{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "24e7652f",
   "metadata": {},
   "source": [
    "# \"Python für ML\" Kurzeinführung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92bb5be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports überall im Code möglich, aber die Konvention ist alle benötigten import statements\n",
    "# gleich zu Beginn einer Datei zu machen\n",
    "\n",
    "# numpy ist ein Python-Modul für Numerik, das sowohl Funktionalität als auch Effizienz bietet\n",
    "import numpy as np\n",
    "\n",
    "# pandas ist sehr gut zum Arbeiten mit tabellarischen Daten, egal ob csv, xls oder xlsx\n",
    "import pandas as pd\n",
    "\n",
    "# plotting settings\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "\n",
    "# matplotlib ist ein sehr umfangreiches Modul zum Erstellen von Visualisierungen/Plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# seaborn erleichtert das Erstellen von oft verwendeten Plot-Typen;\n",
    "# es basiert selbst auf matplotlib und man kann beides kombinieren\n",
    "# eine schöne Einführung in Seaborn: https://www.kaggle.com/learn/data-visualization\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0562db47",
   "metadata": {},
   "source": [
    "Es gibt verschiedene Zelltypen in Jupyter - Code oder Markdown. Mit Markdown kann man den Code schöner dokumentieren als durch Kommentare im Code selbst. Es sind *verschiedene* **Formatierungen** und sogar LaTeX-ähnliche mathematische Formeln möglich. Sowohl inline ($h_\\theta(x) = \\theta^Tx$) als auch zentriert in separaten Zeilen:\n",
    "\n",
    "$$h_\\theta(x) = \\theta^Tx$$\n",
    "\n",
    "<p><b>HTML</b> wird ebenfalls erkannt.</p>\n",
    "\n",
    "Wir laden jetzt eine CSV-Datei mit Pandas:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f91177",
   "metadata": {},
   "source": [
    "## Daten laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "724b4875",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file_path = '../data/exam-iq.csv'\n",
    "data = pd.read_csv(data_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5661e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pass</th>\n",
       "      <th>Hours</th>\n",
       "      <th>IQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2.25</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2.75</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3.25</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>4.25</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>4.50</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>5.50</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pass  Hours   IQ\n",
       "0      0   0.50  110\n",
       "1      0   0.75   95\n",
       "2      0   1.00  118\n",
       "3      0   1.25   97\n",
       "4      0   1.50  100\n",
       "5      0   1.75  110\n",
       "6      0   1.75  115\n",
       "7      1   2.00  104\n",
       "8      1   2.25  120\n",
       "9      0   2.50   98\n",
       "10     1   2.75  118\n",
       "11     0   3.00   88\n",
       "12     1   3.25  108\n",
       "13     1   4.00  109\n",
       "14     1   4.25  110\n",
       "15     1   4.50  112\n",
       "16     1   4.75   97\n",
       "17     1   5.00  102\n",
       "18     1   5.50  109\n",
       "19     0   3.50  125"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66f73953",
   "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>Pass</th>\n",
       "      <th>Hours</th>\n",
       "      <th>IQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Pass  Hours   IQ\n",
       "0     0   0.50  110\n",
       "1     0   0.75   95\n",
       "2     0   1.00  118\n",
       "3     0   1.25   97\n",
       "4     0   1.50  100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2feffb",
   "metadata": {},
   "source": [
    "**Nützliche Shortcuts:<br>**\n",
    "b - fügt eine leere Zelle unterhalb der aktuell aktiven hinzu<br>\n",
    "a - fügt eine leere Zelle oberhalb der aktuell aktiven hinzu<br>\n",
    "CTRL + ENTER - führt aktive Zelle aus (Mac: CMD + ENTER)<br>\n",
    "SHIFT + ENTER - führt aktive Zelle aus und wechselt zur nächsten Zelle<br>\n",
    "ENTER - wechselt in den Bearbeiten-Modus einer Zelle<br>\n",
    "ESC - wechselt in den Ansicht-Modus einer Zelle<br>\n",
    "d d - (2x d) - löscht aktive Zelle<br>\n",
    "CTRL + C und CTRL + V funktionieren wie erwartet<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "099ff4d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Hours', ylabel='IQ'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=data['Hours'], y=data['IQ'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e62008e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Hours', ylabel='IQ'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=data['Hours'], y=data['IQ'], hue=data['Pass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f6f2ee",
