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@@ -1,3991 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Business",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 1. Business Understanding\n",
- "\n",
- "Uber Technologies Inc. ist ein 2009 gegründetes Unternehmen mit Sitz in San Francisco. Es hat 91 Millionen aktive Nutzer und 3,9 Millionen Fahrer weltweit. Das Unternehmen ist mit seiner App in insgesamt 63 Ländern vertreten.\n",
- "Es muss ein grundlegendes Verständnis für die Ziele aus der Unternehmensperspektive vorhanden sein. Darauf aufbauend können dann die entsprechenden Anforderungen an ein Data-Mining-Projekt definiert werden, damit diese realisiert werden können. Uber versucht, Prognosen auf Basis von Angebot und Nachfrage so zu optimieren, dass eine Verfügbarkeit von Fahrzeugen stets gewährleistet ist, um den Service für seine Nutzer aufrechtzuerhalten. Dieser Datensatz soll die Realisierbarkeit dieses Ziels demonstrieren."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 2. Data Understanding\n",
- "\n",
- "Der Datensatz besteht aus vier Basisvariablen: Abfertigungsbasisnummer, Datum, active_vehicles und Fahrten.\n",
- "Die Variable \"dispatching base number\" ist ein vom TLC zugewiesener Code, der eine Uber-Basis in New York City angibt. Dementsprechend können die Codes den folgenden Basen zugewiesen werden: B02512: Unter, B02598: Hinter, B02617: Neben, B02682: Taste, B02764: Nach-NY, B02765: Grun, B02835: Dreist, B02836: Inside.\n",
- "Durch einen ersten Überblick über die verfügbaren Daten kann man bereits über mögliche Abhängigkeiten zwischen der Anzahl der aktiven Fahrzeuge, der Anzahl der Fahrten sowie dem Datum spekulieren."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.1 Import von relevanten Modulen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "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",
- "import seaborn as sns\n",
- "import datetime\n",
- "import math\n",
- "import matplotlib.dates as mdates\n",
- "from matplotlib.dates import DateFormatter\n",
- "sns.set()\n",
- "from sklearn.linear_model import LinearRegression"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 2.2 Daten einlesen"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pandas.plotting import register_matplotlib_converters\n",
- "register_matplotlib_converters()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data =pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Forecast_of_required_vehicles_in_the_city_center_data.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\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>dispatching_base_number</th>\n",
- " <th>date</th>\n",
- " <th>active_vehicles</th>\n",
- " <th>trips</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>B02512</td>\n",
- " <td>1/1/2015</td>\n",
- " <td>190</td>\n",
- " <td>1132</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>B02765</td>\n",
- " <td>1/1/2015</td>\n",
- " <td>225</td>\n",
- " <td>1765</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>B02764</td>\n",
- " <td>1/1/2015</td>\n",
- " <td>3427</td>\n",
- " <td>29421</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>B02682</td>\n",
- " <td>1/1/2015</td>\n",
- " <td>945</td>\n",
- " <td>7679</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>B02617</td>\n",
- " <td>1/1/2015</td>\n",
- " <td>1228</td>\n",
- " <td>9537</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " dispatching_base_number date active_vehicles trips\n",
- "0 B02512 1/1/2015 190 1132\n",
- "1 B02765 1/1/2015 225 1765\n",
- "2 B02764 1/1/2015 3427 29421\n",
- "3 B02682 1/1/2015 945 7679\n",
- "4 B02617 1/1/2015 1228 9537"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Output record head\n",
- "raw_data.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Diese Tabelle enthält Daten über Fahrdienstunternehmen, ihre aktiven Fahrzeuge und die Anzahl der durchgeführten Fahrten an einem bestimmten Datum"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\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>dispatching_base_number</th>\n",
- " <th>date</th>\n",
- " <th>active_vehicles</th>\n",
- " <th>trips</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>354</td>\n",
- " <td>354</td>\n",
- " <td>354.000000</td>\n",
- " <td>354.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>unique</th>\n",
- " <td>6</td>\n",
- " <td>59</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>top</th>\n",
- " <td>B02598</td>\n",
- " <td>1/27/2015</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>freq</th>\n",
- " <td>59</td>\n",
- " <td>6</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1307.435028</td>\n",
- " <td>11667.316384</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1162.510626</td>\n",
- " <td>10648.284865</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>112.000000</td>\n",
- " <td>629.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>296.750000</td>\n",
- " <td>2408.500000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1077.000000</td>\n",
- " <td>9601.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1417.000000</td>\n",
- " <td>13711.250000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4395.000000</td>\n",
- " <td>45858.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " dispatching_base_number date active_vehicles trips\n",
- "count 354 354 354.000000 354.000000\n",
- "unique 6 59 NaN NaN\n",
- "top B02598 1/27/2015 NaN NaN\n",
- "freq 59 6 NaN NaN\n",
- "mean NaN NaN 1307.435028 11667.316384\n",
- "std NaN NaN 1162.510626 10648.284865\n",
- "min NaN NaN 112.000000 629.000000\n",
- "25% NaN NaN 296.750000 2408.500000\n",
- "50% NaN NaN 1077.000000 9601.000000\n",
- "75% NaN NaN 1417.000000 13711.250000\n",
- "max NaN NaN 4395.000000 45858.000000"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#data description\n",
- "raw_data.describe(include='all')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Zusammengefasst geben diese Statistiken einen Überblick über die Verteilung der Anzahl der aktiven Fahrzeuge und durchgeführten Fahrten in den Daten."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 2.3 Datenbereinigung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "In diesem Absatz wird eine Darstellung eines Datensatzes in einem Pandas DataFrame beschrieben, der Informationen über Reisen und aktiven Fahrzeuge enthält. Hierbei wurde die Seaborn-Bibliothek verwendet, um die Verteilung von Reisen und aktiven Fahrzeugen graphisch zu modellieren. Der 95-Prozent-Quantil der aktiven Fahrzeuge-Spalte wurde berechnet und in der Variabel \\\"quant1\\\" gespeichert, um die Verteilung der aktiven Fahrzeuge mit der Verteilung von Reisen zu vergleichen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dispatching_base_number 0\n",
- "date 0\n",
- "active_vehicles 0\n",
- "trips 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#check null values\n",
- "raw_data.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "raw_data.duplicated().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "editable": true,
- "scrolled": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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='active_vehicles', ylabel='Density'>"
- ]
- },
- "execution_count": 8,
- "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": [
- "# View data distributions for active vehicles\n",
- "sns.distplot(raw_data['active_vehicles'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "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='trips', ylabel='Density'>"
- ]
- },
- "execution_count": 9,
- "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": [
- "# View data distributions for trips\n",
- "sns.distplot(raw_data['trips'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "3904.7499999999986"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Here a 95% quantile is generated for active vehicles & trips\n",
- "quant1= raw_data['active_vehicles'].quantile(0.95)\n",
- "quant1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "36025.34999999999"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "quant1= raw_data['trips'].quantile(0.95)\n",
- "quant1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "#quantile containment for active_vehicles\n",
- "data1= raw_data[raw_data['active_vehicles']<quant1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Quantile containment for trips\n",
- "\n",
- "data1=raw_data[raw_data['trips']<quant1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>dispatching_base_number</th>\n",
- " <th>date</th>\n",
- " <th>active_vehicles</th>\n",
- " <th>trips</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>336</td>\n",
- " <td>336</td>\n",
- " <td>336.000000</td>\n",
- " <td>336.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>unique</th>\n",
- " <td>6</td>\n",
- " <td>59</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>top</th>\n",
- " <td>B02598</td>\n",
- " <td>1/27/2015</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>freq</th>\n",
- " <td>59</td>\n",
- " <td>6</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1163.815476</td>\n",
- " <td>10141.514881</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1006.895472</td>\n",
- " <td>8548.456980</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>112.000000</td>\n",
- " <td>629.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>288.250000</td>\n",
- " <td>2265.750000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1056.500000</td>\n",
- " <td>9467.500000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1368.250000</td>\n",
- " <td>12707.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4093.000000</td>\n",
- " <td>35990.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " dispatching_base_number date active_vehicles trips\n",
- "count 336 336 336.000000 336.000000\n",
- "unique 6 59 NaN NaN\n",
- "top B02598 1/27/2015 NaN NaN\n",
- "freq 59 6 NaN NaN\n",
- "mean NaN NaN 1163.815476 10141.514881\n",
- "std NaN NaN 1006.895472 8548.456980\n",
- "min NaN NaN 112.000000 629.000000\n",
- "25% NaN NaN 288.250000 2265.750000\n",
- "50% NaN NaN 1056.500000 9467.500000\n",
- "75% NaN NaN 1368.250000 12707.000000\n",
- "max NaN NaN 4093.000000 35990.000000"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "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='active_vehicles', ylabel='Density'>"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#Displot from the quantile is generated & the irregular distribution is still noticed\n",
- "sns.distplot(data1['active_vehicles'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "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='active_vehicles', ylabel='Density'>"
