got_prjt_v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "battles = pd.read_csv('battles.csv')\n",
    "char_deaths = pd.read_csv('character-deaths.csv')\n",
    "char_pred = pd.read_csv('character-predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>battle_number</th>\n",
       "      <th>attacker_king</th>\n",
       "      <th>defender_king</th>\n",
       "      <th>attacker_1</th>\n",
       "      <th>attacker_2</th>\n",
       "      <th>attacker_3</th>\n",
       "      <th>attacker_4</th>\n",
       "      <th>defender_1</th>\n",
       "      <th>defender_2</th>\n",
       "      <th>defender_3</th>\n",
       "      <th>defender_4</th>\n",
       "      <th>attacker_outcome</th>\n",
       "      <th>battle_type</th>\n",
       "      <th>major_death</th>\n",
       "      <th>major_capture</th>\n",
       "      <th>attacker_size</th>\n",
       "      <th>defender_size</th>\n",
       "      <th>attacker_commander</th>\n",
       "      <th>defender_commander</th>\n",
       "      <th>summer</th>\n",
       "      <th>location</th>\n",
       "      <th>region</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Battle of the Golden Tooth</td>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>win</td>\n",
       "      <td>pitched battle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Jaime Lannister</td>\n",
       "      <td>Clement Piper, Vance</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Golden Tooth</td>\n",
       "      <td>The Westerlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Battle at the Mummer's Ford</td>\n",
       "      <td>298</td>\n",
       "      <td>2</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>win</td>\n",
       "      <td>ambush</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Gregor Clegane</td>\n",
       "      <td>Beric Dondarrion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mummer's Ford</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of Riverrun</td>\n",
       "      <td>298</td>\n",
       "      <td>3</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>win</td>\n",
       "      <td>pitched battle</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Jaime Lannister, Andros Brax</td>\n",
       "      <td>Edmure Tully, Tytos Blackwood</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Riverrun</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  year  battle_number             attacker_king  \\\n",
       "0   Battle of the Golden Tooth   298              1  Joffrey/Tommen Baratheon   \n",
       "1  Battle at the Mummer's Ford   298              2  Joffrey/Tommen Baratheon   \n",
       "2           Battle of Riverrun   298              3  Joffrey/Tommen Baratheon   \n",
       "\n",
       "  defender_king attacker_1 attacker_2 attacker_3 attacker_4 defender_1  \\\n",
       "0    Robb Stark  Lannister        NaN        NaN        NaN      Tully   \n",
       "1    Robb Stark  Lannister        NaN        NaN        NaN  Baratheon   \n",
       "2    Robb Stark  Lannister        NaN        NaN        NaN      Tully   \n",
       "\n",
       "  defender_2  defender_3  defender_4 attacker_outcome     battle_type  \\\n",
       "0        NaN         NaN         NaN              win  pitched battle   \n",
       "1        NaN         NaN         NaN              win          ambush   \n",
       "2        NaN         NaN         NaN              win  pitched battle   \n",
       "\n",
       "   major_death  major_capture  attacker_size  defender_size  \\\n",
       "0          1.0            0.0        15000.0         4000.0   \n",
       "1          1.0            0.0            NaN          120.0   \n",
       "2          0.0            1.0        15000.0        10000.0   \n",
       "\n",
       "             attacker_commander             defender_commander  summer  \\\n",
       "0               Jaime Lannister           Clement Piper, Vance     1.0   \n",
       "1                Gregor Clegane               Beric Dondarrion     1.0   \n",
       "2  Jaime Lannister, Andros Brax  Edmure Tully, Tytos Blackwood     1.0   \n",
       "\n",
       "        location           region note  \n",
       "0   Golden Tooth  The Westerlands  NaN  \n",
       "1  Mummer's Ford   The Riverlands  NaN  \n",
       "2       Riverrun   The Riverlands  NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "battles.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>DateoFdeath</th>\n",
       "      <th>mother</th>\n",
       "      <th>father</th>\n",
       "      <th>heir</th>\n",
       "      <th>house</th>\n",
       "      <th>spouse</th>\n",
       "      <th>book1</th>\n",
       "      <th>book2</th>\n",
       "      <th>book3</th>\n",
       "      <th>book4</th>\n",
       "      <th>book5</th>\n",
       "      <th>isAliveMother</th>\n",
       "      <th>isAliveFather</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.946</td>\n",
       "      <td>Viserys II Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhaenyra Targaryen</td>\n",
       "      <td>Daemon Targaryen</td>\n",
       "      <td>Aegon IV Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.613</td>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Frey</td>\n",
       "      <td>Perra Royce</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.507</td>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Swyft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S.No  actual  pred  alive   plod                  name  \\\n",
       "0     1       0     0  0.054  0.946  Viserys II Targaryen   \n",
       "1     2       1     0  0.387  0.613           Walder Frey   \n",
       "2     3       1     0  0.493  0.507          Addison Hill   \n",
       "\n",
       "                  title  male   culture  dateOfBirth  DateoFdeath  \\\n",
       "0                   NaN     1       NaN          NaN          NaN   \n",
       "1  Lord of the Crossing     1  Rivermen        208.0          NaN   \n",
       "2                   Ser     1       NaN          NaN          NaN   \n",
       "\n",
       "               mother            father                heir        house  \\\n",
       "0  Rhaenyra Targaryen  Daemon Targaryen  Aegon IV Targaryen          NaN   \n",
       "1                 NaN               NaN                 NaN   House Frey   \n",
       "2                 NaN               NaN                 NaN  House Swyft   \n",
       "\n",
       "        spouse  book1  book2  book3  book4  book5  isAliveMother  \\\n",
       "0          NaN      0      0      0      0      0            1.0   \n",
       "1  Perra Royce      1      1      1      1      1            NaN   \n",
       "2          NaN      0      0      0      1      0            NaN   \n",
       "\n",
       "   isAliveFather  isAliveHeir  isAliveSpouse  isMarried  isNoble   age  \\\n",
       "0            0.0          0.0            NaN          0        0   NaN   \n",
       "1            NaN          NaN            1.0          1        1  97.0   \n",
       "2            NaN          NaN            NaN          0        1   NaN   \n",
       "\n",
       "   numDeadRelations  boolDeadRelations  isPopular  popularity  isAlive  \n",
       "0                11                  1          1    0.605351        0  \n",
       "1                 1                  1          1    0.896321        1  \n",
       "2                 0                  0          0    0.267559        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Allegiances</th>\n",
       "      <th>Death Year</th>\n",
       "      <th>Book of Death</th>\n",
       "      <th>Death Chapter</th>\n",
       "      <th>Book Intro Chapter</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nobility</th>\n",
       "      <th>GoT</th>\n",
       "      <th>CoK</th>\n",
       "      <th>SoS</th>\n",
       "      <th>FfC</th>\n",
       "      <th>DwD</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Addam Marbrand</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>Aegon Frey (Jinglebell)</td>\n",
       "      <td>None</td>\n",
       "      <td>299.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>Aegon Targaryen</td>\n",
       "      <td>House Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name      Allegiances  Death Year  Book of Death  \\\n",
       "0           Addam Marbrand        Lannister         NaN            NaN   \n",
       "1  Aegon Frey (Jinglebell)             None       299.0            3.0   \n",
       "2          Aegon Targaryen  House Targaryen         NaN            NaN   \n",
       "\n",
       "   Death Chapter  Book Intro Chapter  Gender  Nobility  GoT  CoK  SoS  FfC  \\\n",
       "0            NaN                56.0       1         1    1    1    1    1   \n",
