From 2c0899824a258e81d1b1bbb2d7d6cf977e01387c Mon Sep 17 00:00:00 2001
From: Andreas Buzer <andreas.buzer@student.reutlingen-university.de>
Date: Mon, 1 Jul 2024 17:59:36 +0000
Subject: [PATCH] deleted notebooks with old content and renamed newer
notebooks
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.../1Notebook.ipynb | 4263 -----------------
.../notebook.ipynb | 696 ++-
.../notebook1.ipynb | 3985 ---------------
3 files changed, 689 insertions(+), 8255 deletions(-)
delete mode 100644 CRM/Increase customer satisfaction/1Notebook.ipynb
delete mode 100644 Health/Risk prediction of heart disease/notebook1.ipynb
diff --git a/CRM/Increase customer satisfaction/1Notebook.ipynb b/CRM/Increase customer satisfaction/1Notebook.ipynb
deleted file mode 100644
index f245c97..0000000
--- a/CRM/Increase customer satisfaction/1Notebook.ipynb
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@@ -1,4263 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Business",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 1. Business Understanding\n",
- "\n",
- "Aufgrund der großen Auswahl, die Netflix zu bieten hat, ist es für die Nutzer schwierig, geeignete Filme für sich zu finden. Die Suche in der Bibliothek nimmt viel Zeit in Anspruch und schafft ein schlechtes Nutzererlebnis, was wiederum zu höheren Abbruchquoten führt.\n",
- "Um die Abbruchquoten zu senken, muss geprüft werden, ob die Kundenzufriedenheit durch die Anwendung von maschinellem Lernen in Bezug auf Filmempfehlungen erhöht werden kann.\n",
- "\n",
- "\n",
- "Der Datensatz enthält Filmdaten aus dem tmdb Dataset.\n",
- "Finden Sie heraus, welche Faktoren auf der Grundlage der Daten über die Beliebtheit oder Bewertung der Filme ergriffen werden können, um Strategien für das Unternehmen zu entwickeln.\n",
- "Basierend auf dem obigen Geschäftsproblem definieren wir die abhängige Variable (y)\n",
- "\n",
- "Problem 1: y = Popularität / Voting-Durchschnitt (Regressionsproblem)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 2. Daten und Datenverständnis\n",
- "\n",
- "Aus dem Datensatz ist ersichtlich, dass sowohl Zahlen als auch kategoriale Werte enthalten sind. Jede Kategorie bezieht sich auf den entsprechenden Film in der Zeile. So enthält beispielsweise die Spalte \"Crew\" mehrere Mitwirkende wie Autoren, Filmeditor usw., während \"Cast\" die Schauspieler enthält, die in den jeweiligen Filmen mitspielen. Außerdem hat jeder Film eine eindeutige ID, z. B. movie_id/id, die identisch ist und es ermöglicht, beide Datensätze zu kombinieren. Alle Daten sind sehr verständlich und selbsterklärend, und der Inhalt ist auf kaggle.com ausdrücklich beschrieben."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.1 Import von relevanten Modulen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Dieser Code-Block importiert Bibliotheken und Module, die für Datenanalyse, statistische Modellierung,\n",
- "maschinelles Lernen und Visualisierung in Python verwendet werden.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import statsmodels.api as sm\n",
- "import matplotlib.pyplot as plt<\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.tree import DecisionTreeClassifier\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.ensemble import RandomForestClassifier\n",
- "from sklearn import metrics\n",
- "from sklearn.linear_model import LinearRegression\n",
- "from sklearn.metrics import confusion_matrix, classification_report\n",
- "import seaborn as sns\n",
- "sns.set()\n",
- "\n",
- "\n",
- "# statsmodels benötigt diese Funktion (chisqprob) von skipy für Berichte\n",
- "from scipy import stats\n",
- "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.2 Daten einlesen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "original_data = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Increase_customer_satisfaction_tmdb_5000_movies.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "original_data2 = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Increase_customer_satisfaction_tmdb_5000_credits.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "original_data = pd.merge(original_data, original_data2)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Datenverständnis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle listet Filme mit verschiedenen Attributen wie \n",
- "Budget, Genres, Webseite, ID, Schlüsselwörter, Originalsprache, Originaltitel, Zusammenfassung, \n",
- "Popularität, Produktionsfirmen, Laufzeit, gesprochene Sprachen, Status, Tagline, Titel, \n",
- "durchschnittliche Bewertung, Anzahl der Stimmen, Film-ID, Besetzung und Crew auf."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
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- "2 http://www.sonypictures.com/movies/spectre/ 206647 \n",
- "3 http://www.thedarkknightrises.com/ 49026 \n",
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- "paragraph": "Daten",
- "slideshow": {
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- "tags": []
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- "Die Tabelle fasst verschiedene Eigenschaften von Filmen zusammen, \n",
- "wie Budget, Genres, Homepage, ID, Schlüsselwörter, Originalsprache, Originaltitel, Zusammenfassung, \n",
- "Popularität, Produktionsfirmen, Laufzeit, gesprochene Sprachen, Status, Tagline, Titel, \n",
- "durchschnittliche Bewertung, Anzahl der Stimmen, Film-ID, Besetzung und Crew."
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- " <td>4809</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>unique</th>\n",
- " <td>NaN</td>\n",
- " <td>1175</td>\n",
- " <td>1691</td>\n",
- " <td>NaN</td>\n",
- " <td>4222</td>\n",
- " <td>37</td>\n",
- " <td>4801</td>\n",
- " <td>4800</td>\n",
- " <td>NaN</td>\n",
- " <td>3697</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>544</td>\n",
- " <td>3</td>\n",
- " <td>3944</td>\n",
- " <td>4800</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4761</td>\n",
- " <td>4776</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>top</th>\n",
- " <td>NaN</td>\n",
- " <td>[{\"id\": 18, \"name\": \"Drama\"}]</td>\n",
- " <td>http://www.missionimpossible.com/</td>\n",
- " <td>NaN</td>\n",
- " <td>[]</td>\n",
- " <td>en</td>\n",
- " <td>Out of the Blue</td>\n",
- " <td>Dennis Hopper is a hard-drinking truck driver ...</td>\n",
- " <td>NaN</td>\n",
- " <td>[]</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
- " <td>Released</td>\n",
- " <td>Based on a true story.</td>\n",
- " <td>Out of the Blue</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>[]</td>\n",
- " <td>[]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>freq</th>\n",
- " <td>NaN</td>\n",
- " <td>372</td>\n",
- " <td>4</td>\n",
- " <td>NaN</td>\n",
- " <td>412</td>\n",
- " <td>4510</td>\n",
- " <td>4</td>\n",
- " <td>2</td>\n",
- " <td>NaN</td>\n",
- " <td>352</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>3175</td>\n",
- " <td>4801</td>\n",
- " <td>3</td>\n",
- " <td>4</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>43</td>\n",
- " <td>28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>2.902780e+07</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>57120.571429</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>21.491664</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>106.882255</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>6.092514</td>\n",
- " <td>690.331670</td>\n",
- " <td>57120.571429</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>4.070473e+07</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>88653.369849</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>31.803366</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>22.602535</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1.193989</td>\n",
- " <td>1234.187111</td>\n",
- " <td>88653.369849</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>0.000000e+00</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>5.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>5.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>7.800000e+05</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>9012.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.667230</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>94.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>5.600000</td>\n",
- " <td>54.000000</td>\n",
- " <td>9012.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>1.500000e+07</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>14624.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>12.921594</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>103.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>6.200000</td>\n",
- " <td>235.000000</td>\n",
- " <td>14624.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>4.000000e+07</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>58595.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>28.350529</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>118.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>6.800000</td>\n",
- " <td>737.000000</td>\n",
- " <td>58595.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>3.800000e+08</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>459488.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>875.581305</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>338.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>10.000000</td>\n",
- " <td>13752.000000</td>\n",
- " <td>459488.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>11 rows × 23 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " budget genres \\\n",
- "count 4.809000e+03 4809 \n",
- "unique NaN 1175 \n",
- "top NaN [{\"id\": 18, \"name\": \"Drama\"}] \n",
- "freq NaN 372 \n",
- "mean 2.902780e+07 NaN \n",
- "std 4.070473e+07 NaN \n",
- "min 0.000000e+00 NaN \n",
- "25% 7.800000e+05 NaN \n",
- "50% 1.500000e+07 NaN \n",
- "75% 4.000000e+07 NaN \n",
- "max 3.800000e+08 NaN \n",
- "\n",
- " homepage id keywords \\\n",
- "count 1713 4809.000000 4809 \n",
- "unique 1691 NaN 4222 \n",
- "top http://www.missionimpossible.com/ NaN [] \n",
- "freq 4 NaN 412 \n",
- "mean NaN 57120.571429 NaN \n",
- "std NaN 88653.369849 NaN \n",
- "min NaN 5.000000 NaN \n",
- "25% NaN 9012.000000 NaN \n",
- "50% NaN 14624.000000 NaN \n",
- "75% NaN 58595.000000 NaN \n",
- "max NaN 459488.000000 NaN \n",
- "\n",
- " original_language original_title \\\n",
- "count 4809 4809 \n",
- "unique 37 4801 \n",
- "top en Out of the Blue \n",
- "freq 4510 4 \n",
- "mean NaN NaN \n",
- "std NaN NaN \n",
- "min NaN NaN \n",
- "25% NaN NaN \n",
- "50% NaN NaN \n",
- "75% NaN NaN \n",
- "max NaN NaN \n",
- "\n",
- " overview popularity \\\n",
- "count 4806 4809.000000 \n",
- "unique 4800 NaN \n",
- "top Dennis Hopper is a hard-drinking truck driver ... NaN \n",
- "freq 2 NaN \n",
- "mean NaN 21.491664 \n",
- "std NaN 31.803366 \n",
- "min NaN 0.000000 \n",
- "25% NaN 4.667230 \n",
- "50% NaN 12.921594 \n",
- "75% NaN 28.350529 \n",
- "max NaN 875.581305 \n",
- "\n",
- " production_companies ... runtime \\\n",
- "count 4809 ... 4807.000000 \n",
- "unique 3697 ... NaN \n",
- "top [] ... NaN \n",
- "freq 352 ... NaN \n",
- "mean NaN ... 106.882255 \n",
- "std NaN ... 22.602535 \n",
- "min NaN ... 0.000000 \n",
- "25% NaN ... 94.000000 \n",
- "50% NaN ... 103.000000 \n",
- "75% NaN ... 118.000000 \n",
- "max NaN ... 338.000000 \n",
- "\n",
- " spoken_languages status \\\n",
- "count 4809 4809 \n",
- "unique 544 3 \n",
- "top [{\"iso_639_1\": \"en\", \"name\": \"English\"}] Released \n",
- "freq 3175 4801 \n",
- "mean NaN NaN \n",
- "std NaN NaN \n",
- "min NaN NaN \n",
- "25% NaN NaN \n",
- "50% NaN NaN \n",
- "75% NaN NaN \n",
- "max NaN NaN \n",
- "\n",
- " tagline title vote_average vote_count \\\n",
- "count 3965 4809 4809.000000 4809.000000 \n",
- "unique 3944 4800 NaN NaN \n",
- "top Based on a true story. Out of the Blue NaN NaN \n",
- "freq 3 4 NaN NaN \n",
- "mean NaN NaN 6.092514 690.331670 \n",
- "std NaN NaN 1.193989 1234.187111 \n",
- "min NaN NaN 0.000000 0.000000 \n",
- "25% NaN NaN 5.600000 54.000000 \n",
- "50% NaN NaN 6.200000 235.000000 \n",
- "75% NaN NaN 6.800000 737.000000 \n",
- "max NaN NaN 10.000000 13752.000000 \n",
- "\n",
- " movie_id cast crew \n",
- "count 4809.000000 4809 4809 \n",
- "unique NaN 4761 4776 \n",
- "top NaN [] [] \n",
- "freq NaN 43 28 \n",
- "mean 57120.571429 NaN NaN \n",
- "std 88653.369849 NaN NaN \n",
- "min 5.000000 NaN NaN \n",
- "25% 9012.000000 NaN NaN \n",
- "50% 14624.000000 NaN NaN \n",
- "75% 58595.000000 NaN NaN \n",
- "max 459488.000000 NaN NaN \n",
- "\n",
- "[11 rows x 23 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "original_data.describe(include=\"all\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Das ist eine Zusammenfassung eines Pandas DataFrame mit 4809 Einträgen und 23 Spalten, die verschiedene Informationen über Filme enthält, wie Budget, Genres, Homepage, ID, Schlüsselwörter, Sprache, Titel, Popularität, Produktionsfirmen, Einnahmen, Laufzeit, Bewertung, Stimmenzahl,\n",
- "Besetzung und Crew."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "Int64Index: 4809 entries, 0 to 4808\n",
- "Data columns (total 23 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 budget 4809 non-null int64 \n",
- " 1 genres 4809 non-null object \n",
- " 2 homepage 1713 non-null object \n",
- " 3 id 4809 non-null int64 \n",
- " 4 keywords 4809 non-null object \n",
- " 5 original_language 4809 non-null object \n",
- " 6 original_title 4809 non-null object \n",
- " 7 overview 4806 non-null object \n",
- " 8 popularity 4809 non-null float64\n",
- " 9 production_companies 4809 non-null object \n",
- " 10 production_countries 4809 non-null object \n",
- " 11 release_date 4808 non-null object \n",
- " 12 revenue 4809 non-null int64 \n",
- " 13 runtime 4807 non-null float64\n",
- " 14 spoken_languages 4809 non-null object \n",
- " 15 status 4809 non-null object \n",
- " 16 tagline 3965 non-null object \n",
- " 17 title 4809 non-null object \n",
- " 18 vote_average 4809 non-null float64\n",
- " 19 vote_count 4809 non-null int64 \n",
- " 20 movie_id 4809 non-null int64 \n",
- " 21 cast 4809 non-null object \n",
- " 22 crew 4809 non-null object \n",
- "dtypes: float64(3), int64(5), object(15)\n",
- "memory usage: 901.7+ KB\n"
- ]
- }
- ],
- "source": [
- "original_data.info()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.3 Datenbereinigung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Auf Nullwerte prüfen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle zeigt die Anzahl der fehlenden Werte in jeder Spalte eines DataFrame an, wobei z.B. die Spalte 'homepage' 3096 fehlende Werte hat, \n",
- "während viele andere Spalten keine fehlenden Werte aufweisen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "budget 0\n",
- "genres 0\n",
- "homepage 3096\n",
- "id 0\n",
- "keywords 0\n",
- "original_language 0\n",
- "original_title 0\n",
- "overview 3\n",
- "popularity 0\n",
- "production_companies 0\n",
- "production_countries 0\n",
- "release_date 1\n",
- "revenue 0\n",
- "runtime 2\n",
- "spoken_languages 0\n",
- "status 0\n",
- "tagline 844\n",
- "title 0\n",
- "vote_average 0\n",
- "vote_count 0\n",
- "movie_id 0\n",
- "cast 0\n",
- "crew 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "original_data.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null = original_data.dropna(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "budget 0\n",
- "genres 0\n",
- "homepage 0\n",
- "id 0\n",
- "keywords 0\n",
- "original_language 0\n",
- "original_title 0\n",
- "overview 0\n",
- "popularity 0\n",
- "production_companies 0\n",
- "production_countries 0\n",
- "release_date 0\n",
- "revenue 0\n",
- "runtime 0\n",
- "spoken_languages 0\n",
- "status 0\n",
- "tagline 0\n",
- "title 0\n",
- "vote_average 0\n",
- "vote_count 0\n",
- "movie_id 0\n",
- "cast 0\n",
- "crew 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_wo_null.isnull().sum()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Auf Duplikate prüfen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "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>budget</th>\n",
- " <th>genres</th>\n",
- " <th>homepage</th>\n",
- " <th>id</th>\n",
- " <th>keywords</th>\n",
- " <th>original_language</th>\n",
- " <th>original_title</th>\n",
- " <th>overview</th>\n",
- " <th>popularity</th>\n",
- " <th>production_companies</th>\n",
- " <th>...</th>\n",
- " <th>runtime</th>\n",
- " <th>spoken_languages</th>\n",
- " <th>status</th>\n",
- " <th>tagline</th>\n",
- " <th>title</th>\n",
- " <th>vote_average</th>\n",
- " <th>vote_count</th>\n",
- " <th>movie_id</th>\n",
- " <th>cast</th>\n",
- " <th>crew</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>0 rows × 23 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- "Empty DataFrame\n",
- "Columns: [budget, genres, homepage, id, keywords, original_language, original_title, overview, popularity, production_companies, production_countries, release_date, revenue, runtime, spoken_languages, status, tagline, title, vote_average, vote_count, movie_id, cast, crew]\n",
- "Index: []\n",
- "\n",
- "[0 rows x 23 columns]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_wo_null[df_wo_null.duplicated(keep=False)]"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.4 Test auf Multikollinearität"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle zeigt, dass es keine fehlenden Werte in den Spalten für verschiedene Merkmale wie Budget, Genres, Homepage, usw. gibt."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Keine nicht-signifikanten Variablen mehr. Das endgültige Modell wird erstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code erstellt eine Wärmebildkarte, die die Korrelationen zwischen den Spalten des DataFrames df_wo_null zeigt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1296x1296 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "f,ax=plt.subplots(figsize = (18,18))\n",
- "sns.heatmap(df_wo_null.corr(),annot= True,linewidths=0.5,fmt = \".1f\",ax=ax)\n",
- "plt.xticks(rotation=90)\n",
- "plt.yticks(rotation=0)\n",
- "plt.title('Correlation Map')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "Der Code entfernt die Spalten 'tagline', 'homepage', 'id' und 'movie_id' aus dem DataFrame df_wo_null.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null = df_wo_null.drop(['tagline', 'homepage', 'id', 'movie_id'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code erstellt und zeigt eine große Wärmebildkarte der Korrelationen \n",
- "zwischen den Spalten von df_wo_null mit Beschriftungen, Linien und einem Titel an.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1296x1296 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "f,ax=plt.subplots(figsize = (18,18))\n",
- "sns.heatmap(df_wo_null.corr(),annot= True,linewidths=0.5,fmt = \".1f\",ax=ax)\n",
- "plt.xticks(rotation=90)\n",
- "plt.yticks(rotation=0)\n",
- "plt.title('Correlation Map')\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.5 Deskriptive Analysise "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null = df_wo_null.drop(['status', 'original_title', 'overview'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null = df_wo_null.drop(['production_countries', 'original_language', 'crew', 'spoken_languages'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null = df_wo_null.drop(['runtime', 'keywords', 'vote_average', 'budget'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle listet Filme mit ihren Genres, Popularität, Produktionsfirmen, Veröffentlichungsdatum, Einnahmen, Titel, \n",
- "Anzahl der Stimmen und Besetzung auf."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "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>genres</th>\n",
- " <th>popularity</th>\n",
- " <th>production_companies</th>\n",
- " <th>release_date</th>\n",
- " <th>revenue</th>\n",
- " <th>title</th>\n",
- " <th>vote_count</th>\n",
- " <th>cast</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
- " <td>150.437577</td>\n",
- " <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
- " <td>2009-12-10</td>\n",
- " <td>2787965087</td>\n",
- " <td>Avatar</td>\n",
- " <td>11800</td>\n",
- " <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
- " <td>139.082615</td>\n",
- " <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
- " <td>2007-05-19</td>\n",
- " <td>961000000</td>\n",
- " <td>Pirates of the Caribbean: At World's End</td>\n",
- " <td>4500</td>\n",
- " <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
- " <td>107.376788</td>\n",
- " <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
