{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maskinlæring (Titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Lese dataene\n",
    "titanic = pd.read_csv(\"Datafiler/titanic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utforsking og opprydding av datasettet\n",
    "La oss undersøke dataene og rydde litt, dersom vi trenger det."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  sex   age  sibsp  parch     fare embarked  class deck  \\\n",
       "0         0       3    0  22.0      1      0   7.2500        S  Third  NaN   \n",
       "1         1       1    1  38.0      1      0  71.2833        C  First    C   \n",
       "2         1       3    1  26.0      0      0   7.9250        S  Third  NaN   \n",
       "3         1       1    1  35.0      1      0  53.1000        S  First    C   \n",
       "4         0       3    0  35.0      0      0   8.0500        S  Third  NaN   \n",
       "\n",
       "   embark_town alive  alone  \n",
       "0  Southampton    no  False  \n",
       "1    Cherbourg   yes  False  \n",
       "2  Southampton   yes   True  \n",
       "3  Southampton   yes  False  \n",
       "4  Southampton    no   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skriver ut fem første linjer\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vi ser at det ikke er alle kategoriene vi trenger. Siden vi er interessert i hvem som overlevde, og hvorfor, kan det også være lurt å sjekke hvor mange dette var."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.38 % overlevde\n"
     ]
    }
   ],
   "source": [
    "# Hvor mange overlevde?\n",
    "overlevde_prosent = (titanic['survived'].sum()/titanic['survived'].count())*100\n",
    "print(f'{overlevde_prosent:.2f} % overlevde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Southampton\n",
       "1        Cherbourg\n",
       "2      Southampton\n",
       "3      Southampton\n",
       "4      Southampton\n",
       "          ...     \n",
       "886    Southampton\n",
       "887    Southampton\n",
       "888    Southampton\n",
       "889      Cherbourg\n",
       "890     Queenstown\n",
       "Name: embark_town, Length: 891, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sletter kategorier vi ikke er interessert i\n",
    "titanic.pop('deck')\n",
    "titanic.pop('fare')\n",
    "titanic.pop('embarked')\n",
    "titanic.pop('embark_town')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived      0\n",
      "pclass        0\n",
      "sex           0\n",
      "age         177\n",
      "sibsp         0\n",
      "parch         0\n",
      "class         0\n",
      "alive         0\n",
      "alone         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Print antall manglende verdier i kolonnene\n",