       "1           51.0                49.0       1         1    0    0    1    0   \n",
       "2            NaN                 5.0       1         1    0    0    0    0   \n",
       "\n",
       "   DwD  \n",
       "0    0  \n",
       "1    0  \n",
       "2    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_deaths.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our task is to predict characters death. We will focus on Character dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>DateoFdeath</th>\n",
       "      <th>mother</th>\n",
       "      <th>father</th>\n",
       "      <th>heir</th>\n",
       "      <th>house</th>\n",
       "      <th>spouse</th>\n",
       "      <th>book1</th>\n",
       "      <th>book2</th>\n",
       "      <th>book3</th>\n",
       "      <th>book4</th>\n",
       "      <th>book5</th>\n",
       "      <th>isAliveMother</th>\n",
       "      <th>isAliveFather</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.946</td>\n",
       "      <td>Viserys II Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhaenyra Targaryen</td>\n",
       "      <td>Daemon Targaryen</td>\n",
       "      <td>Aegon IV Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.613</td>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Frey</td>\n",
       "      <td>Perra Royce</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.507</td>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Swyft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S.No  actual  pred  alive   plod                  name  \\\n",
       "0     1       0     0  0.054  0.946  Viserys II Targaryen   \n",
       "1     2       1     0  0.387  0.613           Walder Frey   \n",
       "2     3       1     0  0.493  0.507          Addison Hill   \n",
       "\n",
       "                  title  male   culture  dateOfBirth  DateoFdeath  \\\n",
       "0                   NaN     1       NaN          NaN          NaN   \n",
       "1  Lord of the Crossing     1  Rivermen        208.0          NaN   \n",
       "2                   Ser     1       NaN          NaN          NaN   \n",
       "\n",
       "               mother            father                heir        house  \\\n",
       "0  Rhaenyra Targaryen  Daemon Targaryen  Aegon IV Targaryen          NaN   \n",
       "1                 NaN               NaN                 NaN   House Frey   \n",
       "2                 NaN               NaN                 NaN  House Swyft   \n",
       "\n",
       "        spouse  book1  book2  book3  book4  book5  isAliveMother  \\\n",
       "0          NaN      0      0      0      0      0            1.0   \n",
       "1  Perra Royce      1      1      1      1      1            NaN   \n",
       "2          NaN      0      0      0      1      0            NaN   \n",
       "\n",
       "   isAliveFather  isAliveHeir  isAliveSpouse  isMarried  isNoble   age  \\\n",
       "0            0.0          0.0            NaN          0        0   NaN   \n",
       "1            NaN          NaN            1.0          1        1  97.0   \n",
       "2            NaN          NaN            NaN          0        1   NaN   \n",
       "\n",
       "   numDeadRelations  boolDeadRelations  isPopular  popularity  isAlive  \n",
       "0                11                  1          1    0.605351        0  \n",
       "1                 1                  1          1    0.896321        1  \n",
       "2                 0                  0          0    0.267559        1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       433.000000\n",
       "mean      -1293.563510\n",
       "std       19564.340993\n",
       "min     -298001.000000\n",
       "25%          18.000000\n",
       "50%          27.000000\n",
       "75%          50.000000\n",
       "max         100.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing age properties\n",
    "char_pred['age'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are negative value for date of birth. Negative mean for age.\n",
    "Let's investigate that."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research was made and according to [Fandom.com](https://gameofthrones.fandom.com/wiki/Doreah) Doreah is 25 years old.\n",
    "\n",
    "According to [Fanthom.com](https://gameofthrones.fandom.com/wiki/Rhaego) Rhaego was never born."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "char = char_pred.copy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the age\n",
    "char_pred.loc[1684, 'age'] = 25.0\n",
    "char_pred.loc[1868, 'age'] = 0.0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title            1008\n",
       "culture          1269\n",
       "dateOfBirth      1513\n",
       "DateoFdeath      1502\n",
       "mother           1925\n",
       "father           1920\n",
       "heir             1923\n",
       "house             427\n",
       "spouse           1670\n",
       "isAliveMother    1925\n",
       "isAliveFather    1920\n",
       "isAliveHeir      1923\n",
       "isAliveSpouse    1670\n",
       "age              1513\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss = char.isna().sum()\n",
    "miss[miss > 0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 14 columns with NaN vales. We will replace that of age with the average age value.\n",
    "\n",
    "Replace date of birth and date of death with the median value.\n",
    "\n",
    "Replace other categorical variable with unknown\n",
    "\n",
    "Replace other numerical variable with -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing age with average age\n",
    "char[\"age\"].fillna(round(char[\"age\"].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "char['dateOfBirth'].fillna(char['dateOfBirth'].median(), inplace=True)\n",
    "char['DateoFdeath'].fillna(char['DateoFdeath'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = ['title', 'culture', 'mother', 'father', 'heir', 'house', 'spouse']\n",
    "for col in cat:\n",
    "    char[col].fillna('unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "char.fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unknown', 'Rivermen', 'Dornish', 'Valyrian', 'Ironborn',\n",
       "       'Free Folk', 'Northmen', 'Summer Isles', 'Braavosi', 'Dothraki',\n",
       "       'Ghiscari', 'Vale mountain clans', 'Reach', 'Tyroshi', 'Lhazarene',\n",
       "       'Free folk', 'Ironmen', 'Qartheen', 'Lysene', 'westermen',\n",
       "       'Westerman', 'Qarth', 'Lyseni', 'northmen', 'Qohor', 'Westeros',\n",
       "       'Norvoshi', 'First Men', 'Meereenese', 'Andal', 'Astapori',\n",
       "       'Westermen', 'ironborn', 'Ghiscaricari', 'Braavos', 'Stormlands',\n",
       "       'Valemen', 'Myrish', 'Lhazareen', 'Dornishmen', 'Sistermen',\n",
       "       'Northern mountain clans', 'Andals', 'Vale', 'Crannogmen',\n",
       "       'Wildling', 'Dorne', 'Pentoshi', 'free folk', 'Summer Islander',\n",
       "       'Westerlands', 'Summer Islands', 'Asshai', 'Riverlands', 'Naathi',\n",
       "       'Rhoynar', 'Meereen', 'Norvos', 'Stormlander', 'Wildlings',\n",
       "       'Astapor', 'Reachmen', \"Asshai'i\", 'Ibbenese', 'The Reach'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char['culture'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some culture are spelt wrongly thus making duplicate. E.g. 'Westermen', 'Westerman' and 'westermen' are all the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From  Shail Daliwala's kernel on Kaggle\n",
    "cult = {\n",
    "    'Summer Islands': ['summer islands', 'summer islander', 'summer isles'],\n",
    "    'Ghiscari': ['ghiscari', 'ghiscaricari',  'ghis'],\n",
    "    'Asshai': [\"asshai'i\", 'asshai'],\n",
    "    'Lysene': ['lysene', 'lyseni'],\n",
    "    'Andal': ['andal', 'andals'],\n",
    "    'Braavosi': ['braavosi', 'braavos'],\n",
    "    'Dornish': ['dornishmen', 'dorne', 'dornish'],\n",
    "    'Myrish': ['myr', 'myrish', 'myrmen'],\n",
    "    'Westermen': ['westermen', 'westerman', 'westerlands'],\n",
    "    'Westerosi': ['westeros', 'westerosi'],\n",
    "    'Stormlander': ['stormlands', 'stormlander'],\n",
    "    'Norvoshi': ['norvos', 'norvoshi'],\n",
    "    'Northmen': ['the north', 'northmen'],\n",
    "    'Free Folk': ['wildling', 'first men', 'free folk'],\n",
    "    'Qartheen': ['qartheen', 'qarth'],\n",
    "    'Reach': ['the reach', 'reach', 'reachmen'],\n",
    "}\n",
    "\n",
    "def get_cult(value):\n",
    "    value = value.lower()\n",
    "    v = [k for (k, v) in cult.items() if value in v]\n",
    "    return v[0] if len(v) > 0 else value.title()\n",
    "\n",
    "char.loc[:, \"culture\"] = [get_cult(x) for x in char.culture.fillna(\"\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>DateoFdeath</th>\n",