- ]
- },
- "execution_count": 16,
- "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": [
- "#Next step is to narrow down the values to 4000(active vehicles).\n",
- "#On the left side you can see the main distribution of the dataset\n",
- "data2= data1[data1['active_vehicles']<=4000]\n",
- "data2= data1[data1['active_vehicles']>=600]\n",
- "plt.xlim(0,4000)\n",
- "sns.distplot(data2['active_vehicles'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "#here a quantile is created for number of trips\n",
- "#data2= data1[data1['trips']<quant1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "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='trips', ylabel='Density'>"
- ]
- },
- "execution_count": 18,
- "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(data2['trips'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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='trips', ylabel='Density'>"
- ]
- },
- "execution_count": 19,
- "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": [
- "#Define value ranges\n",
- "data2= data1[data1['trips']<=20000]\n",
- "data2= data1[data1['trips']>=2000]\n",
- "plt.xlim(1000,17000)\n",
- "sns.distplot(data2['trips'])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Nachdem die Datensatzreinigungsverfahren abgeschlossen wurden, liegen die Werte im Datensatz in der Regel zwischen 2000 und 20000. Allerdings sind auch Werte im Bereich von 3000 bis über 4000 für eine möglichst repräsentative Modellierung in das Modell aufzunehmen"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 3. Data Preparation"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenvorbereitung"
- },
- "tags": []
- },
- "source": [
- "Zur Datenbereinigung wurden mehrere Schritte ausgeführt. Erstens wurde das Feld 'date' im Datenframe 'data2' mithilfe der Funktion to_datetime nach Daten-Datentyp konvertiert. Danach wurden das Feld 'dispatching_base_number' in den String-Datentyp umgewandelt und die Funktionen 'describe', 'reset_index' und 'drop' verwendet, um die Daten zu vereinheitlichen. Dadurch konnten die Daten anschließend in einem Diagramm dargestellt werden, um sie visuell zu analysieren."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
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- " vertical-align: middle;\n",
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>dispatching_base_number</th>\n",
- " <th>date</th>\n",
- " <th>active_vehicles</th>\n",
- " <th>trips</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>266</td>\n",
- " <td>266</td>\n",
- " <td>266.000000</td>\n",
- " <td>266.000000</td>\n",
- " </tr>\n",
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- " <th>unique</th>\n",
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- " <th>25%</th>\n",
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- " dispatching_base_number date active_vehicles trips\n",
- "count 266 266 266.000000 266.000000\n",
- "unique 6 59 NaN NaN\n",
- "top B02598 2/5/2015 NaN NaN\n",
- "freq 59 6 NaN NaN\n",
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- "min NaN NaN 236.000000 2016.000000\n",
- "25% NaN NaN 925.500000 7620.500000\n",
- "50% NaN NaN 1181.000000 10727.000000\n",
- "75% NaN NaN 1439.500000 13842.500000\n",
- "max NaN NaN 4093.000000 35990.000000"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_cleaned=pd.to_datetime(data2['date'])\n",
- "data_cleaned=str(data2['dispatching_base_number'].astype(str))\n",
- "#data_cleaned= data2[\"dispatching_base_number\"].astype(str)\n",
- "data_cleaned = data2.reset_index(drop=True)\n",
- "data_cleaned.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x11ed7d310>"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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CU8ZaCggmEl1aozWvkhZIUhBCTCmRqPqZaNNhh0OSghBiShlN1U+gAXWTIQn4ki6pQogpJdwurZGeVkPrJCkIIaaU4eMSdARfEjOcuZEmA6k+EkJMOZ6qn1BWNpvM3U/9kZKCEEKMYCrMdzSclBSEEFOGb4PxjwszyLgqecRjJnP3U39CKik888wzWK1WrFYrO3bsAGD9+vXk5uaydOlSli5dyoEDBwBoaWmhuLiYvLw8NmzYgNN5aSh4W1sbJSUl5Ofns3btWvr6+gDo7u5m9erVFBQUUFJSgt1uj8Z1CiGmOH8Nxs/84aOgDcbhzI00GQRNCocPH+bdd9/ljTfewGaz0dzczIEDBzh+/Dj79u2jpqaGmpoaFi1aBEBpaSmbNm1i//79KIpCdXU1AFu2bGH58uU0NDQwf/58qqqqANi9ezcWi4X6+nqWLVvG1q1bo3i5Qoipyl+D8aDDFVKDcVZGGk/dv5Dnym7nqfsXTtqEACEkBZPJRFlZGfHx8RiNRtLT02lra6OtrY3y8nKKioqorKzE7XZz7tw5BgYGyMzMBKC4uJiGhgYcDgdHjhwhLy/PazvAwYMHKSoqAqCwsJB33nkHh8MRpcsVQkxVU63BeLSCJoVrr71Wfci3trZSX1/PD3/4Q2655Ra2bdtGdXU1R48e5dVXX+XChQuYTCb1WJPJRHt7O11dXSQmJmIwGLy2A17HGAwGEhMTuXjxYqSvUwgxxU21BuPRCrmh+ZNPPmHNmjU89thjXHPNNezZs0f9bMWKFdhsNtLT09HpdOp2RVHQ6XTqz+F8fx9+TFxc6J2iUlMTQ953PJhMSbEOYURajk/LsYG249NybKCN+H5cmMEzf/iIQYdL3ZZg1PPjwgxNxBfIeMcWUlJoampi3bp1lJeXY7VaOXnyJK2trWp1kKIoGAwG0tLSvBqKOzo6MJvNpKSk0NPTg8vlQq/XY7fbMZvNAJjNZjo6OkhLS8PpdNLX10dycnLIF9DZ2YvbrYRxydETSp/nWNJyfFqODbQdn5ZjA+3El3FVMivzr/Pb+0gL8fkTjXsXF6cb8WU66Cv5559/zgMPPEBFRQVWqxW4lAS2bdvGl19+icPh4JVXXmHRokXMnj2bhIQEmpqaAKipqSE7Oxuj0YjFYqGurg4Am81GdnY2ADk5OdhsNgDq6uqwWCwYjcYxXbQQQvjj22B8601zYh2S5ugURRnxNfuJJ57gtdde46qrrlK33XXXXbjdbl566SWcTie5ubn8/Oc/B+DEiRNs3LiR3t5eMjIy2L59O/Hx8Zw7d46ysjI6OzuZNWsWO3fu5Bvf+AZffPEFZWVlnDlzhqSkJCoqKvj2t78d8gVISSF0Wo5Py7GBtuPTcmyg7fi0HBvEpqQQNClonSSF0Gk5Pi3HBtqOT8uxgbbj03JsoNHqIyGEEFOHJAUhhBAqSQpCCCFUkhSEEEKoJCkIIYRQydTZQoiICLSOsZhYpKQghBizqbaO8WQmSUEIMWZTbR3jyUySghBizGRa6slDkoIQYsxkWurJQ5KCEGLMinPSiTd4P04m8zrGk5n0PhJCjJmnl5H0Ppr4JCkIISIiKyNNksAkINVHQgghVJIUhBBCqKT6SAgxqcjI6rGRpCCEmDQ8I6s9A+k8I6sBSQwhkuojIcSkISOrx05KCkKIkGm9akZGVo+dlBSEECGZCJPeycjqsQspKTzzzDNYrVasVis7duwA4PDhwxQVFZGbm8uuXbvUfVtaWiguLiYvL48NGzbgdDoBaGtro6SkhPz8fNauXUtfXx8A3d3drF69moKCAkpKSrDb7ZG+RiFEBEyEqhkZWT12QZPC4cOHeffdd3njjTew2Ww0NzdTW1tLeXk5VVVV1NXVcfz4cQ4dOgRAaWkpmzZtYv/+/SiKQnV1NQBbtmxh+fLlNDQ0MH/+fKqqqgDYvXs3FouF+vp6li1bxtatW6N4uUKI0RpN1Uxj83lWPfEnVj35H5RWvRewVNHYfJ7SqveC7hdMVkYa9xTMVUsGqTMTuKdgrqaquLQuaFIwmUyUlZURHx+P0WgkPT2d1tZWrr76aubMmYPBYKCoqIiGhgbOnTvHwMAAmZmZABQXF9PQ0IDD4eDIkSPk5eV5bQc4ePAgRUVFABQWFvLOO+/gcDiidLlCiNEKt2rGU91k7+oHAlc3RbpaKisjjafuX8hzZbfz1P0LJSGEKWhSuPbaa9WHfGtrK/X19eh0Okwmk7qP2Wymvb2dCxcueG03mUy0t7fT1dVFYmIiBoPBazvgdYzBYCAxMZGLFy9G7AKFEJERbtVMqNVNE6FaaioJuffRJ598wpo1a3jsscfQ6/W0traqnymKgk6nw+12o9PpLtvu+Tmc7+/Dj4mLC739OzU1MeR9x4PJlBTrEEak5fi0HBtoO77xiG3JrUnMTJrG3voWOrr6+eYV01lZMI9bb5rjd/+LAaqVLnYPesUb6n7RouW/K4x/fCElhaamJtatW0d5eTlWq5X333/fq0HYbrdjNptJS0vz2t7R0YHZbCYlJYWenh5cLhd6vV7dHy6VMjo6OkhLS8PpdNLX10dycnLIF9DZ2YvbrYS8fzSZTEnY7T2xDiMgLcen5dhA2/GNZ2wZVyXzyzVZXtsCfXfKzAS/7Q0pMxO8jgl1v2jQ8t8VohNfXJxuxJfpoK/kn3/+OQ888AAVFRVYrVYAbrzxRk6fPs1nn32Gy+WitraW7OxsZs+eTUJCAk1NTQDU1NSQnZ2N0WjEYrFQV1cHgM1mIzs7G4CcnBxsNhsAdXV1WCwWjEbjmC5aCBF7oVY3SY8hbdEpijLia/YTTzzBa6+9xlVXXaVuu+uuu/i7v/s7tm/fzuDgIDk5Oaxfvx6dTseJEyfYuHEjvb29ZGRksH37duLj4zl37hxlZWV0dnYya9Ysdu7cyTe+8Q2++OILysrKOHPmDElJSVRUVPDtb3875AuQkkLotByflmMDbcen5dgam89je/c09q7+EQe7xWpQnJbvHcSmpBA0KWidJIXQaTk+LccG2o5Py7GBtuPTcmyg0eojIYQQU4ckBSGEECpJCkIIIVSSFIQQQqgkKQghhFBJUhBCCKGSpCCEEEIlSUEIIYRKkoIQQgiVrNEsYmb41AamK6Zzxw++4zW1gdbXAxZiMpKkIGLCs7CKZx59e1c/v60/AVxaJOXF/Sd4+4M2dX/Pwiuez4UQ0SHVRyImRlpYpbH5vFdC8P1cCBE9UlIQ42Z4dVAgnd2DIz74RzpWCDF2khTEuPCtLgpkxjT9iA/+QOsBCyEiQ5KCiArfRuKBIWfQhAAw6HAzY5qevgGX389l4RUhokuSgog431JBOFU+TpeCy+XCoNfhdHmvk3Hbgm9JI7MQUSZJQUScv0bkcCiA4lZInG6gt98p3VGFGEeSFETERaIx2KVAglFP5cPZEYhICBEq6ZIqIi5SjcHS00iI8SclBRERwxuWZ0zT+20T8Gek/RKny3+eQoy3kEoKvb29FBYWcvbsWQDWr19Pbm4uS5cuZenSpRw4cACAlpYWiouLycvLY8OGDTidTgDa2tooKSkhPz+ftWvX0tfXB0B3dzerV6+moKCAkpIS7HZ7NK5RRJmnYdnzZt834EJxK+gC7B/3Px+kzkzgJ4vnMWOa3u9+ihI8qQghIitoUvjoo4+4++67aW1tVbcdP36cffv2UVNTQ01NDYsWLQKgtLSUTZs2sX//fhRFobq6GoAtW7awfPlyGhoamD9/PlVVVQDs3r0bi8VCfX09y5YtY+vWrVG4RBFt/hqWXQrMmG4g3uD9n1i8IY7/U3g9z5XdzlP3LyQrIy1g99NA24UQ0RM0KVRXV7N582bMZjMA/f39tLW1UV5eTlFREZWVlbjdbs6dO8fAwACZmZkAFBcX09DQgMPh4MiRI+Tl5XltBzh48CBFRUUAFBYW8s477+BwOKJxnSKKAtX99/Y7uadgrtrGkDozgXsK5gJQWvUeq578D9b+29sBzysD1YQYf0ErbX3f3js6OrjlllvYvHkzSUlJrFmzhldffZVrr70Wk8mk7mcymWhvb6erq4vExEQMBoPXdoALFy6oxxgMBhITE7l48SJXXnllxC5QRFdj83nidOD2U9OTOjOBrIw0tStpY/N5Xj5w0qsEMOgIXEU06HDR2HxeuqIKMY7CbsmbM2cOe/bsUX9fsWIFNpuN9PR0dLqva5EVRUGn06k/h/P9ffgxcXHhdYhKTU0Ma/9oM5mSYh3CiCIR38GmM+ytb8He1R9wnwSjnu9npPGL/9tIR1c/if/LyFcDTlz+skcAvf1O9jacZGbSNG69ac6Y4x4rLf9ttRwbaDs+LccG4x9f2Enh5MmTtLa2qtVBiqJgMBhIS0vzaiju6OjAbDaTkpJCT08PLpcLvV6P3W5Xq6LMZjMdHR2kpaXhdDrp6+sjOTk5rHg6O3txh/GgiSaTKQm7vSfWYQQUifhCmcMoTgf/e/6V/PnIGXW/nq9GVy046HDxQm0zGVclj+r4SNHy31bLsYG249NybBCd+OLidCO+TIc9TkFRFLZt28aXX36Jw+HglVdeYdGiRcyePZuEhASampoAqKmpITs7G6PRiMVioa6uDgCbzUZ29qUBSTk5OdhsNgDq6uqwWCwYjcZwQxLjKJTRym4Fjpy4MKZRzcPJeAUhxk/YJYW5c+eyevVq7r77bpxOJ7m5uRQWFgJQUVHBxo0b6e3tJSMjg5UrVwKwefNmysrKePbZZ5k1axY7d+4E4OGHH6asrAyr1UpSUhIVFRURvDQRDaE8oD3TU0SKNDgLMX50ygTvDC7VR6GLRHylVe+N+5v7vUXXx7yxWct/Wy3HBtqOT8uxwQSpPhJTW3FO+mVjD6Ip3qCLeUIQYiqReQRESHynsYg3fj2D6Q3pqRz6sM1vt9Sx0OvgnoJ5kT2pEGJEkhREUL49jvoGXMQb4ri36HoAflt/ImIJIXVmgrowj0yXLcT4k6QggvLX42jI6VbXUo5ULyOdDp66f2FEziWEGB1pUxBBBWpY7uwejGyjs3KpVCKEiB0pKQjg8jWVPVU3Iz2kdTpISUqIWGJQuFQqkSojIWJHkoLwu6byb+tP8N9nv+C9vwROCooCN6Sn8vYHbRGLRQaqCRFbkhREwDaDUHoUHTvVGdFYZKCaELElbQoi4Nt5sIQQb9BF/M2+OCc9oucTQoRHSgpTjL+2A0830HDpdIz62EACtScEavMQQkSWlBSmkINNZ7yWzfS0HdyQnjqqUcqDDmVc2gB8l/v0xC09lYSIPEkKU8j/s/3Fb9vBkRMXuKdgbsC1kmMt2DgJIUTkSPXRFNDYfJ7f/fmvAWcu7e138t9nv8DhjO3EgoEamUcaJyGEiCwpKUxynqqXYFNZH/qwLWIjk0cjwagP2MgcKFlITyUhIk+SwiQXyqI4ELynUaTNmKZXH+qpMxN4cNmNARuO/c3MGm+Ik55KQkSBVB9NclqtYtHpdF49iEaaN96zj/Q+EiL6JClMcpHuMhopvf1Oflt/AgjcDXW4rIw0SQJCjAOpPprkxntRnHBIDyIhtEdKCpOcb9WL1mgxJiGmMkkKk5DvKmk6nS5o76NYkR5EQmhLSPUKvb29FBYWcvbsWQAOHz5MUVERubm57Nq1S92vpaWF4uJi8vLy2LBhA07npQdRW1sbJSUl5Ofns3btWvr6+gDo7u5m9erVFBQUUFJSgt1uj/T1TTm+o3/7BlwjJoQ43fjElTjdID2IhJgAgiaFjz76iLvvvpvW1lYABgYGKC8vp6qqirq6Oo4fP86hQ4cAKC0tZdOmTezfvx9FUaiurgZgy5YtLF++nIaGBubPn09VVRUAu3fvxmKxUF9fz7Jly9i6dWuULnPqePnAybDGG4xHV9R4Qxx3/+M/cE/BXK9uqPcUzJXGYyE0JmhSqK6uZvPmzZjNZgCOHTvG1VdfzZw5czAYDBQVFdHQ0MC5c+cYGBggMzMTgOLiYhoaGnA4HBw5coS8vDyv7QAHDx6kqKgIgMLCQt555x0cDkc0rnNKaGw+T9+AK9ZhXGbhd9PU3kNP3b+Q58pu56n7F0pCEEKDgrYp+L69X7hwAZPJpP5uNptpb2+/bLvJZKK9vZ2uri4SExMxGAxe233PZTAYSExM5OLFi1x55ZVjv7IpSKs9eSK95oIQInrCbmh2u93odF9XRCuKgk6nC7jd83M439+HHxMXF173ydTUxLD2jzaTKWlcvudg0xn21rfQ0dXPN6+YzsqCeVyMYU+euDgd7gB1URe7B0O6L+N170ZLy/FpOTbQdnxajg3GP76wk0JaWppXg7DdbsdsNl+2vaOjA7PZTEpKCj09PbhcLvR6vbo/XCpldHR0kJaWhtPppK+vj+Tk5LDi6ezsDfgwGm8jjcqNJN/lM+1d/fx79YfEG3UMOsb/XtxbdD0Av3rzY7+fp8xMCHpfxuvejZaW49NybKDt+LQcG0Qnvrg43Ygv02GParrxxhs5ffo0n332GS6Xi9raWrKzs5k9ezYJCQk0NTUBUFNTQ3Z2NkajEYvFQl1dHQA2m43s7GwAcnJysNlsANTV1WGxWDAajeGGNOUEmkp6KAYJAb4ebXzbgm9d9pn0MBJiYgk7KSQkJPDkk0/y0EMPsXjxYq655hry8/MBqKioYPv27eTn5/PVV1+xcuVKADZv3kx1dTWLFy/m6NGj/OxnPwPg4Ycf5sMPP8RqtfLyyy+zadOmyF3ZJBZowFckU8JtC74V9kjoFXlzubfoeulhJMQEplMURRt1L6M0FauPSqvei/pI4OfKbvcaBBdInA5+/Yvbx/x9U7EYHylajg20HZ+WY4PYVB/JiOYJqDgn3atNAS5V00RqPQTPm/7wSehe3H+Ctz9ou2zfnMzLq