- " <td>2015-10-26</td>\n",
- " <td>880674609</td>\n",
- " <td>Spectre</td>\n",
- " <td>4466</td>\n",
- " <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
- " <td>112.312950</td>\n",
- " <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
- " <td>2012-07-16</td>\n",
- " <td>1084939099</td>\n",
- " <td>The Dark Knight Rises</td>\n",
- " <td>9106</td>\n",
- " <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
- " <td>43.926995</td>\n",
- " <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
- " <td>2012-03-07</td>\n",
- " <td>284139100</td>\n",
- " <td>John Carter</td>\n",
- " <td>2124</td>\n",
- " <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " genres popularity \\\n",
- "0 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... 150.437577 \n",
- "1 [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"... 139.082615 \n",
- "2 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... 107.376788 \n",
- "3 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam... 112.312950 \n",
- "4 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... 43.926995 \n",
- "\n",
- " production_companies release_date revenue \\\n",
- "0 [{\"name\": \"Ingenious Film Partners\", \"id\": 289... 2009-12-10 2787965087 \n",
- "1 [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"... 2007-05-19 961000000 \n",
- "2 [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam... 2015-10-26 880674609 \n",
- "3 [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"... 2012-07-16 1084939099 \n",
- "4 [{\"name\": \"Walt Disney Pictures\", \"id\": 2}] 2012-03-07 284139100 \n",
- "\n",
- " title vote_count \\\n",
- "0 Avatar 11800 \n",
- "1 Pirates of the Caribbean: At World's End 4500 \n",
- "2 Spectre 4466 \n",
- "3 The Dark Knight Rises 9106 \n",
- "4 John Carter 2124 \n",
- "\n",
- " cast \n",
- "0 [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"... \n",
- "1 [{\"cast_id\": 4, \"character\": \"Captain Jack Spa... \n",
- "2 [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr... \n",
- "3 [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba... \n",
- "4 [{\"cast_id\": 5, \"character\": \"John Carter\", \"c... "
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_wo_null.head()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 3. Datenaufbereitung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3.1 Erfassung kategorialer Variablen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null['genre1'] = df_wo_null['genres'].str.split(',').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null['genre1'] = df_wo_null['genre1'].str.split(':').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null['genre1'] = df_wo_null['genre1'].str.split('\"').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null['genre2'] = df_wo_null['genres'].str.split(',').str[3]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null['genre2'] = df_wo_null['genre2'].str.split(':').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_wo_null['genre2'] = df_wo_null['genre2'].str.split('\"').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_1 = df_wo_null.drop(['genres'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle zeigt die Anzahl der fehlenden Werte in den angegebenen Spalten eines DataFrames,\n",
- "wobei z.B. 'genre2' 232 fehlende Werte und 'popularity' keine fehlenden Werte aufweist.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "popularity 0\n",
- "production_companies 0\n",
- "release_date 0\n",
- "revenue 0\n",
- "title 0\n",
- "vote_count 0\n",
- "cast 0\n",
- "genre1 2\n",
- "genre2 232\n",
- "dtype: int64"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_1.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_1[\"new_genres\"] = df_1[\"genre1\"] +\",\"+ df_1[\"genre2\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_2 = df_1.drop(['genre1', 'genre2'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle zeigt die Anzahl der fehlenden Werte in bestimmten Spalten eines DataFrames, \n",
- "wobei die Spalte 'new_genres' 232 fehlende Werte und die anderen aufgeführten Spalten keine fehlenden Werte aufweisen.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "popularity 0\n",
- "production_companies 0\n",
- "release_date 0\n",
- "revenue 0\n",
- "title 0\n",
- "vote_count 0\n",
- "cast 0\n",
- "new_genres 232\n",
- "dtype: int64"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_2.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_2 = df_2.dropna(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_2['year'] = df_2['release_date'].str.split('-').str[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_2['year'] = df_2['year'].astype(int)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle beschreibt einen Pandas DataFrame mit 1262 Einträgen und 9 Spalten, \n",
- "die verschiedene Filmattribute wie Popularität, Produktionsfirmen, Veröffentlichungsdatum, \n",
- "Einnahmen, Titel, Stimmenanzahl, Besetzung, #neue Genres und Jahr enthalten, wobei keine Spalte fehlende Werte aufweist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "Int64Index: 1262 entries, 0 to 4802\n",
- "Data columns (total 9 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 popularity 1262 non-null float64\n",
- " 1 production_companies 1262 non-null object \n",
- " 2 release_date 1262 non-null object \n",
- " 3 revenue 1262 non-null int64 \n",
- " 4 title 1262 non-null object \n",
- " 5 vote_count 1262 non-null int64 \n",
- " 6 cast 1262 non-null object \n",
- " 7 new_genres 1262 non-null object \n",
- " 8 year 1262 non-null int64 \n",
- "dtypes: float64(1), int64(3), object(5)\n",
- "memory usage: 98.6+ KB\n"
- ]
- }
- ],
- "source": [
- "df_2.info()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3 = df_2.drop(['release_date'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle enthält Informationen über Filme mit Attributen wie Popularität, Produktionsfirmen, \n",
- "Einnahmen, Titel, Stimmenanzahl, Besetzung, Genres und Jahr für insgesamt 1262 Filme.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "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>popularity</th>\n",
- " <th>production_companies</th>\n",
- " <th>revenue</th>\n",
- " <th>title</th>\n",
- " <th>vote_count</th>\n",
- " <th>cast</th>\n",
- " <th>new_genres</th>\n",
- " <th>year</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>150.437577</td>\n",
- " <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
- " <td>2787965087</td>\n",
- " <td>Avatar</td>\n",
- " <td>11800</td>\n",
- " <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2009</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>139.082615</td>\n",
- " <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
- " <td>961000000</td>\n",
- " <td>Pirates of the Caribbean: At World's End</td>\n",
- " <td>4500</td>\n",
- " <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
- " <td>Adventure,Fantasy</td>\n",
- " <td>2007</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>107.376788</td>\n",
- " <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
- " <td>880674609</td>\n",
- " <td>Spectre</td>\n",
- " <td>4466</td>\n",
- " <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2015</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>112.312950</td>\n",
- " <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
- " <td>1084939099</td>\n",
- " <td>The Dark Knight Rises</td>\n",
- " <td>9106</td>\n",
- " <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
- " <td>Action,Crime</td>\n",
- " <td>2012</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>43.926995</td>\n",
- " <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
- " <td>284139100</td>\n",
- " <td>John Carter</td>\n",
- " <td>2124</td>\n",
- " <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2012</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4764</th>\n",
- " <td>27.662696</td>\n",
- " <td>[{\"name\": \"Automatik Entertainment\", \"id\": 281...</td>\n",
- " <td>600896</td>\n",
- " <td>The Signal</td>\n",
- " <td>631</td>\n",
- " <td>[{\"cast_id\": 1, \"character\": \"Nic Eastman\", \"c...</td>\n",
- " <td>Thriller,Science Fiction</td>\n",
- " <td>2014</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4772</th>\n",
- " <td>3.277287</td>\n",
- " <td>[{\"name\": \"FM Productions\", \"id\": 12601}, {\"na...</td>\n",
- " <td>321952</td>\n",
- " <td>The Last Waltz</td>\n",
- " <td>64</td>\n",
- " <td>[{\"cast_id\": 1, \"character\": \"Himself\", \"credi...</td>\n",
- " <td>Documentary,Music</td>\n",
- " <td>1978</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4778</th>\n",
- " <td>1.330379</td>\n",
- " <td>[]</td>\n",
- " <td>10000</td>\n",
- " <td>Down Terrace</td>\n",
- " <td>26</td>\n",
- " <td>[{\"cast_id\": 4, \"character\": \"Bill\", \"credit_i...</td>\n",
- " <td>Drama,Action</td>\n",
- " <td>2009</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4787</th>\n",
- " <td>0.048948</td>\n",
- " <td>[]</td>\n",
- " <td>0</td>\n",
- " <td>Dry Spell</td>\n",
- " <td>1</td>\n",
- " <td>[{\"cast_id\": 4, \"character\": \"Sasha\", \"credit_...</td>\n",
- " <td>Comedy,Romance</td>\n",
- " <td>2013</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4802</th>\n",
- " <td>23.307949</td>\n",
- " <td>[{\"name\": \"Thinkfilm\", \"id\": 446}]</td>\n",
- " <td>424760</td>\n",
- " <td>Primer</td>\n",
- " <td>658</td>\n",
- " <td>[{\"cast_id\": 1, \"character\": \"Aaron\", \"credit_...</td>\n",
- " <td>Science Fiction,Drama</td>\n",
- " <td>2004</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>1262 rows × 8 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " popularity production_companies \\\n",
- "0 150.437577 [{\"name\": \"Ingenious Film Partners\", \"id\": 289... \n",
- "1 139.082615 [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"... \n",
- "2 107.376788 [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam... \n",
- "3 112.312950 [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"... \n",
- "4 43.926995 [{\"name\": \"Walt Disney Pictures\", \"id\": 2}] \n",
- "... ... ... \n",
- "4764 27.662696 [{\"name\": \"Automatik Entertainment\", \"id\": 281... \n",
- "4772 3.277287 [{\"name\": \"FM Productions\", \"id\": 12601}, {\"na... \n",
- "4778 1.330379 [] \n",
- "4787 0.048948 [] \n",
- "4802 23.307949 [{\"name\": \"Thinkfilm\", \"id\": 446}] \n",
- "\n",
- " revenue title vote_count \\\n",
- "0 2787965087 Avatar 11800 \n",
- "1 961000000 Pirates of the Caribbean: At World's End 4500 \n",
- "2 880674609 Spectre 4466 \n",
- "3 1084939099 The Dark Knight Rises 9106 \n",
- "4 284139100 John Carter 2124 \n",
- "... ... ... ... \n",
- "4764 600896 The Signal 631 \n",
- "4772 321952 The Last Waltz 64 \n",
- "4778 10000 Down Terrace 26 \n",
- "4787 0 Dry Spell 1 \n",
- "4802 424760 Primer 658 \n",
- "\n",
- " cast \\\n",
- "0 [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"... \n",
- "1 [{\"cast_id\": 4, \"character\": \"Captain Jack Spa... \n",
- "2 [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr... \n",
- "3 [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba... \n",
- "4 [{\"cast_id\": 5, \"character\": \"John Carter\", \"c... \n",
- "... ... \n",
- "4764 [{\"cast_id\": 1, \"character\": \"Nic Eastman\", \"c... \n",
- "4772 [{\"cast_id\": 1, \"character\": \"Himself\", \"credi... \n",
- "4778 [{\"cast_id\": 4, \"character\": \"Bill\", \"credit_i... \n",
- "4787 [{\"cast_id\": 4, \"character\": \"Sasha\", \"credit_... \n",
- "4802 [{\"cast_id\": 1, \"character\": \"Aaron\", \"credit_... \n",
- "\n",
- " new_genres year \n",
- "0 Action,Adventure 2009 \n",
- "1 Adventure,Fantasy 2007 \n",
- "2 Action,Adventure 2015 \n",
- "3 Action,Crime 2012 \n",
- "4 Action,Adventure 2012 \n",
- "... ... ... \n",
- "4764 Thriller,Science Fiction 2014 \n",
- "4772 Documentary,Music 1978 \n",
- "4778 Drama,Action 2009 \n",
- "4787 Comedy,Romance 2013 \n",
- "4802 Science Fiction,Drama 2004 \n",
- "\n",
- "[1262 rows x 8 columns]"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3['cast'] = df_3['cast'].str.split(',').str[5]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3['cast'] = df_3['cast'].str.split(':').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3['cast'] = df_3['cast'].str.split('\"').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3['production_companies'] = df_3['production_companies'].str.split(',').str[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3['production_companies'] = df_3['production_companies'].str.split(':').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_3['production_companies'] = df_3['production_companies'].str.split('\"').str[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code bearbeitet die Spalten 'cast' und 'production_companies', \n",
- "um bestimmte Informationen zu extrahieren und zu trennen, wobei fehlende Werte in diesen Spalten identifiziert werden.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "popularity 0\n",
- "production_companies 22\n",
- "revenue 0\n",
- "title 0\n",
- "vote_count 0\n",
- "cast 5\n",
- "new_genres 0\n",
- "year 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_3.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_5 = df_3.dropna(axis=0)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 4. Modellierung und Evaluation"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4.1 Test und Trainieren der Daten"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle listet Filme mit ihren Popularitätsbewertungen, Produktionsfirmen, Einnahmen, Titeln, \n",
- "Stimmenanzahl, Hauptdarstellern, Genres und Erscheinungsjahren auf.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>popularity</th>\n",
- " <th>production_companies</th>\n",
- " <th>revenue</th>\n",
- " <th>title</th>\n",
- " <th>vote_count</th>\n",
- " <th>cast</th>\n",
- " <th>new_genres</th>\n",
- " <th>year</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>150.437577</td>\n",
- " <td>Ingenious Film Partners</td>\n",
- " <td>2787965087</td>\n",
- " <td>Avatar</td>\n",
- " <td>11800</td>\n",
- " <td>Sam Worthington</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2009</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>139.082615</td>\n",
- " <td>Walt Disney Pictures</td>\n",
- " <td>961000000</td>\n",
- " <td>Pirates of the Caribbean: At World's End</td>\n",
- " <td>4500</td>\n",
- " <td>Johnny Depp</td>\n",
- " <td>Adventure,Fantasy</td>\n",
- " <td>2007</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>107.376788</td>\n",
- " <td>Columbia Pictures</td>\n",
- " <td>880674609</td>\n",
- " <td>Spectre</td>\n",
- " <td>4466</td>\n",
- " <td>Daniel Craig</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2015</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>112.312950</td>\n",
- " <td>Legendary Pictures</td>\n",
- " <td>1084939099</td>\n",
- " <td>The Dark Knight Rises</td>\n",
- " <td>9106</td>\n",
- " <td>Christian Bale</td>\n",
- " <td>Action,Crime</td>\n",
- " <td>2012</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>43.926995</td>\n",
- " <td>Walt Disney Pictures</td>\n",
- " <td>284139100</td>\n",
- " <td>John Carter</td>\n",
- " <td>2124</td>\n",
- " <td>Taylor Kitsch</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2012</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " popularity production_companies revenue \\\n",
- "0 150.437577 Ingenious Film Partners 2787965087 \n",
- "1 139.082615 Walt Disney Pictures 961000000 \n",
- "2 107.376788 Columbia Pictures 880674609 \n",
- "3 112.312950 Legendary Pictures 1084939099 \n",
- "4 43.926995 Walt Disney Pictures 284139100 \n",
- "\n",
- " title vote_count cast \\\n",
- "0 Avatar 11800 Sam Worthington \n",
- "1 Pirates of the Caribbean: At World's End 4500 Johnny Depp \n",
- "2 Spectre 4466 Daniel Craig \n",
- "3 The Dark Knight Rises 9106 Christian Bale \n",
- "4 John Carter 2124 Taylor Kitsch \n",
- "\n",
- " new_genres year \n",
- "0 Action,Adventure 2009 \n",
- "1 Adventure,Fantasy 2007 \n",
- "2 Action,Adventure 2015 \n",
- "3 Action,Crime 2012 \n",
- "4 Action,Adventure 2012 "
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_5.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_6 = df_5.rename({\"cast\":\"star\"}, axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle enthält Informationen über den Erfolg von Filmen, darunter Popularität, \n",
- "Produktionsunternehmen, Einnahmen, Titel, Stimmenzahl, Hauptdarsteller, Genres und Jahr der Veröffentlichung."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>popularity</th>\n",
- " <th>production_companies</th>\n",
- " <th>revenue</th>\n",
- " <th>title</th>\n",
- " <th>vote_count</th>\n",
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- " <th>year</th>\n",
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- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>150.437577</td>\n",
- " <td>Ingenious Film Partners</td>\n",
- " <td>2787965087</td>\n",
- " <td>Avatar</td>\n",
- " <td>11800</td>\n",
- " <td>Sam Worthington</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2009</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>139.082615</td>\n",
- " <td>Walt Disney Pictures</td>\n",
- " <td>961000000</td>\n",
- " <td>Pirates of the Caribbean: At World's End</td>\n",
- " <td>4500</td>\n",
- " <td>Johnny Depp</td>\n",
- " <td>Adventure,Fantasy</td>\n",
- " <td>2007</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>107.376788</td>\n",
- " <td>Columbia Pictures</td>\n",
- " <td>880674609</td>\n",
- " <td>Spectre</td>\n",
- " <td>4466</td>\n",
- " <td>Daniel Craig</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2015</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>112.312950</td>\n",
- " <td>Legendary Pictures</td>\n",
- " <td>1084939099</td>\n",
- " <td>The Dark Knight Rises</td>\n",
- " <td>9106</td>\n",
- " <td>Christian Bale</td>\n",
- " <td>Action,Crime</td>\n",
- " <td>2012</td>\n",
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- " <th>4</th>\n",
- " <td>43.926995</td>\n",
- " <td>Walt Disney Pictures</td>\n",
- " <td>284139100</td>\n",
- " <td>John Carter</td>\n",
- " <td>2124</td>\n",
- " <td>Taylor Kitsch</td>\n",
- " <td>Action,Adventure</td>\n",
- " <td>2012</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " popularity production_companies revenue \\\n",
- "0 150.437577 Ingenious Film Partners 2787965087 \n",
- "1 139.082615 Walt Disney Pictures 961000000 \n",
- "2 107.376788 Columbia Pictures 880674609 \n",
- "3 112.312950 Legendary Pictures 1084939099 \n",
- "4 43.926995 Walt Disney Pictures 284139100 \n",
- "\n",
- " title vote_count star \\\n",
- "0 Avatar 11800 Sam Worthington \n",
- "1 Pirates of the Caribbean: At World's End 4500 Johnny Depp \n",
- "2 Spectre 4466 Daniel Craig \n",
- "3 The Dark Knight Rises 9106 Christian Bale \n",
- "4 John Carter 2124 Taylor Kitsch \n",
- "\n",
- " new_genres year \n",
- "0 Action,Adventure 2009 \n",
- "1 Adventure,Fantasy 2007 \n",
- "2 Action,Adventure 2015 \n",
- "3 Action,Crime 2012 \n",
- "4 Action,Adventure 2012 "
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_6.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_7 = df_6.drop(['year'], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_cleaned = df_7.dropna(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Ausgabe zeigt ein Array von Diagrammen, das die Verteilung der Popularität, \n",
- "Einnahmen und Stimmenzahl der Filme in der Tabelle visualisiert.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[<AxesSubplot:title={'center':'popularity'}>,\n",
- " <AxesSubplot:title={'center':'revenue'}>],\n",
- " [<AxesSubplot:title={'center':'vote_count'}>, <AxesSubplot:>]],\n",
- " dtype=object)"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 720x720 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_cleaned.hist(figsize=(10,10), bins=50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Diese Meldung warnt davor, dass die Funktion distplot in zukünftigen Versionen von Seaborn nicht mehr unterstützt wird \n",
- "und stattdessen displot oder histplot verwendet werden sollte.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/Jumana/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='popularity', ylabel='Density'>"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(df_cleaned['popularity'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [],
- "source": [
- "q = df_cleaned['popularity'].quantile(0.99)\n",
- "\n",
- "data_1 = df_cleaned[df_cleaned['popularity']<q]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Meldung weist darauf hin, dass die Funktion distplot in zukünftigen Versionen nicht mehr unterstützt wird \n",
