ub_regression_vorlage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import der benötigten Bibliotheken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ____ import ____\n",
    "from ____ import ____\n",
    "from ____ import ____\n",
    "from ____ import ____\n",
    "from ____ import ____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import des Datensatzes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\".../SportscarsData.csv\", sep = \";\", decimal =\",\")\n",
    "df = df.astype({\"Leistung_PS\":'float', \"Drehmoment_Nm\": 'float', \"Hubraum_cm3\" : 'float', \"Höchstgeschwindigkeit_kmh\" : 'float', \"Leergewicht_kg\" : 'float', \"Preis_euro\" : 'float'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aufbereitung der Daten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def onehot_encode(data, columns, prefix):\n",
    "    for column in columns:\n",
    "        dummies = pd.get_dummies(data[column], prefix = prefix)\n",
    "        data = pd.concat([data, dummies], axis = 1)\n",
    "        data.drop(column, axis = 1, inplace = True)\n",
    "    return data\n",
    "\n",
    "df = onehot_encode(df, ['Hersteller'], \"Hersteller\")\n",
    "\n",
    "X = df.drop(['Modell', 'BeschleunigungViertelmeile_sek'], axis = 1).values\n",
    "y = df['BeschleunigungViertelmeile_sek'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Erstellen des Modells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr = ____(____, ____, ____)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aufteilen des Datensatzes in Trainings- und Testmenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "____, ____, ____, ____ = ____(____, ____, ____, ____)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trainieren des Modells und Treffen der Vorhersagen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "____.____(____, ____)\n",
    "predictions = ____.____(____)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berechnen des MAE Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mae = ____(____, ____)\n",
    "print(\"MAE Score: {}\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berechnen des RMSE Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(____(____, ____))\n",
    "print(\"RMSE Score: {}\".format(rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berechnen des Bestimmtheitsmaßes R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2 = ____(____, ____)\n",
    "print(\"R^2 Score: {}\".format(r2))"
   ]
  }
 ],
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