   "metadata": {},
   "source": [
    "Ein anderer Datensatz..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f7c4ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "melbourne_file_path = 'data/melb_data.csv'\n",
    "melbourne_data = pd.read_csv(melbourne_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5e017fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "melbourne_data = melbourne_data.dropna(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0de6a41c",
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>124 Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1876000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>7/05/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>98 Charles St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1636000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb          Address  Rooms Type      Price Method SellerG  \\\n",
       "1  Abbotsford  25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "2  Abbotsford     5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "4  Abbotsford      55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "6  Abbotsford     124 Yarra St      3    h  1876000.0      S  Nelson   \n",
       "7  Abbotsford    98 Charles St      2    h  1636000.0      S  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
       "6  7/05/2016       2.5    3067.0  ...       2.0  0.0     245.0         210.0   \n",
       "7  8/10/2016       2.5    3067.0  ...       1.0  2.0     256.0         107.0   \n",
       "\n",
       "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
       "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
       "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
       "6     1910.0        Yarra  -37.8024    144.9993  Northern Metropolitan   \n",
       "7     1890.0        Yarra  -37.8060    144.9954  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "4        4019.0  \n",
       "6        4019.0  \n",
       "7        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melbourne_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3523f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 1000.0)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=melbourne_data['BuildingArea'], y=melbourne_data['Price'])\n",
    "ax.set(xlim=(0, 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3cc3971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xplot = [1,2]\n",
    "yplot = [3, 8]\n",
    "ax = sns.lineplot(x=xplot, y=yplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f8a1a5",
   "metadata": {},
   "source": [
    "Sie plotten verschiedene Dinge in eine Grafik, indem Sie seaborn mehrfach hintereinander innerhalb derselben Codezelle aufrufen. Wir plotten jetzt sowohl die Datenpunkte von oben, als auch eine Gerade:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "410a31ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6196"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(melbourne_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb578f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BuildingArea', ylabel='Price'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=melbourne_data['BuildingArea'][:100], y=melbourne_data['Price'][:100])\n",
    "xplot = [0,300]\n",
    "yplot = [1e5, 3e6]\n",
    "sns.lineplot(x=xplot, y=yplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "223eeae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,2,3]\n",
    "l.append(4)\n",
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e335cc5",
   "metadata": {},
   "source": [
    "So könnte man die Feature-Matrix X und den Vektor der Outputs erstellen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30b87fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = [1,2,3]\n",
    "l2 = [4]\n",
    "l1 + l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5215abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = [\n",
    "    [1,2,3],\n",
    "    [4,5,6]\n",
    "]\n",
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "791052f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79166874",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "Y = []\n",
    "for i, row in melbourne_data.iterrows():\n",
    "    X.append([1] + [row['BuildingArea']])\n",
    "    Y.append(row['Price'])\n",
    "\n",
    "# Für Matrixmultiplikation eiget sich numpy besser, daher wandeln wir die Python-Listen in numpy arrays um:\n",