- "und empfiehlt die Verwendung von displot oder histplot als Alternative."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/Jumana/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='popularity', ylabel='Density'>"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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jeP8gKmubOCutHyHEVVw2zbHdbm/V3aMoSqvtjtLb8sorrzh+/8EPfsDq1auprq6+7lhFUVCrOx9XQ0Pd1zq4FaWWOgz+bQ8mKK5oGWXWP8JAbX37a+zodNp2z9EVeUbd5sXBk6X846sSvjtpkNNzuILRaOj21/QkUj/OSf24jsuCT0REBAcPHnRsm81mTCZTq3Sz2ezYLisra5V+Lbvdzu9///vrWkQajYbw8HBKS0sZMGBAp851rfLyGux2D+wW0miorml7KHNxWQ0qWoZZF5tr2j2F1Wpr9xxdlWdwZACHTpSQf74cg6+X0/N0JaPRgNksM2y3R+rHOamf9qnVqlv+0u6ybrfJkyeTnZ2NxWKhvr6eXbt2ERcX50iPjo5Gr9dz6NAhADIzM1ulX1dQtZrdu3ezc+dOADIyMhg7diy+vr7Ex8eTmZkJwMGDB9Hr9URFRbnq0jxCZW0T/r46dFr3j6a/MuPB57l9uytUCPEtl30yhYeHs2jRIlJSUpg1axaJiYmMGTOG1NRUcnNzAUhPT2flypVMnTqVuro6UlJSnJ7ztdde47333mPGjBl88MEHrFixAoDHHnuMpqYmZsyYwauvvsqqVatcdVke43JNI0H+3ftwaXuCDXqGRAXwyeFCGXgghABApcingcd2uykaDZ8cunDdfrtdYcPuU4wcHMITiXdw5JS5jaNbjB1udJreVXkUBdbvPEnavHHcPijE6bm6inSbOCf145zUT/t6dLebcJ+quiYUBYL8u+/+SkfGDQ/Dz1vL3sN9ewi8EKKFBJ9eqLKmCYBAv57R7QbgpdVw7+hIvjhlprK2yd3FEUK4mQSfXqiypmVOt8Ae1PIBiB8XRbNd4bOj0voRoq+T4NMLXa5pwt9Hh1bTs/57I0P9iBkQxL4jRdjlVqMQfVrP+nQSXaKytqnHtXquiB8XjflyA1+ds7i7KEIIN5Lg08soikJ1XRMB3fgw5424c7gRfx8de7+Urjch+jIJPr1MQ1MztmYFg6/O3UVpk06r5v4xkRw+XeZY7E4I0fdI8OllqupaRpJ15zQ2NypuXBR2RQYeCNGXSfDpZWrqrAA9tuUDEB7syx2DgvnkSBHNdru7iyOEcAMJPr1MVZ0VlQr8fXpu8AF4YHw/LFWNHD5d5u6iCCHcQIJPL1Nd24Sftw612j2rl3bW+GFhhAV6szunwN1FEUK4gQSfXqa6ztqju9yuUKtVPHhXP05drOR8scyfJURfI8Gnl6mubyLAr+cONrja/WOi0Htp2H1QWj9C9DUSfHqRxqZmmqx2j2j5APh6a7lvdCT//KrEMSWQEKJvkODTi1R7wDDra333rn7Y7Qoff1no7qIIIbqRBJ9epNoDhllfKzzElzFDQ9n7ZSFWW7O7iyOE6CYuDT7btm1j+vTpTJkyhQ0bNlyXnpeXx+zZs0lISGDp0qXYbLZW6WvXruW3v/2tY/vs2bMsWLCA5ORkHnnkEfLy8gAoLCxk/PjxJCcnk5yczFNPPeXKy+qxHC2fHjjMWqVWUdtoa/Mnbnw0VXVW9h2VZbaF6Cu0rjpxSUkJa9asYevWrXh5eTFv3jwmTpzIbbfd5siTlpbGihUrGDduHEuWLGHz5s3Mnz+f6upqVq5cyd/+9jd++MMfOvK/9NJL/OhHP+I73/kO2dnZvPDCC/z1r3/l2LFjJCUl8corr7jqcjxCVZ0VX28tmh42mzVAo7W53ZVOFUUhLNCbnf88z3fGRaJR97zyCyG6Vqf+yp999ln2799/Qyfev38/kyZNIigoCF9fXxISEsjKynKkFxYW0tDQwLhx4wCYPXu2I33Pnj0MGjSIJ554otU5586dy/333w/AiBEjuHTpEgC5ubmcOnWK5ORkUlJSOHny5A2VtbeormvyqC63K1QqFSMHh1BW2cChk86X6xZC9A6davl873vf4/XXX2f58uU8/PDDfP/73ycoKMjpMaWlpRiNRse2yWTi6NGj7aYbjUZKSkoAmDVrFkCrLjdoCVBXrFu3ju9+97sA6PV6Zs6cybx58/j000955pln2L59O15enbvxfqtrkbtLqaUOg7+3Y7um3sbgqIBW+3Q6bavta3WU3lV5OkofOVTPyYLL7Dp4ken3D0Wl6pqHZI1GQ5ecp7eS+nFO6sd1OhV8Zs6cycyZMzl79iwffPABc+bMYfz48Tz22GOMGTOmzWPsdnurDxBFUVptd5TeHkVRWLVqFUeOHOG9994DWlpmV8THx7N69Wry8/OJiYnpzOVRXl6D3e6Bi5tpNFTXNABgtdmpb7ThrVM79gFYrbZW29fqKL2r8nTmHP9yZz827j7F3pzzjBoc6jRvZxiNBsxmeYC1PVI/zkn9tE+tVt3yl/ZOd67b7XbOnz/PuXPnaG5uJjQ0lP/8z/9k3bp1beaPiIjAbP62C8VsNmMymdpNLysra5XeFpvNxvPPP09ubi7vvfceBkPLt5L169dTUVHhyKcoClqty25n9UieOMz6WrExJoINerZnn3d3UYQQLtap4LNmzRri4+P54x//yPTp09m1axeLFy/mT3/6U5uj2AAmT55MdnY2FouF+vp6du3aRVxcnCM9OjoavV7PoUOHAMjMzGyV3pbXXnuNmpoa3nnnHUfgAcjJyWHLli0AHDhwALvdzpAhQzpzab2GJw6zvpZOq2bKhP6cuHCZs4WV16Xb7LQ7Yu7Kj00myRbCI3SqeWCxWHjrrbeu68by9fVl9erVbR4THh7OokWLSElJwWq1MmfOHMaMGUNqaioLFy5k9OjRpKen89JLL1FTU8PIkSNJSUlxWoYNGzbQr18/5s6d69ifmZnJ0qVLWbx4MZmZmej1elavXo26j42Y6g0tH4D4cVF8uP8cH+4/x3Nzx7ZKa7TayMkrcXr8hNvD0er7VqtXCE/Uqb/S5ubm6wLPwoULWbduHffdd1+7xyUlJZGUlNRq31tvveX4PSYmxtFiacvV93JCQkL46quv2swXHh7Ou+++6/QaeruqOiveXhp0Ws8Out5eWqZM6M9fPv2a88XVDIyQG75C9EZOg8/LL79MSUkJhw4dwmKxOPbbbDYKCmQyyJ7EU4dZt+XBu/qRdeACf8s+x08fGu3u4gghXMBp8JkzZw6nT5/m5MmTJCQkOPZrNBrH8zmiZ6iusxIR4uvuYnQJX28dD97Vj7/tP09hWS3RYX7uLpIQoos5DT6jR49m9OjR3HvvvYSHh3dXmcQNsjXbqWuw9ZqWD8D3YvuzK6eA7dnnSE0a6e7iCCG6mNPg89xzz/Gb3/ym1RQ3V9u2bZtLCiVuTE39lZFunj3Y4GoGXy8eGB/NrpwCku8bjCm4d7TqhBAtnAaf1NRUAH7xi190S2HEzbkyzDqgF7V8ABLuHsCeQ4Vs/8d5/nXa7e4ujhCiCzkdGjVq1CgA7r77biIjI7n77rupq6sjJyeH22+XD4OeorcMs75WkL+e+8dG8nluMZYq57MjCCE8S6fG5S5btoy33nqLs2fP8tJLL3Hx4kWWLFni6rKJTqqus+KlVaP30ri7KF1u2sQBAOz45wU3l0QI0ZU6FXyOHTvGf/7nf7J7924eeughVq5cSWGhrDzZU1TVNvW6Vs8VYYE+3DMqgn1HiqiqbXJ3cYQQXaRTwUdRFNRqNZ9//jmTJk0CoKFBukF6ipp6a68a6XatGZMGYmu28/dDF91dFCFEF+lU8BkwYACpqalcvHiRu+++m3//939nxIgRri6b6AS7Xek1wae91U79/by4a4SRT48W0dAkS20L0Rt0anqdlStXsnv3bu666y50Oh2xsbGONXeEe9XUW1GU3jHYwNlqp1Ghfhw8YebE+QrGDQvr5pIJIbpap1o+vr6+xMbGUlVVxfHjxxkzZgz5+fmuLpvoBMds1n6e3/JxJsigZ/TQUPLOV9BkldaPEJ6uUy2f3/zmN7zzzjuEhn67wJdKpWLPnj0uK5jonCvDrAN6QcunIw9O6E/u2XJOXrjM6KG3vticEMJ9OhV8MjMz2bVrl0yx0wNV11nRalR498Jh1tfqb/InKsyPvPMV3DE4GE0fWzZDiN6kU3+9kZGREnh6qJbZrL06tQR5b3DHoGAampr5ukiWNxbCk3Wq5XPPPfewatUqHnzwQby9vR37R46UCR/drbrOSqB/7+9yuyIy1Jcgfy/yzlcwNDqgzwRdIXqbTgWfrVu3ApCVleXY15l7Ptu2beONN97AZrPx+OOPs2DBglbpeXl5LF26lNraWmJjY1m+fDla7bdFWrt2LRqNxrGoXFVVFc8//zwFBQWEhISwdu1ajEYjTU1NLF26lGPHjuHt7U16ejpDhw7tXA14MLtdobrOSj9T31lyQKVScfvAYLKPl1BiqSciVCYcFcITdarb7e9///t1Px0FnpKSEtasWcPGjRvJyMhg06ZNnDlzplWetLQ0li1bxs6dO1EUhc2bNwNQXV3NkiVLrluddO3atcTGxrJjxw7mzp3Lq6++CsD69evx8fFhx44dLFmyhBdffLHTFeDJKqobsCtKrxhmfSMGRwWg12n46nyFu4sihLhJnQo+tbW1vPLKKzz++ONcvnyZZcuWUVtb6/SY/fv3M2nSJIKCgvD19SUhIaFVy6mwsJCGhgbHonSzZ892pO/Zs4dBgwbxxBNPtDrn3r17HctyJyYmsm/fPqxWK3v37mXmzJkATJgwAYvFQlFRUedqwIOVVtQD9IoHTG+EVqNm+IAgLpbWyJQ7QnioTnW7rVixApPJRHl5OXq9npqaGpYtW8bq1avbPaa0tBSj0ejYNplMHD16tN10o9FISUkJgOMB1t/+9rftnlOr1eLv74/FYmnzXMXFxURFRXXm8ggN9e9Uvp6m9OglACKNhnZbPzqdFoO/d5tpnUnvqjxdfY67YsI5nm/hbFEVceP7OfL4+uoxXrWiq9FocPqafZ3Uj3NSP67TqeCTl5fHypUr+eSTT/Dx8SE9PZ3ExESnx9jt9lY3gxVFabXdUXpnXJlz7tpjr+zvrPLyGux25YZeuycorahHrVJhb26muqbtufasVlu7aZ1J76o8rjjHoEgDJ85VMGpwCDpty/93XV0j5uaWh1CNRgNms4yKa4/Uj3NSP+1Tq1W3/KW9U5/Q136QNzc3d/jhHhERgdn87VQpZrMZk8nUbnpZWVmr9LaYTCbKysoAsNls1NbWEhQURHh4OKWlpTd0rt6g1FKHwVeHuo+O+BrRPwhrs52vL1W5uyhCiBvUqeAzYcIE/t//+380NDTw6aef8m//9m9MnDjR6TGTJ08mOzsbi8VCfX09u3btIi4uzpEeHR2NXq/n0KFDQMuDrFentyU+Pp6MjAwAtm/fTmxsLDqdjvj4eDIzMwE4ePAger2+011unsxcUdfn7vdcLSzIm2CDnlMFl1EUz2u5CtGXdSr4PP/88/j6+mIwGFi7di0xMTH8x3/8h9NjwsPDWbRoESkpKcyaNYvExETGjBlDamoqubm5AKSnp7Ny5UqmTp1KXV0dKSkpTs/53HPPcfjwYWbMmMHGjRtZtmwZAI899hhNTU3MmDGDV199lVWrVnXmsjyaoiiUVtT3uZFuV1OpVAzvH4ilqpHySlniQwhP0uE9n927d/P2229z8uRJvL29GTFiBHfeeSd6vb7DkyclJTlGp13x1ltvOX6PiYlhy5Yt7R5/5fmeK4KCgnjzzTevy6fX63nttdc6LE9vUlXbRKO1uU+3fKBl2PWhk2ZOFlwmLMjH3cURQnSS0+CzY8cO1qxZw8KFC4mJiUGlUpGbm8urr75KY2MjU6ZM6a5yimuUOIZZ992WD4CXVsPgyADyi6qIjen99/mE6C2cBp/33nuP//mf/2l1/2To0KGMHTuWJUuWSPBxI/PlvvmMT1uGDwji9MVK8guruG9M77/XJ0Rv4PSeT21tbZs37gcPHkxjY6PLCiU6VvLNMGt/Hwk+oQHehAV6y8ADITyI0+Cj0bQ/Tb/8kbtXaUUdoYHeqNV9c5j1tYb1D6Syton8Ihl2LYQnkAVRPFRpRT2mEJlU84pBEQFoNSr2515yd1GEEJ3g9J7PyZMnufPOO6/brygKTU0yp5Y7mS/XMygq0N3F6DF0WjVDogL48nQZdQ1WfL2lO1KInsxp8Nm9e3d3lUPcgJp6K7UNNmn5XGNYvyBOFVSSfbyEB+/q1/EBQgi3cRp8oqOju6sc4gZcmc06PNiHymp5uPKK0EBv+pn82XekiH+5U967QvRkcs/HA5VergPAFCwtn2tNHhVBQWkN54plQkghejIJPh7oSstHnui/XmyMCS+dmk8O9/71nITwZBJ8PFBpRT3BBj1euvaHwvdVPnotE2JM/DOvhPpGm7uLI4RohwQfD1RaUU94sLR62hM/NprGpmb2fVno7qIIIdohwccDlV6uxyhdbu0aGh1AVJgfu/55zt1FEUK0Q4KPh6lvtFFV24RJWj7tUqlUxI2N4tSFyxSU1ri7OEKINkjw8TBXJhQNl5FuTk0eFYFWo2afDDwQokeS4ONhrox0k5aPc/4+OiaPiST7eDFN1mZ3F0cIcY0OF5O7Fdu2beONN97AZrPx+OOPs2DBglbpeXl5LF26lNraWmJjY1m+fDlarZaioiLS0tIoLy9n8ODBpKen4+fnx+zZs2lubvkgaWhooKCggH379tHY2EhiYiIDBgwAICwsjLffftuVl+Y2pd+0fOSeT8cSJg1k35eFHDxZyuRRke4ujhDiKi5r+ZSUlLBmzRo2btxIRkYGmzZt4syZM63ypKWlsWzZMnbu3ImiKGzevBmA5cuXM3/+fLKyshg1ahSvv/46AFu3biUzM5PMzEzGjh3LwoULCQsL49ixYyQlJTnSemvggZbZrAN8dfjoXfq9oVcYPTQMU7CPdL0J0QO5LPjs37+fSZMmERQUhK+vLwkJCWRlZTnSCwsLaWhoYNy4cQDMnj2brKwsrFYrOTk5JCQktNp/tezsbE6cOEFqaioAubm5nDp1iuTkZFJSUjh58qSrLsvtSivqZWaDTnIMPLhYyaXyWncXRwhxFZd9fS4tLcVoNDq2TSYTR48ebTfdaDRSUlJCRUUF/v7+aLXaVvuvtm7dOhYtWuRYb0iv1zNz5kzmzZvHp59+yjPPPMP27dvx8urcEtOhof43fZ3drayqkdFDQzEaDZRa6jD4ezvNr9NpnebpKL2r8nTXOXx99RivmnB15ndu4y/78sk5VcZTMyOcHtsXGY0GdxehR5P6cR2XBR+73Y5K9e1CZ4qitNpuL/3afECr7dOnT1NRUcEDDzzg2Pfss886fo+Pj2f16tXk5+cTExPTqbKWl9dgt/f8xfGarM2UX64n0EeH2VwNGg3VNc4nFrVabU7zdJTeVXm66xx1dY2Yv7kvaDQasDVYGTcsjI8OXGDahP7otDLG5gqj0dDyPhJtkvppn1qtuuUv7S77S4yIiMBsNju2zWYzJpOp3fSysjJMJhMhISFUV1c7BhZce9xHH33E9OnTW73W+vXrqaiocGwriuJoOfUmpRX1KEBEqHS73Yj4sVHU1Fv58rS548xCiG7hsuAzefJksrOzsVgs1NfXs2vXLuLi4hzp0dHR6PV6Dh06BEBmZiZxcXHodDpiY2PZvn07ABkZGa2OO3z4MLGxsa1eKycnhy1btgBw4MAB7HY7Q4YMcdWluU2xpWU2a3nGp30qtYraRhu1jTZKLXXUNtoYGBVAaIA3Ow8UUNtow2Z3dymFEC5rHoSHh7No0SJSUlKwWq3MmTOHMWPGkJqaysKFCxk9ejTp6em89NJL1NTUMHLkSFJSUgB4+eWXWbx4MW+88QaRkZH8+te/dpy3oKCA8PDwVq+1dOlSFi9eTGZmJnq9ntWrV6NW977uFUfwCZFh1u1ptDZz5FRLC8fg7+3ophsSHUBOXinbs88x/Z5BaGW0oBBu5dK/wKSkJJKSklrte+uttxy/x8TEOFosV4uOjmb9+vVtnvNKi+hq4eHhvPvuu7dY2p6v2FJHsEGPt5d8cN6o26IDOXK6jK++tjD9nkHuLo4QfV7vax70YiWWOpnN+ibptGqG9w/iQkkNZd88qCuEcB8JPh6k2FJHRKifu4vhsWIGBqNSwV5ZakEIt5Pg4yGq65qobbARIS2fm+brrWVwZADZx4upqbe6uzhC9GkSfDxEieWb2axDZKTbrbhjcDBNVru0foRwMwk+HuLKSDd5xufWBBu8uWNwCDsPXJBltoVwIwk+HqLYUodGrSIs0Pn0MqJj0ycNpLbBxp5DF91dFCH6LAk+HqLEUocp2AdNL3x+qbsNjDAwdmgoOw9coK5B7v0I4Q7ySeYhiivqZGaDLvRQ3BDqGmz87R/n3V0UIfokCT4ewG5XKLHUEyGDDbrMgHADk0ZGsDvnImWV8tyPEN1Ngo8HsFQ1YGu2y2CDLjY7bggqFWzZe9bdRRGiz5Hg4wG+nVBUnvHpSqGB3kybOIADeaXknbO4uzhC9CkSfDzAt8OsZXaDrjZ90kDCAr350+5T2JplumshuosEHw9QYqnHR68hwFfn7qL0Ol46DY9OGc6l8joyP/saAJsdx7IM7f3IsgxC3BqZHtkDFFtqCQ/2vW6FV9E1xgwN477RkWz/x3nG3hZGZJgfOXklTo+ZcHu4LMsgxC2Qlo8HKLbUy2ADF/vBd4cRYvDmjx9+RaO12d3FEaLXk+DTwzVZm7FUNRAhz/i4lI9eyw8Tb8dcUU/Gvnx3F0eIXs+lwWfbtm1Mnz6dKVOmsGHDhuvS8/LymD17NgkJCSxduhSbrWWuraKiIhYsWMDUqVP5yU9+Qm1tLdCyRPbEiRNJTk4mOTmZF198EYCmpibS0tKYNm0aDz30EGfP9p6hs5fK61CAqDAZbOBqIwYE870J/fns6CUulFS7uzhC9GouCz4lJSWsWbOGjRs3kpGRwaZNmzhz5kyrPGlpaSxbtoydO3eiKAqbN28GYPny5cyfP5+srCxGjRrF66+/DsCxY8d48sknyczMJDMzk5UrVwKwfv16fHx82LFjB0uWLHEEpd6gsKwGkODTXb4fP4T+Jn8+zy2mqrbJ3cURotdyWfDZv38/kyZNIigoCF9fXxISEsjKynKkFxYW0tDQwLhx4wCYPXs2WVlZWK1WcnJySEhIaLUfIDc3l88++4ykpCR+/OMfc+nSJQD27t3LzJkzAZgwYQIWi4WioiJXXVq3KjTXotWoMMkzPt1Cp9Xww6Q7UKtU7P2yEKsMaxPCJVw2XKe0tBSj0ejYNplMHD16tN10o9FISUkJFRUV+Pv7o9VqW+0HMBgMTJs2jSlTpvDnP/+ZRYsW8f7777d5ruLiYqKiojpV1tBQ/1u6VlcyVzXSz2QgMiLwurRSSx0Gf+ezXOt0Wqd5OkrvqjzuOkdb+X199RidTFWkaDQkTBrItk/zyTlhZsrEAdeNNOzoHJ7CaDS4uwg9mtSP67gs+Njt9lZ/sIqitNpuL/3afIBj+5VXXnHs+8EPfsDq1auprq6+7hhFUVDfwOzP5eU12O1K5y+uG50rqmRodCBmcxv3IDQaqmsanB5vtdqc5ukovavyuOMcBn/vNvPX1TVibm5/RFtdo40gPx3jhofx5akyDD5aRg8NvaFzeAKj0dD2+0oAUj/OqNWqW/7S7rJut4iICMxms2PbbDZjMpnaTS8rK8NkMhESEkJ1dTXN3/xhXznObrfzxhtvOPZfodFoCA8Pp7S09LpzebqGJhtllQ1yv8dNRg0OYVCkgS9Pl3GuWD6EhOhKLgs+kydPJjs7G4vFQn19Pbt27SIuLs6RHh0djV6v59ChQwBkZmYSFxeHTqcjNjaW7du3A5CRkUFcXBxqtZrdu3ezc+dOx/6xY8fi6+tLfHw8mZmZABw8eBC9Xt/pLreerKisZVqdaAk+bqFSqbh3VATGIB8+P3oJ82WZ/VqIruKy4BMeHs6iRYtISUlh1qxZJCYmMmbMGFJTU8nNzQUgPT2dlStXMnXqVOrq6khJSQHg5ZdfZvPmzUyfPp2DBw/ys5/9DIDXXnuN9957jxkzZvDBBx+wYsUKAB577DGampqYMWMGr776KqtWrXLVZXWrQnPLSLdoowQfd9Fo1DxwZxQ+ei0ff1FITZ0sPidEV3Dp/CBJSUkkJSW12vfWW285fo+JiWHLli3XHRcdHc369euv2z9s2DDef//96/br9Xpee+21Lihxz1JYVotOq8YYKCPd3MnbS8uDd0Wz4x8X2PPFRaZNHODuIgnh8WSGgx6soLSGfkY/1GqZ083dAv31fGd8NFW1Tew9XCQzYAtxiyT49FCKonChpJoB4TLUs6eICPVl8qgIisvrWL/zJHalZ46QFMITSPDpoSxVjdQ22Bhg6rnPIPVFQ6MDuXN4GF+cNPP+R6dRJAAJcVNkTvge6srcYv2l5dPlVGoVtY22dtM7euRr5OAQAv31fHToIgF+XiROHtS1BRSiD5Dg00NdKK1BBfQ3SsunqzVamzlyytxu+tjhxnbToGUI9qy4ITQ02ti6L58APy/ixnr+0H4hupMEnx7qQkk14SG+6L007i6KaINapeKJ6bdTXW/lf7NO4KvXEhvj+Q82C9Fd5J5PD3WhpIYB4dLq6cm0GjXPzBrN0KhA3sw8Ts6J0o4PEkIAEnx6pJp6K+VVDTLSzQPovTQsengsQ6ID+H3mcQ50sPy2EKKFBJ8e6NylKgAGR0jw8QQ+ei2L5o7ltugAfv/X43x29JK7iyREjyfBpwfKL6pCBQyKDHB3UUQn+ei1LHp4HLcPDOad7Xls3XdWngMSwgkJPj1Q/qUqIsP88NHLeBBPovfS8LO5Y4kbG8mH+8/zZuZxmqyeveyCEK4in249jKIo5BdVMe62MHcXRdwErUbN41NjiAjx4/8+PsOl8lpSE++Q+3dCXENaPj2M+XI9NfVWhkRJl5unUqlUTJ04gJ89PJaaOiu//N+DbP/H+R67YKEQ7iDBp4fJL2oZbCDBx/PdPiiUxY/dxeghoWzZe5bl/5PD4TNl1DbaqG20YZO5SUUfJt1uPUx+URVeOrWs4dPDdTRFD7RM05N3zsLooSEY/HQcOmlm3Zaj9DP6MX64kSl3D0Ar9/VEHyXv/B7mVMFlhkQGoFFLo7Qn62iKHvh2mh6VSsXgyAD6m/w5cb6C3HwL2z4/R35RFYn3DGR4/yBUKlk2Q/QtLv2E27ZtG9OnT2fKlCls2LDhuvS8vDxmz55NQkICS5cuxWZr+SZZVFTEggULmDp1Kj/5yU+ora0F4OzZsyxYsIDk5GQeeeQR8vLyACgsLGT8+PEkJyeTnJzMU0895crLcpmaeisFpTXEDAx2d1GEC2g1akYNCeWhuCGMuy2U88XVvLbxS17534N8eqSIRhkZJ/oQlwWfkpIS1qxZw8aNG8nIyGDTpk2cOXOmVZ60tDSWLVvGzp07URSFzZs3A7B8+XLmz59PVlYWo0aN4vXXXwfgpZdeIjU1lczMTH72s5/xwgsvAHDs2DGSkpLIzMwkMzOTt99+21WX5VKnCy6jADEDJPj0Zt5eGsbcFsbyH97NYwkjsNnsvLvjBM//7nPe33OaYkudu4sohMu5LPjs37+fSZMmERQUhK+vLwkJCWRlZTnSCwsLaWhoYNy4cQDMnj2brKwsrFYrOTk5JCQktNoPMHfuXO6//34ARowYwaVLLU+S5+bmcurUKZKTk0lJSeHkyZOuuiyXOnHhMjqtmsHycGmfoPfScvcd4bzw6J08N3cMIwYE89Ghiyz5wz9YtfEL9h8vplFGJYheymX3fEpLSzEav52a3mQycfTo0XbTjUYjJSUlVFRU4O/vj1arbbUfWgLRFevWreO73/0uAHq9npkzZzJv3jw+/fRTnnnmGbZv346Xl1enyhoa2jMm8DxbVMXtg0KIigzsVP5SSx0Gf2+neXQ6rdM8HaV3VR53naOt/D2lTuyoOFlw+ZstFfeMiWLMcCN5X1s4nl/OH7d9xQd7zzLj3sFMmTiQ4ADnr3czjEZ5/sgZqR/XcVnwsdvtrW6iKorSaru99GvzAdflW7VqFUeOHOG9994D4Nlnn3Wkx8fHs3r1avLz84mJielUWcvLa9z+DEZNvZWviypJvn8wZnN15w7SaKiuaXCaxWq1Oc3TUXpX5XHHOQz+3m3m7+l1MqJ/IMOiA7horqHYUs+fsk6wcddJxt0Wxv1jIxkaHdjqb0Kv06K9iT4Mo9HQ+fdaHyT10z61WnXLX9pdFnwiIiI4ePCgY9tsNmMymVqlm83fjhYqKyvDZDIREhJCdXU1zc3NaDSaVsfZbDZeeOEFSkpKeO+99zAYWr6VrF+/nsTERIKDW+6VKIriaDl5iq/OWVCAOwaFuLsoogdQq1UMCDeQdP8QPv2ykJMXLnMsv5wvTpkJ8vdi+IAghkQF4KXVMOH2cBmyLTyOy+75TJ48mezsbCwWC/X19ezatYu4uDhHenR0NHq9nkOHDgGQmZlJXFwcOp2O2NhYtm/fDkBGRobjuNdee42amhreeecdR+AByMnJYcuWLQAcOHAAu93OkCFDXHVpLnHkTBn+PjqGyP0ecY0APy8m3G5izgNDuWdUBGq1igNflbLl47P843gJRWW17i6iEDfMZV+XwsPDWbRoESkpKVitVubMmcOYMWNITU1l4cKFjB49mvT0dF566SVqamoYOXIkKSkpALz88sssXryYN954g8jISH79619jsVjYsGED/fr1Y+7cuY7XyczMZOnSpSxevJjMzEz0ej2rV69G7UHPydjtCkfPljP2tjDUanneQ7RNq1EzrF8gt0UHUF7ZwMkLlzlTWMnK9YcY1i+QB+6MJnaECa3Gc977ou9yaVs9KSmJpKSkVvveeustx+8xMTGOFsvVoqOjWb9+/XX7v/rqqzZfJzw8nHffffcWS+s+Z4sqqW2wMVYmExWdoFKpCAvyISzIh7tiTDRZm/k89xJ/+OtXvO97mvvHRhE/LoqwQB93F1WIdklHcQ9w5Ew5GrWKkXK/R9wgby8N94+NInHyII5/beHjLwrZnn2e7dnnGRIdwJ3Djdw5zEh4iK+7iypEKxJ83ExRFHJOlDBiQBC+3vLfIW6OWqVi9JBQRg8Jpayynv3HivnilJn/+/gs//fxWcICvYkZGMzt3/wE+evdXWTRx8mnnZudLarCfLmBmfcOdndRhIe6dpJTH28dD8b258HY/liqGsg7d5m88xa+OGl2LPFtCvZhxMAQBob7MzQ6gKhQf3Qaud8ouo8EHzf7x/FivLRq7hxu7DizEG3oaJLTCSNNeOlUjB4aQkVVI8WWOkosdRw4Xsynh1vmkwvy92J4/6CWn35BRBn9UMtkp8KFJPi4ka3ZzoG8UsYNC5Mls4XLqVUqQgO9CQ30ZuTgEPz99BQUV1FSUYetWeH0xUoO5JUC4Oet5bboQIb3D2JY/yAGRRhkFJ3oUvKJ50Zfni6jpt7KPSMj3F0U0QepVCqCDXqCDXom3B6Or5eGssoGThVc5vTFy5wqqOTI2XIAvLQtw7xb7huFMDDCX5b9ELdEgo8b7cq5gCnIh9FDQt1dFNHHqdQq6pqa8fXRMW64kXHfdANX1zVxtrCKry9VcepCBR98kg/k46PXMKJ/sGMQQ7R004kbJMHHTc4UVnK2sIoF3xsuD5YKt+vovtFDcUPw02upqm3ixIUKTpyvIO98BYfPlAHg76NrNZouPNhHFsgTTknwcZOd/7yAr17LvaOly014jgA/L+6+PZy7bw8HaBlN900gyjtfwcETLfeMgvy9GHZlAEP/IEK+mZH7ZidBFb2PBB83OHOxkkOnzCTfNxhvL/kvEJ4rJMCbe0dHcu/oSGoarPz90EWKy+u4ZKnj6Jlycr4ZwGDw1REe7EtsjImYAUFEG/3knlEfJ5983cyuKPx5z2mC/L2YevcAdxdHiE659lmitiioCPDzIsCvZdZtRVG4XNPUEozKa7lQWs2ZwkoAvHRqBoUb6B9uIDrMj6gwP6KNfvh567qkvDY7NFrbL6+0wNxPgk8323e4iK8vVfHk9NvRe2ncXRwhOqWje0IAY695Vu3q0XS3DwpGURQGRwVSXFZLflHLIIbPci/R2NTsOCbAz4sgfz1BBi+C/fUEGfT4++jw9tLio9fg7aXF4OuFj5canVaDTqNGp1Wj1aha3WNqtNrIyStpt6yyDIX7Se13o0JzDX/ec5qRg4KZLPd6RB+jUqkwhfhiDPJh9DeT6CqKQkV1I5e+aR2VWOo5X1zF+eJq8uorsDZ3fhlx7TeB6EowsjUr6DQqvPVafPRa/Ly1BPi2tMwarc34SfBxK6n9blJV18Tv/nIMby8NP0y8Q4alij7JWQsq0M+LuPHRrdKbrM00Wpux2uyOn/7hBtQqsNnsWJsVrLZv0ptb0m02O3VNzZRa6rDa7NQ12CivbKDhqhbW9uzzRIb5MTjCwKDIAAZHBtDf5I9O+uK6jQSfblBTb+XX7x+mvKqBnz88lkCZ1FGITvHSafDSte6ejo0xddhqqW28vtvN1mynus5KVW0Tvt5aCs215OaX8/mxYgC0GhUDIwwMjQrktuhA7pbBQC4ltetiX1+q4vW/HKOytpFnvz+GEQOC3V0kITxaZwY/2JXr92k1asc9qLtHRqDYFUe334WSas5dqia/qIq/f3GRXTkFvJ5xjNAAPUOjAxkaFUi00Y/IUD+C/L06/QxTRwMfoO8OfnBp8Nm2bRtvvPEGNpuNxx9/nAULFrRKz8vLY+nSpdTW1hIbG8vy5cvRarUUFRWRlpZGeXk5gwcPJj09HT8/P6qqqnj++ecpKCggJCSEtWvXYjQaaWpqYunSpRw7dgxvb2/S09MZOnSoKy+tQ+WVDfztH+f55HAhwQY9Lz56F4NliWwhbtnNDH7ozDmijS0j7prt4ViqGgk0eHOm4HKrOe+gZQ2lyNCWe1cGXy8CfHUYfL0w+OrQ6zRoNeqWH60Ka7NC3jkLapUKtVqFRt36X+i7gx9cdsUlJSWsWbOGrVu34uXlxbx585g4cSK33XabI09aWhorVqxg3LhxLFmyhM2bNzN//nyWL1/O/PnzmTFjBr/73e94/fXXSUtLY+3atcTGxvKHP/yBjIwMXn31VdauXcv69evx8fFhx44d5OTk8OKLL7J582ZXXVqbrLZmCstqOX2xkqNny/nqmzfcg3f2Y+Z9g/H36ZohpEII19Ko1RiDfLh3XD/uHdUyMKiy5sps4PWUVNRRXF5HflEVNfXWVveSboQKUKtVbPn4LL7eWvx8dPh7a/H11uHno8PPW4uftw5/Hx1+Plr8fa783pLm6c9JuSz47N+/n0mTJhEUFARAQkICWVlZ/Nu//RsAhYWFNDQ0MG7cOABmz57NunXrmDt3Ljk5Ofzud79z7H/00UdJS0tj7969bNiwAYDExEReeeUVrFYre/fu5bnnngNgwoQJWCwWioqKiIqK6lRZb3Z6m79+fo78okqqapuoqbc69ocFevPIg8OYdHs4QQbX3d9R1Cp8O3guQqtRO83TUXpX5XHHOXz0Wppt1+fvTXVyK69zdf309LK643Wa7XaOf21pte/KrOB3fLPq8NjbwtBpVNTUW6mtt2Frbhn4YG9WsNrt1DfayC+qwq4o2O0KzXYFxa5gV1p+t9sVggze2JpbBkY0NNqobbRRVtVAfYMNu9JG/+E3vL20+Hpr0Ou0eOnUjmHnOq0GL60KtVqNWtXSTalWtQxFV6laZjdXq1So1DAsOpCh0YFO66EtXTElmMuCT2lpKUbjt01fk8nE0aNH2003Go2UlJRQUVGBv78/Wq221f5rj9Fqtfj7+2OxWNo8V3FxcaeDT3Cw301d4xMzR93UcV1pxv0ddy8O6ef8PlNH6V2Vp6ecQ17HfefwtNfpH965rvLwTuUSV3NZu81ut7e6KacoSqvt9tKvzQe0e3NPURTUavV1x1zZL4QQomdy2Sd0REQEZvO3N/TMZjMmk6nd9LKyMkwmEyEhIVRXV9Pc3HzdcSaTibKylll0bTYbtbW1BAUFER4eTmlp6XXnEkII0TO5LPhMnjyZ7OxsLBYL9fX17Nq1i7i4OEd6dHQ0er2eQ4cOAZCZmUlcXBw6nY7Y2Fi2b98OQEZGhuO4+Ph4MjIyANi+fTuxsbHodDri4+PJzMwE4ODBg+j1+k53uQkhhOh+KkVxckfrFm3bto3f//73WK1W5syZQ2pqKqmpqSxcuJDRo0dz4sQJXnrpJWpqahg5ciQrV67Ey8uLwsJCFi9eTHl5OZGRkfz6178mMDCQy5cvs3jxYgoKCjAYDKSnp9OvXz8aGxtZtmwZx44dw8vLixUrVjBy5EhXXZYQQohb5NLgI4QQQrRF7soLIYTodhJ8hBBCdDsJPkIIIbqdBB8hhBDdToKPB9q2bRvTp09nypQpjumG+rrHHnuMGTNmkJycTHJyMkeOHGH//v0kJSUxZcoU1qxZ4+4iukVNTQ2JiYlcvHgRoN06ycvLY/bs2SQkJLB06VJsNuczMfcW19bPiy++yJQpUxzvo927dwN9s37++7//mxkzZjBjxgxWrVoFdPH7RxEepbi4WHnggQeUiooKpba2VklKSlJOnz7t7mK5ld1uV+677z7FarU69tXX1yvx8fHKhQsXFKvVqjz55JPK3r173VjK7nf48GElMTFRGTlypFJQUOC0TmbMmKF8+eWXiqIoyosvvqhs2LDBjSXvHtfWj6IoSmJiolJSUnJd3r5WP59//rnyyCOPKI2NjUpTU5OSkpKibNu2rUvfP9Ly8TBXT9jq6+vrmLC1L8vPzwfgySefZObMmfzpT3/i6NGjDBw4kP79+6PVaklKSupz9bR582Zefvllx2wf7dVJW5P89oW6urZ+6uvrKSoqYsmSJSQlJbFu3TrsdnufrB+j0cjixYvx8vJCp9MxdOhQzp0716Xvn763iISH62jC1r6oqqqKe+65h1/84hdYrVZSUlL44Q9/eF09XZmgtq949dVXW2239d4pKSlpd5Lf3u7a+ikrK2PSpEm8/PLLGAwGfvSjH7FlyxaGDRvW5+pn2LBhjt/PnTvHjh07ePTRR7v0/SPBx8N0NGFrXzR+/HjGjx/v2J4zZw7r1q3jrrvucuyTemr/vSPvqRb9+/d3LOUCLfcRMzIyGDp0aJ+tn9OnT/OjH/2I//iP/0Cj0XDu3DlH2q2+f6TbzcN0NGFrX3Tw4EGys7Md24qiEB0dLfV0jfbeO+1N8tvXnDx5kp07dzq2FUVBq9X22fo5dOgQ//qv/8q///u/89BDD3X5+0eCj4fpaMLWvqi6uppVq1bR2NhITU0Nf/nLX/j5z3/O119/zfnz52lububDDz/s8/U0duzYNuukvUl++xpFUfiv//ovKisrsVqtbNq0ie9973t9sn4uXbrEM888Q3p6OjNmzAC6/v0j3W4eJjw8nEWLFpGSkuKYsHXMmDHuLpZbPfDAAxw5coRZs2Zht9uZP38+48eP51e/+hXPPvssjY2NxMfHM3XqVHcX1a30en27dZKent5qkt+UlBQ3l7b7xcTE8PTTT/ODH/wAm83GlClTSExMBPpe/bz99ts0Njbyq1/9yrFv3rx5Xfr+kYlFhRBCdDvpdhNCCNHtJPgIIYTodhJ8hBBCdDsJPkIIIbqdBB8hhBDdToKPED3c4sWLefvtt2/omD179rBixQoA9u7dy29+8xtXFE2ImybP+QjRCz344IM8+OCDAOTm5lJZWenmEgnRmgQfIW7SP//5T9LT04mKiiI/Px9vb29+9atfYTKZWL58OSdOnEClUnH//ffz85//HK1Wyx133EFqaiqffvopdXV1/PznP2fKlCls3bqVnTt38vvf/x7guu0rtmzZwqZNm7BarVRWVpKamsr8+fPZunUrW7Zsob6+Hn9/fx566CF27tzJT3/6U95//32am5sxGAwcPXqUadOm8fDDDwPw+uuvc/nyZZYsWdLt9Sf6Ngk+QtyCY8eO8cILLxAbG8uf//xn0tLSGDZsGEFBQWzbtg2r1cpPfvIT3nnnHZ5++mmam5vx8fFh69atnDhxgkcffZTY2NhOvVZtbS3/93//xx/+8AeCg4M5fPgwTzzxBPPnzwfgzJkz/P3vf8ff35+tW7cCLVOizJs3j4qKChYtWsRHH33Em2++ycMPP4zdbmfLli388Y9/dFn9CNEeuecjxC2IiYlxBI/vf//75OXl8eGHH/Loo4+iUqnw8vJi3rx57Nu3z3HMo48+6jh2+PDh5OTkdOq1/Pz8ePPNN/nkk09Yu3Ytb775JnV1dY70ESNG4O/v7/QcDzzwAOXl5Zw4cYJPP/2Ufv36MWTIkBu9bCFumQQfIW6BRqO5bt+1U8rb7fZWywpffYzdbkej0aBSqbh6piur1XrdeYuLi5k1axaFhYXcdddd/OxnP2uV7uvr26nyPvLII2zZsoUPPviAefPmdXiMEK4gwUeIW3DixAlOnDgBwKZNmxg/fjzTpk3jT3/6E4qi0NTUxObNm5k8ebLjmIyMDACOHz/O119/zYQJEwgJCeH06dM0NjZitVpbTe1/xbFjxwgJCeGnP/0p9913Hx9//DEAzc3NTsuo0WhaBb+5c+fy0Ucfcfz4cb73ve/dahUIcVPkno8QtyAsLIy1a9dSWFhISEgIq1atws/PjxUrVpCUlITVauX+++/nxz/+seOYL774gs2bN2O321mzZg2BgYHce++9TJgwgWnTpmE0Gpk4cSInT55s9Vr33nsvW7ZsYerUqahUKu6++25CQkI4f/680zJOmjSJ559/nl/+8pf84he/IDQ0lFGjRjF06FB0Op1L6kWIjsis1kLcpH/+85/88pe/5MMPP+z0MSNGjCA7O5uQkBAXlsw5i8XCnDlz2LBhA5GRkW4rh+jbpNtNiD5k8+bNTJ8+naeeekoCj3ArafkIIYTodtLyEUII0e0k+AghhOh2EnyEEEJ0Owk+Qgghup0EHyGEEN1Ogo8QQohu9/8Bx+lK2ODFbWsAAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(data_1['popularity'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [],
- "source": [
- "q = data_1['vote_count'].quantile(0.01)\n",
- "\n",
- "data_2 = data_1[data_1['vote_count']>q]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle liefert statistische Informationen über die Popularität, Einnahmen und Stimmenzahl von Filmen, \n",
- "einschließlich der Anzahl der Datensätze, Durchschnittswerte, Standardabweichungen und Quartile."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "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>popularity</th>\n",
- " <th>revenue</th>\n",
- " <th>vote_count</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>1208.000000</td>\n",
- " <td>1.208000e+03</td>\n",
- " <td>1208.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>35.506440</td>\n",
- " <td>1.669286e+08</td>\n",
- " <td>1405.486755</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>29.940760</td>\n",
- " <td>2.455862e+08</td>\n",
- " <td>1752.879308</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>0.132878</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>9.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>13.768277</td>\n",