       "      <th>mother</th>\n",
       "      <th>father</th>\n",
       "      <th>heir</th>\n",
       "      <th>house</th>\n",
       "      <th>spouse</th>\n",
       "      <th>book1</th>\n",
       "      <th>book2</th>\n",
       "      <th>book3</th>\n",
       "      <th>book4</th>\n",
       "      <th>book5</th>\n",
       "      <th>isAliveMother</th>\n",
       "      <th>isAliveFather</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.946</td>\n",
       "      <td>Viserys II Targaryen</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>268.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>Rhaenyra Targaryen</td>\n",
       "      <td>Daemon Targaryen</td>\n",
       "      <td>Aegon IV Targaryen</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.613</td>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>208.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Frey</td>\n",
       "      <td>Perra Royce</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.507</td>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>268.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Swyft</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.924</td>\n",
       "      <td>Aemma Arryn</td>\n",
       "      <td>Queen</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>82.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Arryn</td>\n",
       "      <td>Viserys I Targaryen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.183946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.383</td>\n",
       "      <td>Sylva Santagar</td>\n",
       "      <td>Greenstone</td>\n",
       "      <td>0</td>\n",
       "      <td>Dornish</td>\n",
       "      <td>276.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Santagar</td>\n",
       "      <td>Eldon Estermont</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>1942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.197</td>\n",
       "      <td>Luwin</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Westerosi</td>\n",
       "      <td>268.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Stark</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>1943</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.013</td>\n",
       "      <td>Reek</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>268.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Bolton</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.096990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.480</td>\n",
       "      <td>Symeon Star-Eyes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>268.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>1945</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.363</td>\n",
       "      <td>Coldhands</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>268.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Three-eyed crow</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>1946</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Tytos Lannister</td>\n",
       "      <td>Casterly Rock</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>220.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>House Lannister</td>\n",
       "      <td>Jeyne Marbrand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S.No  actual  pred  alive   plod                  name  \\\n",
       "0        1       0     0  0.054  0.946  Viserys II Targaryen   \n",
       "1        2       1     0  0.387  0.613           Walder Frey   \n",
       "2        3       1     0  0.493  0.507          Addison Hill   \n",
       "3        4       0     0  0.076  0.924           Aemma Arryn   \n",
       "4        5       1     1  0.617  0.383        Sylva Santagar   \n",
       "...    ...     ...   ...    ...    ...                   ...   \n",
       "1941  1942       0     1  0.803  0.197                 Luwin   \n",
       "1942  1943       0     1  0.987  0.013                  Reek   \n",
       "1943  1944       1     1  0.520  0.480      Symeon Star-Eyes   \n",
       "1944  1945       1     1  0.637  0.363             Coldhands   \n",
       "1945  1946       0     0  0.093  0.907       Tytos Lannister   \n",
       "\n",
       "                     title  male    culture  dateOfBirth  DateoFdeath  \\\n",
       "0                  unknown     1    Unknown        268.0        299.0   \n",
       "1     Lord of the Crossing     1   Rivermen        208.0        299.0   \n",
       "2                      Ser     1    Unknown        268.0        299.0   \n",
       "3                    Queen     0    Unknown         82.0        105.0   \n",
       "4               Greenstone     0    Dornish        276.0        299.0   \n",
       "...                    ...   ...        ...          ...          ...   \n",
       "1941               unknown     1  Westerosi        268.0        299.0   \n",
       "1942               unknown     1    Unknown        268.0        299.0   \n",
       "1943               unknown     1    Unknown        268.0        299.0   \n",
       "1944               unknown     1    Unknown        268.0        299.0   \n",
       "1945         Casterly Rock     1    Unknown        220.0        267.0   \n",
       "\n",
       "                  mother            father                heir  \\\n",
       "0     Rhaenyra Targaryen  Daemon Targaryen  Aegon IV Targaryen   \n",
       "1                unknown           unknown             unknown   \n",
       "2                unknown           unknown             unknown   \n",
       "3                unknown           unknown             unknown   \n",
       "4                unknown           unknown             unknown   \n",
       "...                  ...               ...                 ...   \n",
       "1941             unknown           unknown             unknown   \n",
       "1942             unknown           unknown             unknown   \n",
       "1943             unknown           unknown             unknown   \n",
       "1944             unknown           unknown             unknown   \n",
       "1945             unknown           unknown             unknown   \n",
       "\n",
       "                house               spouse  book1  book2  book3  book4  book5  \\\n",
       "0             unknown              unknown      0      0      0      0      0   \n",
       "1          House Frey          Perra Royce      1      1      1      1      1   \n",
       "2         House Swyft              unknown      0      0      0      1      0   \n",
       "3         House Arryn  Viserys I Targaryen      0      0      0      0      0   \n",
       "4      House Santagar      Eldon Estermont      0      0      0      1      0   \n",
       "...               ...                  ...    ...    ...    ...    ...    ...   \n",
       "1941      House Stark              unknown      1      1      1      1      1   \n",
       "1942     House Bolton              unknown      0      1      0      1      1   \n",
       "1943          unknown              unknown      1      1      1      1      1   \n",
       "1944  Three-eyed crow              unknown      0      0      1      1      1   \n",
       "1945  House Lannister       Jeyne Marbrand      0      0      1      1      1   \n",
       "\n",
       "      isAliveMother  isAliveFather  isAliveHeir  isAliveSpouse  isMarried  \\\n",
       "0               1.0            0.0          0.0           -1.0          0   \n",
       "1              -1.0           -1.0         -1.0            1.0          1   \n",
       "2              -1.0           -1.0         -1.0           -1.0          0   \n",
       "3              -1.0           -1.0         -1.0            0.0          1   \n",
       "4              -1.0           -1.0         -1.0            1.0          1   \n",
       "...             ...            ...          ...            ...        ...   \n",
       "1941           -1.0           -1.0         -1.0           -1.0          0   \n",
       "1942           -1.0           -1.0         -1.0           -1.0          0   \n",
       "1943           -1.0           -1.0         -1.0           -1.0          0   \n",
       "1944           -1.0           -1.0         -1.0           -1.0          0   \n",
       "1945           -1.0           -1.0         -1.0            1.0          1   \n",
       "\n",
       "      isNoble     age  numDeadRelations  boolDeadRelations  isPopular  \\\n",
       "0           0 -1294.0                11                  1          1   \n",
       "1           1    97.0                 1                  1          1   \n",
       "2           1 -1294.0                 0                  0          0   \n",