    "X = np.array(X)\n",
    "Y = np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23f97e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.  ,  79.  ],\n",
       "       [  1.  , 150.  ],\n",
       "       [  1.  , 142.  ],\n",
       "       ...,\n",
       "       [  1.  ,  35.64],\n",
       "       [  1.  ,  61.6 ],\n",
       "       [  1.  , 388.5 ]], shape=(6196, 2))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860aea24",
   "metadata": {},
   "source": [
    "### Slicen von numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69d5eed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.  79.]\n",
      " [  1. 150.]\n",
      " [  1. 142.]\n",
      " [  1. 210.]\n",
      " [  1. 107.]]\n",
      "[1035000. 1465000. 1600000. 1876000. 1636000. 1097000. 1350000.]\n"
     ]
    }
   ],
   "source": [
    "# slicen von numpy arrays\n",
    "print(X[:5])\n",
    "print(Y[:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a5ebf4dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 79.   150.   142.   ...  35.64  61.6  388.5 ]\n"
     ]
    }
   ],
   "source": [
    "# slicen von numpy arrays: Auswahl einer Spalte\n",
    "print(X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "796dfee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 79. 150. 142. 210. 107.]\n"
     ]
    }
   ],
   "source": [
    "# slicen von numpy arrays: die ersten 5 Zeilen von Spalte 2 (also index 1):\n",
    "print(X[:5,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "681e36cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# slicen von numpy arrays: die ersten 5 Zeilen von Spalte 1 (also index ):\n",
    "print(X[:5,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce97c986",
   "metadata": {},
   "source": [
    "### Multiplikation bei numpy arrays\n",
    "\n",
    "Sie können numpy arrays sowohl elementweise multiplizieren als auch als Matrixmultiplikation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6f4cabc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'asdas {}'\n",
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "73cc9eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a: 7.1234567'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 7.1234567\n",
    "s = 'a: {}'.format(a)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c853f2d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: [1 1 1]\n",
      "b: [1 2 3]\n",
      "elementweise Operationen:\n",
      "a+b: [2 3 4]\n",
      "a*b: [1 2 3]\n",
      "a/b: [1.         0.5        0.33333333]\n",
      "\n",
      "Skalarprodukt:\n",
      "a@b: 6\n",
      "\n",
      "Matrix x Vektor:\n",
      "M:\n",
      "[[1 3]\n",
      " [2 1]]\n",
      "x: [1 1]\n",
      "M@x: [4 3]\n",
      "\n",
      "Matrix transponieren:\n",
      "M.T:\n",
      "[[1 2]\n",
      " [3 1]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,1,1])\n",
    "b = np.array([1,2,3])\n",
    "print('a: {}'.format(a))\n",
    "print('b: {}'.format(b))\n",
    "\n",
    "print('elementweise Operationen:')\n",
    "print('a+b: {}'.format(a+b))\n",
    "print('a*b: {}'.format(a*b))\n",
    "print('a/b: {}'.format(a/b))\n",
    "\n",
    "print()\n",
    "print('Skalarprodukt:')\n",
    "print('a@b: {}'.format(a@b))\n",
    "\n",
    "print()\n",
    "print('Matrix x Vektor:')\n",
    "M = np.array([[1,3], [2,1]])\n",
    "x = np.array([1,1])\n",
    "print('M:\\n{}'.format(M))\n",
    "print('x: {}'.format(x))\n",
    "print('M@x: {}'.format(M@x))\n",
    "\n",
    "print()\n",
    "print('Matrix transponieren:')\n",
    "print('M.T:\\n{}'.format(M.T))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0577f21",
   "metadata": {},
   "source": [
    "## Analytische Lösung der linearen Regression\n",
    "\n",
    "`np.linalg.solve(A, b)` berechnet $w$ im linearen Gleichungssystem\n",
    "\n",
    "$ A w = b $\n",
    "\n",
    "$A$ - Matrix,\n",
    "$w$ - Vektor (unsere unbekannten),\n",
    "$b$ - Vektor.\n",
    "\n",
    "Wir suchen die Lösung $w$ im folgenden Gleichungssystem:\n",
    "\n",
    "$$ X^T X w = X^T Y $$\n",
    "\n",
    "Mit $A = X^TX$ und $b = X^T Y$ berechnet `np.linalg.solve(A, b)` unsere gesuchten Paramter für die lineare Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35a78137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[510531.72552189   3943.64499525]\n",
      "CPU times: user 302 μs, sys: 10 μs, total: 312 μs\n",
      "Wall time: 316 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "w_ana = np.linalg.solve(X.T @ X, X.T @ Y)\n",
    "print(w_ana)"
   ]
  }
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