- " <td>1.529117e+07</td>\n",
- " <td>279.250000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>27.153374</td>\n",
- " <td>7.233776e+07</td>\n",
- " <td>712.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>46.832955</td>\n",
- " <td>2.054128e+08</td>\n",
- " <td>1805.500000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>167.932870</td>\n",
- " <td>2.787965e+09</td>\n",
- " <td>13752.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " popularity revenue vote_count\n",
- "count 1208.000000 1.208000e+03 1208.000000\n",
- "mean 35.506440 1.669286e+08 1405.486755\n",
- "std 29.940760 2.455862e+08 1752.879308\n",
- "min 0.132878 0.000000e+00 9.000000\n",
- "25% 13.768277 1.529117e+07 279.250000\n",
- "50% 27.153374 7.233776e+07 712.000000\n",
- "75% 46.832955 2.054128e+08 1805.500000\n",
- "max 167.932870 2.787965e+09 13752.000000"
- ]
- },
- "execution_count": 54,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_2.describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Meldung informiert darüber, dass die Funktion distplot in Zukunft nicht mehr unterstützt wird \n",
- "und empfiehlt stattdessen die Verwendung von displot oder histplot."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/Jumana/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='vote_count', ylabel='Density'>"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(data_2['vote_count'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [],
- "source": [
- "#q = data_2['revenue'].quantile(0.99)\n",
- "\n",
- "data_3 = data_2[data_2['revenue']<1.5e+09]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Ausgabe zeigt ein Array von Diagrammen, das die Verteilung der Popularität, \n",
- "Einnahmen und Stimmenzahl von Filmen visualisiert."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[<AxesSubplot:title={'center':'popularity'}>,\n",
- " <AxesSubplot:title={'center':'revenue'}>],\n",
- " [<AxesSubplot:title={'center':'vote_count'}>, <AxesSubplot:>]],\n",
- " dtype=object)"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1800x1800 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "data_3.hist(figsize=(25,25), bins=50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_final = data_3.reset_index(drop=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_final['revenue'] = data_final['revenue'].astype(float)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle enthält Informationen über die Popularität, Produktionsunternehmen, Einnahmen, Titel, \n",
- "Stimmenanzahl, Hauptdarsteller und Genres von verschiedenen Filmen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "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>popularity</th>\n",
- " <th>production_companies</th>\n",
- " <th>revenue</th>\n",
- " <th>title</th>\n",
- " <th>vote_count</th>\n",
- " <th>star</th>\n",
- " <th>new_genres</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>139.082615</td>\n",
- " <td>Walt Disney Pictures</td>\n",
- " <td>9.610000e+08</td>\n",
- " <td>Pirates of the Caribbean: At World's End</td>\n",
- " <td>4500</td>\n",
- " <td>Johnny Depp</td>\n",
- " <td>Adventure,Fantasy</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>107.376788</td>\n",
- " <td>Columbia Pictures</td>\n",
- " <td>8.806746e+08</td>\n",
- " <td>Spectre</td>\n",
- " <td>4466</td>\n",
- " <td>Daniel Craig</td>\n",
- " <td>Action,Adventure</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>112.312950</td>\n",
- " <td>Legendary Pictures</td>\n",
- " <td>1.084939e+09</td>\n",
- " <td>The Dark Knight Rises</td>\n",
- " <td>9106</td>\n",
- " <td>Christian Bale</td>\n",
- " <td>Action,Crime</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>43.926995</td>\n",
- " <td>Walt Disney Pictures</td>\n",
- " <td>2.841391e+08</td>\n",
- " <td>John Carter</td>\n",
- " <td>2124</td>\n",
- " <td>Taylor Kitsch</td>\n",
- " <td>Action,Adventure</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>115.699814</td>\n",
- " <td>Columbia Pictures</td>\n",
- " <td>8.908716e+08</td>\n",
- " <td>Spider-Man 3</td>\n",
- " <td>3576</td>\n",
- " <td>Tobey Maguire</td>\n",
- " <td>Fantasy,Action</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " popularity production_companies revenue \\\n",
- "0 139.082615 Walt Disney Pictures 9.610000e+08 \n",
- "1 107.376788 Columbia Pictures 8.806746e+08 \n",
- "2 112.312950 Legendary Pictures 1.084939e+09 \n",
- "3 43.926995 Walt Disney Pictures 2.841391e+08 \n",
- "4 115.699814 Columbia Pictures 8.908716e+08 \n",
- "\n",
- " title vote_count star \\\n",
- "0 Pirates of the Caribbean: At World's End 4500 Johnny Depp \n",
- "1 Spectre 4466 Daniel Craig \n",
- "2 The Dark Knight Rises 9106 Christian Bale \n",
- "3 John Carter 2124 Taylor Kitsch \n",
- "4 Spider-Man 3 3576 Tobey Maguire \n",
- "\n",
- " new_genres \n",
- "0 Adventure,Fantasy \n",
- "1 Action,Adventure \n",
- "2 Action,Crime \n",
- "3 Action,Adventure \n",
- "4 Fantasy,Action "
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_final.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle gibt statistische Zusammenfassungen über die Merkmale \"Popularität\", \"Produktionsunternehmen\", \n",
- "\"Einnahmen\", \"Titel\", \"Stimmenanzahl\", \"Hauptdarsteller\" und \"Genres\" von Filmen an, \n",
- "einschließlich Anzahl der Datensätze, eindeutiger Werte, häufigster Wert, Mittelwerte, \n",
- "Standardabweichungen, und Quartile.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
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- " <th>75%</th>\n",
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- "text/plain": [
- " popularity production_companies revenue title \\\n",
- "count 1205.000000 1205 1.205000e+03 1205 \n",
- "unique NaN 413 NaN 1204 \n",
- "top NaN Universal Pictures NaN The Host \n",
- "freq NaN 91 NaN 2 \n",
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- "max 167.932870 NaN 1.405404e+09 NaN \n",
- "\n",
- " vote_count star new_genres \n",
- "count 1205.000000 1205 1205 \n",
- "unique NaN 659 132 \n",
- "top NaN Matt Damon Comedy,Drama \n",
- "freq NaN 14 73 \n",
- "mean 1383.145228 NaN NaN \n",
- "std 1693.870514 NaN NaN \n",
- "min 9.000000 NaN NaN \n",
- "25% 277.000000 NaN NaN \n",
- "50% 705.000000 NaN NaN \n",
- "75% 1798.000000 NaN NaN \n",
- "max 13752.000000 NaN NaN "
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_final.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle zeigt, dass es keine fehlenden Werte für die Merkmale \n",
- "\"Popularität\", \"Produktionsunternehmen\", \"Einnahmen\", \"Titel\", \"Stimmenanzahl\", \"Hauptdarsteller\" und \"Genres\" gibt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "popularity 0\n",
- "production_companies 0\n",
- "revenue 0\n",
- "title 0\n",
- "vote_count 0\n",
- "star 0\n",
- "new_genres 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_final.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['popularity', 'production_companies', 'revenue', 'title',\n",
- " 'vote_count', 'star', 'new_genres'], dtype=object)"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_final.columns.values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code definiert ein Pandas DataFrame mit 1205 Einträgen und 7 Spalten, \n",
- "die verschiedene Datentypen wie Float, Integer und Object enthalten."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 1205 entries, 0 to 1204\n",
- "Data columns (total 7 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 popularity 1205 non-null float64\n",
- " 1 production_companies 1205 non-null object \n",
- " 2 revenue 1205 non-null float64\n",
- " 3 title 1205 non-null object \n",
- " 4 vote_count 1205 non-null int64 \n",
- " 5 star 1205 non-null object \n",
- " 6 new_genres 1205 non-null object \n",
- "dtypes: float64(2), int64(1), object(4)\n",
- "memory usage: 66.0+ KB\n"
- ]
- }
- ],
- "source": [
- "data_final.info()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code berechnet die VIF (Variance Inflation Factor) für numerische Variablen wie \n",
- "\"Einnahmen\" und \"Stimmenanzahl\" \n",
- "aus einem DataFrame und speichert die Ergebnisse zusammen mit den Variablennamen in einem neuen DataFrame."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {},
- "outputs": [],
- "source": [
- "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
- "\n",
- "# Since categorical data is not preprocessed, take only the numerical data.\n",
- "variables = data_final[['revenue', 'vote_count']]\n",
- "\n",
- "# Create a new data frame which includes all VIFs (Variance Inflation Factor)\n",
- "# Each variable has its own variance inflation factor. This measure is variable specific\n",
- "vif = pd.DataFrame()\n",
- "\n",
- "# Make use of the variance_inflation_factor module, output the respective VIFs \n",
- "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
- "\n",
- "# Include variable names so it is easier to explore the result\n",
- "vif[\"Features\"] = variables.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle zeigt die berechneten VIF (Variance Inflation Factor) Werte für die Merkmale \n",
- "\"Einnahmen\" und \"Stimmenanzahl\", sowie die zugehörigen Merkmalsnamen.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "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>VIF</th>\n",
- " <th>Features</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>3.741807</td>\n",
- " <td>revenue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3.741807</td>\n",
- " <td>vote_count</td>\n",
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- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " VIF Features\n",
- "0 3.741807 revenue\n",
- "1 3.741807 vote_count"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Explore the result\n",
- "vif"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Remove 'Year' as feature with the highest VIF from the model\n",
- "data_final = data_final.drop(['star', 'production_companies'],axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle bietet eine statistische Zusammenfassung der Merkmale \"Popularität\", \"Einnahmen\", \"Titel\", \"Stimmenanzahl\" und \"Genres\" von Filmen, einschließlich der Anzahl der Datensätze, der eindeutigen Werte, des am häufigsten auftretenden Titels, der Durchschnittswerte,\n",
- "der Standardabweichungen und der Quartile."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "metadata": {},
- "outputs": [
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- " <th>top</th>\n",
- " <td>NaN</td>\n",
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- " <td>The Host</td>\n",
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- " <th>mean</th>\n",
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- " <td>NaN</td>\n",
- " <td>1383.145228</td>\n",
- " <td>NaN</td>\n",
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- " <tr>\n",
- " <th>std</th>\n",
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- " <th>min</th>\n",
- " <td>0.132878</td>\n",
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- " <td>9.000000</td>\n",
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- " <th>25%</th>\n",
- " <td>13.707843</td>\n",
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- " <td>NaN</td>\n",
- " <td>277.000000</td>\n",
- " <td>NaN</td>\n",
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- " <tr>\n",
- " <th>50%</th>\n",
- " <td>27.082182</td>\n",
- " <td>7.210861e+07</td>\n",
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- " <td>705.000000</td>\n",
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- " <tr>\n",
- " <th>75%</th>\n",
- " <td>46.630062</td>\n",
- " <td>2.034276e+08</td>\n",
- " <td>NaN</td>\n",
- " <td>1798.000000</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>167.932870</td>\n",
- " <td>1.405404e+09</td>\n",
- " <td>NaN</td>\n",
- " <td>13752.000000</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " </tbody>\n",
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- ],
- "text/plain": [
- " popularity revenue title vote_count new_genres\n",
- "count 1205.000000 1.205000e+03 1205 1205.000000 1205\n",
- "unique NaN NaN 1204 NaN 132\n",
- "top NaN NaN The Host NaN Comedy,Drama\n",
- "freq NaN NaN 2 NaN 73\n",
- "mean 35.267109 1.622383e+08 NaN 1383.145228 NaN\n",
- "std 29.569232 2.255582e+08 NaN 1693.870514 NaN\n",
- "min 0.132878 0.000000e+00 NaN 9.000000 NaN\n",
- "25% 13.707843 1.525000e+07 NaN 277.000000 NaN\n",
- "50% 27.082182 7.210861e+07 NaN 705.000000 NaN\n",
- "75% 46.630062 2.034276e+08 NaN 1798.000000 NaN\n",
- "max 167.932870 1.405404e+09 NaN 13752.000000 NaN"
- ]
- },
- "execution_count": 68,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_final.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_with_dummies = pd.get_dummies(data_final, drop_first=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Tabelle enthält binäre Indikatoren für das Vorhandensein bestimmter Filme in den Spalten \n",
- "\"Titel\" und Genres, zusammen mit numerischen Daten wie \"Popularität\", \n",
- "\"Einnahmen\" und \"Stimmenanzahl\" für jede dieser Filme.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <th>title_(500) Days of Summer</th>\n",
- " <th>title_10 Cloverfield Lane</th>\n",
- " <th>title_12 Rounds</th>\n",
- " <th>title_13 Hours: The Secret Soldiers of Benghazi</th>\n",
- " <th>title_1408</th>\n",
- " <th>title_1911</th>\n",
- " <th>title_2 Guns</th>\n",
- " <th>...</th>\n",
- " <th>new_genres_Thriller,Crime</th>\n",
- " <th>new_genres_Thriller,Documentary</th>\n",
- " <th>new_genres_Thriller,Drama</th>\n",
- " <th>new_genres_Thriller,Horror</th>\n",
- " <th>new_genres_Thriller,Mystery</th>\n",
- " <th>new_genres_Thriller,Science Fiction</th>\n",
- " <th>new_genres_War,Action</th>\n",
- " <th>new_genres_War,Crime</th>\n",
- " <th>new_genres_War,Drama</th>\n",
- " <th>new_genres_Western,Drama</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>139.082615</td>\n",
- " <td>9.610000e+08</td>\n",
- " <td>4500</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>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",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>107.376788</td>\n",
- " <td>8.806746e+08</td>\n",
- " <td>4466</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>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",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>112.312950</td>\n",
- " <td>1.084939e+09</td>\n",
- " <td>9106</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>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",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>43.926995</td>\n",
- " <td>2.841391e+08</td>\n",
- " <td>2124</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>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",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>115.699814</td>\n",
- " <td>8.908716e+08</td>\n",
- " <td>3576</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>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",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 1337 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " popularity revenue vote_count title_(500) Days of Summer \\\n",
- "0 139.082615 9.610000e+08 4500 0 \n",
- "1 107.376788 8.806746e+08 4466 0 \n",
- "2 112.312950 1.084939e+09 9106 0 \n",
- "3 43.926995 2.841391e+08 2124 0 \n",
- "4 115.699814 8.908716e+08 3576 0 \n",
- "\n",
- " title_10 Cloverfield Lane title_12 Rounds \\\n",
- "0 0 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " title_13 Hours: The Secret Soldiers of Benghazi title_1408 title_1911 \\\n",
- "0 0 0 0 \n",
- "1 0 0 0 \n",
- "2 0 0 0 \n",
- "3 0 0 0 \n",
- "4 0 0 0 \n",
- "\n",
- " title_2 Guns ... new_genres_Thriller,Crime \\\n",
- "0 0 ... 0 \n",
- "1 0 ... 0 \n",
- "2 0 ... 0 \n",
- "3 0 ... 0 \n",
- "4 0 ... 0 \n",
- "\n",
- " new_genres_Thriller,Documentary new_genres_Thriller,Drama \\\n",
- "0 0 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " new_genres_Thriller,Horror new_genres_Thriller,Mystery \\\n",
- "0 0 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " new_genres_Thriller,Science Fiction new_genres_War,Action \\\n",
- "0 0 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " new_genres_War,Crime new_genres_War,Drama new_genres_Western,Drama \n",
- "0 0 0 0 \n",
- "1 0 0 0 \n",
- "2 0 0 0 \n",
- "3 0 0 0 \n",
- "4 0 0 0 \n",
- "\n",
- "[5 rows x 1337 columns]"
- ]
- },
- "execution_count": 70,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_with_dummies.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "metadata": {},
- "outputs": [],
- "source": [
- "target = data_with_dummies['popularity']\n",
- "predictors = data_with_dummies.drop(['popularity'],axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 80-20 split into training and test data\n",
- "X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.2, random_state=123)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code standardisiert die Merkmale eines Trainingsdatensatzes \n",
- "und wendet dieselbe Transformation auf Trainings- und Testdaten an.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {},
- "outputs": [],
- "source": [
- "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": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code erstellt ein lineares Regressionsmodell und passt es an den Trainingsdatensatz an."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "LinearRegression()"
- ]
- },
- "execution_count": 74,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "reg = LinearRegression()\n",
- "reg.fit(X_train,y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code berechnet die Leistung eines linearen Regressionsmodells auf dem Trainingsdatensatz \n",
- "und dem Testdatensatz und gibt die Ergebnisse aus."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "training performance\n",
- "1.0\n",
- "test performance\n",
- "0.5546065264388957\n"
- ]
- }
- ],
- "source": [
- "print('training performance')\n",
- "print(reg.score(X_train,y_train))\n",
- "print('test performance')\n",
- "print(reg.score(X_test,y_test))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4.2 Lineare Regression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Der Code führt eine Vorhersage basierend auf einem Modell durch, vergleicht die Vorhersagen mit den tatsächlichen Werten \n",
- "und visualisiert die Ergebnisse durch Diagramme und Streudiagramme mit einer Regressionslinie.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "metadata": {},
- "outputs": [
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 1152x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 432x432 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "y_pred = reg.predict(X_test)\n",
- "test = pd.DataFrame({'Predicted':y_pred,'Actual':y_test})\n",
- "fig= plt.figure(figsize=(16,8))\n",
- "test = test.reset_index()\n",
- "test = test.drop(['index'],axis=1)\n",
- "plt.plot(test[:50])\n",
- "plt.legend(['Actual','Predicted'])\n",
- "sns.jointplot(x='Actual',y='Predicted',data=test,kind='reg',);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# 5 Evaluation "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Die Analyse des Datensatzes aus dem tmdb-Dataset zur Verbesserung der Filmempfehlungen auf Netflix legt nahe, \n",
- "dass eine Vielzahl von Faktoren die Beliebtheit oder Bewertungen der Filme beeinflussen können.\n",
- "Dazu gehören sowohl numerische als auch kategoriale Daten wie Crewmitglieder, Besetzung und eindeutige IDs. \n",
- "Durch die Anwendung von maschinellem Lernen auf diese Daten können Strategien entwickelt werden, \n",
- "um die Kundenzufriedenheit zu erhöhen und Abbruchquoten zu senken. \n",
- "Eine gründliche Analyse und Modellierung dieser Faktoren ermöglicht es, personalisierte Empfehlungen zu generieren \n",
- "und das Nutzererlebnis zu verbessern. Das Fazit dieser Umsetzung ist, dass die Daten eine solide Grundlage bieten, \n",
- "um innovative Lösungen zur Filmempfehlung zu entwickeln, die die Zufriedenheit der Netflix-Nutzer steigern können."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Umsetzung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# 6 Umsetzung "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Umsetzung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "Um die Abbruchquoten bei Netflix zu senken und die Kundenzufriedenheit zu steigern, können maschinelles Lernen und Datenanalyse genutzt werden, um relevante Faktoren wie Crewmitglieder, Besetzung und Genre zu identifizieren und ein Modell zu trainieren, das die Beliebtheit oder Bewertungen von Filmen vorhersagt,\n",
- "was zu personalisierten Empfehlungen führt."
- ]
- }
- ],
- "metadata": {
- "category": "CRM",
- "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": "3.8.2"
- },
- "skipNotebookInDeployment": false,
- "title": "Increase customer satisfaction"
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Health/Risk prediction of heart disease/notebook.ipynb b/Health/Risk prediction of heart disease/notebook.ipynb
index 7382239..8afd126 100644
--- a/Health/Risk prediction of heart disease/notebook.ipynb
+++ b/Health/Risk prediction of heart disease/notebook.ipynb
@@ -1,15 +1,91 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Business",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 1.Business Understanding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Business",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "Das Unternehmen, das in der Medizinbranche tätig ist, hat das Ziel, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) basierend auf verschiedenen demografischen, verhaltensbezogenen und medizinischen Faktoren zu bestimmen. Mit dieser Risikovorhersage können frühzeitige Maßnahmen ergriffen werden, um die Krankheit im besten Fall zu verhindern und die Gesundheit der Patienten langfristig zu verbessern.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 2.Data Understanding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "Das Unternehmen in der Medizinbranche strebt danach, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) basierend auf verschiedenen demografischen, verhaltensbezogenen und medizinischen Faktoren zu bestimmen. Diese Risikovorhersage ermöglicht es, frühzeitig Maßnahmen zu ergreifen, um die Krankheit im besten Fall zu verhindern und langfristig die Gesundheit der Patienten zu verbessern.\n",
+ "\n"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
+ "source": [
+ "Der Code importiert Bibliotheken für Datenanalyse, numerische Berechnungen und Datenvisualisierung, und legt fest, dass Diagramme direkt in das Jupyter Notebook eingebettet werden, um eine Analyse zur Vorhersage des Risikos einer koronaren Herzkrankheit anhand der Zielvariable TenYearCHD durchzuführen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
"source": [
"# Import Libraries"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -21,6 +97,13 @@
"#Ziel: Vorhersage, ob der Patient ein Risiko hat an koronare Herzkrankheit zu erkranken. Zielvariable ist TenYearCHD."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code importiert Bibliotheken für die Erstellung von Klassifikationsdatensätzen, das Aufteilen von Daten in Trainings- und Testsets, die Durchführung logistischer Regressionen, die Bewertung von Klassifikationsmodellen und das Ausbalancieren von Klassenverteilungen, um zu überprüfen, ob sklearn und imblearn kompatible Versionen haben."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 5,
@@ -36,6 +119,13 @@
"#Check if sklearn and imblearn are in a compatible version"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code lädt einen Datensatz zur Risikoanalyse von Herzerkrankungen aus einer angegebenen URL in ein Pandas DataFrame."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 6,
@@ -46,6 +136,13 @@
"#Quelle: https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Tabelle zeigt verschiedene Merkmale von Patienten, wie Geschlecht, Alter, Bildungsniveau, Rauchgewohnheiten, Blutdruckmedikamente, Vorerkrankungen, Cholesterinwerte, Blutdruck, Body-Mass-Index, Herzfrequenz und Blutzuckerspiegel, sowie die Zielvariable, ob der Patient in den nächsten zehn Jahren eine koronare Herzkrankheit entwickelt hat."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 7,
@@ -223,6 +320,13 @@
"train.head()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Tabelle liefert eine statistische Zusammenfassung (Anzahl, Mittelwert, Standardabweichung, Minimum, 25., 50. und 75. Perzentil sowie Maximum) verschiedener Merkmale von Patienten, darunter Geschlecht, Alter, Bildungsniveau, Rauchgewohnheiten, Medikamenteneinnahme, Vorerkrankungen, Cholesterinwerte, Blutdruck, Body-Mass-Index, Herzfrequenz, Blutzuckerspiegel und die zehnjährige Wahrscheinlichkeit, an einer koronaren Herzkrankheit zu erkranken."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 8,
@@ -475,6 +579,41 @@
"train.describe(include='all')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Tabelle zeigt die Struktur eines DataFrames mit 4240 Einträgen und 16 Spalten, einschließlich der Spaltennamen, der Anzahl der nicht-leeren Werte, der Datentypen jeder Spalte und des gesamten Speicherbedarfs."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": false,
+ "paragraph": "Datenvorbereitung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 3.Datenvorbereitung"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": "Datenvorbereitung"
+ },
+ "tags": []
+ },
+ "source": [
+ "Dieser Prozess umfasst das Identifizieren und Behandeln fehlender Werte, die Bereinigung und Transformation der Daten sowie die Sicherstellung der Datenkonsistenz."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 9,
@@ -514,6 +653,13 @@
"train.info()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Tabelle zeigt für jede Zeile und Spalte, ob ein fehlender Wert vorhanden ist, wobei alle Werte \"False\" sind, was darauf hinweist, dass keine fehlenden Werte in den angegebenen Daten vorhanden sind."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 10,
@@ -691,6 +837,13 @@
"train_missingValues.head()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Tabelle zeigt die Anzahl der fehlenden Werte (NaN) für jede Spalte des DataFrames, wobei Spalten wie education, cigsPerDay, BPMeds, totChol, BMI, heartRate und glucose einige fehlende Werte aufweisen.\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 11,
@@ -886,6 +1039,13 @@
"train.columns"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code zeigt die Anzahl der Fälle (572) und Nicht-Fälle (3179) der Zielvariable \"TenYearCHD\" in einem Pandas Series-Objekt an."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 17,
@@ -909,6 +1069,13 @@
"train.TenYearCHD.value_counts()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code stellt ein Balkendiagramm dar, das die Verteilung der Zielvariable \"TenYearCHD\" im DataFrame \"train\" visualisiert, während das Design der Visualisierung auf \"whitegrid\" gesetzt wird."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 18,
@@ -940,6 +1107,13 @@
"sns.countplot(x='TenYearCHD', data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code importiert die Cufflinks-Bibliothek für interaktive Plotly-Diagramme, konfiguriert sie für die Offline-Nutzung und stellt sicher, dass die erstellten Diagramme für andere Benutzer sichtbar sind.\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 19,
@@ -952,6 +1126,13 @@
"#cf.set_config_file(offline=False, world_readable=True)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" für männliche und weibliche Patienten im DataFrame \"train\" und erstellt ein gestapeltes Balkendiagramm, um die Verteilung der Herzkrankheitsrisiken zwischen den Geschlechtern darzustellen, unter Verwendung der Cufflinks-Bibliothek und Plotly für interaktive Visualisierungen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 20,
@@ -965,6 +1146,14 @@
"#df1.iplot(kind='bar',barmode='stack')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code wandelt den DataFrame \"train\" in ein geschmolzenes Format um und erstellt dann eine Kreuztabelle, die die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" für jede Kategorie der Variable \"male\" (0 und 1) zeigt.\n",
+ "\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 21,
@@ -975,6 +1164,13 @@
"#pd.crosstab(index=df1['TenYearCHD'], columns=df1['male'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach Geschlecht im DataFrame \"train\" darstellt, wobei das Design auf \"whitegrid\" gesetzt ist."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 22,
@@ -1010,6 +1206,13 @@
"# 10 Jahres Risko und männlich = 319"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung besagt, dass die distplot Funktion in Seaborn veraltet ist und in zukünftigen Versionen durch displot oder histplot ersetzt werden sollte."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 23,
@@ -1057,6 +1260,13 @@
"sns.distplot(train['age'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt ein Balkendiagramm, das die Häufigkeit der Zielvariable \"TenYearCHD\" für jede Altersgruppe im DataFrame \"train\" darstellt, wobei jede Balkenfarbe den jeweiligen Wert von \"TenYearCHD\" repräsentiert."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 24,
@@ -1087,6 +1297,13 @@
" sns.countplot(x=train['age'], hue=train['TenYearCHD'], data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt ein Balkendiagramm, das die Häufigkeit der Zielvariable \"TenYearCHD\" für verschiedene Werte der Variable \"cigsPerDay\" (Zigaretten pro Tag) im DataFrame \"train\" darstellt, wobei die Balken nach der Werte der Zielvariable \"TenYearCHD\" gefärbt sind."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 25,
@@ -1117,6 +1334,13 @@
" sns.countplot(y=train['cigsPerDay'], hue=train['TenYearCHD'], data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt dann ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach Raucherstatus (\"currentSmoker\") im DataFrame \"train\" darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 26,
@@ -1148,6 +1372,13 @@
"sns.countplot(x='TenYearCHD', hue='currentSmoker', data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach der Einnahme von Blutdruckmedikamenten (\"BPMeds\") im DataFrame \"train\" darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 27,
@@ -1179,6 +1410,13 @@
"sns.countplot(x='TenYearCHD', hue='BPMeds', data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der auskommentierte Code würde ein gruppiertes Balkendiagramm erstellen, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach dem Vorhandensein eines Schlaganfalls (\"prevalentStroke\") im DataFrame \"train\" darstellt, während das Design auf \"whitegrid\" gesetzt ist."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 28,
@@ -1189,6 +1427,13 @@
"#sns.countplot(x='TenYearCHD', hue='prevalentStroke', data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach dem Vorhandensein von Bluthochdruck (\"prevalentHyp\") im DataFrame \"train\" darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 29,
@@ -1220,6 +1465,13 @@
"sns.countplot(x='TenYearCHD', hue='prevalentHyp', data=train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach dem Vorhandensein von Diabetes (\"diabetes\") im DataFrame \"train\" darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 30,
@@ -1258,6 +1510,13 @@
"### Outliers"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung informiert darüber, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code entsprechend anzupassen, um entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen zu verwenden. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 31,
@@ -1305,6 +1564,13 @@
"sns.distplot(train['totChol'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt einen Boxplot, der die Verteilung der Cholesterinwerte (totChol) im DataFrame train nach der Zielvariable TenYearCHD darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 32,
@@ -1335,6 +1601,13 @@
"sns.boxplot(y=train['totChol'], x=train['TenYearCHD'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet das 99. Perzentil der Cholesterinwerte (totChol) im DataFrame train und speichert den Wert in der Variablen q_totChol."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 33,
@@ -1356,6 +1629,13 @@
"q_totChol"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der Cholesterinwert (totChol) kleiner als das zuvor berechnete 99. Perzentil (q_totChol) ist."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 34,
@@ -1365,6 +1645,13 @@
"train = train[train['totChol']<q_totChol]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 35,
@@ -1412,6 +1699,13 @@
"sns.distplot(train['sysBP'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt einen Boxplot, der die Verteilung der systolischen Blutdruckwerte (sysBP) im DataFrame train nach der Zielvariable TenYearCHD darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 36,
@@ -1442,6 +1736,13 @@
"sns.boxplot(y=train['sysBP'], x=train['TenYearCHD'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet das 99. Perzentil der systolischen Blutdruckwerte (sysBP) im DataFrame train und speichert den Wert in der Variablen q_sysBP."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 37,
@@ -1463,6 +1764,13 @@
"q_sysBP"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der systolische Blutdruckwert (sysBP) kleiner als das zuvor berechnete 99. Perzentil (q_sysBP) ist."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 38,
@@ -1472,6 +1780,13 @@
"train = train[train['sysBP']<q_sysBP]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 39,
@@ -1519,6 +1834,13 @@
"sns.distplot(train['diaBP'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt einen Boxplot, der die Verteilung der diastolischen Blutdruckwerte (diaBP) im DataFrame train nach der Zielvariable TenYearCHD darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 40,
@@ -1549,6 +1871,13 @@
"sns.boxplot(y=train['diaBP'], x=train['TenYearCHD'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet das 99. Perzentil der diastolischen Blutdruckwerte (diaBP) im DataFrame train und speichert den Wert in der Variablen q_diaBP."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 41,
@@ -1570,6 +1899,13 @@
"q_diaBP"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der diastolische Blutdruckwert (diaBP) kleiner als das zuvor berechnete 99. Perzentil (q_diaBP) ist."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 42,
@@ -1579,6 +1915,13 @@
"train = train[train['diaBP']<q_diaBP]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 43,
@@ -1626,6 +1969,13 @@
"sns.distplot(train['BMI'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der auskommentierte Code erstellt einen Boxplot, der die Verteilung des Body-Mass-Index (BMI) im DataFrame train nach der Zielvariable TenYearCHD darstellt. Der zweite Codeausschnitt erstellt einen Boxplot, der nur die Verteilung des BMI im DataFrame train darstellt, ohne Berücksichtigung einer weiteren Variablen wie TenYearCHD."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 44,
@@ -1657,6 +2007,13 @@
"sns.boxplot(y=train['BMI'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet das 99. Perzentil der Body-Mass-Index (BMI) Werte im DataFrame train und speichert den Wert in der Variablen q_BMI."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 45,
@@ -1678,6 +2035,13 @@
"q_BMI"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der Body-Mass-Index (BMI) kleiner als das zuvor berechnete 99. Perzentil (q_BMI) ist."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 46,
@@ -1687,6 +2051,13 @@
"train = train[train['BMI']<q_BMI]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung informiert darüber, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 47,
@@ -1734,6 +2105,13 @@
"sns.distplot(train['heartRate'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der auskommentierte Code würde einen Boxplot erstellen, der die Verteilung der Herzfrequenzwerte (heartRate) im DataFrame train nach der Zielvariable TenYearCHD darstellt. Der zweite Codeausschnitt erstellt einen Boxplot, der nur die Verteilung der Herzfrequenzwerte im DataFrame train darstellt, ohne Berücksichtigung einer weiteren Variablen wie TenYearCHD."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 48,
@@ -1765,6 +2143,13 @@
"sns.boxplot(y=train['heartRate'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet das 99. Perzentil der Herzfrequenzwerte (heartRate) im DataFrame train und speichert den berechneten Wert in der Variablen q_heartRate."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 49,
@@ -1786,6 +2171,13 @@
"q_heartRate"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen die Herzfrequenzwerte (heartRate) kleiner sind als das zuvor berechnete 99. Perzentil (q_heartRate)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 50,
@@ -1795,6 +2187,13 @@
"train = train[train['heartRate']<q_heartRate]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 51,
@@ -1842,9 +2241,16 @@
"sns.distplot(train['glucose'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code würde einen Boxplot erstellen, der die Verteilung der Glukosewerte (glucose) im DataFrame train nach der Zielvariable TenYearCHD darstellt. Der zweite Codeausschnitt erstellt einen Boxplot, der nur die Verteilung der Glukosewerte im DataFrame train darstellt, ohne Berücksichtigung einer weiteren Variablen wie TenYearCHD."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -1873,6 +2279,13 @@
"sns.boxplot(x=train['glucose'])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet das 97. Perzentil der Glukosewerte (glucose) im DataFrame train und speichert den berechneten Wert in der Variablen q_glucose."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 53,
@@ -1894,6 +2307,13 @@
"q_glucose"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen die Glukosewerte (glucose) kleiner sind als das zuvor berechnete 97. Perzentil (q_glucose)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 54,
@@ -1903,6 +2323,13 @@
"train = train[train['glucose']<q_glucose]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt einen Boxplot, der die Verteilung der Glukosewerte (glucose) im DataFrame train darstellt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 55,
@@ -1949,6 +2376,13 @@
"### Checking for Multicollinarity"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Mit der Importanweisung from statsmodels.stats.outliers_influence import variance_inflation_factor wird die Funktion variance_inflation_factor aus dem Modul outliers_influence in statsmodels.stats importiert. Diese Funktion wird verwendet, um den Variance Inflation Factor (VIF) zu berechnen, der zur Diagnose von Multikollinearität in Regressionsmodellen verwendet wird."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 57,
@@ -1958,6 +2392,13 @@
"from statsmodels.stats.outliers_influence import variance_inflation_factor"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt ein neues DataFrame vif, das den Variance Inflation Factor (VIF) für jede Variable im DataFrame train, ausgenommen der Zielvariable TenYearCHD, berechnet. Der VIF wird mithilfe der Funktion variance_inflation_factor aus dem Modul statsmodels.stats.outliers_influence für jede Variable einzeln berechnet und zusammen mit den Variablennamen in vif gespeichert, um die Ergebnisse leichter erkunden zu können."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 58,
@@ -1976,6 +2417,11 @@
"vif[\"Features\"] = variables.columns"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 59,
@@ -2108,6 +2554,13 @@
"vif"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code erstellt eine Heatmap der Korrelationsmatrix für die Variablen im DataFrame train, wobei die Größe der Abbildung auf 12x8 Zoll festgelegt ist. Die Heatmap zeigt die Korrelationen zwischen den Variablen, einschließlich spezifischer Anmerkungen zu Korrelationen wie zwischen currentSmoker und cigsPerDay, sysBP und diaBP, sowie prevalentHyp und sysBP und diaBP."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 60,
@@ -2140,6 +2593,13 @@
"#Korrelationen zwischen currentSmoker und cigsPerDay, sysBPund diaBP, prevalentHyp und sysBP und diaBP "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code setzt den Index des DataFrame train zurück und erstellt eine Kopie davon, wobei der ursprüngliche Index verworfen wird."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 61,
@@ -2151,9 +2611,43 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 4.Modeling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": "Datenmodell"
+ },
+ "tags": []
+ },
"source": [
- "### Modeling"
+ "In diesem Abschnitt wird die Feature-Liste estimators definiert, die die relevanten Merkmale für die Modellierung mittels logistischer Regression enthält. Diese Merkmale werden aus dem DataFrame train ausgewählt und der Variablen X_all zugewiesen, während die Zielvariablen y aus dem gleichen DataFrame extrahiert werden. Dabei wurden die Merkmale currentSmoker und sysBP (siehe oben) aus der endgültigen Merkmalsliste entfernt, um die Genauigkeit des Modells zu verbessern."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Der Code definiert die Feature-Liste bestimators, wählt die entsprechenden Merkmale aus dem DataFrame train aus und weist sie der Variablen X_all zu. Zudem werden die Zielvariablen y aus dem DataFrame train extrahiert."
]
},
{
@@ -2169,6 +2663,13 @@
"#currentSmoker & sysBP werden gedropt (siehe oben)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Importanweisung import statsmodels.api as sm importiert das Modul statsmodels unter dem Alias sm, das für statistische Modellierung und Tests verwendet wird."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 63,
@@ -2178,6 +2679,13 @@
"import statsmodels.api as sm"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code führt eine logistische Regression auf den Daten X_all mit der Zielvariable y aus und gibt eine Zusammenfassung der Ergebnisse der Regression zurück, einschließlich statistischer Kennzahlen wie Koeffizienten, p-Werte und Konfidenzintervalle der geschätzten Koeffizienten."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 64,
@@ -2353,6 +2861,13 @@
"#(The closer to 0.000 the p-value, the better, Slides_AI - Part 4-2.pdf, S.27)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet die Odds Ratios und deren Konfidenzintervalle für die Koeffizienten der logistischen Regressionsergebnisse und gibt sie als DataFrame aus, wobei die exponentiellen Transformation der Konfidenzintervalle und des Koeffizienten der Odds Ratio angewendet wird."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 66,
@@ -2388,6 +2903,13 @@
"print(np.exp(conf))"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code entfernt bestimmte Variablen ('BPMeds', 'prevalentStroke', 'diabetes', 'totChol', 'diaBP', 'BMI', 'heartRate', 'glucose') aus dem DataFrame x und speichert das Ergebnis in x_new."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 67,
@@ -2397,6 +2919,13 @@
"#x_new = x.drop(['BPMeds', 'prevalentStroke', 'diabetes', 'totChol','diaBP','BMI','heartRate', 'glucose'], axis=1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code entfernt die Spalten 'BPMeds', 'prevalentStroke', 'diabetes', 'totChol', 'diaBP', 'BMI', 'heartRate' und 'glucose' aus dem DataFrame train."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 68,
@@ -2406,6 +2935,13 @@
"#train = train.drop(['BPMeds', 'prevalentStroke', 'diabetes', 'totChol','diaBP','BMI','heartRate', 'glucose'], axis=1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code fügt eine konstante Spalte zu x_new hinzu, führt eine logistische Regression durch und gibt eine Zusammenfassung der Regressionsergebnisse aus."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 69,
@@ -2418,6 +2954,13 @@
"#results_logit.summary()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code berechnet die Odds Ratio und die Konfidenzintervalle für die Regressionskoeffizienten der logistischen Regression und gibt sie exponentiell transformiert aus."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 70,
@@ -2473,7 +3016,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Undersampling (nachträglich) "
+ "### Undersampling (nachträglich) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code gibt die Versionen der Bibliotheken scikit-learn (sklearn) und imbalanced-learn (imblearn) aus, die in der Umgebung installiert sind."
]
},
{
@@ -2495,6 +3045,13 @@
"print(imblearn.__version__)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dieser Code importiert die Bibliothek imblearn, speziell das Modul InstanceHardnessThreshold für das Unterdampling und die LogisticRegression aus scikit-learn für die logistische Regression."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 74,
@@ -2506,6 +3063,13 @@
"from sklearn.linear_model import LogisticRegression"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code führt das Verfahren des Instance Hardness Threshold (IHT) für das Unterdampling durch. Dabei wird ein Modell der logistischen Regression (mit bestimmten Parametern wie solver='lbfgs' und multi_class='auto') verwendet, um die Instanzen zu bewerten und diejenigen zu entfernen, die schwer klassifizierbar sind, um das Ungleichgewicht in den Klassen zu reduzieren (fit_resample)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 75,
@@ -2574,6 +3138,13 @@
"X_resampled, y_resampled = iht.fit_resample(X, y)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code importiert die Funktion train_test_split aus Scikit-Learn, die verwendet wird, um Datensätze in Trainings- und Testsets aufzuteilen."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 76,
@@ -2583,6 +3154,13 @@
"from sklearn.model_selection import train_test_split"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code verwendet die Methode train_test_split aus Scikit-Learn, um die Datensätze X_resampled und y_resampled in Trainings- und Testsets aufzuteilen, wobei 20% der Daten für das Testset reserviert werden."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 77,
@@ -2602,6 +3180,13 @@
"#### Scaling"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code importiert die StandardScaler-Klasse aus Scikit-Learn, die zur Skalierung von Merkmalen verwendet wird, um sicherzustellen, dass sie eine Nullmittelwert und eine Einheitsvarianz haben."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 78,
@@ -2611,6 +3196,13 @@
"from sklearn.preprocessing import StandardScaler"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code führt eine Standardisierung der Trainingsdaten (X_train) und Testdaten (X_test) mithilfe eines StandardScaler durch, wobei die Daten so transformiert werden, dass sie eine Nullmittelwert und eine Einheitsvarianz haben, basierend auf den statistischen Eigenschaften der Trainingsdaten."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 79,
@@ -2631,6 +3223,13 @@
"### Logistische Regression"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code importiert die LogisticRegression Klasse aus sklearn.linear_model, die für die Logistische Regression zur Klassifikation verwendet wird."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 80,
@@ -3078,6 +3677,13 @@
"log_model.fit(X_train,y_train)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code importiert die classification_report Funktion aus sklearn.metrics, die zur Ausgabe eines Klassifikationsberichts für die Modellleistung verwendet wird, einschließlich Präzision, Recall, F1-Score und Unterstützung für jede Klasse."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 83,
@@ -3142,6 +3748,13 @@
"from sklearn.metrics import confusion_matrix"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code druckt die Verwechselungsmatrix aus, die die Leistung eines Klassifikationsmodells, insbesondere einer logistischen Regression (log_model), durch den Vergleich der vorhergesagten Werte (log_model.predict(X_test)) mit den tatsächlichen Werten (y_test) zeigt."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 86,
@@ -3277,9 +3890,74 @@
"source": [
"#print(classification_report(y_test, rf.predict(X_test)))"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 5.Evaluation "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "Das Unternehmen in der Medizinbranche strebt danach, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) mithilfe verschiedener demografischer, verhaltensbezogener und medizinischer Faktoren zu bestimmen. Durch diese Risikovorhersage sollen rechtzeitig Maßnahmen ergriffen werden, um die Krankheit idealerweise zu verhindern und die langfristige Gesundheit der Patienten zu verbessern."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Deployment",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 6.Deployment "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Deployment",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "Das Unternehmen in der Medizinbranche strebt danach, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) basierend auf verschiedenen demografischen, verhaltensbezogenen und medizinischen Faktoren zu bestimmen. Mit dieser Risikovorhersage können frühzeitige Maßnahmen ergriffen werden, um die Krankheit im besten Fall zu verhindern und die langfristige Gesundheit der Patienten zu verbessern. Die Implementierung dieser Analyse könnte potenziell zur Verbesserung der öffentlichen Gesundheit beitragen, indem sie präventive Strategien fördert und die Behandlung von Risikopersonen priorisiert."