       "3           1    23.0                 0                  0          0   \n",
       "4           1    29.0                 0                  0          0   \n",
       "...       ...     ...               ...                ...        ...   \n",
       "1941        0 -1294.0                 0                  0          1   \n",
       "1942        0 -1294.0                 0                  0          0   \n",
       "1943        0 -1294.0                 0                  0          0   \n",
       "1944        0 -1294.0                 0                  0          0   \n",
       "1945        1    47.0                 4                  1          0   \n",
       "\n",
       "      popularity  isAlive  \n",
       "0       0.605351        0  \n",
       "1       0.896321        1  \n",
       "2       0.267559        1  \n",
       "3       0.183946        0  \n",
       "4       0.043478        1  \n",
       "...          ...      ...  \n",
       "1941    0.351171        0  \n",
       "1942    0.096990        0  \n",
       "1943    0.030100        1  \n",
       "1944    0.130435        1  \n",
       "1945    0.210702        0  \n",
       "\n",
       "[1946 rows x 33 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link to other data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names of all Attackers\n",
    "all_attackers = set(battles.attacker_1).union(set(battles.attacker_2))\\\n",
    ".union(set(battles.attacker_3)).union(set(battles.attacker_4))\n",
    "# Names of all Defenders\n",
    "all_defenders = set(battles.defender_1).union(set(battles.defender_2))\n",
    "\n",
    "# Unique attackers and defenders combined\n",
    "all_warlead = all_attackers.union(all_defenders)\n",
    "all_warlead = list(all_warlead)\n",
    "all_warlead.remove(np.nan)\n",
    "\n",
    "renam_warlead = []\n",
    "for x in all_warlead:\n",
    "    house = 'House ' + x\n",
    "    renam_warlead.append(house)\n",
    "    \n",
    "# All attacking commanders\n",
    "all = []\n",
    "for i in battles['attacker_commander'].dropna().values:\n",
    "    splitted = i.split(', ')\n",
    "    all +=splitted\n",
    "    \n",
    "all_att_commanders = set(all)\n",
    "\n",
    "# All defending commanders\n",
    "all = []\n",
    "for i in battles['defender_commander'].dropna().values:\n",
    "    splitted = i.split(', ')\n",
    "    all +=splitted\n",
    "    \n",
    "all_def_commanders = set(all)\n",
    "\n",
    "# All commanders \n",
    "all_commanders = all_att_commanders.union(all_def_commanders)\n",
    "# characters who swore allegiance in character death data\n",
    "char_with_allgnc = char_deaths[char_deaths['Allegiances'] != 'None']\n",
    "\n",
    "# Extracting the above character from all character data\n",
    "all_char_with_allgnc = char_pred[char_pred['name'].isin(char_with_allgnc['Name'])]\n",
    "\n",
    "# house of character with allegiance to another house (house in character dead allegiance column)\n",
    "hus_char_with_allegnc = all_char_with_allgnc['house']\n",
    "\n",
    "# Extract unique house of allegiance from charater death data.\n",
    "husOfAllgnc = list(set(char_deaths['Allegiances']))\n",
    "husOfAllgnc.remove('None')\n",
    "\n",
    "renam_husOfAllgnc = []\n",
    "for x in husOfAllgnc:\n",
    "    house = 'House ' + x\n",
    "    renam_husOfAllgnc.append(house)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "char['isACommander'] = np.where(char['name'].isin(all_commanders), 1, 0)\n",
    "char['inHouseOfWarLead'] = np.where(char['house'].isin(renam_warlead), 1, 0)\n",
    "char['houseSworeAllegiance'] = np.where(char['house'].isin(hus_char_with_allegnc), 1, 0)\n",
    "char['inHouseOfAllegnc'] = np.where(char['house'].isin(renam_husOfAllgnc), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "char = char.drop(['S.No', 'actual', 'pred', 'alive', 'plod', 'name', 'dateOfBirth',\n",
    "                  'DateoFdeath', 'house'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>mother</th>\n",
       "      <th>father</th>\n",
       "      <th>heir</th>\n",
       "      <th>spouse</th>\n",
       "      <th>book1</th>\n",
       "      <th>book2</th>\n",
       "      <th>book3</th>\n",
       "      <th>book4</th>\n",
       "      <th>book5</th>\n",
       "      <th>isAliveMother</th>\n",
       "      <th>isAliveFather</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "      <th>isACommander</th>\n",
       "      <th>inHouseOfWarLead</th>\n",
       "      <th>houseSworeAllegiance</th>\n",
       "      <th>inHouseOfAllegnc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Rhaenyra Targaryen</td>\n",
       "      <td>Daemon Targaryen</td>\n",
       "      <td>Aegon IV Targaryen</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Perra Royce</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queen</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Viserys I Targaryen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.183946</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Greenstone</td>\n",
       "      <td>0</td>\n",
       "      <td>Dornish</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Eldon Estermont</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Westerosi</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.096990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030100</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>1944</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1294.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130435</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>1945</th>\n",
       "      <td>Casterly Rock</td>\n",
       "      <td>1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Jeyne Marbrand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210702</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title  male    culture              mother  \\\n",
       "0                  unknown     1    Unknown  Rhaenyra Targaryen   \n",
       "1     Lord of the Crossing     1   Rivermen             unknown   \n",
       "2                      Ser     1    Unknown             unknown   \n",
       "3                    Queen     0    Unknown             unknown   \n",
       "4               Greenstone     0    Dornish             unknown   \n",
       "...                    ...   ...        ...                 ...   \n",
       "1941               unknown     1  Westerosi             unknown   \n",
       "1942               unknown     1    Unknown             unknown   \n",
       "1943               unknown     1    Unknown             unknown   \n",
       "1944               unknown     1    Unknown             unknown   \n",
       "1945         Casterly Rock     1    Unknown             unknown   \n",
       "\n",
       "                father                heir               spouse  book1  book2  \\\n",
       "0     Daemon Targaryen  Aegon IV Targaryen              unknown      0      0   \n",
       "1              unknown             unknown          Perra Royce      1      1   \n",
       "2              unknown             unknown              unknown      0      0   \n",
       "3              unknown             unknown  Viserys I Targaryen      0      0   \n",
       "4              unknown             unknown      Eldon Estermont      0      0   \n",
       "...                ...                 ...                  ...    ...    ...   \n",
       "1941           unknown             unknown              unknown      1      1   \n",
       "1942           unknown             unknown              unknown      0      1   \n",
       "1943           unknown             unknown              unknown      1      1   \n",
       "1944           unknown             unknown              unknown      0      0   \n",
       "1945           unknown             unknown       Jeyne Marbrand      0      0   \n",
       "\n",
       "      book3  book4  book5  isAliveMother  isAliveFather  isAliveHeir  \\\n",
       "0         0      0      0            1.0            0.0          0.0   \n",
       "1         1      1      1           -1.0           -1.0         -1.0   \n",
       "2         0      1      0           -1.0           -1.0         -1.0   \n",
       "3         0      0      0           -1.0           -1.0         -1.0   \n",
       "4         0      1      0           -1.0           -1.0         -1.0   \n",
       "...     ...    ...    ...            ...            ...          ...   \n",
       "1941      1      1      1           -1.0           -1.0         -1.0   \n",
       "1942      0      1      1           -1.0           -1.0         -1.0   \n",
       "1943      1      1      1           -1.0           -1.0         -1.0   \n",