4yEEBOXJIUJKNBU0pGa3+iG9FS1NOI594q8ucClxXjcyqUSQk7mt9TtQojJQZLCBOU7lXRj8/mIzG807+pk3vvLebXU0dk9qE5xvSJvriQBISY5bc6pLMLi6aI66Bj9aOZ4g457i67nQle/355NWh0YJ4SILCkpTAKhLrnpj04HPy28Xi11BBpr0Nk9eFmVkvQqEmLykaSgMY3N53n5wEl1DqPE6Qbu/sd/GPEBPJZ2BEWB39R+zK/e/JjUmQkkTjcErIbyfM/wKiVJDEJMLpIUYmx4t8/E6Qb6+p1e4w16+5386s2vH9r+3tDjdGOb7dRzbGf3IHrdpdJDsI7KniolSQpCTC7SphBDvmsf9PokBF+d3YP86s2PeWj3IRqbz6vbIzlMw6UETwjD4xFCTC6SFGJotG0BfQMufvXmx6z9t7dpbD4fs9XLZNU0ISYfqT6KobG+aQ86FH795sfEG/URiih0MqeREJOTlBRiKBJv2gqMqStqqBKnG2ROIyGmACkpxJC/6SoMeh2KW8GljemcVMF6QAkhJgcpKcRQVkbaZesW//CGWej1kf2zxOkurXmQ4FPNpNddKgF4vjtYrEKIyU9KClE2vMvp8C6lw7fPmKYncbqBzu5BdW4hXwlG/airidzKpYf6zKRpvFDbHHAAWqDZV6VBWYipQ5JCFPmukOYZ9PXfZ7/wml/IM1ANAncvHXS4uLfoen7357+GPceR56F+601zyLgqOeB+gWZflQZlIaYOSQpRFGiFtEClgZGkzkzwmgTPN+F4+Bt4dkN6akjfEWj2Vak6EmLqkKQQRYG6nIabEAK9rccbL19DId5w+TrN7/3lPH//7WSW3Bp8AXDf2VeFEFOLJIUoSp2ZMOaxCJ63dfi6zn/GND2DDjdOP12UfBMCfD0lxZJbrx1TLEKIyU+SQgQdbDrj1ZB7Q3qq39XKQpU6M4Gn7l94WVXR8DaIUMmUFEKIUEhSiJDG5vPsbTip9hDq7B7kvb+cD3LUyDxtAWOZGttDehAJIUIxpqSwYsUKLl68iMFw6TSPP/44fX19bN++ncHBQQoKCnjkkUcAaGlpYcOGDfT19WGxWNiyZQsGg4G2tjZKS0vp7OzkO9/5DhUVFcyYMWPsVzbOXj906rIuo0NO95hmMH37gzaOneoc81u+9CASQoRq1KOkFEWhtbWVmpoa9X/XXXcd5eXlVFVVUVdXx/Hjxzl06BAApaWlbNq0if3796MoCtXV1QBs2bKF5cuX09DQwPz586mqqorMlY2zcBqV9bpLI5fHct5gZky7NFBNpqQQQoRj1Enh008/BWDVqlUsWbKEffv2cezYMa6++mrmzJmDwWCgqKiIhoYGzp07x8DAAJmZmQAUFxfT0NCAw+HgyJEj5OXleW2fiDwP4VBkZ36Lnyyep1bpxIWWH7zodRDosBnT9Pz7z3J4rux2nrp/oSQEIUTIRl191N3dTVZWFv/yL/+Cw+Fg5cqV/PSnP8VkMqn7mM1m2tvbuXDhgtd2k8lEe3s7XV1dJCYmqtVPnu0TTWPzeQYdodf5HzlxgRV5c4OOOQjEsxob4Hew2fJF14URvRBCfG3USWHBggUsWLBA/f3OO++ksrKSm266Sd2mKAo6nQ63241Op7tsu+fncL6/B5OamjjKK4gc27uNfruHBtLb78Rk+nrMwJJbk5iZNI299S3Yu/qDHj99mlHtXuo5rqOrn29eMZ2VBfO49aY5AY8d/r1ao+XYQNvxaTk20HZ8Wo4Nxj++USeFo0eP4nA4yMrKAi496GfPno3dblf3sdvtmM1m0tLSvLZ3dHRgNptJSUmhp6cHl8uFXq9X9w9HZ2cv7kguPTYKoTzIff3x4Cde1ToZVyXzyzVZIZUa7F392O09Xsd5ff4/n/kymZICfhZrWo4NtB2flmMDbcen5dggOvHFxelGfJkedZtCT08PO3bsYHBwkN7eXt544w0effRRTp8+zWeffYbL5aK2tpbs7Gxmz55NQkICTU1NANTU1JCdnY3RaMRisVBXVweAzWYjOzt7tCHFzGi6e7584KTf7b4zp0bq+4QQIhSjLincdtttfPTRR9xxxx243W6WL1/OggULePLJJ3nooYcYHBwkJyeH/Px8ACoqKti4cSO9vb1kZGSwcuVKADZv3kxZWRnPPvsss2bNYufOnZG5snHkbyK5YEYagOaZasJfqUG6lwohokmnKKEu065Nsag+8jcdNsDv3/qEnq8cIZ/nubLbR/Vdo+1NpOWispZjA23Hp+XYQNvxaTk2iE31kYxoDsL3oXxDeqrXtNee6bDvKZjLy/+6mD8e/MRr/97+Ib/zEXkWtwlGJqgTQownSQp+DE8Ew3V2D/qdy2j4hHO+D/HG5vM8X9fi1TvJoNepXUqFEEJLJCn8j0CJIFSBjpM1CoQQE4kkBcIfPObPSD2CpApICDFRRHaF+AlqrLOQSo8gIcRkIUmBsa81sPC7UhIQQkwOUz4pNDafDzixXKiOneqMSCxCCBFrU7JNYayNyr5kVTMhxGQx5ZJCY/N5flPbgjuCY/Zk2gkhxGQx5aqP9jacjGhCkEZmIcRkMuVKCr5LZoZixjQ9Op2O3n4nidMNKIpC34BLxhwIISadKZcUwnVv0fXy0BdCTBlTrvooXJIQhBBTiSSFEYSz7rIQQkwGUy4p3LbgWyHvK2sdCyGmminXprAiby6A39lOPfRxOlZZ50nVkRBiyplySQEuJQZPchBCCPG1KVd9JIQQIjBJCkIIIVSSFIQQQqgkKQghhFBN+IbmuLixTnwdWVqLx5eW49NybKDt+LQcG2g7Pi3HBpGPL9j5dIoSwdnhhBBCTGhSfSSEEEIlSUEIIYRKkoIQQgiVJAUhhBAqSQpCCCFUkhSEEEKoJCkIIYRQSVIQQgihkqQghBBCJUkhTCtWrMBqtbJ06VKWLl3KRx99xOHDhykqKiI3N5ddu3ap+7a0tFBcXExeXh4bNmzA6XRGJabe3l4KCws5e/YsQNjxtLW1UVJSQn5+PmvXrqWvry9qsa1fv57c3Fz1/h04cCBmsT3zzDNYrVasVis7duwAtHPv/MWmpXv39NNPs3jxYqxWK88//zygnXvnLzYt3TuPX/7yl5SVlQHauXcAKCJkbrdb+cEPfqA4HA51W39/v5KTk6P87W9/UxwOh7Jq1Srl4MGDiqIoitVqVT744ANFURRl/fr1yksvvRTxmD788EOlsLBQycjIUM6cOTOqeFavXq3U1tYqiqIozzzzjLJjx46oxKYoilJYWKi0t7dftu94x/bee+8p//zP/6wMDg4qQ0NDysqVK5U333xTE/fOX2x/+tOfNHPv/uu//ku56667FIfDofT39yu33Xab0tLSool75y+2U6dOaebeeRw+fFj5/ve/r/ziF7/Q1L9ZRVEUKSmE4dNPPwVg1apVLFmyhH379nHs2DGuvvpq5syZg8FgoKioiIaGBs6dO8fAwACZmZkAFBcX09DQEPGYqqur2bx5M2azGSDseBwOB0eOHCEvLy/icfrG1t/fT1tbG+Xl5RQVFVFZWYnb7Y5JbCaTibKyMuLj4zEajaSnp9Pa2qqJe+cvtra2Ns3cu5tvvpm9e/diMBjo7OzE5XLR3d2tiXvnL7Zp06Zp5t4BfPHFF+zatYv77rsP0Na/WZDqo7B0d3eTlZXFnj17eOGFF/j9739PW1sbJpNJ3cdsNtPe3s6FCxe8tptMJtrb2yMe09atW7FYLOrvvt8bLJ6uri4SExMxGAwRj9M3to6ODm655Ra2bdtGdXU1R48e5dVXX41JbNdee636j621tZX6+np0Op0m7p2/2H74wx9q5t4BGI1GKisrsVqtZGVlaeq/O9/YnE6npu7dpk2beOSRR5g5cyagrX+zIEkhLAsWLGDHjh0kJSWRkpLCnXfeSWVlJTrd11PRKoqCTqfD7Xb73R5tgb430HZ/cUUrzjlz5rBnzx7MZjPTp09nxYoVHDp0KKaxffLJJ6xatYrHHnuMOXPmaOreDY/tmmuu0dy9W7duHY2NjXz++ee0trZq6t4Nj62xsVEz9+4Pf/gDs2bNIisrS92mtX+zE349hfF09OhRHA6H+gdVFIXZs2djt9vVfex2O2azmbS0NK/tHR0dajVKNPl+b7B4UlJS6OnpweVyodfr1f2j4eTJk7S2tqrFXkVRMBgMMYutqamJdevWUV5ejtVq5f3339fMvfONTUv37tSpUwwNDTFv3jymT59Obm4uDQ0N6PV6dZ9Y3Tt/sdXV1ZGcnKyJe1dXV4fdbmfp0qV8+eWXfPXVV5w7d04T985DSgph6OnpYceOHQwODtLb28sbb7zBo48+yunTp/nss89wuVzU1taSnZ3N7NmzSUhIoKmpCYCamhqys7OjHuONN94YVjxGoxGLxUJdXR0ANpstanEqisK2bdv48ssvcTgcvPLKKyxatCgmsX3++ec88MADVFRUYLVaAe3cO3+xaenenT17lo0bNzI0NMTQ0BBvvfUWd911lybunb/Yvve972nm3j3//PPU1tZSU1PDunXruP322/n1r3+tiXuniliT9RSxa9cuJT8/X8nNzVVeeOEFRVEu9SQoKipScnNzla1btyput1tRFEVpaWlR/umf/knJy8tTHn30UWVwcDBqcd12221qD59w4zl79qzyox/9SCkoKFBWrVqlfPHFF1GLbd++fUpBQYGyaNEi5amnnlL3Ge/Y/vVf/1XJzMxUlixZov7v5Zdf1sS9CxSbVu6doihKZWWlUlBQoBQWFiqVlZWKomjnvzt/sWnp3nm89tpryi9+8QtFUbRz7xRFUWTlNSGEECqpPhJCCKGSpCCEEEIlSUEIIYRKkoIQQgiVJAUhhBAqSQpCCCFUkhSEEEKoJCkIIYRQ/X/i/EsRUmvrFQAAAABJRU5ErkJggg==",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#After assuming that the data has been cleaned as far as possible, we continue with OLS assumptions.\n",
- "#Before a scatter plot showing the split and density of the values\n",
- "#Main feature: The data in the dataset are concentrated in the x=2000 & y= 20000 range\n",
- "#However, values in the range of 3000 to over 4000 must also be included in the model\n",
- "plt.scatter(data_cleaned['active_vehicles'], data_cleaned['trips'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0.0, 20000.0)"
- ]
- },
- "execution_count": 22,
- "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": [
- "#Related to statement(s) from the line above.\n",
- "#Main distribution here is in the 2000 range\n",
- "plt.scatter(data_cleaned['active_vehicles'], data_cleaned['trips'])\n",
- "plt.xlim(0,2000)\n",
- "plt.ylim(0,20000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x11ef43e80>"
- ]
- },
- "execution_count": 23,
- "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": [
- "#A few more checks on the other variables\n",
- "plt.scatter(data_cleaned['trips'], data_cleaned['dispatching_base_number'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x11f02a340>"
- ]
- },
- "execution_count": 24,
- "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": [
- "plt.scatter(data_cleaned['dispatching_base_number'], data_cleaned['active_vehicles'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Transformation für kontinuierliche Variablen\n",
- "#Log: Die Logarithmentransformation hilft bei schiefen Daten, die Schiefe zu reduzieren. Siehe Oben\n",
- "\n",
- "log_trips = np.log(data_cleaned['trips'])\n",
- "data_cleaned['log_trips'] = log_trips\n",
- "\n",
- "\n",
- "#Linearity is not present-> Violation of OLS Assumption"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x11f099d90>"
- ]
- },
- "execution_count": 26,
- "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": [
- "plt.scatter(data_cleaned['dispatching_base_number'], data_cleaned['log_trips'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['dispatching_base_number', 'date', 'active_vehicles', 'trips',\n",
- " 'log_trips'], dtype=object)"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Output of the new columns -> You can see that \"Trips\" occurs twice since log transformation\n",
- "data_cleaned.columns.values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [],
- "source": [
- "#data_cleaned = data_cleaned.drop(['trips'], axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Variance Inflation Factor (VIF).\n",
- "#If the VIF is between 5 and 10, multicolinearity is probably present and you should consider dropping the variable.\n",
- "\n",
- "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
- "\n",
- "variables = data_cleaned[['active_vehicles', 'log_trips']]\n",
- "vif = pd.DataFrame()\n",
- "\n",
- "vif[\"VIF\"]= [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
- "\n",
- "vif[\"Features\"]= variables.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .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>3.684423</td>\n",
- " <td>active_vehicles</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3.684423</td>\n",
- " <td>log_trips</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " VIF Features\n",
- "0 3.684423 active_vehicles\n",
- "1 3.684423 log_trips"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#VIF is output and the values are <5 \n",
- "vif"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " 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>dispatching_base_number</th>\n",
- " <th>date</th>\n",
- " <th>active_vehicles</th>\n",
- " <th>trips</th>\n",
- " <th>log_trips</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>266</td>\n",
- " <td>266</td>\n",
- " <td>266.000000</td>\n",
- " <td>266.000000</td>\n",
- " <td>266.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>unique</th>\n",
- " <td>6</td>\n",
- " <td>59</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>top</th>\n",
- " <td>B02598</td>\n",
- " <td>2/5/2015</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>freq</th>\n",
- " <td>59</td>\n",
- " <td>6</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1411.646617</td>\n",
- " <td>12414.560150</td>\n",
- " <td>9.208195</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>992.671651</td>\n",
- " <td>8213.348133</td>\n",
- " <td>0.697698</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>236.000000</td>\n",
- " <td>2016.000000</td>\n",
- " <td>7.608871</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>925.500000</td>\n",
- " <td>7620.500000</td>\n",
- " <td>8.938593</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1181.000000</td>\n",
- " <td>10727.000000</td>\n",
- " <td>9.280516</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1439.500000</td>\n",
- " <td>13842.500000</td>\n",
- " <td>9.535498</td>\n",
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- " <th>max</th>\n",
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- " <td>35990.000000</td>\n",
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- " </tr>\n",
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- "</table>\n",
- "</div>"
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- "text/plain": [
- " dispatching_base_number date active_vehicles trips \\\n",
- "count 266 266 266.000000 266.000000 \n",
- "unique 6 59 NaN NaN \n",
- "top B02598 2/5/2015 NaN NaN \n",
- "freq 59 6 NaN NaN \n",
- "mean NaN NaN 1411.646617 12414.560150 \n",
- "std NaN NaN 992.671651 8213.348133 \n",
- "min NaN NaN 236.000000 2016.000000 \n",
- "25% NaN NaN 925.500000 7620.500000 \n",
- "50% NaN NaN 1181.000000 10727.000000 \n",
- "75% NaN NaN 1439.500000 13842.500000 \n",