    "print(titanic.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fyller inn manglende alder med gjennomsnittet\n",
    "gjennomsnitt = titanic['age'].mean()\n",
    "titanic['age'].fillna(gjennomsnitt, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualiseringer\n",
    "La oss først se hvilken effekt klasse og kjønn hadde på overlevelsessjansene:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B4adMmcItt9wCwKxZs5gyZQpr1qzhkUce4dhjj2XMmDGcdtppLF++HIDf/va3HH/88QBMnTq105bTzOzD6hHnFHbbbTfmzZvHnXfeyXnnnce4cePo3bt33uTT+n6AbbbZJu8eOnQogwcPZsGCBdxyyy1cffXVH5j+xIkTOffcc3nllVeYO3cuhx12GG+++SYDBw5k/vz5JWPyJadm1hX1iCOFF198ka233prPfe5zfO1rX2PevHmMGDGCuXPnAnDbbbeVHX/KlClccsklvPbaa+y1114fqO/Xrx/7778/06dP56ijjqJXr15su+22jBw5kp/+9KdAdpfyE088AcDBBx/MrFmzALjppps6c1HNzD6UHpEUnnzySQ444ADGjBnDhRdeyHnnncf555/P9OnTaWhooFevXmXHP+aYY5g1axaTJ09uc5gpU6bwk5/8hClTpuRlN910E9dddx177703u+++O7fffjsAl19+OVdeeSV77rkny5Yt65yFNDPrBN36P5obGhqi9Z/sLF68mI9//OM1iqhzbU7LYj2bL0ntWiTNjYiGUnU94kjBzMzap2JJQVJfSY9LekLSU5IuTOUzJD0naX56jUnlkvR9SUskLZC0b6ViMzOz0ip59dE7wGERsUZSH+BhSb9KdV+LiFtbDX8EMCq9PgFcld7NzKxKKnakEJk1qbdPepU7gTEJuDGN9ygwUJLv7DIzq6KKnlOQ1EvSfGAFcHdEPJaqLkpNRJdJ2jKVDQWWFkZvSmWtpzlNUqOkxubm5kqGb2bW41Q0KUTEuogYA9QDB0jaA/gG8DFgf2AQcHYHp3lNRDRERENdXV1nh2xm1qNV5Y7miHhV0n3A+Ii4NBW/I+nHwP9J/cuAYYXR6lNZp+rsS+Pae+naXXfdxfTp01m3bh2nnnoq55xzTqfGYWbWGSp59VGdpIGpeyvgM8DTLecJlD3n4WhgYRplDvD5dBXSgcBrEbG8UvFV07p16zjzzDP51a9+xaJFi5g5cyaLFi2qdVhmZh9QySOFIcANknqRJZ/ZEXGHpHsl1QEC5gOnp+HvBCYAS4C3gJMqGFtVPf744+y6667svPPOABx33HHcfvvtjB49usaRmZltqGJJISIWAPuUKD+sjeEDOLNS8dTSsmXLGDZsfctYfX09jz32WJkxzMxqw3c0m5lZzkmhCoYOHcrSpeuvtm1qamLo0A9cbWtmVnNOClWw//7788c//pHnnnuOd999l1mzZvn/nc2sS+oRf7JTVIunH/bu3Zsf/OAHHH744axbt46TTz6Z3XffvepxmJltTI9LCrUyYcIEJkyYUOswzMzKcvORmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyPe6S1B2+/sNOnd6KS85o13Ann3wyd9xxBzvssAMLFy7c+AhmZjXgI4UqOfHEE7nrrrtqHYaZWVlOClVy6KGHMmjQoFqHYWZWlpOCmZnlnBTMzCznpGBmZjknBTMzy/W4S1LbewlpZzv++OO5//77WblyJfX19Vx44YWccsopNYnFzKwtFUsKkvoCDwJbpvncGhHnSxoJzAIGA3OBqRHxrqQtgRuB/YBVwJSIeL5S8VXbzJkzax2CmdlGVbL56B3gsIjYGxgDjJd0IPDvwGURsSuwGmj5uXwKsDqVX5aGMzOzKqpYUojMmtTbJ70COAy4NZXfAByduielflL9OEmqVHxmZvZBFT3RLKmXpPnACuBu4Bng1YhYmwZpAlr+wX4osBQg1b9G1sTUeprTJDVKamxubi4534jozMWoic1hGcys+6loUoiIdRExBqgHDgA+1gnTvCYiGiKioa6u7gP1ffv2ZdWqVd16pxoRrFq1ir59+9Y6FDPrYapy9VFEvCrpPuAgYKCk3ulooB5YlgZbBgwDmiT1BgaQnXDukPr6epqammjrKKK76Nu3L/X19bUOw8x6mEpefVQHvJcSwlbAZ8hOHt8HHEN2BdIJwO1plDmp/3ep/t7YhJ/7ffr0YeTIkZ2wBGZmPU8ljxSGADdI6kXWTDU7Iu6QtAiYJenbwH8D16XhrwP+U9IS4BXguArGZmZmJVQsKUTEAmCfEuXPkp1faF3+NnBspeIxM7ON82MuzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZrmKJQVJwyTdJ2mRpKckTU/lF0haJml+ek0ojPMNSUsk/UHS4ZWKzczMSqvYfzQDa4GvRsQ8Sf2BuZLuTnWXRcSlxYEljQaOA3YHdgJ+I2m3iFhXwRjNzKygYkcKEbE8Iual7jeAxcDQMqNMAmZFxDsR8RywBDigUvGZmdkHVeWcgqQRwD7AY6noLEkLJF0vabtUNhRYWhitifJJxMzMOlnFk4KkfsBtwJcj4nXgKmAXYAywHPhuB6c3TVKjpMbm5ubODtfMrEeraFKQ1IcsIdwUET8DiIiXI2JdRLwPXMv6JqJlwLDC6PWpbAMRcU1ENEREQ11dXSXDNzPrcSp59ZGA64DFEfG9QvmQwmCfBRam7jnAcZK2lDQSGAU8Xqn4zMzsgyp59dHBwFTgSUnzU9m5wPGSxgABPA+cBhART0maDSwiu3LpTF95ZGZWXRVLChHxMKASVXeWGeci4KJKxWRmZuX5jmYzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOzXJtPSZU0qNyIEfFK54djZma1VO7R2XPJ/vOg1OOvA9i5IhGZmVnNtJkUImJkNQMxM7PaK9d8tG+5ESNiXueHY2ZmtVSu+ei7ZeoCOKyTYzEzsxor13z06WoGYmZmtdeu/2iWtAcwGujbUhYRN25knGHAjcCOZEcW10TE5emqpluAEcDzwOSIWC1JwOXABOAt4EQ3UZmZVddG71OQdD5wRXp9GrgEmNiOaa8FvhoRo4EDgTMljQbOAe6JiFHAPakf4AhgVHpNA67q2KKYmdmH1Z6b144BxgEvRcRJwN7AgI2NFBHLW37pR8QbwGJgKDAJuCENdgNwdOqeBNwYmUeBgZKGdGBZzMzsQ2pPUvhzRLwPrJW0LbACGNaRmUgaAewDPAbsGBHLU9VLZM1LkCWMpYXRmlJZ62lNk9QoqbG5ubkjYZiZ2Ua0Jyk0ShoIXEt2Q9s84HftnYGkfsBtwJcj4vViXUQE2fmGdouIayKiISIa6urqOjKqmZltxEZPNEfEGanzakl3AdtGxIL2TFxSH7KEcFNE/CwVvyxpSEQsT81DK1L5MjY8AqlPZWZmViXtOdF8T0t3RDwfEQuKZWXGE3AdsDgivleomgOckLpPAG4vlH9emQOB1wrNTGZmVgXl7mjuC2wNbC9pO9Y/A2lbSrT1l3AwMBV4UtL8VHYucDEwW9IpwAvA5FR3J9nlqEvILkk9qUNLYmZmH1q55qPTgC8DO5GdS2hJCq8DP9jYhCPiYUo/TA+yq5laDx/AmRubrpmZVU65O5ovBy6X9L8j4ooqxmRmZjXSnhPNV0j6K7I7kHsXysve0WxmZt3PRpOCpP8EdgHmA+tScZA9wsLMzDYj7Xn2UQMwOrX5m5nZZqw9N68tBP6i0oGYmVnttedIYXtgkaTHgXdSWUTEpMqFZWZmtdCepHBBoVvAJ4HjKhKNmZnV1EabjyLiAbJ7E44CZpD949rVlQ3LzMxqodwdzbsBx6fXSrI/xpH/kc3MbPNVrvnoaeAh4KiIWAIg6StVicrMzGqiXPPR3wHLgfskXStpHG0/tsLMzDYDbSaFiPhFRBwHfAy4j+w5SDtIukrS31QpPjMzq6L2nGh+MyJujoi/JfuPg/8Gzq54ZGZmVnXtuXktFxGr0z+ffeApp2Zm1v11KCmYmdnmzUnBzMxyTgpmZpZzUjAzs5yTgpmZ5SqWFCRdL2mFpIWFsgskLZM0P70mFOq+IWmJpD9IOrxScZmZWdsqeaQwAxhfovyyiBiTXncCSBpN9uTV3dM4P5TUq4KxmZlZCRVLChHxIPBKOwefBMyKiHci4jlgCXBApWIzM7PSanFO4SxJC1Lz0napbCiwtDBMUyr7AEnTJDVKamxubq50rGZmPUq1k8JVwC7AGLKH7X23oxNId1Q3RERDXV1dJ4dnZtazVTUpRMTLEbEuIt4HrmV9E9EyYFhh0PpUZmZmVVTVpCBpSKH3s0DLlUlzgOMkbSlpJDAKeLyasZmZWfv+o3mTSJoJjAW2l9QEnA+MlTQGCOB54DSAiHhK0mxgEbAWODMi1lUqNjMzK61iSSEiji9RfF2Z4S8CLqpUPGZmtnG+o9nMzHJOCmZmlqtY85F9eDt8/Ye1DqHDVlxyRq1DMLMPwUcKZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlvMdzWZmrfTkpwn4SMHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZrmKJQVJ10taIWlhoWyQpLsl/TG9b5fKJen7kpZIWiBp30rFZWZmbavkkcIMYHyrsnOAeyJiFHBP6gc4AhiVXtOAqyoYl5mZtaFiSSEiHgReaVU8Cbghdd8AHF0ovzEyjwIDJQ2pVGxmZlZatc8p7BgRy1P3S8COqXsosLQwXFMq+wBJ0yQ1Smpsbm6uXKRmZj1QzU40R0QAsQnjXRMRDRHRUFdXV4HIzMx6rmo/++hlSUMiYnlqHlqRypcBwwrD1acyMwD69Tuy1iF02Jo1v6x1CGYdVu0jhTnACan7BOD2Qvnn01VIBwKvFZqZzMysSip2pCBpJjAW2F5SE3A+cDEwW9IpwAvA5DT4ncAEYAnwFnBSpeIyM7O2VSwpRMTxbVSNKzFsAGdWKhYzM2sf39FsZmY5/8mOWYV0tz9q6aw/abHuzUcKZmaWc1IwM7Ock4KZmeV6zDmF7njz09ZndL+Yzax785GCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWa4mT0mV9DzwBrAOWBsRDZIGAbcAI4DngckRsboW8ZmZ9VS1PFL4dESMiYiG1H8OcE9EjALuSf1mZlZFXan5aBJwQ+q+ATi6dqGYmfVMtUoKAfxa0lxJ01LZjhGxPHW/BOxYakRJ0yQ1Smpsbm6uRqxmZj1Grf557ZCIWCZpB+BuSU8XKyMiJEWpESPiGuAagIaGhpLDmJnZpqnJkUJELEvvK4CfAwcAL0saApDeV9QiNjOznqzqSUHSNpL6t3QDfwMsBOYAJ6TBTgBur3ZsZmY9XS2aj3YEfi6pZf43R8Rdkn4PzJZ0CvACMLkGsZmZ9WhVTwoR8Sywd4nyVcC4asdjZmbrdaVLUs3MrMacFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZma5LpcUJI2X9AdJSySdU+t4zMx6ki6VFCT1Aq4EjgBGA8dLGl3bqMzMeo4ulRSAA4AlEfFsRLwLzAIm1TgmM7MeQxFR6xhyko4BxkfEqal/KvCJiDirMMw0YFrq/Sjwh6oHWj3bAytrHYRtMm+/7mtz33bDI6KuVEXvakfyYUXENcA1tY6jGiQ1RkRDreOwTePt13315G3X1ZqPlgHDCv31qczMzKqgqyWF3wOjJI2U9BHgOGBOjWMyM+sxulTzUUSslXQW8F9AL+D6iHiqxmHVUo9oJtuMeft1Xz1223WpE81mZlZbXa35yMzMashJwczMck4KXZCk6yWtkLSw1rFYx0gaJuk+SYskPSVpeq1jsvaT1FfS45KeSNvvwlrHVG0+p9AFSToUWAPcGBF71Doeaz9JQ4AhETFPUn9gLnB0RCyqcWjWDpIEbBMRayT1AR4GpkfEozUOrWp8pNAFRcSDwCu1jsM6LiKWR8S81P0GsBgYWtuorL0isyb19kmvHvXL2UnBrEIkjQD2AR6rcSjWAZJ6SZoPrADujogetf2cFMwqQFI/4DbgyxHxeq3jsfaLiHURMYbsiQoHSOpRTbhOCmadLLVF3wbcFBE/q3U8tmki4lXgPmB8jUOpKicFs06UTlReByyOiO/VOh7rGEl1kgam7q2AzwBP1zSoKnNS6IIkzQR+B3xUUpOkU2odk7XbwcBU4DBJ89NrQq2DsnYbAtwnaQHZs9jujog7ahxTVfmSVDMzy/lIwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYFaGpDWF7gmS/kfScEkXSPo/tYzNrBKcFMzaQdI44PvAERHxQq3jMasUJwWzjUiPMr8WOCoinilR/wVJv0/P4L9N0tap/FhJC1P5g6ls9/S8/vmSFkgalco/Vyj/kaRe1VxGsxZOCmblbQn8guw/Edp63MHPImL/iNib7FHZLXegfws4PJVPTGWnA5enB641AE2SPg5MAQ5O5euAf6jAsphtlJOCWXnvAY+wfkdfyh6SHpL0JNnOfPdU/ltghqQvAC2//H8HnCvpbGB4RPwZGAfsB/w+PbJ5HLBzpy+JWTs4KZiV9z4wmewRyue2McwM4KyI2BO4EOgLEBGnA+cBw4C5kgZHxM1kRw1/Bu6UdBgg4IaIGJNeH42ICyq5UGZtcVIw24iIeAs4EviHNh5O2B9Ynh6ZnTf7SNolIh6LiG8BzcAwSTsDz0bE94Hbgb2Ae4BjJO2QxhskaXhll8qstN61DsCsO4iIVySNBx6U1Nyq+p/I/l2tOb33T+XfSSeSRbbjfwI4G5gq6T3gJeBf07TPA34taQuyJqszAV/lZFXnp6SamVnOzUdmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWe7/Axa9kZ33J1VzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Passasjerklasse\n",
    "sns.countplot(x='pclass', hue='survived', data=titanic, palette='ocean')\n",
    "plt.title(\"Antall døde (0) og overlevende (1) av hver klasse\")\n",
    "plt.xlabel(\"Klasse\")\n",
    "plt.ylabel(\"Antall\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9914+//nPM3ToUE455RSWLFkCwO9//3vGjx8PwIQJEzpsPTf5NgUzs/bYfffdmTNnDrfeeivnnHMOhx56KD179iyqfFo/D/De97636B4wYAD9+vVj3rx5TJ8+nSuvvPId8x89ejRnn302K1euZPbs2RxyyCG8/PLLbLPNNjz44INVY6rHbbO+UjAza4dnnnmGLbfcki9+8Yt885vfZM6cOTQ3NzN79mwAbrzxxjanHzduHBdeeCEvvPAC++yzzzuG9+nTh/32249JkyYxatQoevTowdZbb82gQYP42c9+BqSnlOfOnQvAQQcdxLRp0wCYMmVKh62nk4KZWTs89NBD7L///gwdOpTzzz+fc845h3PPPZdJkyYxbNgwevTo0eb0Y8aMYdq0aYwdO7bmOOPGjeOnP/0p48aNK8qmTJnC1VdfzZAhQ9hzzz25+eabAbjkkku4/PLL2XvvvVm8eHHHrCTd/Deahw0bFu/2R3Z8S+rbfEuqWbJgwQI+9KEPNTqMDlFtXSTNjohh1cb3lYKZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzAp+otnMbD119K3s7b0d/LbbbmPSpEmsWbOGk046ibPOOqtD4wBfKZiZdQtr1qzhtNNO45e//CXz589n6tSpzJ8/v8OX46RgZtYNPPDAA+y2227suuuubLbZZhx99NHF080dyUnBzKwbWLx4MQMHDiz6m5qaOvT1FhVOCmZmVnBSMDPrBgYMGMCiRYuK/paWFgYMGNDhy3FSMDPrBvbbbz/+9Kc/8cQTT/DGG28wbdq0uvy+s29JNTNbT414o3DPnj257LLLGDFiBGvWrOHEE09kzz337PjldPgczcysLg4//HAOP/zwui7D1UdmZlZwUjAzs4KTgpmZFZwUzMysUPekIKmHpD9KuiX3D5J0v6SFkqZL2iyXb577F+bhzfWOzczM1tYZVwqTgAWl/u8CF0fEbsAqYGIunwisyuUX5/HMzKwT1fWWVElNwBHAd4C/lSTgEOALeZRrgfOAK4AjczfADcBlkhQRUc8YzczW1w5n/KBD57f0wlPXOc6JJ57ILbfcwg477MDDDz/cocsvq/eVwveAM4C3cn8/4PmIWJ37W4DKc9oDgEUAefgLefy1SDpZ0ixJs5YtW1bH0M3Muo7jjz+e2267re7LqVtSkDQKWBoRsztyvhHxw4gYFhHD+vfv35GzNjPrsg4++GC22267ui+nntVHBwGjJR0O9Aa2Bi4BtpHUM18NNAGVd78uBgYCLZJ6An2BFXWMz8zMWqnblUJE/F1ENEVEM3A0cGdEHAPcBYzJox0HVH4lYmbuJw+/0+0JZmadqxHPKZxJanReSGozuDqXXw30y+V/C3T8j4+amVmbOuWFeBFxN3B37n4c2L/KOK8Bn++MeMzMrDq/JdXMbD215xbSjjZ+/Hjuvvtuli9fTlNTE+effz4TJ05c94TryUnBzKwbmDp1aqcsx+8+MjOzgpOCmZkVnBTMzKrYGO6I35B1cFIwM2uld+/erFixolsnhohgxYoV9O7de72mc0OzmVkrTU1NtLS00N3fr9a7d2+amprWaxonBTOzVnr16sWgQYMaHUZDuPrIzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs0LdkoKk3pIekDRX0iOSzs/lgyTdL2mhpOmSNsvlm+f+hXl4c71iMzOz6up5pfA6cEhEDAGGAiMlHQB8F7g4InYDVgET8/gTgVW5/OI8npmZdaK6JYVIXsq9vfJfAIcAN+Tya4GjcveRuZ88/FBJqld8Zmb2TnVtU5DUQ9KDwFLgduAx4PmIWJ1HaQEG5O4BwCKAPPwFoF+VeZ4saZakWcuWLatn+GZmm5y6JoWIWBMRQ4EmYH/ggx0wzx9GxLCIGNa/f/93OzszMyvplLuPIuJ54C7gQGAbST3zoCZgce5eDAwEyMP7Ais6Iz4zM0vqefdRf0nb5O4tgE8DC0jJYUwe7Tjg5tw9M/eTh98ZEVGv+MzM7J16rnuUDbYTcK2kHqTkMyMibpE0H5gm6dvAH4Gr8/hXAz+RtBBYCRxdx9jMzKyKuiWFiJgH7Ful/HFS+0Lr8teAz9crHjMzWzc/0WxmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs0LNW1IlbdfWhBGxsuPDMTOzRmrrOYXZpLeaVntTaQC71iUiMzNrmJpJISIGdWYgZmbWeG1VH324rQkjYk7Hh2NmZo3UVvXRv7UxrPJjOWZmthFpq/roU50ZiJmZNV67XognaS9gD6B3pSwirqtXUGZm1hjrTAqSzgWGk5LCrcBhwO8AJwUzs41Mex5eGwMcCjwbEScAQ0i/imZmZhuZ9iSFVyPiLWC1pK2BpeSfzTQzs41Le9oUZuWf1byK9EDbS8Af6hmUmZk1xjqTQkScmjuvlHQbsHX+VTUzM9vIrLP6SNIdle6IeDIi5pXLzMxs49HWE829gS2B7SVty9vvQNoaGNAJsZmZWSdrq/roFOCrwM6ktoRKUvgzcFl9wzIzs0Zo64nmS4BLJP11RFzaiTGZmVmDtKeh+VJJHwOay+P7iWYzs41Pe55o/gnwfuBBYE0uDvxEs5nZRqc9zykMA/aIiKh3MGZm1exwxg8aHUKXsfTCU9c90rvQnieaHwb+oq5RmJlZl9CeK4XtgfmSHgBez2UREUfWLywzM2uE9iSF80rdAj4BHF2XaMzMrKHWWX0UEb8hPZswCphM+sW1K+sblpmZNUJbTzTvDozPf8uB6YD8i2xmZhuvtqqPHgXuAUZFxEIASV/rlKjMzKwh2qo++hywBLhL0lWSDuXtV12YmdlGqGZSiIibIuJo4IPAXaT3IO0g6QpJn+mk+MzMrBO1p6H55Yi4PiI+CzQBfwTOrHtkZmbW6drz8FohIlZFxA8j4tB6BWRmZo2zXklhfUgaKOkuSfMlPSJpUi7fTtLtkv6U/2+byyXp+5IWSpon6cP1is3MzKqrW1IAVgNfj4g9gAOA0yTtAZwF3BERg4E7cj/AYcDg/HcycEUdYzMzsyrqlhQiYklEzMndLwILSL/YdiRwbR7tWuCo3H0kcF0k9wHbSNqpXvGZmdk71fNKoSCpGdgXuB/YMSKW5EHPAjvm7gHAotJkLVT52U9JJ0uaJWnWsmXL6he0mdkmqO5JQVIf4EbgqxHx5/Kw/Dru9Xold27oHhYRw/r379+BkZqZWV2TgqRepIQwJSJ+noufq1QL5f9Lc/liYGBp8qZcZmZmnaSedx8JuBpYEBH/Xho0Ezgudx8H3FwqPzbfhXQA8EKpmsnMzDpBe16dvaEOAiYAD0l6MJedDVwAzJA0EXgKGJuH3QocDiwEXgFOqGNsZmZWRd2SQkT8jtrvSnrHw2+5feG0esVjZmbr1il3H5mZWffgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMyv0rNeMJV0DjAKWRsReuWw7YDrQDDwJjI2IVZIEXAIcDrwCHB8Rc+oVm1W3wxk/aHQIXcbSC09tdAhmDVHPK4XJwMhWZWcBd0TEYOCO3A9wGDA4/50MXFHHuMzMrIa6JYWI+C2wslXxkcC1ufta4KhS+XWR3AdsI2mnesVmZmbVdXabwo4RsSR3PwvsmLsHAItK47XkMjMz60QNa2iOiABifaeTdLKkWZJmLVu2rA6RmZltujo7KTxXqRbK/5fm8sXAwNJ4TbnsHSLihxExLCKG9e/fv67Bmpltajo7KcwEjsvdxwE3l8qPVXIA8EKpmsnMzDpJPW9JnQoMB7aX1AKcC1wAzJA0EXgKGJtHv5V0O+pC0i2pJ9QrLjMzq61uSSEixtcYdGiVcQM4rV6xmJlZ+/iJZjMzKzgpmJlZwUnBzMwKdWtTMLN3p0+fIxodQpex5aneFp3FVwpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCl0qKUgaKel/JS2UdFaj4zEz29R0maQgqQdwOXAYsAcwXtIejY3KzGzT0mWSArA/sDAiHo+IN4BpwJENjsnMbJOiiGh0DABIGgOMjIiTcv8E4KMRcXqr8U4GTs69HwD+t1MD3bhtDyxvdBBmVfiz2bF2iYj+1Qb07OxI3q2I+CHww0bHsTGSNCsihjU6DrPW/NnsPF2p+mgxMLDU35TLzMysk3SlpPA/wGBJgyRtBhwNzGxwTGZmm5QuU30UEaslnQ78N9ADuCYiHmlwWJsaV8tZV+XPZifpMg3NZmbWeF2p+sjMzBrMScHMzApOCubXi1iXJekaSUslPdzoWDYVTgqbOL9exLq4ycDIRgexKXFSML9exLqsiPgtsLLRcWxKnBRsALCo1N+Sy8xsE+SkYGZmBScF8+tFzKzgpGB+vYiZFZwUNnERsRqovF5kATDDrxexrkLSVOAPwAcktUia2OiYNnZ+zYWZmRV8pWBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUrC6k/RSqftwSf8naRdJX5Z0bCcs/wZJu+buj0h6KL8R9vuS9C7n3dwZb/CU9B1Ji8rbsgPm2ZHzmiZpcEfNzxrHScE6jaRDge8Dh0XEUxFxZURcV2Pcpg5a5p5Aj4h4PBddAXwJGJz/ussbOP+L9PLCruoK4IxGB2HvnpOCdQpJBwNXAaMi4rFcdp6kb+TuoZLukzRP0kzgIiV3S/qupAfyFcYn8vjHS/q5pNsk/UnShTUWfQxwc55mJ2DriLgv0gM61wFHdcDq9ZB0laRHJP1K0hZ5eV+S9D+S5kq6UdKWuXxyvkq5V9LjksasawE55iUdEOta8hXI3LztdyzFN6Y0zkv5//C8P26Q9KikKaUrrXuAv5TUZX733TaMk4J1hs2Bm4CjIuLRGuNcB5xJOhveGXgu3n6ysmdE7A98FTi3NM1QYBywNzBOUvkdThUHAbNz9wDSW2Arqr4RVtIxkh6s8ndDjdgHA5dHxJ7A88Bf5fKfR8R+ETGE9LR4+WncnYCPA6OAC2rMd71I+lSNuO+tMcl7gftyfL8lXUGty76k/bAHsCtp+xIRbwELgSHvdj2ssZzVrTO8CdxLOihOaj1QUl9g24j4jaRLSMnhotIoP8//ZwPNpfI7IuKFPI/5wC6s/RpwSAffZesTbERMAaasxyRPRMSDVWLcS9K3gW2APqRXiVTclA+k8ytn6O9WRNxFSpTt9QZwS+6eDXy6HdM8EBEtAJIeJK3r7/KwpaSEPrvqlNYtOClYZ3gLGAvcIensiPjnVsN7AG9KOpVUDfE0UH7/yuv5/xrW/sy+XupuPaziVaB37l5MegtsRdU3wko6BvhmlXktjIhqVT2t49gid08mXR3NlXQ8MLzGNO+qsbuYifQp4OIqg16JiI9VKX+zdDVW3n6rybUIkt4DbFaapq1t3pu0va0bc1KwThERr0g6ArhH0nMRcXVp2EpJLwJDIuIUSd8iVWd0hAXAbsCTEbFE0p8lHQDcDxwLXFol1vW9UqhlK2CJpF6kto11vpJc0qMR8cENWdgGXCnU8iTwEWAGMBro1c7pdgf8W8rdnNsUrNNExErS3T7nSBpdKZa0A+nqYKikeaQ2gvM7aLG/YO0z9FOBH5Hqvx8DftlBy6nmH0jJ5/dArbaUgqTtqXHVIOlCSS3Alvltoed1ZKCtXAV8UtJc4EDg5XVNkKvAXo2IZ+sYl3UCvyXVGkbSpcAc4Hqgb0QsrcMytgDuAg6KiDUdPf+OJGkUsGtEfL/RsawvSV8D/ly+ArTuyUnBGiJXEY0gPbOwos7LGgEsiIin67mcTZmkE4Cf5N/nsG7MScHMzApuUzAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMyv8f5uPEzh+h+24AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Passasjerklasse\n",
    "sns.countplot(x='sex', hue='survived', data=titanic, palette='ocean')\n",
    "plt.title(\"Antall døde (0) og overlevende (1) av hvert kjønn\")\n",
    "plt.xlabel(\"Kjønn (0 = han, 1 = hun)\")\n",