       "1944      1      1      1           -1.0           -1.0         -1.0   \n",
       "1945      1      1      1           -1.0           -1.0         -1.0   \n",
       "\n",
       "      isAliveSpouse  isMarried  isNoble     age  numDeadRelations  \\\n",
       "0              -1.0          0        0 -1294.0                11   \n",
       "1               1.0          1        1    97.0                 1   \n",
       "2              -1.0          0        1 -1294.0                 0   \n",
       "3               0.0          1        1    23.0                 0   \n",
       "4               1.0          1        1    29.0                 0   \n",
       "...             ...        ...      ...     ...               ...   \n",
       "1941           -1.0          0        0 -1294.0                 0   \n",
       "1942           -1.0          0        0 -1294.0                 0   \n",
       "1943           -1.0          0        0 -1294.0                 0   \n",
       "1944           -1.0          0        0 -1294.0                 0   \n",
       "1945            1.0          1        1    47.0                 4   \n",
       "\n",
       "      boolDeadRelations  isPopular  popularity  isAlive  isACommander  \\\n",
       "0                     1          1    0.605351        0             0   \n",
       "1                     1          1    0.896321        1             1   \n",
       "2                     0          0    0.267559        1             0   \n",
       "3                     0          0    0.183946        0             0   \n",
       "4                     0          0    0.043478        1             0   \n",
       "...                 ...        ...         ...      ...           ...   \n",
       "1941                  0          1    0.351171        0             0   \n",
       "1942                  0          0    0.096990        0             0   \n",
       "1943                  0          0    0.030100        1             0   \n",
       "1944                  0          0    0.130435        1             0   \n",
       "1945                  1          0    0.210702        0             0   \n",
       "\n",
       "      inHouseOfWarLead  houseSworeAllegiance  inHouseOfAllegnc  \n",
       "0                    0                     0                 0  \n",
       "1                    1                     1                 0  \n",
       "2                    0                     1                 0  \n",
       "3                    0                     1                 1  \n",
       "4                    0                     1                 0  \n",
       "...                ...                   ...               ...  \n",
       "1941                 1                     1                 1  \n",
       "1942                 1                     1                 0  \n",
       "1943                 0                     0                 0  \n",
       "1944                 0                     0                 0  \n",
       "1945                 1                     1                 1  \n",
       "\n",
       "[1946 rows x 28 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = char.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book3                   0.006693\n",
       "inHouseOfWarLead        0.029822\n",
       "isAliveSpouse           0.031536\n",
       "book5                   0.032846\n",
       "isNoble                 0.042211\n",
       "age                     0.045790\n",
       "isMarried               0.050037\n",
       "houseSworeAllegiance    0.052159\n",
       "isACommander            0.057968\n",
       "book2                   0.067200\n",
       "inHouseOfAllegnc        0.113380\n",
       "isAliveHeir             0.116365\n",
       "isAliveFather           0.123615\n",
       "isAliveMother           0.140661\n",
       "male                    0.146982\n",
       "book1                   0.147401\n",
       "isPopular               0.153860\n",
       "popularity              0.183223\n",
       "numDeadRelations        0.192444\n",
       "boolDeadRelations       0.193736\n",
       "book4                   0.268975\n",
       "isAlive                 1.000000\n",
       "Name: isAlive, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Heatmap of numerical feature in the training dataset\n",
    "num_features = new.select_dtypes(['int', 'float'])\n",
    "corr_mat = num_features.corr()['isAlive'].abs().sort_values()\n",
    "corr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of numerical feature in the training dataset\n",
    "num_features = new.select_dtypes(['int', 'float'])\n",
    "corr_mat = num_features.corr()\n",
    "sb.heatmap(data=corr_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>isAliveFather</td>\n",
       "      <td>162.811858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>isAliveMother</td>\n",
       "      <td>93.193337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>isAliveHeir</td>\n",
       "      <td>76.982831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isAliveSpouse</td>\n",
       "      <td>26.787287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>isMarried</td>\n",
       "      <td>14.672507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>popularity</td>\n",
       "      <td>7.606625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>isPopular</td>\n",
       "      <td>4.350619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>houseSworeAllegiance</td>\n",
       "      <td>3.579476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>numDeadRelations</td>\n",
       "      <td>3.242963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>book4</td>\n",
       "      <td>3.216016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>boolDeadRelations</td>\n",
       "      <td>3.030660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>book3</td>\n",
       "      <td>2.887907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>2.764721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>book2</td>\n",
       "      <td>2.471624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>isNoble</td>\n",
       "      <td>2.130500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>book5</td>\n",
       "      <td>2.125789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>inHouseOfWarLead</td>\n",
       "      <td>2.064615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>inHouseOfAllegnc</td>\n",
       "      <td>2.023452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>book1</td>\n",
       "      <td>1.717622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>isACommander</td>\n",
       "      <td>1.291037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age</td>\n",
       "      <td>1.041899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                features         VIF\n",
       "7          isAliveFather  162.811858\n",
       "6          isAliveMother   93.193337\n",
       "8            isAliveHeir   76.982831\n",
       "9          isAliveSpouse   26.787287\n",
       "10             isMarried   14.672507\n",
       "16            popularity    7.606625\n",
       "15             isPopular    4.350619\n",
       "19  houseSworeAllegiance    3.579476\n",
       "13      numDeadRelations    3.242963\n",
       "4                  book4    3.216016\n",
       "14     boolDeadRelations    3.030660\n",
       "3                  book3    2.887907\n",
       "0                   male    2.764721\n",
       "2                  book2    2.471624\n",
       "11               isNoble    2.130500\n",
       "5                  book5    2.125789\n",
       "18      inHouseOfWarLead    2.064615\n",
       "20      inHouseOfAllegnc    2.023452\n",
       "1                  book1    1.717622\n",
       "17          isACommander    1.291037\n",
       "12                   age    1.041899"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "x = num_features.drop(columns='isAlive')\n",
    "vif = pd.DataFrame()\n",
    "vif['features'] = x.columns\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]\n",
    "vif.sort_values(by='VIF', ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multicolinearity in the features. Let's drop some columns with multicolinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns with colinearity\n",
    "new = new.drop(['isAliveFather', 'isAliveMother', 'isAliveSpouse'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book3                   0.006693\n",
       "inHouseOfWarLead        0.029822\n",
       "book5                   0.032846\n",
       "isNoble                 0.042211\n",
       "age                     0.045790\n",