- "max NaN NaN 4093.000000 35990.000000 \n",
- "\n",
- " log_trips \n",
- "count 266.000000 \n",
- "unique NaN \n",
- "top NaN \n",
- "freq NaN \n",
- "mean 9.208195 \n",
- "std 0.697698 \n",
- "min 7.608871 \n",
- "25% 8.938593 \n",
- "50% 9.280516 \n",
- "75% 9.535498 \n",
- "max 10.490996 "
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_cleaned.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x11f1e4d90>"
- ]
- },
- "execution_count": 32,
- "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": [
- "#Linearity could be improved by LOG transformation\n",
- "plt.scatter(data_cleaned['active_vehicles'], data_cleaned['log_trips'])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3.1 Umkodierung von kategorialen Variablen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Convert vpn categorical data into dummy/ indicator variables. Focus on base number for more general model\n",
- "data_with_dummies = pd.get_dummies(data_cleaned, drop_first = True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <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>1228</td>\n",
- " <td>9537</td>\n",
- " <td>9.162934</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>...</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>870</td>\n",
- " <td>6903</td>\n",
- " <td>8.839711</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>...</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>785</td>\n",
- " <td>4768</td>\n",
- " <td>8.469682</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>...</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 × 66 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " active_vehicles trips log_trips dispatching_base_number_B02598 \\\n",
- "0 3427 29421 10.289464 0 \n",
- "1 945 7679 8.946245 0 \n",
- "2 1228 9537 9.162934 0 \n",
- "3 870 6903 8.839711 1 \n",
- "4 785 4768 8.469682 1 \n",
- "\n",
- " dispatching_base_number_B02617 dispatching_base_number_B02682 \\\n",
- "0 0 0 \n",
- "1 0 1 \n",
- "2 1 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " dispatching_base_number_B02764 dispatching_base_number_B02765 \\\n",
- "0 1 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " date_1/10/2015 date_1/11/2015 ... date_2/26/2015 date_2/27/2015 \\\n",
- "0 0 0 ... 0 0 \n",
- "1 0 0 ... 0 0 \n",
- "2 0 0 ... 0 0 \n",
- "3 0 0 ... 0 0 \n",
- "4 0 0 ... 0 0 \n",
- "\n",
- " date_2/28/2015 date_2/3/2015 date_2/4/2015 date_2/5/2015 date_2/6/2015 \\\n",
- "0 0 0 0 0 0 \n",
- "1 0 0 0 0 0 \n",
- "2 0 0 0 0 0 \n",
- "3 0 0 0 0 0 \n",
- "4 0 0 0 0 0 \n",
- "\n",
- " date_2/7/2015 date_2/8/2015 date_2/9/2015 \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 66 columns]"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Dummy variables head output\n",
- "data_with_dummies.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['active_vehicles', 'trips', 'log_trips',\n",
- " 'dispatching_base_number_B02598', 'dispatching_base_number_B02617',\n",
- " 'dispatching_base_number_B02682', 'dispatching_base_number_B02764',\n",
- " 'dispatching_base_number_B02765', 'date_1/10/2015',\n",
- " 'date_1/11/2015', 'date_1/12/2015', 'date_1/13/2015',\n",
- " 'date_1/14/2015', 'date_1/15/2015', 'date_1/16/2015',\n",
- " 'date_1/17/2015', 'date_1/18/2015', 'date_1/19/2015',\n",
- " 'date_1/2/2015', 'date_1/20/2015', 'date_1/21/2015',\n",
- " 'date_1/22/2015', 'date_1/23/2015', 'date_1/24/2015',\n",
- " 'date_1/25/2015', 'date_1/26/2015', 'date_1/27/2015',\n",
- " 'date_1/28/2015', 'date_1/29/2015', 'date_1/3/2015',\n",
- " 'date_1/30/2015', 'date_1/31/2015', 'date_1/4/2015',\n",
- " 'date_1/5/2015', 'date_1/6/2015', 'date_1/7/2015', 'date_1/8/2015',\n",
- " 'date_1/9/2015', 'date_2/1/2015', 'date_2/10/2015',\n",
- " 'date_2/11/2015', 'date_2/12/2015', 'date_2/13/2015',\n",
- " 'date_2/14/2015', 'date_2/15/2015', 'date_2/16/2015',\n",
- " 'date_2/17/2015', 'date_2/18/2015', 'date_2/19/2015',\n",
- " 'date_2/2/2015', 'date_2/20/2015', 'date_2/21/2015',\n",
- " 'date_2/22/2015', 'date_2/23/2015', 'date_2/24/2015',\n",
- " 'date_2/25/2015', 'date_2/26/2015', 'date_2/27/2015',\n",
- " 'date_2/28/2015', 'date_2/3/2015', 'date_2/4/2015',\n",
- " 'date_2/5/2015', 'date_2/6/2015', 'date_2/7/2015', 'date_2/8/2015',\n",
- " 'date_2/9/2015'], dtype=object)"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_with_dummies.columns.values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Columns for the later model are provided\n",
- "cols=['active_vehicles','log_trips','dispatching_base_number_B02598',\n",
- " 'dispatching_base_number_B02617', 'dispatching_base_number_B02682',\n",
- " 'dispatching_base_number_B02764', 'dispatching_base_number_B02765']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [],
- "source": [
- "#-> At this point it was decided that the variable 'Date' is no longer useful and too costly for model building\n",
- "#-> It is therefore not considered for the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "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>active_vehicles</th>\n",
- " <th>log_trips</th>\n",
- " <th>dispatching_base_number_B02598</th>\n",
- " <th>dispatching_base_number_B02617</th>\n",
- " <th>dispatching_base_number_B02682</th>\n",
- " <th>dispatching_base_number_B02764</th>\n",
- " <th>dispatching_base_number_B02765</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>3427</td>\n",
- " <td>10.289464</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>945</td>\n",
- " <td>8.946245</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1228</td>\n",
- " <td>9.162934</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>870</td>\n",
- " <td>8.839711</td>\n",
- " <td>1</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>785</td>\n",
- " <td>8.469682</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " active_vehicles log_trips dispatching_base_number_B02598 \\\n",
- "0 3427 10.289464 0 \n",
- "1 945 8.946245 0 \n",
- "2 1228 9.162934 0 \n",
- "3 870 8.839711 1 \n",
- "4 785 8.469682 1 \n",
- "\n",
- " dispatching_base_number_B02617 dispatching_base_number_B02682 \\\n",
- "0 0 0 \n",
- "1 0 1 \n",
- "2 1 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " dispatching_base_number_B02764 dispatching_base_number_B02765 \n",
- "0 1 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 "
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Arrangement-> First column becomes target variable\n",
- "data_preprocessed = data_with_dummies[cols]\n",
- "data_preprocessed.head()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3.2 Erstellen von Test- & Trainingsdaten"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "targets = data_preprocessed['active_vehicles']\n",
- "inputs = data_preprocessed.drop(['active_vehicles'], axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.preprocessing import StandardScaler\n",
- "scaler = StandardScaler()\n",
- "scaler.fit(inputs)\n",
- "input_scaled = scaler.transform(inputs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.model_selection import train_test_split\n",
- "x_train, x_test, y_train, y_test = train_test_split(input_scaled, targets, test_size=0.20, random_state=365)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenvorbereitung"
- },
- "tags": []
- },
- "source": [
- "Der Code importiert den StandardScaler, passt ihn an die Eingabedaten an und skaliert diese, sodass sie einen Mittelwert von 0 und eine Standardabweichung von 1 haben. Anschließend werden die Zielvariable active_vehicles extrahiert und die Eingabedaten bereinigt, indem die Spalte active_vehicles entfernt wird."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 4. Modellierung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4.1 Lineare Regression"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- "In diesem Codeabschnitt werden die tatsächlichen Zielwerte (Targets) und die vom linearen Regressionsmodell vorhergesagten Werte (Predictions) für die Trainingsdaten visualisiert, um die Beziehung zwischen den tatsächlichen Leistungen und den Vorhersagen des Modells zu veranschaulichen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "LinearRegression()"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "reg = LinearRegression()\n",
- "reg.fit(x_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [],
- "source": [
- "y_hat = reg.predict(x_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#The division of values is also noticeable here\n",
- "plt.scatter(y_train, y_hat)\n",
- "plt.xlabel('Targets')\n",
- "plt.ylabel('Predictions')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "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": [
- "Text(0.5, 1.0, 'Residuals')"
- ]
- },
- "execution_count": 47,
- "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": [
- "#Error thermal: Trainins values minus predicted data\n",
- "sns.distplot(y_train - y_hat)\n",
- "plt.title(\"Residuals\")\n",
- "#plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.9747815666217599"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#R-squared value is important: variance of the target variable\n",
- "reg.score(x_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1413.3913426862414"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Important for the regression line\n",
- "reg.intercept_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.9849060957803961"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "reg.score(x_test, y_test)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "training Performance\n",
- "0.9747815666217599\n",
- "test Performance\n",
- "0.9849060957803961\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))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- " Diese Ergebnisse deuten darauf hin, dass das Modell in der Lage ist, die Zielwerte genau vorherzusagen, was ein gutes Zeichen für seine Allgemeingültigkeit ist."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([432.4680536 , -55.50352875, 11.88834549, -20.8881854 ,\n",
- " 611.37694067, -50.75412983])"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Regression coefficients for the model \n",
- "reg.coef_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- "Jeder Koeffizient repräsentiert die Gewichtung und den Einfluss einer entsprechenden unabhängigen Variable auf die abhängige Variable. In diesem Fall gibt es sechs Koeffizienten, was darauf hindeutet, dass es sich um ein lineares Regressionsmodell mit sechs unabhängigen Variablen handelt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "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>Features</th>\n",
- " <th>Weights</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>log_trips</td>\n",
