_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "id": "94166399-0d04-4122-a54e-0cae01a8e63c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 85.5\n",
+ "Precision: 88.88888888888889\n",
+ "Recall: 37.2093023255814\n"
+ ]
+ }
+ ],
+ "source": [
+ "# test data\n",
+ "print('Accuracy:', accuracy_score(y_test, svc.predict(x_test))*100)\n",
+ "print('Precision:', precision_score(y_test, svc.predict(x_test))*100)\n",
+ "print('Recall:', recall_score(y_test, svc.predict(x_test))*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "id": "d1d76420-6b11-4e0e-936a-c7a88fdee70c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm = confusion_matrix(y_test, svc.predict(x_test))\n",
+ "sns.heatmap(cm, annot=True, cmap='terrain', fmt='g')\n",
+ "plt.xlabel('Real data')\n",
+ "plt.ylabel('Predicted data')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cb5c8da-e422-4c3e-aead-2eadd9eea441",
+ "metadata": {},
+ "source": [
+ "# 5. Deployment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "id": "dfc8b109-c296-4ef4-a72a-6043f2e0987c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select one scaled person of the dataset\n",
+ "sample_df = x_test[72]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "3956ab7d-de04-4518-aa5d-6f1a0199a921",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1.35903462, -0.65270587, 1.10833761, -0.37363236, -0.42828957,\n",
+ " 2.30200187, -0.41181385, -0.40137644, -0.38655567, -0.27958383,\n",
+ " -0.30478874, -0.28730468, -0.24413654, -0.24124895, -0.31207962,\n",
+ " -0.26636529, -0.26636529, -0.30478874, -0.26636529, -0.28217394,\n",
+ " -0.28984624, 3.79270555, -0.18328047, -0.22941573, -0.24983394,\n",
+ " -0.24124895, -0.22331316, 5.06622805, -0.21707238, -0.24699789,\n",
+ " -0.23241869, -0.24413654, -0.23833416, -0.23833416, -0.23833416,\n",
+ " -0.22021079, -0.25264558, -0.22021079, -0.19044535, -0.2353911 ,\n",
+ " -0.24983394, 2.19986728, -0.47248449, -0.46255869, -0.40973554,\n",
+ " -0.43033148, -0.27958383, -0.82502865, -0.31926223, -0.91370804,\n",
+ " -0.6352234 , -0.74390729, 1.60356745, -0.29488391, -0.51752183,\n",
+ " -0.29738086, -0.50780078, -0.65660263, -0.6644106 , -0.67419986,\n",
+ " -1.6511054 , -1.04810348, 0.18475885, -0.48560679, -0.92537512,\n",
+ " 0.963709 , 1.11630666, -1.18253256, 0.45167913, 0.85886085,\n",
+ " 0.85043965, 0.74218584, 0.10204472])"
+ ]
+ },
+ "execution_count": 104,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Features of the selected sample\n",
+ "sample_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "id": "3f83393e-01b1-423d-a9ae-72760952476e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Execute prediction\n",
+ "sample_pred = svc.predict([sample_df])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "id": "5a256794-ed23-4157-884f-8b1e3e8029fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Interpret the result\n",
+ "def check_prediction(pred):\n",
+ " if pred[0] == 1:\n",
+ " print(\"Fraud.\")\n",
+ " else:\n",
+ " print(\"No Fraud.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "feee5e9d-2453-4429-90f9-11d86c5ec1d5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fraud.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# call the prediciton method\n",
+ "check_prediction(sample_pred)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e9f6e4e-ee04-4853-97c3-2ccb925de6e4",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Umsetzung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Die Umsetzung bzw. Einbindung des Datenmodells bietet sich in CRM-Systemen an. Auf Basis von Vorfalls Merkmalen kann automatisiert eine Vorhersage über eine potenziellen Betrugsversuch erstellt werden. Auf diese Weise lassen sich Betrugsfälle identifizieren, in Form von Dashboards visualisieren sowie teil-automatisiert bearbeiten."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc5b70d3-89bd-4a69-8033-e88b14a8fa6c",
+ "metadata": {},
+ "source": [