+ ]
}
],
"metadata": {
+ "branche": "Medizin",
+ "dataSource": "https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset",
+ "funktion": "Risikomanagment",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@@ -3295,8 +3973,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
- }
+ "version": "3.9.2"
+ },
+ "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/tree/main/Health/Risk%20prediction%20of%20heart%20disease",
+ "skipNotebookInDeployment": false,
+ "teaser": "Mit der Vorhersage des Risikos einer koronaren Herzkrankheit können frühzeitig Maßnahmen für den Patienten ergriffen werden, um die spätere Erkrankung im besten Fall zu vermeiden.",
+ "title": "Risikovorhersage von Herzkrankheiten"
},
"nbformat": 4,
"nbformat_minor": 4
diff --git a/Health/Risk prediction of heart disease/notebook1.ipynb b/Health/Risk prediction of heart disease/notebook1.ipynb
deleted file mode 100644
index 8afd126..0000000
--- a/Health/Risk prediction of heart disease/notebook1.ipynb
+++ /dev/null
@@ -1,3985 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Business",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 1.Business Understanding"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Business",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "\n",
- "Das Unternehmen, das in der Medizinbranche tätig ist, hat das Ziel, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) basierend auf verschiedenen demografischen, verhaltensbezogenen und medizinischen Faktoren zu bestimmen. Mit dieser Risikovorhersage können frühzeitige Maßnahmen ergriffen werden, um die Krankheit im besten Fall zu verhindern und die Gesundheit der Patienten langfristig zu verbessern.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 2.Data Understanding"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "\n",
- "Das Unternehmen in der Medizinbranche strebt danach, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) basierend auf verschiedenen demografischen, verhaltensbezogenen und medizinischen Faktoren zu bestimmen. Diese Risikovorhersage ermöglicht es, frühzeitig Maßnahmen zu ergreifen, um die Krankheit im besten Fall zu verhindern und langfristig die Gesundheit der Patienten zu verbessern.\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert Bibliotheken für Datenanalyse, numerische Berechnungen und Datenvisualisierung, und legt fest, dass Diagramme direkt in das Jupyter Notebook eingebettet werden, um eine Analyse zur Vorhersage des Risikos einer koronaren Herzkrankheit anhand der Zielvariable TenYearCHD durchzuführen."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# Import Libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "%matplotlib inline\n",
- "#Ziel: Vorhersage, ob der Patient ein Risiko hat an koronare Herzkrankheit zu erkranken. Zielvariable ist TenYearCHD."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert Bibliotheken für die Erstellung von Klassifikationsdatensätzen, das Aufteilen von Daten in Trainings- und Testsets, die Durchführung logistischer Regressionen, die Bewertung von Klassifikationsmodellen und das Ausbalancieren von Klassenverteilungen, um zu überprüfen, ob sklearn und imblearn kompatible Versionen haben."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.datasets import make_classification\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.metrics import classification_report\n",
- "from imblearn.under_sampling import RandomUnderSampler\n",
- "from imblearn.over_sampling import SMOTE\n",
- "#Check if sklearn and imblearn are in a compatible version"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code lädt einen Datensatz zur Risikoanalyse von Herzerkrankungen aus einer angegebenen URL in ein Pandas DataFrame."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Risk_prediction_of_heart_disease_data.csv')\n",
- "#Quelle: https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Tabelle zeigt verschiedene Merkmale von Patienten, wie Geschlecht, Alter, Bildungsniveau, Rauchgewohnheiten, Blutdruckmedikamente, Vorerkrankungen, Cholesterinwerte, Blutdruck, Body-Mass-Index, Herzfrequenz und Blutzuckerspiegel, sowie die Zielvariable, ob der Patient in den nächsten zehn Jahren eine koronare Herzkrankheit entwickelt hat."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "<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>male</th>\n",
- " <th>age</th>\n",
- " <th>education</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
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- " <th>0</th>\n",
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- " <td>0</td>\n",
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- " <td>195.0</td>\n",
- " <td>106.0</td>\n",
- " <td>70.0</td>\n",
- " <td>26.97</td>\n",
- " <td>80.0</td>\n",
- " <td>77.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0</td>\n",
- " <td>46</td>\n",
- " <td>2.0</td>\n",
- " <td>0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>250.0</td>\n",
- " <td>121.0</td>\n",
- " <td>81.0</td>\n",
- " <td>28.73</td>\n",
- " <td>95.0</td>\n",
- " <td>76.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>48</td>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>20.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>245.0</td>\n",
- " <td>127.5</td>\n",
- " <td>80.0</td>\n",
- " <td>25.34</td>\n",
- " <td>75.0</td>\n",
- " <td>70.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0</td>\n",
- " <td>61</td>\n",
- " <td>3.0</td>\n",
- " <td>1</td>\n",
- " <td>30.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>225.0</td>\n",
- " <td>150.0</td>\n",
- " <td>95.0</td>\n",
- " <td>28.58</td>\n",
- " <td>65.0</td>\n",
- " <td>103.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0</td>\n",
- " <td>46</td>\n",
- " <td>3.0</td>\n",
- " <td>1</td>\n",
- " <td>23.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>285.0</td>\n",
- " <td>130.0</td>\n",
- " <td>84.0</td>\n",
- " <td>23.10</td>\n",
- " <td>85.0</td>\n",
- " <td>85.0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " male age education currentSmoker cigsPerDay BPMeds prevalentStroke \\\n",
- "0 1 39 4.0 0 0.0 0.0 0 \n",
- "1 0 46 2.0 0 0.0 0.0 0 \n",
- "2 1 48 1.0 1 20.0 0.0 0 \n",
- "3 0 61 3.0 1 30.0 0.0 0 \n",
- "4 0 46 3.0 1 23.0 0.0 0 \n",
- "\n",
- " prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose \\\n",
- "0 0 0 195.0 106.0 70.0 26.97 80.0 77.0 \n",
- "1 0 0 250.0 121.0 81.0 28.73 95.0 76.0 \n",
- "2 0 0 245.0 127.5 80.0 25.34 75.0 70.0 \n",
- "3 1 0 225.0 150.0 95.0 28.58 65.0 103.0 \n",
- "4 0 0 285.0 130.0 84.0 23.10 85.0 85.0 \n",
- "\n",
- " TenYearCHD \n",
- "0 0 \n",
- "1 0 \n",
- "2 0 \n",
- "3 1 \n",
- "4 0 "
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.to_csv('train.csv', index=False)\n",
- "train.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Tabelle liefert eine statistische Zusammenfassung (Anzahl, Mittelwert, Standardabweichung, Minimum, 25., 50. und 75. Perzentil sowie Maximum) verschiedener Merkmale von Patienten, darunter Geschlecht, Alter, Bildungsniveau, Rauchgewohnheiten, Medikamenteneinnahme, Vorerkrankungen, Cholesterinwerte, Blutdruck, Body-Mass-Index, Herzfrequenz, Blutzuckerspiegel und die zehnjährige Wahrscheinlichkeit, an einer koronaren Herzkrankheit zu erkranken."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\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>male</th>\n",
- " <th>age</th>\n",
- " <th>education</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4135.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4211.000000</td>\n",
- " <td>4187.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4190.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4240.000000</td>\n",
- " <td>4221.000000</td>\n",
- " <td>4239.000000</td>\n",
- " <td>3852.000000</td>\n",
- " <td>4240.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>0.429245</td>\n",
- " <td>49.580189</td>\n",
- " <td>1.979444</td>\n",
- " <td>0.494104</td>\n",
- " <td>9.005937</td>\n",
- " <td>0.029615</td>\n",
- " <td>0.005896</td>\n",
- " <td>0.310613</td>\n",
- " <td>0.025708</td>\n",
- " <td>236.699523</td>\n",
- " <td>132.354599</td>\n",
- " <td>82.897759</td>\n",
- " <td>25.800801</td>\n",
- " <td>75.878981</td>\n",
- " <td>81.963655</td>\n",
- " <td>0.151887</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>0.495027</td>\n",
- " <td>8.572942</td>\n",
- " <td>1.019791</td>\n",
- " <td>0.500024</td>\n",
- " <td>11.922462</td>\n",
- " <td>0.169544</td>\n",
- " <td>0.076569</td>\n",
- " <td>0.462799</td>\n",
- " <td>0.158280</td>\n",
- " <td>44.591284</td>\n",
- " <td>22.033300</td>\n",
- " <td>11.910394</td>\n",
- " <td>4.079840</td>\n",
- " <td>12.025348</td>\n",
- " <td>23.954335</td>\n",
- " <td>0.358953</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>0.000000</td>\n",
- " <td>32.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>107.000000</td>\n",
- " <td>83.500000</td>\n",
- " <td>48.000000</td>\n",
- " <td>15.540000</td>\n",
- " <td>44.000000</td>\n",
- " <td>40.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>0.000000</td>\n",
- " <td>42.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>206.000000</td>\n",
- " <td>117.000000</td>\n",
- " <td>75.000000</td>\n",
- " <td>23.070000</td>\n",
- " <td>68.000000</td>\n",
- " <td>71.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>0.000000</td>\n",
- " <td>49.000000</td>\n",
- " <td>2.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>234.000000</td>\n",
- " <td>128.000000</td>\n",
- " <td>82.000000</td>\n",
- " <td>25.400000</td>\n",
- " <td>75.000000</td>\n",
- " <td>78.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>1.000000</td>\n",
- " <td>56.000000</td>\n",
- " <td>3.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>20.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>263.000000</td>\n",
- " <td>144.000000</td>\n",
- " <td>90.000000</td>\n",
- " <td>28.040000</td>\n",
- " <td>83.000000</td>\n",
- " <td>87.000000</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>1.000000</td>\n",
- " <td>70.000000</td>\n",
- " <td>4.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>70.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>696.000000</td>\n",
- " <td>295.000000</td>\n",
- " <td>142.500000</td>\n",
- " <td>56.800000</td>\n",
- " <td>143.000000</td>\n",
- " <td>394.000000</td>\n",
- " <td>1.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " male age education currentSmoker cigsPerDay \\\n",
- "count 4240.000000 4240.000000 4135.000000 4240.000000 4211.000000 \n",
- "mean 0.429245 49.580189 1.979444 0.494104 9.005937 \n",
- "std 0.495027 8.572942 1.019791 0.500024 11.922462 \n",
- "min 0.000000 32.000000 1.000000 0.000000 0.000000 \n",
- "25% 0.000000 42.000000 1.000000 0.000000 0.000000 \n",
- "50% 0.000000 49.000000 2.000000 0.000000 0.000000 \n",
- "75% 1.000000 56.000000 3.000000 1.000000 20.000000 \n",
- "max 1.000000 70.000000 4.000000 1.000000 70.000000 \n",
- "\n",
- " BPMeds prevalentStroke prevalentHyp diabetes totChol \\\n",
- "count 4187.000000 4240.000000 4240.000000 4240.000000 4190.000000 \n",
- "mean 0.029615 0.005896 0.310613 0.025708 236.699523 \n",
- "std 0.169544 0.076569 0.462799 0.158280 44.591284 \n",
- "min 0.000000 0.000000 0.000000 0.000000 107.000000 \n",
- "25% 0.000000 0.000000 0.000000 0.000000 206.000000 \n",
- "50% 0.000000 0.000000 0.000000 0.000000 234.000000 \n",
- "75% 0.000000 0.000000 1.000000 0.000000 263.000000 \n",
- "max 1.000000 1.000000 1.000000 1.000000 696.000000 \n",
- "\n",
- " sysBP diaBP BMI heartRate glucose \\\n",
- "count 4240.000000 4240.000000 4221.000000 4239.000000 3852.000000 \n",
- "mean 132.354599 82.897759 25.800801 75.878981 81.963655 \n",
- "std 22.033300 11.910394 4.079840 12.025348 23.954335 \n",
- "min 83.500000 48.000000 15.540000 44.000000 40.000000 \n",
- "25% 117.000000 75.000000 23.070000 68.000000 71.000000 \n",
- "50% 128.000000 82.000000 25.400000 75.000000 78.000000 \n",
- "75% 144.000000 90.000000 28.040000 83.000000 87.000000 \n",
- "max 295.000000 142.500000 56.800000 143.000000 394.000000 \n",
- "\n",
- " TenYearCHD \n",
- "count 4240.000000 \n",
- "mean 0.151887 \n",
- "std 0.358953 \n",
- "min 0.000000 \n",
- "25% 0.000000 \n",
- "50% 0.000000 \n",
- "75% 0.000000 \n",
- "max 1.000000 "
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.describe(include='all')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Tabelle zeigt die Struktur eines DataFrames mit 4240 Einträgen und 16 Spalten, einschließlich der Spaltennamen, der Anzahl der nicht-leeren Werte, der Datentypen jeder Spalte und des gesamten Speicherbedarfs."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": false,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 3.Datenvorbereitung"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenvorbereitung"
- },
- "tags": []
- },
- "source": [
- "Dieser Prozess umfasst das Identifizieren und Behandeln fehlender Werte, die Bereinigung und Transformation der Daten sowie die Sicherstellung der Datenkonsistenz."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 4240 entries, 0 to 4239\n",
- "Data columns (total 16 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 male 4240 non-null int64 \n",
- " 1 age 4240 non-null int64 \n",
- " 2 education 4135 non-null float64\n",
- " 3 currentSmoker 4240 non-null int64 \n",
- " 4 cigsPerDay 4211 non-null float64\n",
- " 5 BPMeds 4187 non-null float64\n",
- " 6 prevalentStroke 4240 non-null int64 \n",
- " 7 prevalentHyp 4240 non-null int64 \n",
- " 8 diabetes 4240 non-null int64 \n",
- " 9 totChol 4190 non-null float64\n",
- " 10 sysBP 4240 non-null float64\n",
- " 11 diaBP 4240 non-null float64\n",
- " 12 BMI 4221 non-null float64\n",
- " 13 heartRate 4239 non-null float64\n",
- " 14 glucose 3852 non-null float64\n",
- " 15 TenYearCHD 4240 non-null int64 \n",
- "dtypes: float64(9), int64(7)\n",
- "memory usage: 530.1 KB\n"
- ]
- }
- ],
- "source": [
- "train.info()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Tabelle zeigt für jede Zeile und Spalte, ob ein fehlender Wert vorhanden ist, wobei alle Werte \"False\" sind, was darauf hinweist, dass keine fehlenden Werte in den angegebenen Daten vorhanden sind."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "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>male</th>\n",
- " <th>age</th>\n",
- " <th>education</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
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- " <td>False</td>\n",
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- " <td>False</td>\n",
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- " <td>False</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
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- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " male age education currentSmoker cigsPerDay BPMeds \\\n",
- "0 False False False False False False \n",
- "1 False False False False False False \n",
- "2 False False False False False False \n",
- "3 False False False False False False \n",
- "4 False False False False False False \n",
- "\n",
- " prevalentStroke prevalentHyp diabetes totChol sysBP diaBP BMI \\\n",
- "0 False False False False False False False \n",
- "1 False False False False False False False \n",
- "2 False False False False False False False \n",
- "3 False False False False False False False \n",
- "4 False False False False False False False \n",
- "\n",
- " heartRate glucose TenYearCHD \n",
- "0 False False False \n",
- "1 False False False \n",
- "2 False False False \n",
- "3 False False False \n",
- "4 False False False "
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_missingValues = train.isna()\n",
- "train_missingValues.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Tabelle zeigt die Anzahl der fehlenden Werte (NaN) für jede Spalte des DataFrames, wobei Spalten wie education, cigsPerDay, BPMeds, totChol, BMI, heartRate und glucose einige fehlende Werte aufweisen.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "male 0\n",
- "age 0\n",
- "education 105\n",
- "currentSmoker 0\n",
- "cigsPerDay 29\n",
- "BPMeds 53\n",
- "prevalentStroke 0\n",
- "prevalentHyp 0\n",
- "diabetes 0\n",
- "totChol 50\n",
- "sysBP 0\n",
- "diaBP 0\n",
- "BMI 19\n",
- "heartRate 1\n",
- "glucose 388\n",
- "TenYearCHD 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_missingValues.sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: >"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.heatmap(train.isnull(),yticklabels=False, cbar=False, cmap='viridis')\n",
- "# Die Daten zeigen, dass es nur wenige Zeilen gibt, die keinen Wert haben "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: >"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Wir löschen alle Zeilen mit den fehlenden Werten und die Spalte die für die Auswertung nicht relevant ist oder nicht benötigt wird\n",
- "train = train.drop('education', axis=1)\n",
- "train = train.dropna(axis=0)\n",
- "sns.heatmap(train.isnull(),yticklabels=False, cbar=False, cmap='viridis')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "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>male</th>\n",
- " <th>age</th>\n",
- " <th>currentSmoker</th>\n",
- " <th>cigsPerDay</th>\n",
- " <th>BPMeds</th>\n",
- " <th>prevalentStroke</th>\n",
- " <th>prevalentHyp</th>\n",
- " <th>diabetes</th>\n",
- " <th>totChol</th>\n",
- " <th>sysBP</th>\n",
- " <th>diaBP</th>\n",
- " <th>BMI</th>\n",
- " <th>heartRate</th>\n",
- " <th>glucose</th>\n",
- " <th>TenYearCHD</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- "Empty DataFrame\n",
- "Columns: [male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose, TenYearCHD]\n",
- "Index: []"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train[train.duplicated(keep=False)] #keine Duplikate vorhanden "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "### Explorative Datenanalyse"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['male', 'age', 'currentSmoker', 'cigsPerDay', 'BPMeds',\n",
- " 'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP',\n",
- " 'diaBP', 'BMI', 'heartRate', 'glucose', 'TenYearCHD'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code zeigt die Anzahl der Fälle (572) und Nicht-Fälle (3179) der Zielvariable \"TenYearCHD\" in einem Pandas Series-Objekt an."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "TenYearCHD\n",
- "0 3179\n",
- "1 572\n",
- "Name: count, dtype: int64"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.TenYearCHD.value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code stellt ein Balkendiagramm dar, das die Verteilung der Zielvariable \"TenYearCHD\" im DataFrame \"train\" visualisiert, während das Design der Visualisierung auf \"whitegrid\" gesetzt wird."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert die Cufflinks-Bibliothek für interaktive Plotly-Diagramme, konfiguriert sie für die Offline-Nutzung und stellt sicher, dass die erstellten Diagramme für andere Benutzer sichtbar sind.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "#import cufflinks as cf\n",
- "#import plotly.offline\n",
- "#cf.go_offline()\n",
- "#cf.set_config_file(offline=False, world_readable=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" für männliche und weibliche Patienten im DataFrame \"train\" und erstellt ein gestapeltes Balkendiagramm, um die Verteilung der Herzkrankheitsrisiken zwischen den Geschlechtern darzustellen, unter Verwendung der Cufflinks-Bibliothek und Plotly für interaktive Visualisierungen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "#TenYearCHD_male = train[train['male']==1]['TenYearCHD'].value_counts()\n",
- "#TenYearCHD_female = train[train['male']==0]['TenYearCHD'].value_counts()\n",
- "#df1 = pd.DataFrame([TenYearCHD_male,TenYearCHD_female])\n",
- "#df1.index = ['Male','Female']\n",
- "#df1.iplot(kind='bar',barmode='stack')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code wandelt den DataFrame \"train\" in ein geschmolzenes Format um und erstellt dann eine Kreuztabelle, die die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" für jede Kategorie der Variable \"male\" (0 und 1) zeigt.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "#df1 =train.melt(var_name='male', value_name='TenYearCHD')\n",
- "#pd.crosstab(index=df1['TenYearCHD'], columns=df1['male'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach Geschlecht im DataFrame \"train\" darstellt, wobei das Design auf \"whitegrid\" gesetzt ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='male', data=train)\n",
- "# kein 10-Jahres Risiko und weiblich = 1828\n",
- "# kein 10 Jahres Risko und männlich = 1351\n",
- "# 10-Jahres Risiko und weiblich = 253\n",
- "# 10 Jahres Risko und männlich = 319"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung besagt, dass die distplot Funktion in Seaborn veraltet ist und in zukünftigen Versionen durch displot oder histplot ersetzt werden sollte."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\3613199035.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['age'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='age', ylabel='Density'>"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['age'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt ein Balkendiagramm, das die Häufigkeit der Zielvariable \"TenYearCHD\" für jede Altersgruppe im DataFrame \"train\" darstellt, wobei jede Balkenfarbe den jeweiligen Wert von \"TenYearCHD\" repräsentiert."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='age', ylabel='count'>"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- " sns.countplot(x=train['age'], hue=train['TenYearCHD'], data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt ein Balkendiagramm, das die Häufigkeit der Zielvariable \"TenYearCHD\" für verschiedene Werte der Variable \"cigsPerDay\" (Zigaretten pro Tag) im DataFrame \"train\" darstellt, wobei die Balken nach der Werte der Zielvariable \"TenYearCHD\" gefärbt sind."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='count', ylabel='cigsPerDay'>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- " sns.countplot(y=train['cigsPerDay'], hue=train['TenYearCHD'], data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt dann ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach Raucherstatus (\"currentSmoker\") im DataFrame \"train\" darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='currentSmoker', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach der Einnahme von Blutdruckmedikamenten (\"BPMeds\") im DataFrame \"train\" darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='BPMeds', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der auskommentierte Code würde ein gruppiertes Balkendiagramm erstellen, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach dem Vorhandensein eines Schlaganfalls (\"prevalentStroke\") im DataFrame \"train\" darstellt, während das Design auf \"whitegrid\" gesetzt ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [],
- "source": [
- "#sns.set_style('whitegrid')\n",
- "#sns.countplot(x='TenYearCHD', hue='prevalentStroke', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach dem Vorhandensein von Bluthochdruck (\"prevalentHyp\") im DataFrame \"train\" darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='prevalentHyp', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code setzt das Design der Diagramme auf \"whitegrid\" und erstellt ein gruppiertes Balkendiagramm, das die Anzahl der Fälle und Nicht-Fälle der Zielvariable \"TenYearCHD\" nach dem Vorhandensein von Diabetes (\"diabetes\") im DataFrame \"train\" darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='count'>"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.set_style('whitegrid')\n",
- "sns.countplot(x='TenYearCHD', hue='diabetes', data=train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Outliers"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung informiert darüber, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code entsprechend anzupassen, um entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen zu verwenden. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\3350716391.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['totChol'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='totChol', ylabel='Density'>"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['totChol'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt einen Boxplot, der die Verteilung der Cholesterinwerte (totChol) im DataFrame train nach der Zielvariable TenYearCHD darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='totChol'>"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(y=train['totChol'], x=train['TenYearCHD'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet das 99. Perzentil der Cholesterinwerte (totChol) im DataFrame train und speichert den Wert in der Variablen q_totChol."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "352.0"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_totChol = train['totChol'].quantile(0.99)\n",
- "q_totChol"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der Cholesterinwert (totChol) kleiner als das zuvor berechnete 99. Perzentil (q_totChol) ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['totChol']<q_totChol]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\923562017.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['sysBP'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='sysBP', ylabel='Density'>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['sysBP'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt einen Boxplot, der die Verteilung der systolischen Blutdruckwerte (sysBP) im DataFrame train nach der Zielvariable TenYearCHD darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='sysBP'>"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(y=train['sysBP'], x=train['TenYearCHD'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet das 99. Perzentil der systolischen Blutdruckwerte (sysBP) im DataFrame train und speichert den Wert in der Variablen q_sysBP."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "199.95499999999993"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_sysBP = train['sysBP'].quantile(0.99)\n",