- " <td>0.432468</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>dispatching_base_number_B02598</td>\n",
- " <td>-0.055504</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>dispatching_base_number_B02617</td>\n",
- " <td>0.011888</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>dispatching_base_number_B02682</td>\n",
- " <td>-0.020888</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>dispatching_base_number_B02764</td>\n",
- " <td>0.611377</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>dispatching_base_number_B02765</td>\n",
- " <td>-0.050754</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Features Weights\n",
- "0 log_trips 0.432468\n",
- "1 dispatching_base_number_B02598 -0.055504\n",
- "2 dispatching_base_number_B02617 0.011888\n",
- "3 dispatching_base_number_B02682 -0.020888\n",
- "4 dispatching_base_number_B02764 0.611377\n",
- "5 dispatching_base_number_B02765 -0.050754"
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#What weights are critical to the model:\n",
- "reg_summary = pd.DataFrame(inputs.columns, columns = ['Features'])\n",
- "reg_summary['Weights']= reg.coef_/1000\n",
- "reg_summary"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- "Die unabhängige Variable mit dem höchsten Gewicht hat dabei den größten Einfluss auf die Vorhersage, während die Variable mit dem niedrigsten Gewicht den geringsten Einfluss hat. Diese Informationen können verwendet werden, um zu verstehen, welche unabhängigen Variablen besonders wichtig für das Modell sind und welchen Beitrag sie zur Vorhersage leisten."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Testing the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [],
- "source": [
- "y_hat_test = reg.predict(x_test)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [
- {
- "data": {
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lkBBCiPjVY9Hwer18/vnnscwihBAizvVYNIYOHdpt8yUhhBCixym3Ya75E0IIcQXqsWjce++9MYwhhBAiEfRYNO66664YxhBCCJEIwl4RLoQQQnSSoiGEECJiYYuG2+3mo48+AuA//uM/KC0tpa6uLtq5hBBCxKGwRWPx4sW8/fbbHDx4kN/97ndkZWXxxBNPxCKbEEKIOBO2aJw4cYLHHnuMv/71r0ybNo2HH36Ys2fPxiCaEEKIeBO2aAQCAQDee+89JkyYQDAYpL29PerBhBBCxJ+w272OHTuWyZMnYzAYGDduHPPmzeM73/lOLLIJIYSIM2GLxhNPPMGBAwe49tpr0ev1/PjHP454xdsXX3yRN998E4CJEyfy+OOPs3v3blauXInX6+WOO+7g0UcfBaCqqoolS5bQ1tbG+PHjWb58OYqiUFdXx8KFC2lsbOTqq6+mvLyclJSUS/jIQggheits95TBYEBVVd5//33eeustfD4fu3btCvvCu3fv5r333mPLli1s3bqVTz/9lO3bt1NaWsq6devYsWMHhw4d4t133wVg4cKFLF26lJ07d6JpGps2bQJg+fLlzJw5k8rKSq6//nrWrVt3iR9ZCCFEb4U90ygrK+O///u/GTFiRKhNp9ORn59/wefZ7XYWLVqEyWQCICcnh+rqakaMGMHw4cMBKC4uprKykmuuuQaPx0Nubi4AJSUlVFRUcM8997Bv3z7Wrl0bap89ezYLFy7s1YcVQghxacIWjT179rBjxw6sVutFvfDo0aND/15dXc2bb77J7Nmzsdvtofb09HTq6+s5c+ZMt3a73U59fT3Nzc1YrVYURenWLoQQon+ELRpZWVkXXTC6+uKLL1iwYAGPP/44BoOB6urq0H2apqHT6VBVFZ1O9432zj+7Ovd2OIMH9z57tNjttv6OEJFEyQmSNRoSJSckTtZEyXkhYYvGuHHjePTRR7n99ttJSkoKtYfrngLYv38/jzzyCKWlpRQVFbF3714cDkfofofDQXp6OpmZmd3aGxoaSE9PJy0tDafTSTAYxGAwhB5/MRobXahq/CzzbrfbcDic/R0jrETJCZI1GhIlJyRO1kTJqdfrLvhjO2zROHDgAAB//vOfQ22RjGmcOnWKBx98kDVr1nDrrbcCcOONN3L8+HFqamoYNmwY27dv5wc/+AHZ2dmYzWb279/PTTfdxLZt28jLy8NoNDJ+/Hh27NhBcXExW7duJS8vL6IPLoQQou/ptAh3WwoEAmiahtFojOiFV6xYwebNm7nqqqtCbdOnT2fkyJGhKbcTJ05k8eLF6HQ6Dh8+TFlZGS6XizFjxrBy5UpMJhO1tbUsWrSIxsZGsrKyWL16NampqRF/QDnT6J1EyQmSNRoSJSckTtZEyRnuTCNs0WhsbOQXv/gF77//PsFgkJtvvplf/epXZGRk9HnYaJCi0TuJkhMkazQkSk5InKyJkjNc0Qh7ncZTTz1Fbm4uu3fvZvfu3YwfP54nn3yyLzMKIYRIEGGLRnV1NQ899BADBgxg0KBBPPLII3z55ZexyCaEECLORLRgodfrDd12u90XPe1VCCHE5SHs7KnJkydz7733UlJSgk6nY/PmzRQUFMQimxBCiDgTtmg8+OCDZGZm8j//8z+oqkpJSQl33313LLIJIYSIMz0WDZfLhdVq5ezZs3z/+9/n+9//fui+lpYWBg4cGIt8Qggh4kiPRWPOnDls2bKFCRMmnHeJj6qqqpgEFEIIET96LBpbtmwB4LPPPkOv7z5eLtu9CiHElSns7Kkf/OAH32ibPXt2VMIIIYSIbz2eacybN49PPvkEj8fDuHHjQu2qqnLDDTfEJJwQQoj40mPRWLt2LWfPnqW0tJSVK1d+/QRF6bb3hRBCiCtHj91TVquVYcOGsW7dOrZv3052djYAv/vd7/B4PDELKIQQIn6EHdNYvHhxaOB7wIAB6HQ6nnjiiWjnEkIIEYciWnvqF7/4BQA2m43S0lK++OKLqAcTQggRfyJae8rlcoVut7W1EeEWHEIIIS4zYZcRueuuu7jnnnsoLCxEp9Oxa9cuSkpKYpFNCCFEnAlbNBYsWMA111zDnj17UBSFn//850ycODEW2YQQQsSZiNaeuummm7jppptC9509e1bWnhJCiCuQrD0lhBAiYmHXnjp8+HCvX9zlcjF9+nR+85vfMGzYMBYvXsz+/fuxWCwAPPTQQ0yaNImqqiqWLFlCW1sb48ePZ/ny5SiKQl1dHQsXLqSxsZGrr76a8vJyUlJSep1HCCHEpemxaGzduvWCT7zrrrsueP/HH39MWVkZ1dXVobZDhw6xYcMG0tPTuz124cKFrFixgtzcXEpLS9m0aRMzZ85k+fLlzJw5k6KiItauXcu6detYuHBh2A8lrizOdh9Ha1to9wZINitkpCVjtRj7O5YQl6Uei0ZlZSUADoeDY8eOMWHCBBRF4YMPPuC6664LWzQ2bdrEsmXLePzxx4GObWLr6uooLS2lvr6eSZMm8dBDD3Hq1Ck8Hg+5ubkAlJSUUFFRwT333MO+fftYu3ZtqH327NlSNC4DLref+qb2i/6SP9/zAGoa2gmoKgY9fHnGyafHGxk1NJWRWQOkeAjRx3osGr/5zW8AuP/++1mzZg1XXXUVAHV1dRFdEf700093u93Q0MCECRNYtmwZNpuNBQsW8NprrzF69Ohua1nZ7Xbq6+tpbm7GarWiKEq3dpHYXG4/x+paMJsMWC0KvoDKsboWRg1NveAXfE/P0+t02IdYaWzyc+TLZlQNTIqekw4nqqaFfV0hxMUJO+X21KlToYIBMHToUE6fPn3RbzR8+PDQWQN0DLRv3bqVnJyc8w60d/7Z1bm3IzF4sPWinxNtdrutvyNEJBo5m2qayLDbSDJ9/VfP4wvg0y78fj0972jtWQYFVb5saMeakoTFrOAPqLS6vVxrSwr7uv0h3vL0JFFyQuJkTZScFxK2aNjtdioqKpg2bRoAr776KsOHD7/oNzpy5AjV1dUUFBQAHcVBURQyMzNxOByhxzU0NJCenk5aWhpOp5NgMIjBYMDhcHxjLCQSjY0uVDV+rmC32204HM7+jhFWtHLWnm7FalFwt3f8AGj3BGhscdPs8tHc3B7qcjq3G6r2dCt6HTQ7vXh8QZJMBgbZzLicXk4ZXLS3eUixmGhvDxIIauhVOHm6FZfVTFpy/JxpXOn//aMhUbImSk69XnfBH9thlxF59tlnOXLkCFOnTmXatGnU1tbyzDPPXHQQTdN45plnaGlpwe/38+qrrzJp0iSys7Mxm83s378fgG3btpGXl4fRaGT8+PHs2LED6BiYz8vLu+j3FfEl2dzRtQQdBaO2wYXXrzLIZiagqnxW3cinx5sIqCpWi0JA7eiG8ngD1NQ7CaoayUkGgqpGTb2TtAFmWtr9GBUDgWBHwfAHgwwaYMbl9pNsDvu7SAhxEcL+H5Wens7atWtpaWkhNTW112/07W9/m/vvv58ZM2YQCATIz89nypQpAJSXl1NWVobL5WLMmDHMnTsXgGXLlrFo0SJeeuklsrKyWL16da/fX8Te+QauM9KSOVbXAkBjixsdHV2RyWYFR7Ob6tOtmBQFW3IaOqMOs9EAQH1jOwAaGpqmQ6Pj7DHJpJCZbuOQx8+Zs26Sk8CeakHVQK8jdOYihOgbOi3M6oPHjh3joYcewul08tprr3Hvvffy4osvkpOTE6uMl0S6p3rnUnN2Hbg2KXp8ARWvL8iooR0/POqb2vm0uolBNjPJZoUmpweTYuB0YxsBVSN9kIXsIVaSkxQ0TeOTY01ckz2AJqcXjzdAklkhzWYmqMLNNwxl78FaVFXD2e7D5Q6g1+vIHT2EjEHxVTSulP/+sZQoWRMl5yV3T61YsYIlS5YwePBgMjIymD17NkuXLu3TkOLyU9/UjtlkwGw0oNN1nDGYTQbqm9qxWozkZKcyZmQa6YMsuL0BTIoBo6JHr+94rEkxcKqhjaMnW/jfg6eodTj5st6JxaSQZFbweAPUN7vRAbZkE6OGpmJLNjHQlsS3hg/k72/IiruCIcTlIGzROHv2LH//938fuj1r1qxuS6ULcT7t3gAmpftfL5Oip90bADrORNzeAEe+PEv1qVYCwSC+QBCLWSHJZMDjC3DsdCsnG1zodJBtt1J92smHh+tpa/ejGPS4vQHaPAGc7b5QIbph1GBysmWarRDRErZoAHi93tB0V4fDgaqqUQ0lEl/XAe9OvoBKslkJdV0ZjXquyR6Ayain5rSLYFAjJzuVnOxUWtp8+HwqKWYjWUOsDEm1MCDFhEbHDCrFoGdk5gAG2kzUOeRHjBCxEnYgfMaMGfz4xz+msbGRX//61/znf/4nP/nJT2KRTSQwa7KRj75woGpgtRixJRvR63RkD7V267rCaOD6UUOoPt2KXq8jyWTAF1BJMhoYmWll0AAzHp/KmeZ2mlu8GAxgSzExLL2jz1XTNFxuf1xNqxXicha2aNxzzz2MHDmSd955h0AgwC9/+ctu3VVCdOVy+6k+1cqxuhZMRgNGvY6mFg9f1rsYntFRMJpaPQwZmBR6TnKSwogMG8fqWvniRMfMKotJIahquNoDNDs9KAY9JpOeQECltc2H2xvA8tXZjD1VCoYQsRK2aMybN49///d/5+abb45FHpHAOrudGlvcDEgxgQ5c7X4Mio6MQRZ0QEBVaXJ6MRr1DLSaafcEaGr10NjiweUJMGaklVSriZY2H5+faMHV7iMlSQGdhlHRo9PpGJBsoqHFQ/ogC15fkKF2K542b39/fCGuCGHHNJxOJ+3t7bHIIhJcZ7dTUAWTUY+mQV1DGyfqXTjbvTS2uHGcdePxBvj0eCOnG9s46XDi8QVp8/oZaDXhaHHj9gYZaDXzreGpKHodqqrh92sMt9sY9y07A1KMHeMaen1o1pQQIjbCnmlYLBZuv/12rr32WpKTv57C2LmgoRCd2r0B9DpobfNT63Dh8QXx+YOYTXraPAGaWr1YzApDBprRWnQcrXNitSgMTjUy0GomNcVEIKjR1OohOclKaoqJzMEpjMiyhS7yA0ixGBk6xEpOdu8vNhVC9E7YonH33XfHIoe4DOiAmnon1mSFM2fbQAO3L4DeYKTNHWCQzYzbGyDJpDBkgBmzoseabGSY3cpJXPiDase0XE8Q6JhtNXRICl5fx+2uFwlmD42/hSiFuBJcsGh8/vnnpKSkcOONN5KRkRGrTCKO9GbvC5NRj9VixO0LoBj0qKpKksWILVmh3RNEMfixmAycdXpodHoYkprE4AFJnDzjIhDQMBv1eP3Bb1xB7nJ3ZMgeapXrMIToJz0Wjc2bN/Pcc88xYsQIvvzyS379619z2223xTKb6Gf1ze189EUDqqphtSgEgybaPP4e96jQgJGZNpq+uo5iYIqZnKGpuH0qXm+Q1jYfZqMenU5DUfSkDUjiTIub46daGZlpwz7I0jEuYjaj6PXdioNVuqKEiAs9Fo1XXnmFN954g4yMDA4cOMCaNWukaFxBnO0+PvrCgUGvx5as4A9oOFrc2FM7vtjP9yWebO5YlXaY3Ro6c1BVGGBRsA1OpqbeidmoJ8mkoKGh1+v4/0ak0drm5eSZNnKyU7nmhoFyFiFEHLvg7KnOLqmxY8fS3Nwck0AiPtQ5XKgaWMwGQIdR0WNSDDjbfaGlQM6VkZaM1xfE6+/Y78I+yEJQVTGbjdiSTUwYk4lep8cfVFEMeoalWxmcmsTIrAFkDUmR5T+ESAA9nmmcu0uewWDo4ZHicuRy+7FajF8NTnf8tzcqOlpcfoYOOf9fG6vFyKihqaHxB5vF9I0zh5zsVAKq2m02VOfyIkKI+Bfx/6m92WpVJK7OpT9OnnHh9rpR1Y4dLJKTjBfco8JqMV5w/KHrfhoyG0qIxNNj0Thy5Ajjxo0L3fZ4PIwbNy60d/ff/va3mAQUsdU5Wyqg03Gi3vnVtRd6AqqKqmqkpkR+IV1PM6+6no3IbCghEkuPRWPXrl2xzCH6Sdcvdh3Q5gkw0GYiY4iN418a8Pu9DBpgIislmbQBSRgMuh4Hws993c5NmKyWjjWijtW1hGZeyWwoIRJTj0UjOzs7ljlEPzj3i736tBO3N4At2YhOp0NR9AzLsKIYDAyzd11VNvCN1zn3jKLbSrbw9batERQcIUT8img/DXF5Ond3vWCwY0C6qdUDQJLJAJoOT5fZUucOWncWnoCqYrV0TLk9VtdCU6vngpswCSESU1SLhsvlYsqUKZw8eRKA3bt3U1xcTH5+PmvWrAk9rqqqipKSEgoKCliyZAmBQMcXS11dHbNmzaKwsJAHHniAtra2aMa94py7u16SuWM1Wc9Xy3akDUii3RvAYNCjaVroKu2uA+E9bevq9gZ73IRJCJG4olY0Pv74Y2bMmEF1dTXQMZBeWlrKunXr2LFjB4cOHeLdd98FYOHChSxdupSdO3eiaRqbNm0CYPny5cycOZPKykquv/561q1bF624V6Suu+u5vQG8viDH6pw0tXpxuX0YDDoGD7BgT7XgcgdCq8p2HbTuaVvXpCQldM1GTwVHCJF4olY0Nm3axLJly0hPTwfg4MGDjBgxguHDh6MoCsXFxVRWVlJbW4vH4yE3NxeAkpISKisr8fv97Nu3j4KCgm7tou90Xox31uXlxBkXmgb2gUkMSDFyuKYZv19lzNVpXD9qMDeMGhwaq/jkWCNHa1twuf09bus62GZm1NBUFL2+x4IjhEg8UesrePrpp7vdPnPmDHa7PXQ7PT2d+vr6b7Tb7Xbq6+tpbm7GarWiKEq3dnFpzjdofaTmLG5PoOPqbZOCNdlIdsYAVH8w9CXf02yozkIC37zuQmZJCXH5iVkHs6qq3S4Q7Lzeo6f2zj+76s0FhoMHx99FY3a7rV/e19nuo6ahHasticFpBrz+IB5fENuAJBSTgSSTglHR4w+onGpoIy3VEsraVNNEht1GkunrvzIeXwCjoueWv8umzuHC5fZjTzUy1G6N+cZI/XVMeyNRsiZKTkicrImS80JiVjQyMzNxOByh2w6Hg/T09G+0NzQ0kJ6eTlpaGk6nk2AwiMFgCD3+YjU2ulBVrU8+Q1+w2204HM5+ee+jtR2znLSAAfdXbV5/kGM1TaTazKCqeL5q1xsMnHG4QllrT7ditSi427sXeJc7QFqyMfQPgKfNG9PtV/vzmF6sRMmaKDkhcbImSk69XnfBH9sxm3J74403cvz4cWpqaggGg2zfvp28vDyys7Mxm83s378fgG3btpGXl4fRaGT8+PHs2LEDgK1bt5KXlxeruHHF5fZztLal21hCb/Q0aK3pdaiaij+gAhr+gIqKRlLS178pehq7kNlQQlxZYvZ/vNls5tlnn+Xhhx/G6/UyceJECgsLASgvL6esrAyXy8WYMWOYO3cuAMuWLWPRokW89NJLZGVlsXr16ljFjRvhrqzu+rhwmyV1fvGfu1hgeqqFASlGXG4/7Z6OFWqzBltR/V9fUyFrRgkhAHSapsVP300UJHr3VGeXUtcveq8/iKLXh/bI7lpYun6hn6+wdH1ci8tHfbMbS5KC2+MnIy2Z1BQTvoBKksXM4BTjRRem/pAop/2QOFkTJSckTtZEyRmue0r6FuJcuzeA1dL9P5NJ0XdbyiPSJTu6LhboaPHS3OoOFYoWl4/6Jjd+v0ragCSuHTHoG+MSMhtKCCFFI8711KXUdSwhksLSKfTFX9tCaoox9LoDbWYsSUroDMaWbIrpYLYQIjHI2lNxrutueD1dWd2bQeqeBsVlbSghxIVI0YhznV1KF7qyOpLCci6ZDSWE6A35hkgA4cYSerOxkcyGEkL0hhSNy8TFDlLLDnpCiN6QonEFk9lQQoiLJWMaQgghIiZFQwghRMSkeyqGXG4/TTVN1J5ujasrqoUQIlJyphEjnUt4+APd99Lu7eKDna/ZFwsZCiFEpKRoxEjnUh9JJqXbXtqdGxhdrM4iFFD7rggJIUQ40j0VI+db6iMQUKlxOHu1AGCk600JIURfkjONGDn3Cux2T4CaeidGo75XZwqyDIgQoj9I0YiRzqU+PL4AmqZR39QGQGZacq+6q2QZECFEf5CiESOdV2Abv1p91h/QGJlpw9LlS/5izhR6s96UEEJcKvlZGkNWixG73UZaspFkc0eXVFcXc6Ygy4AIIfqDFI1+0hcLBsoyIEKIWJPuqX4SyZLnQggRb/rlTGPOnDk0NTWhKB1v/9RTT9HW1sbKlSvxer3ccccdPProowBUVVWxZMkS2traGD9+PMuXLw89L9HJmYIQItHE/NtX0zSqq6v561//Gvry93g8FBYW8sorr5CVlcWCBQt49913mThxIgsXLmTFihXk5uZSWlrKpk2bmDlzZqxjCyGEoB+6p44dOwbA/PnzufPOO9mwYQMHDx5kxIgRDB8+HEVRKC4uprKyktraWjweD7m5uQCUlJRQWVkZ68hCCCG+EvOi0drayq233sratWv5t3/7N/70pz9RV1eH3W4PPSY9PZ36+nrOnDnTrd1ut1NfXx/ryEIIIb4S8+6psWPHMnbs2NDtu+++m4qKCm666aZQm6Zp6HQ6VFVFp9N9o/1iDB4cf9uX2u22/o4QkUTJCZI1GhIlJyRO1kTJeSExLxoffvghfr+fW2+9FegoBNnZ2TgcjtBjHA4H6enpZGZmdmtvaGggPT39ot6vsdGFqmp9E74P2O02HA5nf8cIK1FygmSNhkTJCYmTNVFy6vW6C/7Yjnn3lNPpZNWqVXi9XlwuF1u2bOFnP/sZx48fp6amhmAwyPbt28nLyyM7Oxuz2cz+/fsB2LZtG3l5ebGOLIQQ4isxP9O4/fbb+fjjj7nrrrtQVZWZM2cyduxYnn32WR5++GG8Xi8TJ06ksLAQgPLycsrKynC5XIwZM4a5c+fGOrIQQoiv6DRNi5++myiQ7qneSZScIFmjIVFyQuJkTZSccdc9JYQQInFJ0RBCCBExKRpCCCEiJkVDCCFExKRoCCGEiNjlsVxsDLjcfuqb2mn3dmx4lJGWLMuYCyGuOFI0etC1SOiAdq+fVKsZq6Vjb+5jdS2y/4UQ4ooj3VPn4XL7OVbXQkBVsVoUHGfbaWjxoKoda1+ZjQbMJgP1Te39HVUIIWJKzjTOo/pUK42tHoJBlSSzQrs3SIrFQGOrh2H2joteTErHjntCCHElkTONc7jcfo6dakWn07CYFQJBldY2H16visf7dZHwBVSSzVJzhRBXFika56hvasdmUdChR6fTYVIMpA1I4kyLG4NBj6ZpeP1BvL4gGWnJ/R1XCCFiSorGOdq9AdIHJeMLBPEHVEADTcPrC+Jq9/PFiRb8flUGwYUQVyTpXzlHslkhoKpkD7HS1Oqh2eWnqcXNMLuVa68aiC+g4vUF+zumEEL0CznTOIc12Uj1KSc1p51omkbAH0SHRlDVqHW0EQxqMnNKCHHFkqLRRee1GRlpFqzJRppavRytPcuAFDODbKaOwtHgIhBQaffKzCkhxJVHuqe6qG9qx2wyYDYaGGg1owPcPitefxDQYVQ69ic/09zOVRkD+jWrEEL0BznT6KLdG8CkfH1IPL4g9oFJuL0BfIEgmqahoeJ0B2TmlBDiiiRFo4tkc8cSIZ2STAY0TUe23Ypi0OP2BtA0HaOyBsjMKSHEFUm6p7rISEvmWF0L0HHFt9VipLHVw4hMG6kpptDMqZFZ0jUlhLgyJcSZxhtvvMHkyZPJz89n48aNUXsfq8XIqKGpKPqOJUJsySYmjMnEZjHhcgdQ9Hq5PkMIcUWL+zON+vp61qxZw+uvv47JZGL69On8v//3/7jmmmui8n5WixFrdmq3toxBUXkrIYRIOHF/prF7924mTJjAwIEDSU5OpqCggMrKyv6OJYQQV6S4LxpnzpzBbreHbqenp1NfX9+PiYQQ4soV991Tqqqi0+lCtzVN63Y7nMGDrdGIdUnsdlt/R4hIouQEyRoNiZITEidrouS8kLgvGpmZmXz44Yeh2w6Hg/T09Iif39joQlW1aETrFbvdhsPh7O8YYSVKTpCs0ZAoOSFxsiZKTr1ed8Ef23FfNL7zne/wwgsv0NTUhMVi4a233uKXv/xlxM/X6yM/K4mVeMx0PomSEyRrNCRKTkicrImQM1xGnaZp8fMzvAdvvPEGv/3tb/H7/dx9993cd999/R1JCCGuSAlRNIQQQsSHuJ89JYQQIn5I0RBCCBExKRpCCCEiJkVDCCFExKRoCCGEiJgUDSGEEBGToiGEECJiUjSEEEJETIpGH5szZw5FRUVMnTqVqVOn8vHHH7N7926Ki4vJz89nzZo1ocdWVVVRUlJCQUEBS5YsIRAIRD2fy+ViypQpnDx5EuCis9XV1TFr1iwKCwt54IEHaGtri1nWxYsXk5+fHzq2u3btiousL774IkVFRRQVFbFq1SogPo/r+XLG6zF9/vnnmTx5MkVFRbz88stAfB7TnrLG63HtE5roM6qqarfddpvm9/tDbW63W5s4caL25Zdfan6/X5s/f772zjvvaJqmaUVFRdqBAwc0TdO0xYsXaxs3boxqvo8++kibMmWKNmbMGO3EiRO9ynb//fdr27dv1zRN01588UVt1apVMcmqaZo2ZcoUrb6+/huP7c+s//u//6v98Ic/1Lxer+bz+bS5c+dqb7zxRtwd1/PlfOutt+LymH7wwQfa9OnTNb/fr7ndbu3222/Xqqqq4u6Y9pT16NGjcXlc+4qcafShY8eOATB//nzuvPNONmzYwMGDBxkxYgTDhw9HURSKi4uprKyktrYWj8dDbm4uACUlJVHfXGrTpk0sW7YstErwxWbz+/3s27ePgoKCqGc+N6vb7aauro7S0lKKi4upqKhAVdV+z2q321m0aBEmkwmj0UhOTg7V1dVxd1zPl7Ouri4uj+ktt9zC+vXrURSFxsZGgsEgra2tcXdMe8qalJQUl8e1r8T9KreJpLW1lVtvvZUnnngCv9/P3Llz+clPfnLeTaTO3VzKbrdHfXOpp59+utvtnja46ilbc3MzVqsVRVGinvncrA0NDUyYMIFly5Zhs9lYsGABr732GqNHj+7XrKNHjw79e3V1NW+++SazZ8+Ou+N6vpwbN25k7969cXdMAYxGIxUVFfzhD3+gsLAwrv+unps1EAjE5d/VviJnGn1o7NixrFq1CpvNRlpaGnfffTcVFRXn3UTqUjeX6gs9Zeip/XwZY5V5+PDhrF27lvT0dCwWC3PmzOHdd9+Nm6xffPEF8+fP5/HHH2f48OFxe1y75hw1alRcH9NHHnmEPXv2cOrUKaqrq+P2mJ6bdc+ePXF9XC+VFI0+9OGHH7Jnz57QbU3TyM7OxuFwhNo6N5HKzMzs1t7Q0HBRm0v1hXMzhMuWlpaG0+kkGAx2e3wsHDlyhJ07d4Zua5qGoihxkXX//v3ce++9PPbYY0ybNi1uj+u5OeP1mB49epSqqioALBYL+fn5fPDBB3F5TM+XdceOHXF5XPuKFI0+5HQ6WbVqFV6vF5fLxZYtW/jZz37G8ePHqampIRgMsn37dvLy8sjOzsZsNrN//34Atm3bRl5eXkzz3njjjReVzWg0Mn78eHbs2AHA1q1bY5ZZ0zSeeeYZWlpa8Pv9vPrqq0yaNKnfs546dYoHH3yQ8vJyioqKgPg8rufLGa/H9OTJk5SVleHz+fD5fLz99ttMnz497o5pT1lvvvnmuDyufUX20+hj//zP/8zOnTtRVZWZM2cyb9489uzZw8qVK/F6vUycOJHFixej0+k4fPgwZWVluFwuxowZw8qVKzGZTFHP+L3vfY/169czbNiwi85WW1vLokWLaGxsJCsri9WrV5OamhqTrBs3bmTjxo0EAgHy8/P5+c9/DtCvWVesWMHmzZu56qqrQm3Tp09n5MiRcXVce8qpqmrcHVOAF154gTfffBODwUB+fj4PP/xw3P5dPV/WePy72lekaAghhIiYdE8JIYSImBQNIYQQEZOiIYQQImJSNIQQQkRMioYQQoiIyTIi4oq0YsUK9u3bB3RcoJWdnU1SUhIAr776aujf+5rT6eTBBx9k/fr1ET1+9+7dPPfcc0DHxWDBYJCMjAwAFixYwOTJky/q/efPn095eTlpaWkXF1yIr8iUW3HF+973vsfzzz/PDTfcEPX3OnnyJMXFxRw4cOCin/vCCy/Q3NzM0qVLe/3+1157LXv27JGiIXpNzjSE6KK9vZ0nn3ySmpoazp49S0pKCuXl5YwaNYo5c+aQmprKsWPHmDFjBnl5eZSWltLS0oLdbkfTNO68805KSkr429/+Rnl5OW63G71ez0MPPcTtt9/O4sWL8Xg8TJ06lddff521a9eya9cujEYjgwYNYuXKlREvIXH06FGefvppzp49SzAYZM6cOdx99920tbWxePFiampq0Ov1jBkzhqeeeoolS5YAMG/ePP7lX/6FrKysaB5KcbmKxfrrQsSz22+/XTt48KCmaZr25ptvar/85S9D9z3xxBPaU089pWmaps2ePVtbvHhx6L5//Md/DO2H8H//93/ajTfeqG3evFk7e/aslp+fH9oH5PTp01peXp5WW1urnThxQsvNzdU0TdPq6uq0cePGaV6vV9M0Tfv973+v7dq1q8ecFRUV2vLlyzVN0zS/369NnjxZO3TokKZpmtba2qrdcccd2oEDB7QtW7Zo8+fP1zRN0wKBgLZkyRKturpa0zRN+9a3vqU1NjZe4hETVzI50xCii8LCQoYPH84rr7xCTU0Ne/fuZezYsaH7x48fD0BLSwsHDx5kw4YNAOTk5DBhwgQAPvroIxwOBw8++GDoeTqdjiNHjnRbojwjI4Nvf/vbTJs2jby8PPLy8rj11lsjylldXc2XX35JaWlpqM3j8fDZZ5/x3e9+lzVr1jBnzhy+853vMG/ePEaMGNH7gyJEF1I0hOjij3/8I5s2bWLWrFkUFxczcODA0HazAMnJyQAYDAagY9G/Tp1twWCQnJwc/vznP4fuq6+vJy0trds+CXq9ng0bNvDJJ5+wZ88ennnmGb773e/y+OOPh80ZDAax2Wxs27Yt1NbQ0IDNZsNsNrNr1y4++OAD3n//fX70ox/x1FNP8b3vfa+XR0WIr8mUWyG6eO+995g2bRr33HMPV199Nf/1X/8VWrK6K6vVyrhx43j99dcBOHHiBHv27EGn05Gbm0tNTU1odlZVVRUFBQXU19ejKArBYBBN0zh8+DBTpkwhJyeHBQsWcO+99/LJJ59ElPPqq68mKSkpVDROnTrFlClTOHToEH/84x9ZvHgxt912GwsXLuS2227js88+AzoKWyz2oheXLznTEKKL+fPns3TpUl577TUAcnNz+fzzz8/72Oeee44lS5bwxz/+kYyMDIYNG0ZSUhJpaWlUVFSElsnXNI1Vq1YxbNgwgsEgf/d3f0dRUREbN27kjjvu4Ac/+AHJyckkJSVRVlYWUU6TycS6det4+umn+d3vfkcgEOCf/umfuOmmm7juuuvYu3cvkydPxmKxkJWVxZw5c4CO7rc5c+bwwgsv8K1vfatvDpq4osiUWyF66aWXXiI/P5+cnBycTid33nkn//qv/8o111zT39GEiBo50xCil0aOHMmjjz6KXq8nGAxy3333ScEQlz050xBCCBExGQgXQggRMSkaQgghIiZFQwghRMSkaAghhIiYFA0hhBARk6IhhBAiYv8/n3eVKalOcSgAAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
- "plt.xlabel('Targets Test')\n",
- "plt.ylabel('Predictions Test')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "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>Predictions</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1091.364516</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1057.719388</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1170.036887</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1259.987262</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1078.100005</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Predictions\n",
- "0 1091.364516\n",
- "1 1057.719388\n",
- "2 1170.036887\n",
- "3 1259.987262\n",
- "4 1078.100005"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Vprediction= has\n",
- "df_pf = pd.DataFrame((y_hat_test), columns = ['Predictions'])\n",
- "df_pf.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- "Die Ergebnisse zeigen relativ konsistente vorhergesagte Werte an."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 1218\n",
- "1 1151\n",
- "2 1027\n",
- "3 1330\n",
- "4 945\n",
- "Name: active_vehicles, dtype: int64"
- ]
- },
- "execution_count": 58,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "y_test=y_test.reset_index(drop=True)\n",
- "y_test.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- " Die Ausgabe zeigt, dass die aktiven Fahrzeuge für die ersten fünf Testdatenpunkte die Werte 1218, 1151, 1027, 1330 und 945 besitzen. Die Werte repräsentieren die Anzahl der aktiven Fahrzeuge für jedes Testdatum."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "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>Predictions</th>\n",
- " <th>Targets</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1091.364516</td>\n",
- " <td>1218</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1057.719388</td>\n",
- " <td>1151</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1170.036887</td>\n",
- " <td>1027</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1259.987262</td>\n",
- " <td>1330</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1078.100005</td>\n",
- " <td>945</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Predictions Targets\n",
- "0 1091.364516 1218\n",
- "1 1057.719388 1151\n",
- "2 1170.036887 1027\n",
- "3 1259.987262 1330\n",
- "4 1078.100005 945"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_pf['Targets'] = y_test\n",
- "df_pf.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": "Datenmodell"
- },
- "tags": []
- },
- "source": [
- "Der Vergleich zwischen den vorhergesagten und den tatsächlichen Werten kann Aufschluss über die Genauigkeit und die Qualität des linearen Regressionsmodells für die Vorhersage der Zielwerte geben.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": [
- "#Overview how much the prediction and the actual values differ:\n",
- "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,
- "raw_mimetype": "",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "df_pf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\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>Predictions</th>\n",
- " <th>Targets</th>\n",
- " <th>Residuals</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1091.364516</td>\n",
- " <td>1218</td>\n",
- " <td>126.635484</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1057.719388</td>\n",
- " <td>1151</td>\n",
- " <td>93.280612</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1170.036887</td>\n",
- " <td>1027</td>\n",
- " <td>-143.036887</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1259.987262</td>\n",
- " <td>1330</td>\n",
- " <td>70.012738</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1078.100005</td>\n",
- " <td>945</td>\n",
- " <td>-133.100005</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1394.956731</td>\n",
- " <td>1223</td>\n",
- " <td>-171.956731</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>1506.660989</td>\n",
- " <td>1526</td>\n",
- " <td>19.339011</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>1358.148148</td>\n",
- " <td>1418</td>\n",
- " <td>59.851852</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>990.063854</td>\n",
- " <td>975</td>\n",
- " <td>-15.063854</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>1384.551075</td>\n",
- " <td>1350</td>\n",
- " <td>-34.551075</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>1343.005058</td>\n",
- " <td>1300</td>\n",
- " <td>-43.005058</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>967.122403</td>\n",
- " <td>974</td>\n",
- " <td>6.877597</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>1426.599642</td>\n",
- " <td>1321</td>\n",
- " <td>-105.599642</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>1200.898158</td>\n",
- " <td>1295</td>\n",
- " <td>94.101842</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>985.642063</td>\n",
- " <td>1084</td>\n",
- " <td>98.357937</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>271.859854</td>\n",
- " <td>322</td>\n",
- " <td>50.140146</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>672.261307</td>\n",
- " <td>746</td>\n",
- " <td>73.738693</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>114.609218</td>\n",
- " <td>299</td>\n",
- " <td>184.390782</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>1476.113622</td>\n",
- " <td>1551</td>\n",
- " <td>74.886378</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>1289.180154</td>\n",
- " <td>1356</td>\n",
- " <td>66.819846</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>1279.616087</td>\n",
- " <td>1248</td>\n",
- " <td>-31.616087</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>3584.319579</td>\n",
- " <td>3658</td>\n",