       "isMarried               0.050037\n",
       "houseSworeAllegiance    0.052159\n",
       "isACommander            0.057968\n",
       "book2                   0.067200\n",
       "Name: isAlive, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting features with coefficient greater than 0.1\n",
    "num_features = new.select_dtypes(['int', 'float'])\n",
    "corr_mat = num_features.corr()['isAlive'].abs().sort_values()\n",
    "corr_less = corr_mat[corr_mat < 0.1]\n",
    "corr_less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = new.drop(corr_less.index, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of categorical variable\n",
    "nominal_features = ['title', 'culture', 'mother', 'father', 'heir', 'spouse']\n",
    "\n",
    "# Counting unique values in each column\n",
    "unique_count = new[nominal_features].apply(lambda x: len(x.value_counts())).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mother      18\n",
       "father      21\n",
       "heir        23\n",
       "culture     42\n",
       "spouse     255\n",
       "title      263\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arbitrary cutoff of 25 unique count\n",
    "unique_count = unique_count[unique_count > 25].index\n",
    "# Dropping \n",
    "new = new.drop(unique_count, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['culture', 'spouse', 'title'], dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the remaining text column to categorical type\n",
    "txt_col = new.select_dtypes(include=['object'])\n",
    "for col in txt_col:\n",
    "    new[col] = new[col].astype('category')\n",
    "    \n",
    "# Creating dummy columns from txt columns and add back to dataframe\n",
    "new = pd.concat([\n",
    "    new, pd.get_dummies(new.select_dtypes(include=['category']))\n",
    "], axis=1).drop(txt_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>book1</th>\n",
       "      <th>book4</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "      <th>inHouseOfAllegnc</th>\n",
       "      <th>mother_Alicent Hightower</th>\n",
       "      <th>mother_Alyssa Targaryen</th>\n",
       "      <th>mother_Alyssa Velaryon</th>\n",
       "      <th>mother_Betha Blackwood</th>\n",
       "      <th>mother_Cassana Estermont</th>\n",
       "      <th>mother_Catelyn Stark</th>\n",
       "      <th>mother_Cersei Lannister</th>\n",
       "      <th>mother_Daenaera Velaryon</th>\n",
       "      <th>mother_Dyanna Dayne</th>\n",
       "      <th>mother_Lady of House Sunderly</th>\n",
       "      <th>mother_Mariah Martell</th>\n",
       "      <th>mother_Naerys Targaryen</th>\n",
       "      <th>mother_Rhaenyra Targaryen</th>\n",
       "      <th>mother_Rhaenys Targaryen</th>\n",
       "      <th>mother_Shaera Targaryen</th>\n",
       "      <th>...</th>\n",
       "      <th>father_Viserys I Targaryen</th>\n",
       "      <th>father_unknown</th>\n",
       "      <th>heir_Aegon IV Targaryen</th>\n",
       "      <th>heir_Aegon Targaryen</th>\n",
       "      <th>heir_Aemon Targaryen</th>\n",
       "      <th>heir_Aenys Targaryen</th>\n",
       "      <th>heir_Aerys II Targaryen</th>\n",
       "      <th>heir_Arlan V Durrandon</th>\n",
       "      <th>heir_Baelor 'Breakspear' Targaryen</th>\n",
       "      <th>heir_Baelor I Targaryen</th>\n",
       "      <th>heir_Bran Stark</th>\n",
       "      <th>heir_Daeron I Targaryen</th>\n",
       "      <th>heir_Daeron II Targaryen</th>\n",
       "      <th>heir_Duncan Targaryen</th>\n",
       "      <th>heir_Durran the Devout</th>\n",
       "      <th>heir_Halleck Hoare</th>\n",
       "      <th>heir_Jaehaerys Targaryen</th>\n",
       "      <th>heir_Myrcella Baratheon</th>\n",
       "      <th>heir_Rhaegar Targaryen</th>\n",
       "      <th>heir_Rhaegel Targaryen</th>\n",
       "      <th>heir_Rhaenyra Targaryen</th>\n",
       "      <th>heir_Shireen Baratheon</th>\n",
       "      <th>heir_Theon Greyjoy</th>\n",
       "      <th>heir_Tommen Baratheon</th>\n",
       "      <th>heir_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.351171</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>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",
       "      <td>...</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>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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.096990</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030100</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>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",
       "      <td>...</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>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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130435</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>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",
       "      <td>...</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>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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210702</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>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",
       "      <td>...</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>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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  book1  book4  isAliveHeir  numDeadRelations  boolDeadRelations  \\\n",
       "0        1      0      0          0.0                11                  1   \n",
       "1        1      1      1         -1.0                 1                  1   \n",
       "2        1      0      1         -1.0                 0                  0   \n",
       "3        0      0      0         -1.0                 0                  0   \n",
       "4        0      0      1         -1.0                 0                  0   \n",
       "...    ...    ...    ...          ...               ...                ...   \n",
       "1941     1      1      1         -1.0                 0                  0   \n",
       "1942     1      0      1         -1.0                 0                  0   \n",
       "1943     1      1      1         -1.0                 0                  0   \n",
       "1944     1      0      1         -1.0                 0                  0   \n",
       "1945     1      0      1         -1.0                 4                  1   \n",
       "\n",
       "      isPopular  popularity  isAlive  inHouseOfAllegnc  \\\n",
       "0             1    0.605351        0                 0   \n",
       "1             1    0.896321        1                 0   \n",
       "2             0    0.267559        1                 0   \n",
       "3             0    0.183946        0                 1   \n",
       "4             0    0.043478        1                 0   \n",
       "...         ...         ...      ...               ...   \n",
       "1941          1    0.351171        0                 1   \n",
       "1942          0    0.096990        0                 0   \n",
       "1943          0    0.030100        1                 0   \n",
       "1944          0    0.130435        1                 0   \n",
       "1945          0    0.210702        0                 1   \n",
       "\n",
       "      mother_Alicent Hightower  mother_Alyssa Targaryen  \\\n",
       "0                            0                        0   \n",
       "1                            0                        0   \n",
       "2                            0                        0   \n",
       "3                            0                        0   \n",
       "4                            0                        0   \n",
       "...                        ...                      ...   \n",
       "1941                         0                        0   \n",
       "1942                         0                        0   \n",
       "1943                         0                        0   \n",
       "1944                         0                        0   \n",
       "1945                         0                        0   \n",
       "\n",
       "      mother_Alyssa Velaryon  mother_Betha Blackwood  \\\n",
       "0                          0                       0   \n",
       "1                          0                       0   \n",
       "2                          0                       0   \n",
       "3                          0                       0   \n",
       "4                          0                       0   \n",
       "...                      ...                     ...   \n",
       "1941                       0                       0   \n",
       "1942                       0                       0   \n",