- "q_sysBP"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der systolische Blutdruckwert (sysBP) kleiner als das zuvor berechnete 99. Perzentil (q_sysBP) ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['sysBP']<q_sysBP]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\2539230880.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['diaBP'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='diaBP', ylabel='Density'>"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['diaBP'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt einen Boxplot, der die Verteilung der diastolischen Blutdruckwerte (diaBP) im DataFrame train nach der Zielvariable TenYearCHD darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='TenYearCHD', ylabel='diaBP'>"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(y=train['diaBP'], x=train['TenYearCHD'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet das 99. Perzentil der diastolischen Blutdruckwerte (diaBP) im DataFrame train und speichert den Wert in der Variablen q_diaBP."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "113.28999999999996"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_diaBP = train['diaBP'].quantile(0.99)\n",
- "q_diaBP"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der diastolische Blutdruckwert (diaBP) kleiner als das zuvor berechnete 99. Perzentil (q_diaBP) ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['diaBP']<q_diaBP]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\4028512202.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['BMI'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='BMI', ylabel='Density'>"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['BMI'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der auskommentierte Code erstellt einen Boxplot, der die Verteilung des Body-Mass-Index (BMI) im DataFrame train nach der Zielvariable TenYearCHD darstellt. Der zweite Codeausschnitt erstellt einen Boxplot, der nur die Verteilung des BMI im DataFrame train darstellt, ohne Berücksichtigung einer weiteren Variablen wie TenYearCHD."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: ylabel='BMI'>"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#sns.boxplot(y=train['BMI'], x=train['TenYearCHD'])\n",
- "sns.boxplot(y=train['BMI'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet das 99. Perzentil der Body-Mass-Index (BMI) Werte im DataFrame train und speichert den Wert in der Variablen q_BMI."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "38.26239999999998"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_BMI = train['BMI'].quantile(0.99)\n",
- "q_BMI"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen der Body-Mass-Index (BMI) kleiner als das zuvor berechnete 99. Perzentil (q_BMI) ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['BMI']<q_BMI]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung informiert darüber, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\1667855226.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['heartRate'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='heartRate', ylabel='Density'>"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['heartRate'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der auskommentierte Code würde einen Boxplot erstellen, der die Verteilung der Herzfrequenzwerte (heartRate) im DataFrame train nach der Zielvariable TenYearCHD darstellt. Der zweite Codeausschnitt erstellt einen Boxplot, der nur die Verteilung der Herzfrequenzwerte im DataFrame train darstellt, ohne Berücksichtigung einer weiteren Variablen wie TenYearCHD."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: ylabel='heartRate'>"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#sns.boxplot(y=train['heartRate'], x=train['TenYearCHD'])\n",
- "sns.boxplot(y=train['heartRate'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet das 99. Perzentil der Herzfrequenzwerte (heartRate) im DataFrame train und speichert den berechneten Wert in der Variablen q_heartRate."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "110.0"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_heartRate = train['heartRate'].quantile(0.99)\n",
- "q_heartRate"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen die Herzfrequenzwerte (heartRate) kleiner sind als das zuvor berechnete 99. Perzentil (q_heartRate)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['heartRate']<q_heartRate]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Warnung besagt, dass die Funktion distplot in Seaborn veraltet ist und in zukünftigen Versionen (ab v0.14.0) entfernt wird. Es wird empfohlen, den Code so anzupassen, dass entweder displot für eine figure-level Darstellung oder histplot für eine axes-level Darstellung von Histogrammen verwendet wird. Der bereitgestellte Link bietet eine Anleitung zur Aktualisierung des Codes auf die neuen Funktionen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\AppData\\Local\\Temp\\ipykernel_31200\\734497608.py:1: UserWarning: \n",
- "\n",
- "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
- "\n",
- "Please adapt your code to use either `displot` (a figure-level function with\n",
- "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- "\n",
- "For a guide to updating your code to use the new functions, please see\n",
- "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
- "\n",
- " sns.distplot(train['glucose'])\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='glucose', ylabel='Density'>"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(train['glucose'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code würde einen Boxplot erstellen, der die Verteilung der Glukosewerte (glucose) im DataFrame train nach der Zielvariable TenYearCHD darstellt. Der zweite Codeausschnitt erstellt einen Boxplot, der nur die Verteilung der Glukosewerte im DataFrame train darstellt, ohne Berücksichtigung einer weiteren Variablen wie TenYearCHD."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='glucose'>"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#sns.boxplot(y=train['glucose'], x=train['TenYearCHD'])\n",
- "sns.boxplot(x=train['glucose'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet das 97. Perzentil der Glukosewerte (glucose) im DataFrame train und speichert den berechneten Wert in der Variablen q_glucose."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "117.0"
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q_glucose = train['glucose'].quantile(0.97)\n",
- "q_glucose"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code filtert den DataFrame train so, dass nur die Datensätze beibehalten werden, bei denen die Glukosewerte (glucose) kleiner sind als das zuvor berechnete 97. Perzentil (q_glucose)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train[train['glucose']<q_glucose]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt einen Boxplot, der die Verteilung der Glukosewerte (glucose) im DataFrame train darstellt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: xlabel='glucose'>"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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AMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjfIneAJDsPKEquURvIniy5t83Ep5QVaK3ACQ1YgCIsfTNcxK9hSjf2jI30VsAcIHhaQIAAIzjkQEgBvx+v5YvX57obURx7psnKzwez3mdJxgMqqysTJmZmUpJSWmIrdWL3++P+9cEkh0xAMSAx+NRWlpaorcRE8FgUBdddJHS0tISEgMAGh5PEwAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYBwxAACAccQAAADGEQMAABhHDAAAYJyvLgc55yRJwWCwwTcQPmcszn2hYdbkZWleZk1elua1Mmt4vvD9eG087mxHSKqsrNTWrVsbZmcAACCu2rdvr9TU1Fo/XqcYCIVCqqqqktfrlcfjadANAgCA2HDOKRQKyefzyeut/ZUBdYoBAACQvHgBIQAAxhEDAAAYRwwAAGAcMQAAgHHEAAAAxhEDAAAYRwwAAGBc3GMgGAxq4MCBGjNmTGRty5Yt6tOnj7Kzs5WXl6d58+bFe1sNbtmyZWrbtq2ys7MjvwoLCyUl37xHjhzRqFGjdMMNN+j666/X0KFDtX//fknJNevf/va3qOszOztb7dq1U7t27SQl16xh27dv14ABA5STk6MuXbro+eefV2VlpaTkm/eTTz7R/fffr5ycHOXm5mr69OkKhUKSkmfWQ4cOKT8/X6WlpZG1s822cOFC5efnKysrS3fddZc2bdoU722fs5rmlaRNmzapffv21Y5vzLOeNxdnkydPdtddd50bPXq0c865I0eOuJ/85CfujTfecCdPnnRr16512dnZbsuWLfHeWoMaN26cGzNmTLX1ZJz3F7/4hXvkkUfc0aNH3ddff+2GDRvmHnzwwaSc9VT79u1znTt3du+8805SzhoMBl3nzp3da6+95oLBoPviiy9cQUGBmzZtWtLNW15e7nJzc93TTz/tjh075vbu3et69Ojhpk6dmjSzbtiwwd16662uTZs2rqSkxDl39tujkpISl52d7TZs2OAqKyvdq6++6m644QZ3/PjxRI5SJzXNGwqF3Lx581xWVpZr06ZN1PGNedaGENdHBtatW6eVK1eqa9eukbWVK1eqefPmGjBggHw+n37605/q9ttvV3FxcTy31uC2bt0a+RvjqZJt3m3btmnLli0aN26cmjVrpvT0dD333HMaOXJk0s16KuecCgsLlZubq549eyblrEePHtWBAwcUCoUi/8iJ1+tVWlpa0s27ceNGHTx4UGPHjlXTpk11xRVXaMiQIZozZ45WrFjR6GdduHChRo4cqccffzxq/WzX47x583TbbbepY8eOatKkiQYPHqyMjAwtW7YsEWPUWW3zPvXUU5o3b54effTRap/TWGdtKHGLgYMHD+rpp5/WxIkTlZaWFlnfuXOn2rRpE3Vsq1attGPHjnhtrcGFQiFt375dq1ev1s0336wbb7xRzzzzjI4ePZp085aVlalVq1Z66623lJ+fry5dumj8+PG69NJLk27WUy1atEi7du2KPN2VjLNmZGRo8ODBGj9+vNq3b6+bbrpJ11xzjQYPHpx084ZCITVp0kRNmjSJrHk8Hn311VfatGlTo5+1S5cueu+999S9e/eo9bNdj7t27WqUs9c274gRI/Tmm2+qbdu21T6nsc7aUOISA6FQSIWFhbrvvvt03XXXRX3s2LFjUXEgSX6/X8ePH4/H1mLi0KFDatu2rQoKCrRs2TLNnTtXn332mQoLC5Nu3qNHj+rjjz/WZ599poULF+qdd97Rl19+qdGjRyfdrGGhUEjTp0/Xww8/rPT0dEnJ+XMcCoXk9/v1zDPPaPPmzVqyZIk++eQT/fGPf0y6eTt06CC/36+JEyeqoqJC//nPfzRjxozIxxv7rJdeeql8vur/Yv3ZrsfGej3XNu/3vve9Wj+nsc7aUOISA3/+85+VmpqqgQMHVvtYWlqaAoFA1FogENDFF18cj63FxCWXXKLi4mL17t1baWlpatGihQoLC7VmzRo555Jq3vA/ifn0008rPT1dl1xyiR577DF98MEHSTdrWGlpqfbv36/evXtH1pLx5/i9997TihUr1L9/f6Wmpqp169Z65JFHNGfOnKSbt1mzZnrllVe0ZcsW5ebm6rHHHtOdd94pSUpJSUmqWU91tusx2a7nM7E0a03iEgOLFi3S+vXrlZOTo5ycHC1ZskRLlixRTk6O2rRpo507d0Ydv2vXLrVu3ToeW4uJHTt26A9/+EPkeVZJqqyslNfrVWZmZlLN26pVK4VCIZ08eTKyFn4F9o9+9KOkmjVsxYoVys/PV9OmTSNryfhz/MUXX0TeORDm8/nUpEmTpJu3srJSVVVVmjVrlkpLSzVv3jx5vV61atUq6f7Mnups12Pr1q2TdvbTWZq1JnGJgXfffVcfffSRNmzYoA0bNqhHjx7q0aOHNmzYoPz8fH311VeaOXOmTp48qZKSEi1evFh33313PLYWE82bN1dxcbH+8pe/qKqqSp9//rleeOEF9erVSwUFBUk1789+9jNdddVVeuqpp3Ts2DEdOnRIL774om699Vb16NEjqWYN27hxo66//vqotWT8Oe7SpYsOHDigoqIiBYNB7dmzR9OnT9ftt9+elPPef//9mj9/vpxz2rZtm4qKijRo0KCknDXsbLP17t1bixcvVklJiU6ePKmZM2fq4MGDys/PT/DOG56lWWuUiLcwjB49OvLWQuecKysrc3379nXZ2dnulltucQsWLEjEthpUaWlpZKZOnTq55557zgUCAedc8s27b98+99hjj7nOnTu7nJwcN2rUKHf06FHnXPLN6pxzWVlZbvXq1dXWk3HWf/zjH65Pnz6uY8eOLjc3102aNMmdOHHCOZd8865fv9716tXLZWVluVtuucXNmjUr8rFkmvXUt9o5d/bZ3nnnHVdQUOCysrJc79693ebNm+O95fNy+rzOffM2wtPfWuhc45/1fHicO+WxbAAAYA7/O2IAAIwjBgAAMI4YAADAOGIAAADjiAEAAIwjBgAAMI4YAADAOGIAAADjiAEgCbz99tvKy8tL9DYANFLEAAAAxhEDQCPyz3/+U/369VN2drZ69uyp6dOnV3tEoLS0VNdee23U2pgxYzRmzJjI5ddee035+fnKzs7WXXfdpXXr1kn65l+cfPnll3XrrbeqY8eO6t27t/7+979HPm/FihW67bbb1LFjR3Xr1k0vvfRS5GNfffWVRo4cqc6dO6tLly4aO3asysvLY/FtANDAiAGgkSgvL9cDDzygTp06qbS0VBMmTNBbb71V7/O8/fbbeumllzRhwgRt3LhR/fr105AhQ3TkyBH96U9/UnFxsaZMmaLS0lL98pe/1NChQ1VWVqZAIKDCwkKNHTtWGzdu1MSJE/XKK6+orKxMoVBIQ4cOldfr1YoVK7R48WLt379fY8eOjcF3AkBDIwaARmLVqlVKSUnR8OHDlZqaqmuvvVYPPPBAvc+zcOFC9e3bV9nZ2fJ6verTp4/++te/yu/3a8GCBXrwwQf14x//WD6fT927d1deXp7mz58vSfL7/Zo/f77WrVunH/7wh9q4caMyMzO1bds2bd++Xb/97W+Vnp6ujIwMjR49WkuXLtXhw4cb+lsBoIH5Er0BAHWzb98+tWjRQl7v/xr+qquuqvd5Dhw4oBYtWkStdejQQdI3D/Wffs4rr7xSO3bskN/v15w5c/TSSy/pySefVHl5uQoKCvSb3/xGe/fuVTAY1E033RT1uampqdqzZ48yMjLqvU8A8UMMAI1EixYt9Pnnn8s5J4/HI0n6/PPPqx2XkpIiSaqsrFRqaqok6fDhw5E75O9///v64osvoj7nxRdf1B133KErrrhCe/bsifrYnj17dNlll6m8vFz79+/XxIkTJUn/+te/9MQTT6ioqEj5+fny+/0qLS2N+vp79uzR1Vdf3YDfBQCxwNMEQCORl5cn55yKiopUWVmpTz/9VDNmzKh2XMuWLeXz+bR06VJJ0tq1a1VSUhL5+F133aU333wz8lz/ggULVFxcrIyMDPXp00cvv/yytm/frmAwqOXLl2vVqlXq1auXjh07pl/96ldavHixnHO67LLL5PV6lZGRoczMTF199dUaN26cjh07pkAgoN/97ncaPHiwgsFg3L5HAM6NxznnEr0JAHVTVlamZ599Vrt27dI111yjrKwsrVu3Tg899JCmTZumVatWSZKKi4s1c+ZMHTx4UJ06ddLll1+uiooKjRs3LvLxWbNm6cCBA2rVqpV+/etfKzs7W8FgUDNmzND8+fN14MABXX311Ro6dKi6du0q6ZvXLUyePFl79uyR3+9X9+7dNXr0aKWmpmrfvn0aP3681q9frxMnTigzM1NPPfWUWrVqlbDvF4C6IQaARuLw4cP69NNP1bFjx8ja66+/rqVLl2ru3LkJ3BmAxo6nCYBGIhgMatCgQfrggw8kSXv37tXs2bN18803J3hnABo7HhkAGpH3339fU6ZM0d69e9WsWTP16tVLw4YNk8/Ha4EBnDtiAAAA43iaAAAA44gBAACMIwYAADCOGAAAwDhiAAAA44gBAACMIwYAADCOGAAAwLj/B86B0gMh4yOnAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.boxplot(x=train['glucose'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [],
- "source": [
- "#sns.pairplot(train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Checking for Multicollinarity"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Mit der Importanweisung from statsmodels.stats.outliers_influence import variance_inflation_factor wird die Funktion variance_inflation_factor aus dem Modul outliers_influence in statsmodels.stats importiert. Diese Funktion wird verwendet, um den Variance Inflation Factor (VIF) zu berechnen, der zur Diagnose von Multikollinearität in Regressionsmodellen verwendet wird."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "metadata": {},
- "outputs": [],
- "source": [
- "from statsmodels.stats.outliers_influence import variance_inflation_factor"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt ein neues DataFrame vif, das den Variance Inflation Factor (VIF) für jede Variable im DataFrame train, ausgenommen der Zielvariable TenYearCHD, berechnet. Der VIF wird mithilfe der Funktion variance_inflation_factor aus dem Modul statsmodels.stats.outliers_influence für jede Variable einzeln berechnet und zusammen mit den Variablennamen in vif gespeichert, um die Ergebnisse leichter erkunden zu können."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create a new data frame which includes all VIFs (Variance Inflation Factor)\n",
- "# Each variable has its own variance inflation factor. This measure is variable specific\n",
- "variables = train.drop(['TenYearCHD'], axis = 1)\n",
- "vif = pd.DataFrame()\n",
- "\n",
- "# Make use of the variance_inflation_factor module, output the respective VIFs \n",
- "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
- "\n",
- "# Include variable names so it is easier to explore the result\n",
- "vif[\"Features\"] = variables.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>VIF</th>\n",
- " <th>Features</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2.217100</td>\n",
- " <td>male</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>42.056992</td>\n",
- " <td>age</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.959553</td>\n",
- " <td>currentSmoker</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.287163</td>\n",
- " <td>cigsPerDay</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1.118613</td>\n",
- " <td>BPMeds</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1.019975</td>\n",
- " <td>prevalentStroke</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>2.240536</td>\n",
- " <td>prevalentHyp</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>1.023187</td>\n",
- " <td>diabetes</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>37.313994</td>\n",
- " <td>totChol</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>153.968224</td>\n",
- " <td>sysBP</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>147.331914</td>\n",
- " <td>diaBP</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>54.537909</td>\n",
- " <td>BMI</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>45.298946</td>\n",
- " <td>heartRate</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>41.248874</td>\n",
- " <td>glucose</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " VIF Features\n",
- "0 2.217100 male\n",
- "1 42.056992 age\n",
- "2 4.959553 currentSmoker\n",
- "3 4.287163 cigsPerDay\n",
- "4 1.118613 BPMeds\n",
- "5 1.019975 prevalentStroke\n",
- "6 2.240536 prevalentHyp\n",
- "7 1.023187 diabetes\n",
- "8 37.313994 totChol\n",
- "9 153.968224 sysBP\n",
- "10 147.331914 diaBP\n",
- "11 54.537909 BMI\n",
- "12 45.298946 heartRate\n",
- "13 41.248874 glucose"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vif"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code erstellt eine Heatmap der Korrelationsmatrix für die Variablen im DataFrame train, wobei die Größe der Abbildung auf 12x8 Zoll festgelegt ist. Die Heatmap zeigt die Korrelationen zwischen den Variablen, einschließlich spezifischer Anmerkungen zu Korrelationen wie zwischen currentSmoker und cigsPerDay, sysBP und diaBP, sowie prevalentHyp und sysBP und diaBP."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Axes: >"
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1200x800 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(12,8))\n",
- "sns.heatmap(train.corr(), annot=True)\n",
- "#Korrelationen zwischen currentSmoker und cigsPerDay, sysBPund diaBP, prevalentHyp und sysBP und diaBP "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code setzt den Index des DataFrame train zurück und erstellt eine Kopie davon, wobei der ursprüngliche Index verworfen wird."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "metadata": {},
- "outputs": [],
- "source": [
- "train = train.reset_index(drop=True).copy()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 4.Modeling"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- "In diesem Abschnitt wird die Feature-Liste estimators definiert, die die relevanten Merkmale für die Modellierung mittels logistischer Regression enthält. Diese Merkmale werden aus dem DataFrame train ausgewählt und der Variablen X_all zugewiesen, während die Zielvariablen y aus dem gleichen DataFrame extrahiert werden. Dabei wurden die Merkmale currentSmoker und sysBP (siehe oben) aus der endgültigen Merkmalsliste entfernt, um die Genauigkeit des Modells zu verbessern."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Der Code definiert die Feature-Liste bestimators, wählt die entsprechenden Merkmale aus dem DataFrame train aus und weist sie der Variablen X_all zu. Zudem werden die Zielvariablen y aus dem DataFrame train extrahiert."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {},
- "outputs": [],
- "source": [
- "estimators = ['male', 'age', 'currentSmoker', 'BPMeds',\n",
- " 'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'BMI', 'heartRate', 'glucose']\n",
- "X_all = train[estimators]\n",
- "y = train['TenYearCHD']\n",
- "#currentSmoker & sysBP werden gedropt (siehe oben)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Die Importanweisung import statsmodels.api as sm importiert das Modul statsmodels unter dem Alias sm, das für statistische Modellierung und Tests verwendet wird."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "metadata": {},
- "outputs": [],
- "source": [
- "import statsmodels.api as sm"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code führt eine logistische Regression auf den Daten X_all mit der Zielvariable y aus und gibt eine Zusammenfassung der Ergebnisse der Regression zurück, einschließlich statistischer Kennzahlen wie Koeffizienten, p-Werte und Konfidenzintervalle der geschätzten Koeffizienten."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimization terminated successfully.\n",
- " Current function value: 0.356399\n",
- " Iterations 7\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table class=\"simpletable\">\n",
- "<caption>Logit Regression Results</caption>\n",
- "<tr>\n",
- " <th>Dep. Variable:</th> <td>TenYearCHD</td> <th> No. Observations: </th> <td> 3444</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td> 3431</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 12</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Date:</th> <td>Fri, 14 Jun 2024</td> <th> Pseudo R-squ.: </th> <td>0.1008</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Time:</th> <td>14:23:53</td> <th> Log-Likelihood: </th> <td> -1227.4</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td> -1365.0</td> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>Covariance Type:</th> <td>nonrobust</td> <th> LLR p-value: </th> <td>7.410e-52</td>\n",
- "</tr>\n",
- "</table>\n",
- "<table class=\"simpletable\">\n",
- "<tr>\n",
- " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
- "</tr>\n",
- "<tr>\n",
- " <th>const</th> <td> -8.3986</td> <td> 0.805</td> <td> -10.431</td> <td> 0.000</td> <td> -9.977</td> <td> -6.821</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>male</th> <td> 0.6638</td> <td> 0.112</td> <td> 5.943</td> <td> 0.000</td> <td> 0.445</td> <td> 0.883</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>age</th> <td> 0.0703</td> <td> 0.007</td> <td> 10.266</td> <td> 0.000</td> <td> 0.057</td> <td> 0.084</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>currentSmoker</th> <td> 0.4561</td> <td> 0.113</td> <td> 4.031</td> <td> 0.000</td> <td> 0.234</td> <td> 0.678</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>BPMeds</th> <td> -0.1249</td> <td> 0.293</td> <td> -0.427</td> <td> 0.670</td> <td> -0.699</td> <td> 0.449</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>prevalentStroke</th> <td> 1.0221</td> <td> 0.540</td> <td> 1.892</td> <td> 0.058</td> <td> -0.037</td> <td> 2.081</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>prevalentHyp</th> <td> 0.1340</td> <td> 0.150</td> <td> 0.893</td> <td> 0.372</td> <td> -0.160</td> <td> 0.428</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>diabetes</th> <td> -0.0543</td> <td> 0.515</td> <td> -0.106</td> <td> 0.916</td> <td> -1.063</td> <td> 0.954</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>totChol</th> <td> 0.0020</td> <td> 0.001</td> <td> 1.468</td> <td> 0.142</td> <td> -0.001</td> <td> 0.005</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>sysBP</th> <td> 0.0138</td> <td> 0.004</td> <td> 3.760</td> <td> 0.000</td> <td> 0.007</td> <td> 0.021</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>BMI</th> <td> 0.0029</td> <td> 0.015</td> <td> 0.187</td> <td> 0.852</td> <td> -0.027</td> <td> 0.033</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>heartRate</th> <td>-6.527e-05</td> <td> 0.005</td> <td> -0.013</td> <td> 0.989</td> <td> -0.010</td> <td> 0.009</td>\n",
- "</tr>\n",
- "<tr>\n",
- " <th>glucose</th> <td> -0.0009</td> <td> 0.004</td> <td> -0.196</td> <td> 0.845</td> <td> -0.009</td> <td> 0.008</td>\n",
- "</tr>\n",
- "</table>"
- ],
- "text/latex": [
- "\\begin{center}\n",
- "\\begin{tabular}{lclc}\n",
- "\\toprule\n",
- "\\textbf{Dep. Variable:} & TenYearCHD & \\textbf{ No. Observations: } & 3444 \\\\\n",
- "\\textbf{Model:} & Logit & \\textbf{ Df Residuals: } & 3431 \\\\\n",
- "\\textbf{Method:} & MLE & \\textbf{ Df Model: } & 12 \\\\\n",
- "\\textbf{Date:} & Fri, 14 Jun 2024 & \\textbf{ Pseudo R-squ.: } & 0.1008 \\\\\n",
- "\\textbf{Time:} & 14:23:53 & \\textbf{ Log-Likelihood: } & -1227.4 \\\\\n",
- "\\textbf{converged:} & True & \\textbf{ LL-Null: } & -1365.0 \\\\\n",
- "\\textbf{Covariance Type:} & nonrobust & \\textbf{ LLR p-value: } & 7.410e-52 \\\\\n",
- "\\bottomrule\n",
- "\\end{tabular}\n",
- "\\begin{tabular}{lcccccc}\n",
- " & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n",
- "\\midrule\n",
- "\\textbf{const} & -8.3986 & 0.805 & -10.431 & 0.000 & -9.977 & -6.821 \\\\\n",
- "\\textbf{male} & 0.6638 & 0.112 & 5.943 & 0.000 & 0.445 & 0.883 \\\\\n",
- "\\textbf{age} & 0.0703 & 0.007 & 10.266 & 0.000 & 0.057 & 0.084 \\\\\n",
- "\\textbf{currentSmoker} & 0.4561 & 0.113 & 4.031 & 0.000 & 0.234 & 0.678 \\\\\n",
- "\\textbf{BPMeds} & -0.1249 & 0.293 & -0.427 & 0.670 & -0.699 & 0.449 \\\\\n",
- "\\textbf{prevalentStroke} & 1.0221 & 0.540 & 1.892 & 0.058 & -0.037 & 2.081 \\\\\n",
- "\\textbf{prevalentHyp} & 0.1340 & 0.150 & 0.893 & 0.372 & -0.160 & 0.428 \\\\\n",
- "\\textbf{diabetes} & -0.0543 & 0.515 & -0.106 & 0.916 & -1.063 & 0.954 \\\\\n",
- "\\textbf{totChol} & 0.0020 & 0.001 & 1.468 & 0.142 & -0.001 & 0.005 \\\\\n",
- "\\textbf{sysBP} & 0.0138 & 0.004 & 3.760 & 0.000 & 0.007 & 0.021 \\\\\n",
- "\\textbf{BMI} & 0.0029 & 0.015 & 0.187 & 0.852 & -0.027 & 0.033 \\\\\n",
- "\\textbf{heartRate} & -6.527e-05 & 0.005 & -0.013 & 0.989 & -0.010 & 0.009 \\\\\n",
- "\\textbf{glucose} & -0.0009 & 0.004 & -0.196 & 0.845 & -0.009 & 0.008 \\\\\n",
- "\\bottomrule\n",
- "\\end{tabular}\n",
- "%\\caption{Logit Regression Results}\n",
- "\\end{center}"
- ],
- "text/plain": [
- "<class 'statsmodels.iolib.summary.Summary'>\n",
- "\"\"\"\n",
- " Logit Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: TenYearCHD No. Observations: 3444\n",
- "Model: Logit Df Residuals: 3431\n",
- "Method: MLE Df Model: 12\n",
- "Date: Fri, 14 Jun 2024 Pseudo R-squ.: 0.1008\n",
- "Time: 14:23:53 Log-Likelihood: -1227.4\n",
- "converged: True LL-Null: -1365.0\n",
- "Covariance Type: nonrobust LLR p-value: 7.410e-52\n",
- "===================================================================================\n",
- " coef std err z P>|z| [0.025 0.975]\n",
- "-----------------------------------------------------------------------------------\n",
- "const -8.3986 0.805 -10.431 0.000 -9.977 -6.821\n",
- "male 0.6638 0.112 5.943 0.000 0.445 0.883\n",
- "age 0.0703 0.007 10.266 0.000 0.057 0.084\n",
- "currentSmoker 0.4561 0.113 4.031 0.000 0.234 0.678\n",
- "BPMeds -0.1249 0.293 -0.427 0.670 -0.699 0.449\n",
- "prevalentStroke 1.0221 0.540 1.892 0.058 -0.037 2.081\n",
- "prevalentHyp 0.1340 0.150 0.893 0.372 -0.160 0.428\n",
- "diabetes -0.0543 0.515 -0.106 0.916 -1.063 0.954\n",
- "totChol 0.0020 0.001 1.468 0.142 -0.001 0.005\n",
- "sysBP 0.0138 0.004 3.760 0.000 0.007 0.021\n",
- "BMI 0.0029 0.015 0.187 0.852 -0.027 0.033\n",
- "heartRate -6.527e-05 0.005 -0.013 0.989 -0.010 0.009\n",
- "glucose -0.0009 0.004 -0.196 0.845 -0.009 0.008\n",
- "===================================================================================\n",
- "\"\"\""
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = sm.add_constant(X_all)\n",
- "reg_logit = sm.Logit(y,x)\n",
- "results_logit = reg_logit.fit()\n",
- "results_logit.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Der P-Wert bei folgenden Attributen: BPMeds, prevalentStroke, diabetes, totChol,diaBP,BMI,heartRate & glucose\n",
- "#ist relativ hoch und somit weißt es eine geringe statistiche signifikante Beziehung zur Wahrscheinlichkeit einer Herzerkrankung auf\n",
