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+{
+ "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": [
+ {
+ "data": {
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+ " <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
+ " <td>Released</td>\n",
+ " <td>A Plan No One Escapes</td>\n",
+ " <td>Spectre</td>\n",
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+ " <td>Following the death of District Attorney Harve...</td>\n",
+ " <td>112.312950</td>\n",
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+ " <td>7.6</td>\n",
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+ " budget genres \\\n",
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+ "\n",
+ " homepage id \\\n",
+ "0 http://www.avatarmovie.com/ 19995 \n",
+ "1 http://disney.go.com/disneypictures/pirates/ 285 \n",
+ "2 http://www.sonypictures.com/movies/spectre/ 206647 \n",
+ "3 http://www.thedarkknightrises.com/ 49026 \n",
+ "4 http://movies.disney.com/john-carter 49529 \n",
+ "\n",
+ " keywords original_language \\\n",
+ "0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":... en \n",
+ "1 [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na... en \n",
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+ "4 [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":... en \n",
+ "\n",
+ " original_title \\\n",
+ "0 Avatar \n",
+ "1 Pirates of the Caribbean: At World's End \n",
+ "2 Spectre \n",
+ "3 The Dark Knight Rises \n",
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+ "\n",
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+ "0 In the 22nd century, a paraplegic Marine is di... 150.437577 \n",
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+ "3 [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"... ... 165.0 \n",
+ "4 [{\"name\": \"Walt Disney Pictures\", \"id\": 2}] ... 132.0 \n",
+ "\n",
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+ "4 [{\"iso_639_1\": \"en\", \"name\": \"English\"}] Released \n",
+ "\n",
+ " tagline \\\n",
+ "0 Enter the World of Pandora. \n",
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+ "2 A Plan No One Escapes \n",
+ "3 The Legend Ends \n",
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+ " title vote_average vote_count movie_id \\\n",
+ "0 Avatar 7.2 11800 19995 \n",
+ "1 Pirates of the Caribbean: At World's End 6.9 4500 285 \n",
+ "2 Spectre 6.3 4466 206647 \n",
+ "3 The Dark Knight Rises 7.6 9106 49026 \n",
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+ "\n",
+ " cast \\\n",
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+ "[5 rows x 23 columns]"
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+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "original_data.head()"
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+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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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+ "execution_count": 6,
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+ " <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": {
+ "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>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": [
+ "<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",
+ " <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 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": {
+ "image/png": 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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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",
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\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>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>count</th>\n",
+ " <td>1205.000000</td>\n",
+ " <td>1205</td>\n",
+ " <td>1.205000e+03</td>\n",
+ " <td>1205</td>\n",
+ " <td>1205.000000</td>\n",
+ " <td>1205</td>\n",
+ " <td>1205</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
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+ " <td>NaN</td>\n",
+ " <td>1204</td>\n",
+ " <td>NaN</td>\n",
+ " <td>659</td>\n",
+ " <td>132</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>NaN</td>\n",
+ " <td>Universal Pictures</td>\n",
+ " <td>NaN</td>\n",
+ " <td>The Host</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Matt Damon</td>\n",
+ " <td>Comedy,Drama</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>NaN</td>\n",
+ " <td>91</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2</td>\n",
+ " <td>NaN</td>\n",
+ " <td>14</td>\n",
+ " <td>73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
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+ " <td>NaN</td>\n",
+ " <td>1.622383e+08</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1383.145228</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>29.569232</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.255582e+08</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1693.870514</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.132878</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>NaN</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>13.707843</td>\n",
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+ " <td>277.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>27.082182</td>\n",
+ " <td>NaN</td>\n",
+ " <td>7.210861e+07</td>\n",
+ " <td>NaN</td>\n",
+ " <td>705.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>46.630062</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.034276e+08</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1798.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>167.932870</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.405404e+09</td>\n",
+ " <td>NaN</td>\n",
+ " <td>13752.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "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",
+ "mean 35.267109 NaN 1.622383e+08 NaN \n",
+ "std 29.569232 NaN 2.255582e+08 NaN \n",
+ "min 0.132878 NaN 0.000000e+00 NaN \n",
+ "25% 13.707843 NaN 1.525000e+07 NaN \n",
+ "50% 27.082182 NaN 7.210861e+07 NaN \n",
+ "75% 46.630062 NaN 2.034276e+08 NaN \n",
+ "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",
+ " }\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>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",
+ " </tr>\n",
+ " </tbody>\n",
+ "</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": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>popularity</th>\n",
+ " <th>revenue</th>\n",
+ " <th>title</th>\n",
+ " <th>vote_count</th>\n",
+ " <th>new_genres</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1205.000000</td>\n",
+ " <td>1.205000e+03</td>\n",
+ " <td>1205</td>\n",
+ " <td>1205.000000</td>\n",
+ " <td>1205</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1204</td>\n",
+ " <td>NaN</td>\n",
+ " <td>132</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>The Host</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Comedy,Drama</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2</td>\n",
+ " <td>NaN</td>\n",
+ " <td>73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>35.267109</td>\n",
+ " <td>1.622383e+08</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1383.145228</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>29.569232</td>\n",
+ " <td>2.255582e+08</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1693.870514</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.132878</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>NaN</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>13.707843</td>\n",
+ " <td>1.525000e+07</td>\n",
+ " <td>NaN</td>\n",
+ " <td>277.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>27.082182</td>\n",
+ " <td>7.210861e+07</td>\n",
+ " <td>NaN</td>\n",
+ " <td>705.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <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",
+ "</table>\n",
+ "</div>"
+ ],
+ "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": {
+ "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",
+ " <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": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1152x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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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
+}