+ "-----------------------------------------------------------------------------------------------------------------------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13b5c9f4-f6d9-479d-be00-a00b9623a0c3",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Teaser",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Versicherungs Unternehmen werden häufig zu Zielen von Betrügern, weshalb es sehr wichtig ist solche Betrugsversuche frühzeitig zu erkennen. Die Zeilen des Datensatzes stellen jeweils einen Kunden und Seine Vorfall dar. Die Spalten beschreiben die Merkmale der Kunden und die des Vorfalls für welchen sie ihre Versicherung in anspruch nehmen. Daten wie diese, werden von den Versicherungsunternehmen zunehmend automatisiert verarbeitet, ausgewertet und für weitere Versicherungsprozesse genutzt. Ziel ist es für bestehende Versicherungsprodukte das aktuelle Risiko zu berechnen und darauf aufbauend die Prämie und die mögliche Schadenshöhe zu ermitteln. Anhand dieses Datensatz soll mit „Machine-Learning“ ermittelt werden ob sich bei dem jeweiligen Fall um Betrug oder einen legitiemen Anspruch handelt. Logistische Regression, Entscheidungsbäume, Random Forest und Support Vector Machines werden hierbei genutzt um eine Vorhersage zu Fällen zu treffen. Das Finale Modell erreicht eine Genauigkeit von 95 % und einen Recall von 75 %. Die Mehrheit der Betrugsversuche wird mit diesem Modell erkannt. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d942519f-bdff-44a2-b864-c967b94a39da",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Klassifizierungsmodelle sind vielfältig und umfassen zum Beispiel logistische Regression, Entscheidungsbaum, Random Forest und Support Vector Machines. Alle oben genannten Modelle wurden mit dem Datensatz getestet und anschließend wird das mit der Höchste Präzision genutzt. In diesem Fall ist das die Support Vector Machines "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25702f9d-9b4c-44d8-94a8-0b3d5c02b0ea",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Für die Bewertung der Qualität einer Klassifikation werden Metriken wir Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Präzision der Vorhersage der Kundenabwanderung) und Recall (= Menge der abwanderungswilligen Kunden die korrekt klassifiziert wurden) genutzt. In einer ersten Modellstufe wird eine Accuracy von 92%, ein Recall von 75% sowie eine Precision von 95% erreicht. Schlussendlich konnten 85% der Betrugsfälle korrekt erkannt werden. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0edf9cce-4d87-4831-983b-cafd8f113f4f",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Umsetzung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Die Umsetzung bzw. Einbindung des Datenmodells bietet sich in CRM-Systemen an. Auf Basis von Vorfalls Merkmalen kann automatisiert eine Vorhersage über eine potenziellen Betrugsversuch erstellt werden. Auf diese Weise lassen sich Betrugsfälle identifizieren, in Form von Dashboards visualisieren sowie teil-automatisiert bearbeiten."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02d0d7b8-549e-43cf-9fba-44a72a67b2e5",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "category": "Insurance",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.9"
+ },
+ "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/tree/main/Insurance/Insurance%20Fraud%20detection?ref_type=heads",
+ "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Insurance_Fraud_detection_dataset.csv",
+ "skipNotebookInDeployment": false,
+ "teaser": "Versicherungs Unternehmen werden häufig zu Zielen von Betrügern, weshalb es sehr wichtig ist solche Betrugsversuche frühzeitig zu erkennen. Die Zeilen des Datensatzes stellen jeweils einen Kunden und Seine Vorfall dar. Die Spalten beschreiben die Merkmale der Kunden und die des Vorfalls für welchen sie ihre Versicherung in anspruch nehmen. Daten wie diese, werden von den Versicherungsunternehmen zunehmend automatisiert verarbeitet, ausgewertet und für weitere Versicherungsprozesse genutzt. Ziel ist es für bestehende Versicherungsprodukte das aktuelle Risiko zu berechnen und darauf aufbauend die Prämie und die mögliche Schadenshöhe zu ermitteln. Anhand dieses Datensatz soll mit „Machine-Learning“ ermittelt werden ob sich bei dem jeweiligen Fall um Betrug oder einen legitiemen Anspruch handelt. Logistische Regression, Entscheidungsbäume, Random Forest und Support Vector Machines werden hierbei genutzt um eine Vorhersage zu Fällen zu treffen. Das Finale Modell erreicht eine Genauigkeit von 95 % und einen Recall von 75 %. Die Mehrheit der Betrugsversuche wird mit diesem Modell erkannt.",
+ "title": "Versicherungs Betrugserkennung"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
GitLab