    "plt.ylabel(\"Antall\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vi ser, ikke overraskene at menn på 3. klasse hadde særdeles dårlige odds. Vi har alderen til passasjerene, men ikke "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='aldersklasse', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sortere etter alder\n",
    "aldersklasse = []\n",
    "\n",
    "for alder in titanic['age']:\n",
    "    if alder > 15:\n",
    "        aldersklasse.append(\"voksen\")\n",
    "    else:\n",
    "        aldersklasse.append(\"barn\")\n",
    "        \n",
    "titanic['aldersklasse'] = aldersklasse\n",
    "\n",
    "sns.countplot(x='aldersklasse', hue='survived', data=titanic, palette='ocean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# *Frivilling: Erstatte kategorier for visualisering med nye kategorier\n",
    "\"\"\"\n",
    "overlevende = {0: \"døde\", 1: \"overlevde\"}\n",
    "titanic[\"survived\"] = titanic[\"survived\"].map(overlevende)\n",
    "titanic.head(5)\n",
    "\"\"\"\n",
    "# *Frivillig: Telle forekomster av ulike tilfeller\n",
    "\"\"\"\n",
    "titanic[\"survived\"].count()\n",
    "titanic[\"survived\"].value_counts()\n",
    "\"\"\"\n",
    "None # Printer None for å unngå output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maskinlæring\n",
    "Vi skal nå lage en modell som kan forutsi hvorvidt en person overlever på Titanic eller ikke, gitt data om personen. Vi velger ut hvilke data vi ønsker å bruke som kriterium for overlevelse, og spesifiserer kategorien \"survived\" som målkategorien vår:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "kriterier = titanic[['pclass', 'sex', 'age', 'sibsp', 'parch']]\n",
    "kategorier = titanic['survived'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I maskinlæring er det viktig at modellen vår klarer å forutsi data som kommer utenfra datasettet vi trener modellen med. Derfor deler vi ofte opp dataene i et treningssett og et testsett. Treningssettet bruker vi til å trene modellen, testsettet til å teste og evaluere modellen i etterkant. Vi blander ikke disse dataene. Vi kan generere slike data med funksjonen _train\\_test\\_split()_. Her bruker vi 80 \\% av dataene til trening og 20 \\% til testing. Du bør bruke minst 70 \\% av dataene dine til trening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "testandel = 0.2 # Andel brukt til testing\n",
    "ml_data = train_test_split(kriterier, kategorier, test_size=testandel, random_state=42)\n",
    "\n",
    "treningskriterier = ml_data[0]\n",
    "testkriterier = ml_data[1]\n",
    "treningskategorier = ml_data[2]\n",
    "testkategorier = ml_data[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nå kan vi lage modellen vår. Vi bruker en algoritme som heter _Decision Tree Classifier_. Det er basert på sammensatte og forgreinede valgtrær, der alle kombinasjoner av kriterier blir utforsket. Betingede sannsynligheter for ulike hendelser blir beregnet, og de mest sannsynlige utfallene blir framhevet basert på kombinasjonen av kriteriene. Først trener vi modellen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modell = tree.DecisionTreeClassifier()\n",