       "1943                       0                       0   \n",
       "1944                       0                       0   \n",
       "1945                       0                       0   \n",
       "\n",
       "      mother_Cassana Estermont  mother_Catelyn Stark  mother_Cersei Lannister  \\\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",
       "1941                         0                     0                        0   \n",
       "1942                         0                     0                        0   \n",
       "1943                         0                     0                        0   \n",
       "1944                         0                     0                        0   \n",
       "1945                         0                     0                        0   \n",
       "\n",
       "      mother_Daenaera Velaryon  mother_Dyanna Dayne  \\\n",
       "0                            0                    0   \n",
       "1                            0                    0   \n",
       "2                            0                    0   \n",
       "3                            0                    0   \n",
       "4                            0                    0   \n",
       "...                        ...                  ...   \n",
       "1941                         0                    0   \n",
       "1942                         0                    0   \n",
       "1943                         0                    0   \n",
       "1944                         0                    0   \n",
       "1945                         0                    0   \n",
       "\n",
       "      mother_Lady of House Sunderly  mother_Mariah Martell  \\\n",
       "0                                 0                      0   \n",
       "1                                 0                      0   \n",
       "2                                 0                      0   \n",
       "3                                 0                      0   \n",
       "4                                 0                      0   \n",
       "...                             ...                    ...   \n",
       "1941                              0                      0   \n",
       "1942                              0                      0   \n",
       "1943                              0                      0   \n",
       "1944                              0                      0   \n",
       "1945                              0                      0   \n",
       "\n",
       "      mother_Naerys Targaryen  mother_Rhaenyra Targaryen  \\\n",
       "0                           0                          1   \n",
       "1                           0                          0   \n",
       "2                           0                          0   \n",
       "3                           0                          0   \n",
       "4                           0                          0   \n",
       "...                       ...                        ...   \n",
       "1941                        0                          0   \n",
       "1942                        0                          0   \n",
       "1943                        0                          0   \n",
       "1944                        0                          0   \n",
       "1945                        0                          0   \n",
       "\n",
       "      mother_Rhaenys Targaryen  mother_Shaera Targaryen  ...  \\\n",
       "0                            0                        0  ...   \n",
       "1                            0                        0  ...   \n",
       "2                            0                        0  ...   \n",
       "3                            0                        0  ...   \n",
       "4                            0                        0  ...   \n",
       "...                        ...                      ...  ...   \n",
       "1941                         0                        0  ...   \n",
       "1942                         0                        0  ...   \n",
       "1943                         0                        0  ...   \n",
       "1944                         0                        0  ...   \n",
       "1945                         0                        0  ...   \n",
       "\n",
       "      father_Viserys I Targaryen  father_unknown  heir_Aegon IV Targaryen  \\\n",
       "0                              0               0                        1   \n",
       "1                              0               1                        0   \n",
       "2                              0               1                        0   \n",
       "3                              0               1                        0   \n",
       "4                              0               1                        0   \n",
       "...                          ...             ...                      ...   \n",
       "1941                           0               1                        0   \n",
       "1942                           0               1                        0   \n",
       "1943                           0               1                        0   \n",
       "1944                           0               1                        0   \n",
       "1945                           0               1                        0   \n",
       "\n",
       "      heir_Aegon Targaryen  heir_Aemon Targaryen  heir_Aenys Targaryen  \\\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",
       "1941                     0                     0                     0   \n",
       "1942                     0                     0                     0   \n",
       "1943                     0                     0                     0   \n",
       "1944                     0                     0                     0   \n",
       "1945                     0                     0                     0   \n",
       "\n",
       "      heir_Aerys II Targaryen  heir_Arlan V Durrandon  \\\n",
       "0                           0                       0   \n",
       "1                           0                       0   \n",
       "2                           0                       0   \n",
       "3                           0                       0   \n",
       "4                           0                       0   \n",
       "...                       ...                     ...   \n",
       "1941                        0                       0   \n",
       "1942                        0                       0   \n",
       "1943                        0                       0   \n",
       "1944                        0                       0   \n",
       "1945                        0                       0   \n",
       "\n",
       "      heir_Baelor 'Breakspear' Targaryen  heir_Baelor I Targaryen  \\\n",
       "0                                      0                        0   \n",
       "1                                      0                        0   \n",
       "2                                      0                        0   \n",
       "3                                      0                        0   \n",
       "4                                      0                        0   \n",
       "...                                  ...                      ...   \n",
       "1941                                   0                        0   \n",
       "1942                                   0                        0   \n",
       "1943                                   0                        0   \n",
       "1944                                   0                        0   \n",
       "1945                                   0                        0   \n",
       "\n",
       "      heir_Bran Stark  heir_Daeron I Targaryen  heir_Daeron II Targaryen  \\\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",
       "1941                0                        0                         0   \n",
       "1942                0                        0                         0   \n",
       "1943                0                        0                         0   \n",
       "1944                0                        0                         0   \n",
       "1945                0                        0                         0   \n",
       "\n",
       "      heir_Duncan Targaryen  heir_Durran the Devout  heir_Halleck Hoare  \\\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",
       "1941                      0                       0                   0   \n",
       "1942                      0                       0                   0   \n",