- "#(The closer to 0.000 the p-value, the better, Slides_AI - Part 4-2.pdf, S.27)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet die Odds Ratios und deren Konfidenzintervalle für die Koeffizienten der logistischen Regressionsergebnisse und gibt sie als DataFrame aus, wobei die exponentiellen Transformation der Konfidenzintervalle und des Koeffizienten der Odds Ratio angewendet wird."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 5% 95% Odds Ratio\n",
- "const 0.000046 0.001091 0.000225\n",
- "male 1.560329 2.417551 1.942209\n",
- "age 1.058495 1.087278 1.072790\n",
- "currentSmoker 1.264052 1.969618 1.577878\n",
- "BPMeds 0.497151 1.566804 0.882575\n",
- "prevalentStroke 0.964058 8.010118 2.778888\n",
- "prevalentHyp 0.851976 1.534411 1.143364\n",
- "diabetes 0.345451 2.596858 0.947147\n",
- "totChol 0.999340 1.004611 1.001972\n",
- "sysBP 1.006617 1.021175 1.013870\n",
- "BMI 0.972975 1.033720 1.002888\n",
- "heartRate 0.990448 1.009513 0.999935\n",
- "glucose 0.990579 1.007774 0.999140\n"
- ]
- }
- ],
- "source": [
- "#Odds ratio & confidence intervals\n",
- "params = results_logit.params\n",
- "conf = results_logit.conf_int()\n",
- "conf['Odds Ratio'] = params\n",
- "conf.columns = ['5%', '95%', 'Odds Ratio']\n",
- "print(np.exp(conf))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code entfernt bestimmte Variablen ('BPMeds', 'prevalentStroke', 'diabetes', 'totChol', 'diaBP', 'BMI', 'heartRate', 'glucose') aus dem DataFrame x und speichert das Ergebnis in x_new."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "metadata": {},
- "outputs": [],
- "source": [
- "#x_new = x.drop(['BPMeds', 'prevalentStroke', 'diabetes', 'totChol','diaBP','BMI','heartRate', 'glucose'], axis=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code entfernt die Spalten 'BPMeds', 'prevalentStroke', 'diabetes', 'totChol', 'diaBP', 'BMI', 'heartRate' und 'glucose' aus dem DataFrame train."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "metadata": {},
- "outputs": [],
- "source": [
- "#train = train.drop(['BPMeds', 'prevalentStroke', 'diabetes', 'totChol','diaBP','BMI','heartRate', 'glucose'], axis=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code fügt eine konstante Spalte zu x_new hinzu, führt eine logistische Regression durch und gibt eine Zusammenfassung der Regressionsergebnisse aus."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "metadata": {},
- "outputs": [],
- "source": [
- "#x = sm.add_constant(x_new)\n",
- "#reg_logit = sm.Logit(y,x)\n",
- "#results_logit = reg_logit.fit()\n",
- "#results_logit.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code berechnet die Odds Ratio und die Konfidenzintervalle für die Regressionskoeffizienten der logistischen Regression und gibt sie exponentiell transformiert aus."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Odds ratio & confidence intervals\n",
- "#params = results_logit.params\n",
- "#conf = results_logit.conf_int()\n",
- "#conf['Odds Ratio'] = params\n",
- "#conf.columns = ['5%', '95%', 'Odds Ratio']\n",
- "#print(np.exp(conf))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Model Training"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(3444, 15)"
- ]
- },
- "execution_count": 71,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "metadata": {},
- "outputs": [],
- "source": [
- "X = x\n",
- "y = y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Undersampling (nachträglich) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code gibt die Versionen der Bibliotheken scikit-learn (sklearn) und imbalanced-learn (imblearn) aus, die in der Umgebung installiert sind."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1.5.0\n",
- "0.12.3\n"
- ]
- }
- ],
- "source": [
- "print(sklearn.__version__)\n",
- "print(imblearn.__version__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dieser Code importiert die Bibliothek imblearn, speziell das Modul InstanceHardnessThreshold für das Unterdampling und die LogisticRegression aus scikit-learn für die logistische Regression."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 74,
- "metadata": {},
- "outputs": [],
- "source": [
- "import imblearn\n",
- "from imblearn.under_sampling import InstanceHardnessThreshold\n",
- "from sklearn.linear_model import LogisticRegression"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code führt das Verfahren des Instance Hardness Threshold (IHT) für das Unterdampling durch. Dabei wird ein Modell der logistischen Regression (mit bestimmten Parametern wie solver='lbfgs' und multi_class='auto') verwendet, um die Instanzen zu bewerten und diejenigen zu entfernen, die schwer klassifizierbar sind, um das Ungleichgewicht in den Klassen zu reduzieren (fit_resample)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
- " warnings.warn(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
- " warnings.warn(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
- " warnings.warn(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
- " warnings.warn(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
- " warnings.warn(\n",
- "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " n_iter_i = _check_optimize_result(\n"
- ]
- }
- ],
- "source": [
- "iht = InstanceHardnessThreshold(random_state=0,estimator=LogisticRegression (solver='lbfgs', multi_class='auto'))\n",
- " \n",
- "X_resampled, y_resampled = iht.fit_resample(X, y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert die Funktion train_test_split aus Scikit-Learn, die verwendet wird, um Datensätze in Trainings- und Testsets aufzuteilen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.model_selection import train_test_split"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code verwendet die Methode train_test_split aus Scikit-Learn, um die Datensätze X_resampled und y_resampled in Trainings- und Testsets aufzuteilen, wobei 20% der Daten für das Testset reserviert werden."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Methode von train_test_split (sklearn)\n",
- "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
- "#der Datensatz wird übergeben ohne die Zielspalte TenYearCHD für X, dafür wird diese in y eingesetzt\n",
- "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=365)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Scaling"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert die StandardScaler-Klasse aus Scikit-Learn, die zur Skalierung von Merkmalen verwendet wird, um sicherzustellen, dass sie eine Nullmittelwert und eine Einheitsvarianz haben."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.preprocessing import StandardScaler"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code führt eine Standardisierung der Trainingsdaten (X_train) und Testdaten (X_test) mithilfe eines StandardScaler durch, wobei die Daten so transformiert werden, dass sie eine Nullmittelwert und eine Einheitsvarianz haben, basierend auf den statistischen Eigenschaften der Trainingsdaten."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {},
- "outputs": [],
- "source": [
- "scaler = StandardScaler()\n",
- "scaler.fit(X_train)\n",
- "\n",
- "X_train = scaler.transform(X_train)\n",
- "X_test = scaler.transform(X_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Logistische Regression"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert die LogisticRegression Klasse aus sklearn.linear_model, die für die Logistische Regression zur Klassifikation verwendet wird."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.linear_model import LogisticRegression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Ein model wird angelegt\n",
- "log_model = LogisticRegression(random_state=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>#sk-container-id-1 {\n",
- " /* Definition of color scheme common for light and dark mode */\n",
- " --sklearn-color-text: black;\n",
- " --sklearn-color-line: gray;\n",
- " /* Definition of color scheme for unfitted estimators */\n",
- " --sklearn-color-unfitted-level-0: #fff5e6;\n",
- " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
- " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
- " --sklearn-color-unfitted-level-3: chocolate;\n",
- " /* Definition of color scheme for fitted estimators */\n",
- " --sklearn-color-fitted-level-0: #f0f8ff;\n",
- " --sklearn-color-fitted-level-1: #d4ebff;\n",
- " --sklearn-color-fitted-level-2: #b3dbfd;\n",
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- "\n",
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- " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
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- " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
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- "\n",
- "#sk-container-id-1 {\n",
- " color: var(--sklearn-color-text);\n",
- "}\n",
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- "#sk-container-id-1 pre {\n",
- " padding: 0;\n",
- "}\n",
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- "#sk-container-id-1 input.sk-hidden--visually {\n",
- " border: 0;\n",
- " clip: rect(1px 1px 1px 1px);\n",
- " clip: rect(1px, 1px, 1px, 1px);\n",
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- " border: 1px dashed var(--sklearn-color-line);\n",
- " margin: 0 0.4em 0.5em 0.4em;\n",
- " box-sizing: border-box;\n",
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- " background-color: var(--sklearn-color-background);\n",
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- "\n",
- "#sk-container-id-1 div.sk-container {\n",
- " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
- " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
- " so we also need the `!important` here to be able to override the\n",
- " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
- " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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- " position: relative;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-text-repr-fallback {\n",
- " display: none;\n",
- "}\n",
- "\n",
- "div.sk-parallel-item,\n",
- "div.sk-serial,\n",
- "div.sk-item {\n",
- " /* draw centered vertical line to link estimators */\n",
- " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
- " background-size: 2px 100%;\n",
- " background-repeat: no-repeat;\n",
- " background-position: center center;\n",
- "}\n",
- "\n",
- "/* Parallel-specific style estimator block */\n",
- "\n",
- "#sk-container-id-1 div.sk-parallel-item::after {\n",
- " content: \"\";\n",
- " width: 100%;\n",
- " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
- " flex-grow: 1;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-parallel {\n",
- " display: flex;\n",
- " align-items: stretch;\n",
- " justify-content: center;\n",
- " background-color: var(--sklearn-color-background);\n",
- " position: relative;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-parallel-item {\n",
- " display: flex;\n",
- " flex-direction: column;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
- " align-self: flex-end;\n",
- " width: 50%;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
- " align-self: flex-start;\n",
- " width: 50%;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
- " width: 0;\n",
- "}\n",
- "\n",
- "/* Serial-specific style estimator block */\n",
- "\n",
- "#sk-container-id-1 div.sk-serial {\n",
- " display: flex;\n",
- " flex-direction: column;\n",
- " align-items: center;\n",
- " background-color: var(--sklearn-color-background);\n",
- " padding-right: 1em;\n",
- " padding-left: 1em;\n",
- "}\n",
- "\n",
- "\n",
- "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
- "clickable and can be expanded/collapsed.\n",
- "- Pipeline and ColumnTransformer use this feature and define the default style\n",
- "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
- "*/\n",
- "\n",
- "/* Pipeline and ColumnTransformer style (default) */\n",
- "\n",
- "#sk-container-id-1 div.sk-toggleable {\n",
- " /* Default theme specific background. It is overwritten whether we have a\n",
- " specific estimator or a Pipeline/ColumnTransformer */\n",
- " background-color: var(--sklearn-color-background);\n",
- "}\n",
- "\n",
- "/* Toggleable label */\n",
- "#sk-container-id-1 label.sk-toggleable__label {\n",
- " cursor: pointer;\n",
- " display: block;\n",
- " width: 100%;\n",
- " margin-bottom: 0;\n",
- " padding: 0.5em;\n",
- " box-sizing: border-box;\n",
- " text-align: center;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
- " /* Arrow on the left of the label */\n",
- " content: \"â–¸\";\n",
- " float: left;\n",
- " margin-right: 0.25em;\n",
- " color: var(--sklearn-color-icon);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
- " color: var(--sklearn-color-text);\n",
- "}\n",
- "\n",
- "/* Toggleable content - dropdown */\n",
- "\n",
- "#sk-container-id-1 div.sk-toggleable__content {\n",
- " max-height: 0;\n",
- " max-width: 0;\n",
- " overflow: hidden;\n",
- " text-align: left;\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-0);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
- " /* fitted */\n",
- " background-color: var(--sklearn-color-fitted-level-0);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-toggleable__content pre {\n",
- " margin: 0.2em;\n",
- " border-radius: 0.25em;\n",
- " color: var(--sklearn-color-text);\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-0);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-fitted-level-0);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
- " /* Expand drop-down */\n",
- " max-height: 200px;\n",
- " max-width: 100%;\n",
- " overflow: auto;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
- " content: \"â–¾\";\n",
- "}\n",
- "\n",
- "/* Pipeline/ColumnTransformer-specific style */\n",
- "\n",
- "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
- " color: var(--sklearn-color-text);\n",
- " background-color: var(--sklearn-color-unfitted-level-2);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
- " background-color: var(--sklearn-color-fitted-level-2);\n",
- "}\n",
- "\n",
- "/* Estimator-specific style */\n",
- "\n",
- "/* Colorize estimator box */\n",
- "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-2);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
- " /* fitted */\n",
- " background-color: var(--sklearn-color-fitted-level-2);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
- "#sk-container-id-1 div.sk-label label {\n",
- " /* The background is the default theme color */\n",
- " color: var(--sklearn-color-text-on-default-background);\n",
- "}\n",
- "\n",
- "/* On hover, darken the color of the background */\n",
- "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
- " color: var(--sklearn-color-text);\n",
- " background-color: var(--sklearn-color-unfitted-level-2);\n",
- "}\n",
- "\n",
- "/* Label box, darken color on hover, fitted */\n",
- "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
- " color: var(--sklearn-color-text);\n",
- " background-color: var(--sklearn-color-fitted-level-2);\n",
- "}\n",
- "\n",
- "/* Estimator label */\n",
- "\n",
- "#sk-container-id-1 div.sk-label label {\n",
- " font-family: monospace;\n",
- " font-weight: bold;\n",
- " display: inline-block;\n",
- " line-height: 1.2em;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-label-container {\n",
- " text-align: center;\n",
- "}\n",
- "\n",
- "/* Estimator-specific */\n",
- "#sk-container-id-1 div.sk-estimator {\n",
- " font-family: monospace;\n",
- " border: 1px dotted var(--sklearn-color-border-box);\n",
- " border-radius: 0.25em;\n",
- " box-sizing: border-box;\n",
- " margin-bottom: 0.5em;\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-0);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-estimator.fitted {\n",
- " /* fitted */\n",
- " background-color: var(--sklearn-color-fitted-level-0);\n",
- "}\n",
- "\n",
- "/* on hover */\n",
- "#sk-container-id-1 div.sk-estimator:hover {\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-2);\n",
- "}\n",
- "\n",
- "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
- " /* fitted */\n",
- " background-color: var(--sklearn-color-fitted-level-2);\n",
- "}\n",
- "\n",
- "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
- "\n",
- "/* Common style for \"i\" and \"?\" */\n",
- "\n",
- ".sk-estimator-doc-link,\n",
- "a:link.sk-estimator-doc-link,\n",
- "a:visited.sk-estimator-doc-link {\n",
- " float: right;\n",
- " font-size: smaller;\n",
- " line-height: 1em;\n",
- " font-family: monospace;\n",
- " background-color: var(--sklearn-color-background);\n",
- " border-radius: 1em;\n",
- " height: 1em;\n",
- " width: 1em;\n",
- " text-decoration: none !important;\n",
- " margin-left: 1ex;\n",
- " /* unfitted */\n",
- " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
- " color: var(--sklearn-color-unfitted-level-1);\n",
- "}\n",
- "\n",
- ".sk-estimator-doc-link.fitted,\n",
- "a:link.sk-estimator-doc-link.fitted,\n",
- "a:visited.sk-estimator-doc-link.fitted {\n",
- " /* fitted */\n",
- " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
- " color: var(--sklearn-color-fitted-level-1);\n",
- "}\n",
- "\n",
- "/* On hover */\n",
- "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
- ".sk-estimator-doc-link:hover,\n",
- "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
- ".sk-estimator-doc-link:hover {\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-3);\n",
- " color: var(--sklearn-color-background);\n",
- " text-decoration: none;\n",
- "}\n",
- "\n",
- "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
- ".sk-estimator-doc-link.fitted:hover,\n",
- "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
- ".sk-estimator-doc-link.fitted:hover {\n",
- " /* fitted */\n",
- " background-color: var(--sklearn-color-fitted-level-3);\n",
- " color: var(--sklearn-color-background);\n",
- " text-decoration: none;\n",
- "}\n",
- "\n",
- "/* Span, style for the box shown on hovering the info icon */\n",
- ".sk-estimator-doc-link span {\n",
- " display: none;\n",
- " z-index: 9999;\n",
- " position: relative;\n",
- " font-weight: normal;\n",
- " right: .2ex;\n",
- " padding: .5ex;\n",
- " margin: .5ex;\n",
- " width: min-content;\n",
- " min-width: 20ex;\n",
- " max-width: 50ex;\n",
- " color: var(--sklearn-color-text);\n",
- " box-shadow: 2pt 2pt 4pt #999;\n",
- " /* unfitted */\n",
- " background: var(--sklearn-color-unfitted-level-0);\n",
- " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
- "}\n",
- "\n",
- ".sk-estimator-doc-link.fitted span {\n",
- " /* fitted */\n",
- " background: var(--sklearn-color-fitted-level-0);\n",
- " border: var(--sklearn-color-fitted-level-3);\n",
- "}\n",
- "\n",
- ".sk-estimator-doc-link:hover span {\n",
- " display: block;\n",
- "}\n",
- "\n",
- "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
- "\n",
- "#sk-container-id-1 a.estimator_doc_link {\n",
- " float: right;\n",
- " font-size: 1rem;\n",
- " line-height: 1em;\n",
- " font-family: monospace;\n",
- " background-color: var(--sklearn-color-background);\n",
- " border-radius: 1rem;\n",
- " height: 1rem;\n",
- " width: 1rem;\n",
- " text-decoration: none;\n",
- " /* unfitted */\n",
- " color: var(--sklearn-color-unfitted-level-1);\n",
- " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
- " /* fitted */\n",
- " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
- " color: var(--sklearn-color-fitted-level-1);\n",
- "}\n",
- "\n",
- "/* On hover */\n",
- "#sk-container-id-1 a.estimator_doc_link:hover {\n",
- " /* unfitted */\n",
- " background-color: var(--sklearn-color-unfitted-level-3);\n",
- " color: var(--sklearn-color-background);\n",
- " text-decoration: none;\n",
- "}\n",
- "\n",
- "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
- " /* fitted */\n",
- " background-color: var(--sklearn-color-fitted-level-3);\n",
- "}\n",
- "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=0)</pre></div> </div></div></div></div>"
- ],
- "text/plain": [
- "LogisticRegression(random_state=0)"
- ]
- },
- "execution_count": 82,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Trainiere und fitten einer logistisches Regressionsmodell auf das Trainigsset\n",
- "log_model.fit(X_train,y_train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code importiert die classification_report Funktion aus sklearn.metrics, die zur Ausgabe eines Klassifikationsberichts für die Modellleistung verwendet wird, einschließlich Präzision, Recall, F1-Score und Unterstützung für jede Klasse."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.metrics import classification_report"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "train performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.93 0.99 0.96 365\n",
- " 1 0.99 0.93 0.96 380\n",
- "\n",
- " accuracy 0.96 745\n",
- " macro avg 0.96 0.96 0.96 745\n",
- "weighted avg 0.96 0.96 0.96 745\n",
- "\n",
- "test performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.95 0.99 0.97 101\n",
- " 1 0.99 0.94 0.96 86\n",
- "\n",
- " accuracy 0.97 187\n",
- " macro avg 0.97 0.97 0.97 187\n",
- "weighted avg 0.97 0.97 0.97 187\n",
- "\n"
- ]
- }
- ],
- "source": [
- "#Precision= True positive / true positive + false positive \n",
- "#Recall = True positive / true positive + false negative\n",
- "#f1-score = zusammenfassung von der precision und dem recall\n",
- "#accuracy(genauigkeit) liegt bei 0.86 - also 86%\n",
- "print('train performance')\n",
- "print(classification_report(y_train, log_model.predict(X_train)))\n",
- "print('test performance')\n",
- "print(classification_report(y_test, log_model.predict(X_test)))\n",
- "#Bei der logistischen Regression sind die Trainings- und Testleistung sehr ähnlich.\n",
- "# erstellte Modell kann auf neuen Daten gut verallgemeinert werden kann."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 85,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Die Confusion Matrix zeigt eine Zusammenfassung der Vorhersageergebnisse zu dem Klassifizierungsproblem \n",
- "from sklearn.metrics import confusion_matrix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Der Code druckt die Verwechselungsmatrix aus, die die Leistung eines Klassifikationsmodells, insbesondere einer logistischen Regression (log_model), durch den Vergleich der vorhergesagten Werte (log_model.predict(X_test)) mit den tatsächlichen Werten (y_test) zeigt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[100 1]\n",
- " [ 5 81]]\n"
- ]
- }
- ],
- "source": [
- "print(confusion_matrix(y_test, log_model.predict(X_test)))\n",
- "#"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Decision Tree"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from sklearn.tree import DecisionTreeClassifier\n",
- "# overfitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "metadata": {},
- "outputs": [],
- "source": [
- "#tree = DecisionTreeClassifier()\n",
- "#tree.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 89,
- "metadata": {},
- "outputs": [],
- "source": [
- "#confusion_matrix(y_test, tree.predict(X_test)) #true negatives, false positives, false negatives, true positives"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 90,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from sklearn.metrics import classification_report\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 91,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_train, tree.predict(X_train)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 92,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_test, tree.predict(X_test)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Random forest "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "metadata": {},
- "outputs": [],
- "source": [
- "#from sklearn.ensemble import RandomForestClassifier\n",
- "# overfitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {},
- "outputs": [],
- "source": [
- "#rf = RandomForestClassifier()\n",
- "#rf.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "metadata": {},
- "outputs": [],
- "source": [
- "#confusion_matrix(y_test, rf.predict(X_test))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_train, rf.predict(X_train)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [],
- "source": [
- "#print(classification_report(y_test, rf.predict(X_test)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 5.Evaluation "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "\n",
- "Das Unternehmen in der Medizinbranche strebt danach, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) mithilfe verschiedener demografischer, verhaltensbezogener und medizinischer Faktoren zu bestimmen. Durch diese Risikovorhersage sollen rechtzeitig Maßnahmen ergriffen werden, um die Krankheit idealerweise zu verhindern und die langfristige Gesundheit der Patienten zu verbessern."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Deployment",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 6.Deployment "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Deployment",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "\n",
- "Das Unternehmen in der Medizinbranche strebt danach, das Risiko für die Entwicklung einer koronaren Herzkrankheit (KHK) basierend auf verschiedenen demografischen, verhaltensbezogenen und medizinischen Faktoren zu bestimmen. Mit dieser Risikovorhersage können frühzeitige Maßnahmen ergriffen werden, um die Krankheit im besten Fall zu verhindern und die langfristige Gesundheit der Patienten zu verbessern. Die Implementierung dieser Analyse könnte potenziell zur Verbesserung der öffentlichen Gesundheit beitragen, indem sie präventive Strategien fördert und die Behandlung von Risikopersonen priorisiert."
- ]
- }
- ],
- "metadata": {
- "branche": "Medizin",
- "dataSource": "https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset",
- "funktion": "Risikomanagment",
- "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": "3.9.2"
- },
- "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/tree/main/Health/Risk%20prediction%20of%20heart%20disease",
- "skipNotebookInDeployment": false,
- "teaser": "Mit der Vorhersage des Risikos einer koronaren Herzkrankheit können frühzeitig Maßnahmen für den Patienten ergriffen werden, um die spätere Erkrankung im besten Fall zu vermeiden.",
- "title": "Risikovorhersage von Herzkrankheiten"
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
--
GitLab