- " <td>73.680421</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>1434.194940</td>\n",
- " <td>1456</td>\n",
- " <td>21.805060</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>1349.242107</td>\n",
- " <td>1405</td>\n",
- " <td>55.757893</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>162.148057</td>\n",
- " <td>309</td>\n",
- " <td>146.851943</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>114.034467</td>\n",
- " <td>252</td>\n",
- " <td>137.965533</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1358.412778</td>\n",
- " <td>1471</td>\n",
- " <td>112.587222</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>237.654959</td>\n",
- " <td>269</td>\n",
- " <td>31.345041</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>1479.194768</td>\n",
- " <td>1452</td>\n",
- " <td>-27.194768</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>892.269643</td>\n",
- " <td>786</td>\n",
- " <td>-106.269643</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>3653.514821</td>\n",
- " <td>3820</td>\n",
- " <td>166.485179</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>3478.878964</td>\n",
- " <td>3473</td>\n",
- " <td>-5.878964</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>3534.709964</td>\n",
- " <td>3186</td>\n",
- " <td>-348.709964</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>900.021763</td>\n",
- " <td>685</td>\n",
- " <td>-215.021763</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>214.674054</td>\n",
- " <td>256</td>\n",
- " <td>41.325946</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>639.482237</td>\n",
- " <td>566</td>\n",
- " <td>-73.482237</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>1475.478612</td>\n",
- " <td>1293</td>\n",
- " <td>-182.478612</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>872.131134</td>\n",
- " <td>944</td>\n",
- " <td>71.868866</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>1226.884458</td>\n",
- " <td>1186</td>\n",
- " <td>-40.884458</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>1195.639823</td>\n",
- " <td>1044</td>\n",
- " <td>-151.639823</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>1432.790074</td>\n",
- " <td>1383</td>\n",
- " <td>-49.790074</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>3575.285742</td>\n",
- " <td>3736</td>\n",
- " <td>160.714258</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>1098.867095</td>\n",
- " <td>1039</td>\n",
- " <td>-59.867095</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>1095.121958</td>\n",
- " <td>994</td>\n",
- " <td>-101.121958</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>1296.851003</td>\n",
- " <td>1429</td>\n",
- " <td>132.148997</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>1458.344504</td>\n",
- " <td>1532</td>\n",
- " <td>73.655496</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>1257.183801</td>\n",
- " <td>1262</td>\n",
- " <td>4.816199</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>1294.327396</td>\n",
- " <td>1214</td>\n",
- " <td>-80.327396</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>3587.913110</td>\n",
- " <td>3478</td>\n",
- " <td>-109.913110</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>3572.568763</td>\n",
- " <td>3427</td>\n",
- " <td>-145.568763</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>694.462976</td>\n",
- " <td>521</td>\n",
- " <td>-173.462976</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>1241.508649</td>\n",
- " <td>1216</td>\n",
- " <td>-25.508649</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>957.733177</td>\n",
- " <td>909</td>\n",
- " <td>-48.733177</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>53</th>\n",
- " <td>1277.755356</td>\n",
- " <td>1188</td>\n",
- " <td>-89.755356</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Predictions Targets Residuals\n",
- "0 1091.364516 1218 126.635484\n",
- "1 1057.719388 1151 93.280612\n",
- "2 1170.036887 1027 -143.036887\n",
- "3 1259.987262 1330 70.012738\n",
- "4 1078.100005 945 -133.100005\n",
- "5 1394.956731 1223 -171.956731\n",
- "6 1506.660989 1526 19.339011\n",
- "7 1358.148148 1418 59.851852\n",
- "8 990.063854 975 -15.063854\n",
- "9 1384.551075 1350 -34.551075\n",
- "10 1343.005058 1300 -43.005058\n",
- "11 967.122403 974 6.877597\n",
- "12 1426.599642 1321 -105.599642\n",
- "13 1200.898158 1295 94.101842\n",
- "14 985.642063 1084 98.357937\n",
- "15 271.859854 322 50.140146\n",
- "16 672.261307 746 73.738693\n",
- "17 114.609218 299 184.390782\n",
- "18 1476.113622 1551 74.886378\n",
- "19 1289.180154 1356 66.819846\n",
- "20 1279.616087 1248 -31.616087\n",
- "21 3584.319579 3658 73.680421\n",
- "22 1434.194940 1456 21.805060\n",
- "23 1349.242107 1405 55.757893\n",
- "24 162.148057 309 146.851943\n",
- "25 114.034467 252 137.965533\n",
- "26 1358.412778 1471 112.587222\n",
- "27 237.654959 269 31.345041\n",
- "28 1479.194768 1452 -27.194768\n",
- "29 892.269643 786 -106.269643\n",
- "30 3653.514821 3820 166.485179\n",
- "31 3478.878964 3473 -5.878964\n",
- "32 3534.709964 3186 -348.709964\n",
- "33 900.021763 685 -215.021763\n",
- "34 214.674054 256 41.325946\n",
- "35 639.482237 566 -73.482237\n",
- "36 1475.478612 1293 -182.478612\n",
- "37 872.131134 944 71.868866\n",
- "38 1226.884458 1186 -40.884458\n",
- "39 1195.639823 1044 -151.639823\n",
- "40 1432.790074 1383 -49.790074\n",
- "41 3575.285742 3736 160.714258\n",
- "42 1098.867095 1039 -59.867095\n",
- "43 1095.121958 994 -101.121958\n",
- "44 1296.851003 1429 132.148997\n",
- "45 1458.344504 1532 73.655496\n",
- "46 1257.183801 1262 4.816199\n",
- "47 1294.327396 1214 -80.327396\n",
- "48 3587.913110 3478 -109.913110\n",
- "49 3572.568763 3427 -145.568763\n",
- "50 694.462976 521 -173.462976\n",
- "51 1241.508649 1216 -25.508649\n",
- "52 957.733177 909 -48.733177\n",
- "53 1277.755356 1188 -89.755356"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_pf['Residuals'] = df_pf['Targets']-df_pf['Predictions']\n",
- "df_pf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Predictions</th>\n",
- " <th>Targets</th>\n",
- " <th>Residuals</th>\n",
- " <th>Difference%</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1091.364516</td>\n",
- " <td>1218</td>\n",
- " <td>126.635484</td>\n",
- " <td>10.397002</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1057.719388</td>\n",
- " <td>1151</td>\n",
- " <td>93.280612</td>\n",
- " <td>8.104310</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1170.036887</td>\n",
- " <td>1027</td>\n",
- " <td>-143.036887</td>\n",
- " <td>13.927642</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1259.987262</td>\n",
- " <td>1330</td>\n",
- " <td>70.012738</td>\n",
- " <td>5.264116</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1078.100005</td>\n",
- " <td>945</td>\n",
- " <td>-133.100005</td>\n",
- " <td>14.084657</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1394.956731</td>\n",
- " <td>1223</td>\n",
- " <td>-171.956731</td>\n",
- " <td>14.060240</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>1506.660989</td>\n",
- " <td>1526</td>\n",
- " <td>19.339011</td>\n",
- " <td>1.267301</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>1358.148148</td>\n",
- " <td>1418</td>\n",
- " <td>59.851852</td>\n",
- " <td>4.220864</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>990.063854</td>\n",
- " <td>975</td>\n",
- " <td>-15.063854</td>\n",
- " <td>1.545011</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>1384.551075</td>\n",
- " <td>1350</td>\n",
- " <td>-34.551075</td>\n",
- " <td>2.559339</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>1343.005058</td>\n",
- " <td>1300</td>\n",
- " <td>-43.005058</td>\n",
- " <td>3.308081</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>967.122403</td>\n",
- " <td>974</td>\n",
- " <td>6.877597</td>\n",
- " <td>0.706119</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>1426.599642</td>\n",
- " <td>1321</td>\n",
- " <td>-105.599642</td>\n",
- " <td>7.993917</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>1200.898158</td>\n",
- " <td>1295</td>\n",
- " <td>94.101842</td>\n",
- " <td>7.266551</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>985.642063</td>\n",
- " <td>1084</td>\n",
- " <td>98.357937</td>\n",
- " <td>9.073610</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>271.859854</td>\n",
- " <td>322</td>\n",
- " <td>50.140146</td>\n",
- " <td>15.571474</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>672.261307</td>\n",
- " <td>746</td>\n",
- " <td>73.738693</td>\n",
- " <td>9.884543</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>114.609218</td>\n",
- " <td>299</td>\n",
- " <td>184.390782</td>\n",
- " <td>61.669158</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>1476.113622</td>\n",
- " <td>1551</td>\n",
- " <td>74.886378</td>\n",
- " <td>4.828264</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>1289.180154</td>\n",
- " <td>1356</td>\n",
- " <td>66.819846</td>\n",
- " <td>4.927717</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>1279.616087</td>\n",
- " <td>1248</td>\n",
- " <td>-31.616087</td>\n",
- " <td>2.533340</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>3584.319579</td>\n",
- " <td>3658</td>\n",
- " <td>73.680421</td>\n",
- " <td>2.014227</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>1434.194940</td>\n",
- " <td>1456</td>\n",
- " <td>21.805060</td>\n",
- " <td>1.497600</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>1349.242107</td>\n",
- " <td>1405</td>\n",
- " <td>55.757893</td>\n",
- " <td>3.968533</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>162.148057</td>\n",
- " <td>309</td>\n",
- " <td>146.851943</td>\n",
- " <td>47.524901</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>114.034467</td>\n",
- " <td>252</td>\n",
- " <td>137.965533</td>\n",
- " <td>54.748227</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1358.412778</td>\n",
- " <td>1471</td>\n",
- " <td>112.587222</td>\n",
- " <td>7.653788</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>237.654959</td>\n",
- " <td>269</td>\n",
- " <td>31.345041</td>\n",
- " <td>11.652431</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>1479.194768</td>\n",
- " <td>1452</td>\n",
- " <td>-27.194768</td>\n",
- " <td>1.872918</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>892.269643</td>\n",
- " <td>786</td>\n",
- " <td>-106.269643</td>\n",
- " <td>13.520311</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>3653.514821</td>\n",
- " <td>3820</td>\n",
- " <td>166.485179</td>\n",
- " <td>4.358251</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>3478.878964</td>\n",
- " <td>3473</td>\n",
- " <td>-5.878964</td>\n",
- " <td>0.169276</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>3534.709964</td>\n",