    "modell.fit(treningskriterier, treningskategorier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Det var det - da har vi en modell! Den ligger nå i et objekt som vi har kalt _modell_. Vi kan få innsyn i hvordan modellen ser ut, men det kan fort bli litt uoversiktlig og teknisk. La oss først nøye oss med å sjekke hvordan modellen takler testsettet vårt.\n",
    "\n",
    "## Test og validering av modellen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7653631284916201"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modellkategorier_forutsett = modell.predict(testkriterier)\n",
    "accuracy_score(testkategorier, modellkategorier_forutsett)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dette betyr at modellen forutsier riktig ca. 76 % av gangene. Det er en ok modell. For å få bedre oversikt over hva modellen forutsier riktig og hva den feiler på, kan vi konstruere en såkalt \"Confusion Matrix\" (forvirringsmatrise/feilmatrise):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(modellkategorier_forutsett, testkategorier)\n",
    "\n",
    "import seaborn as sns\n",
    "sns.heatmap(cm, annot=True, cmap='viridis')\n",
    "plt.title(\"Forvirringsmatrise\")\n",
    "plt.xlabel(\"Predikerte verdier\")\n",
    "plt.ylabel(\"Sanne verdier\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La oss benytte disse dataene og telle hvor mange datapunkter vi har, hvor mange som overlevde og døde, og deretter beregne hvor stor prosentandel av overlevende og døde som modellen klarte å forutsi korrekt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Andel korrekt forventet død 77.68 %)\n",
      "(Andel korrekt forventet overlevelse 73.13 %)\n"
     ]
    }
   ],
   "source": [
    "presisjon_død = (87/(87+25))*100\n",
    "presisjon_overleve = (49/(49+18))*100\n",
    "print(f'(Andel korrekt forventet død {presisjon_død:.2f} %)')\n",
    "print(f'(Andel korrekt forventet overlevelse {presisjon_overleve:.2f} %)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Det er større presisjon i å forutsi død. Dette er forventet, siden modellen har trent på flere tilfeller av død enn av overlevelse. \n",
    "\n",
    "La oss helt til sist visualisere modellen vår. Vi velger maks dybde på modellen til 3 for at vi ikke skal få alt for mange forgreininger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "titanic.pop('survived')\n",
    "tree.plot_tree(modell, max_depth=2, feature_names=titanic.columns, class_names=['Døde', 'Overlevde'], filled=True, label=None,) \n",
    "None"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}