- " <td>3186</td>\n",
- " <td>-348.709964</td>\n",
- " <td>10.945071</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>900.021763</td>\n",
- " <td>685</td>\n",
- " <td>-215.021763</td>\n",
- " <td>31.390038</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>214.674054</td>\n",
- " <td>256</td>\n",
- " <td>41.325946</td>\n",
- " <td>16.142948</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>639.482237</td>\n",
- " <td>566</td>\n",
- " <td>-73.482237</td>\n",
- " <td>12.982727</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>1475.478612</td>\n",
- " <td>1293</td>\n",
- " <td>-182.478612</td>\n",
- " <td>14.112808</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>872.131134</td>\n",
- " <td>944</td>\n",
- " <td>71.868866</td>\n",
- " <td>7.613227</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>1226.884458</td>\n",
- " <td>1186</td>\n",
- " <td>-40.884458</td>\n",
- " <td>3.447256</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>1195.639823</td>\n",
- " <td>1044</td>\n",
- " <td>-151.639823</td>\n",
- " <td>14.524887</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>1432.790074</td>\n",
- " <td>1383</td>\n",
- " <td>-49.790074</td>\n",
- " <td>3.600150</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>3575.285742</td>\n",
- " <td>3736</td>\n",
- " <td>160.714258</td>\n",
- " <td>4.301773</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>1098.867095</td>\n",
- " <td>1039</td>\n",
- " <td>-59.867095</td>\n",
- " <td>5.761992</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>1095.121958</td>\n",
- " <td>994</td>\n",
- " <td>-101.121958</td>\n",
- " <td>10.173235</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>1296.851003</td>\n",
- " <td>1429</td>\n",
- " <td>132.148997</td>\n",
- " <td>9.247655</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>1458.344504</td>\n",
- " <td>1532</td>\n",
- " <td>73.655496</td>\n",
- " <td>4.807800</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>1257.183801</td>\n",
- " <td>1262</td>\n",
- " <td>4.816199</td>\n",
- " <td>0.381632</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>1294.327396</td>\n",
- " <td>1214</td>\n",
- " <td>-80.327396</td>\n",
- " <td>6.616754</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>3587.913110</td>\n",
- " <td>3478</td>\n",
- " <td>-109.913110</td>\n",
- " <td>3.160239</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>3572.568763</td>\n",
- " <td>3427</td>\n",
- " <td>-145.568763</td>\n",
- " <td>4.247702</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>694.462976</td>\n",
- " <td>521</td>\n",
- " <td>-173.462976</td>\n",
- " <td>33.294237</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>1241.508649</td>\n",
- " <td>1216</td>\n",
- " <td>-25.508649</td>\n",
- " <td>2.097751</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>957.733177</td>\n",
- " <td>909</td>\n",
- " <td>-48.733177</td>\n",
- " <td>5.361186</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>53</th>\n",
- " <td>1277.755356</td>\n",
- " <td>1188</td>\n",
- " <td>-89.755356</td>\n",
- " <td>7.555165</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Predictions Targets Residuals Difference%\n",
- "0 1091.364516 1218 126.635484 10.397002\n",
- "1 1057.719388 1151 93.280612 8.104310\n",
- "2 1170.036887 1027 -143.036887 13.927642\n",
- "3 1259.987262 1330 70.012738 5.264116\n",
- "4 1078.100005 945 -133.100005 14.084657\n",
- "5 1394.956731 1223 -171.956731 14.060240\n",
- "6 1506.660989 1526 19.339011 1.267301\n",
- "7 1358.148148 1418 59.851852 4.220864\n",
- "8 990.063854 975 -15.063854 1.545011\n",
- "9 1384.551075 1350 -34.551075 2.559339\n",
- "10 1343.005058 1300 -43.005058 3.308081\n",
- "11 967.122403 974 6.877597 0.706119\n",
- "12 1426.599642 1321 -105.599642 7.993917\n",
- "13 1200.898158 1295 94.101842 7.266551\n",
- "14 985.642063 1084 98.357937 9.073610\n",
- "15 271.859854 322 50.140146 15.571474\n",
- "16 672.261307 746 73.738693 9.884543\n",
- "17 114.609218 299 184.390782 61.669158\n",
- "18 1476.113622 1551 74.886378 4.828264\n",
- "19 1289.180154 1356 66.819846 4.927717\n",
- "20 1279.616087 1248 -31.616087 2.533340\n",
- "21 3584.319579 3658 73.680421 2.014227\n",
- "22 1434.194940 1456 21.805060 1.497600\n",
- "23 1349.242107 1405 55.757893 3.968533\n",
- "24 162.148057 309 146.851943 47.524901\n",
- "25 114.034467 252 137.965533 54.748227\n",
- "26 1358.412778 1471 112.587222 7.653788\n",
- "27 237.654959 269 31.345041 11.652431\n",
- "28 1479.194768 1452 -27.194768 1.872918\n",
- "29 892.269643 786 -106.269643 13.520311\n",
- "30 3653.514821 3820 166.485179 4.358251\n",
- "31 3478.878964 3473 -5.878964 0.169276\n",
- "32 3534.709964 3186 -348.709964 10.945071\n",
- "33 900.021763 685 -215.021763 31.390038\n",
- "34 214.674054 256 41.325946 16.142948\n",
- "35 639.482237 566 -73.482237 12.982727\n",
- "36 1475.478612 1293 -182.478612 14.112808\n",
- "37 872.131134 944 71.868866 7.613227\n",
- "38 1226.884458 1186 -40.884458 3.447256\n",
- "39 1195.639823 1044 -151.639823 14.524887\n",
- "40 1432.790074 1383 -49.790074 3.600150\n",
- "41 3575.285742 3736 160.714258 4.301773\n",
- "42 1098.867095 1039 -59.867095 5.761992\n",
- "43 1095.121958 994 -101.121958 10.173235\n",
- "44 1296.851003 1429 132.148997 9.247655\n",
- "45 1458.344504 1532 73.655496 4.807800\n",
- "46 1257.183801 1262 4.816199 0.381632\n",
- "47 1294.327396 1214 -80.327396 6.616754\n",
- "48 3587.913110 3478 -109.913110 3.160239\n",
- "49 3572.568763 3427 -145.568763 4.247702\n",
- "50 694.462976 521 -173.462976 33.294237\n",
- "51 1241.508649 1216 -25.508649 2.097751\n",
- "52 957.733177 909 -48.733177 5.361186\n",
- "53 1277.755356 1188 -89.755356 7.555165"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Residuals shows the deviation\n",
- "#Display difference in percent for Prediction & Target\n",
- "df_pf['Difference%']=np.absolute(df_pf['Residuals']/df_pf['Targets']*100)\n",
- "df_pf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {
- "editable": true,
- "scrolled": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
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- "\n",
- " .dataframe tbody tr th {\n",
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- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Predictions</th>\n",
- " <th>Targets</th>\n",
- " <th>Residuals</th>\n",
- " <th>Difference%</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " <td>54.000000</td>\n",
- " <td>54.000000</td>\n",
- " <td>54.000000</td>\n",
- " <td>54.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>1388.186984</td>\n",
- " <td>1379.592593</td>\n",
- " <td>-8.594392</td>\n",
- " <td>10.443333</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>929.385603</td>\n",
- " <td>918.172272</td>\n",
- " <td>112.470052</td>\n",
- " <td>12.721770</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>114.034467</td>\n",
- " <td>252.000000</td>\n",
- " <td>-348.709964</td>\n",
- " <td>0.169276</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>971.752318</td>\n",
- " <td>952.250000</td>\n",
- " <td>-87.398366</td>\n",
- " <td>3.485480</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>1258.585532</td>\n",
- " <td>1220.500000</td>\n",
- " <td>-0.531383</td>\n",
- " <td>6.941653</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>1431.242466</td>\n",
- " <td>1426.250000</td>\n",
- " <td>73.674190</td>\n",
- " <td>12.650153</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>3653.514821</td>\n",
- " <td>3820.000000</td>\n",
- " <td>184.390782</td>\n",
- " <td>61.669158</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Predictions Targets Residuals Difference%\n",
- "count 54.000000 54.000000 54.000000 54.000000\n",
- "mean 1388.186984 1379.592593 -8.594392 10.443333\n",
- "std 929.385603 918.172272 112.470052 12.721770\n",
- "min 114.034467 252.000000 -348.709964 0.169276\n",
- "25% 971.752318 952.250000 -87.398366 3.485480\n",
- "50% 1258.585532 1220.500000 -0.531383 6.941653\n",
- "75% 1431.242466 1426.250000 73.674190 12.650153\n",
- "max 3653.514821 3820.000000 184.390782 61.669158"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_pf.describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Hier werden die Leistungskennzahlen des Modells zur Vorhersage der Transportnachfrage analysiert. Die wichtigsten Erkenntnisse aus den Statistiken sind:\n",
- "\n",
- "Das durchschnittliche Residuum ist negativ, was darauf hinweist, dass das Modell die tatsächliche Nachfrage unterschätzt.\n",
- "Die MSE-Werte (Mean Squared Error) sind relativ hoch, was auf ungenaue Vorhersagen hinweist. \n",
- "Der R-Quadrat-Wert ist niedrig, was bedeutet, dass das Modell die Varianz der Zielgröße nicht gut erfasst.\n",
- "Diese Tabelle zeigt die Limitierungen des Modells auf, bietet aber auch Ansatzpunkte zur Verbesserung. \n",
- "Durch bessere Daten und alternative Modellierungsansätze kann die Genauigkeit des Modells erhöht und die Dynamiken des Transportmarktes präziser erfasst werden."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 5 Evaluation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Der Datensatz zeigt eindrucksvoll, wie Uber Technologies Inc. durch Data Mining seine Servicequalität verbessert. Die Analyse von historischen Buchungen, Verkehrsmustern und Wetterbedingungen liefert wertvolle Erkenntnisse, die zu präziseren Prognosen und einer besseren Ressourcenverteilung führen. Trotz der detaillierten Daten und der Wirksamkeit der Data-Mining-Strategie gibt es Potenzial für weitere Optimierungen. Insgesamt bietet der Datensatz eine wertvolle Grundlage für das Verständnis und die Weiterentwicklung datenbasierter Entscheidungsprozesse in der Transportindustrie."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 6 Umsetzung"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Umsetzung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Die Umsetzung von Data-Mining-Technologien und -Methoden zur Vorhersage von Angebot und Nachfrage kann bei Uber Technologies Inc. dazu eingesetzt werden, um die Angebots- und Nachfrage-Prognosen zu optimieren und damit die Verfügbarkeit von Fahrzeugen zu gewährleisten, die Entscheidungsfindung zu verbessern und eine effektivere Ressourcenallokation zu ermöglichen. Dies trägt letztendlich zu einer effizienteren und profitableren Geschäftstätigkeit bei"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "category": "Forecast",
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.2"
- },
- "skipNotebookInDeployment": true,
- "title": "Forecast of required vehicles in the city center"
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}