       "1943                      0                       0                   0   \n",
       "1944                      0                       0                   0   \n",
       "1945                      0                       0                   0   \n",
       "\n",
       "      heir_Jaehaerys Targaryen  heir_Myrcella Baratheon  \\\n",
       "0                            0                        0   \n",
       "1                            0                        0   \n",
       "2                            0                        0   \n",
       "3                            0                        0   \n",
       "4                            0                        0   \n",
       "...                        ...                      ...   \n",
       "1941                         0                        0   \n",
       "1942                         0                        0   \n",
       "1943                         0                        0   \n",
       "1944                         0                        0   \n",
       "1945                         0                        0   \n",
       "\n",
       "      heir_Rhaegar Targaryen  heir_Rhaegel Targaryen  heir_Rhaenyra Targaryen  \\\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",
       "1941                       0                       0                        0   \n",
       "1942                       0                       0                        0   \n",
       "1943                       0                       0                        0   \n",
       "1944                       0                       0                        0   \n",
       "1945                       0                       0                        0   \n",
       "\n",
       "      heir_Shireen Baratheon  heir_Theon Greyjoy  heir_Tommen Baratheon  \\\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",
       "1941                       0                   0                      0   \n",
       "1942                       0                   0                      0   \n",
       "1943                       0                   0                      0   \n",
       "1944                       0                   0                      0   \n",
       "1945                       0                   0                      0   \n",
       "\n",
       "      heir_unknown  \n",
       "0                0  \n",
       "1                1  \n",
       "2                1  \n",
       "3                1  \n",
       "4                1  \n",
       "...            ...  \n",
       "1941             1  \n",
       "1942             1  \n",
       "1943             1  \n",
       "1944             1  \n",
       "1945             1  \n",
       "\n",
       "[1946 rows x 72 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml = new.copy()\n",
    "ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8039647577092511"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = ml.copy()\n",
    "train1 = data1[:1362]\n",
    "test1 = data1[1362:]\n",
    "\n",
    "features = train1.columns.tolist()\n",
    "features.remove('isAlive')\n",
    "\n",
    "lgr = LogisticRegression(random_state=42, max_iter = 1000)\n",
    "lgr.fit(train1[features], train1['isAlive'])\n",
    "predlgr = lgr.predict(test1[features])\n",
    "lgr.score(train1[features], train1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6815068493150684"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgr.score(test1[features], test1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict, GridSearchCV, KFold, train_test_split \n",
    "from sklearn.metrics import roc_auc_score, accuracy_score, roc_curve \n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8237885462555066"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = ml.copy()\n",
    "train1 = data1[:1362]\n",
    "test1 = data1[1362:]\n",
    "\n",
    "features = train1.columns.tolist()\n",
    "features.remove('isAlive')\n",
    "\n",
    "knc = KNeighborsClassifier(n_neighbors=6, algorithm='brute')\n",
    "knc.fit(train1[features], train1['isAlive'])\n",
    "predknc = knc.predict(test1[features])\n",
    "knc.score(train1[features], train1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7208904109589042"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc.score(test1[features], test1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[128  77]\n",
      " [ 86 293]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.62      0.61       205\n",
      "           1       0.79      0.77      0.78       379\n",
      "\n",
      "    accuracy                           0.72       584\n",
      "   macro avg       0.70      0.70      0.70       584\n",
      "weighted avg       0.72      0.72      0.72       584\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(test1['isAlive'], predknc))\n",
    "print(classification_report(test1['isAlive'], predknc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 67  84]\n",
      " [ 60 373]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.44      0.48       151\n",
      "           1       0.82      0.86      0.84       433\n",
      "\n",
      "    accuracy                           0.75       584\n",
      "   macro avg       0.67      0.65      0.66       584\n",
      "weighted avg       0.74      0.75      0.75       584\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Creating array of values\n",
    "x = new[new.columns.drop('isAlive')].values\n",
    "y = new['isAlive'].values\n",
    "\n",
    "# Splitting data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, shuffle=True, random_state=42)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "# Import confusion matrix\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# Fit the model to the training data\n",
    "knn.fit(x_train, y_train)\n",
    "\n",
    "# Predict the labels of the test data: y_pred\n",
    "y_pred = knn.predict(x_test)\n",
    "\n",
    "# Generate the confusion matrix and classification report\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8788546255506607"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = new.copy()\n",
    "train1 = data1[:1362]\n",
    "test1 = data1[1362:]\n",
    "\n",
    "features = train1.columns.tolist()\n",
    "features.remove('isAlive')\n",
    "\n",
    "rdf = RandomForestClassifier(random_state=42)\n",
    "rdf.fit(train1[features], train1['isAlive'])\n",
    "predrdf = rdf.predict(test1[features])\n",
    "rdf.score(train1[features], train1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6934931506849316"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf.score(test1[features], test1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8788546255506607"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = new.copy()\n",
    "train1 = data1[:1362]\n",
    "test1 = data1[1362:]\n",
    "\n",
    "features = train1.columns.tolist()\n",
    "features.remove('isAlive')\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "dt.fit(train1[features], train1['isAlive'])\n",
    "pred_dt = dt.predict(test1[features])\n",
    "dt.score(train1[features], train1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.672945205479452"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(test1[features], test1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mg:\\My Drive\\Gitlab\\Goodrich_GOT\\got_prjt_v3.ipynb Cell 58\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/My%20Drive/Gitlab/Goodrich_GOT/got_prjt_v3.ipynb#Y111sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Separate our labels from our features\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/g%3A/My%20Drive/Gitlab/Goodrich_GOT/got_prjt_v3.ipynb#Y111sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m x \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39miloc[:,\u001b[39m1\u001b[39m:]\u001b[39m.\u001b[39mvalues\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/My%20Drive/Gitlab/Goodrich_GOT/got_prjt_v3.ipynb#Y111sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m y \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39miloc[:, \u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalues\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "# Separate our labels from our features\n",
    "x = df.iloc[:,1:].values\n",
    "y = df.iloc[:, 0].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate our labels from our features\n",
    "x = new[new.columns.drop('isAlive')].values\n",
    "\n",
    "y = new['isAlive'].values\n",
    "y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into 5 equal groups for validation\n",
    "kfold = KFold(n_splits=5, shuffle=True, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's build different models to train with our data\n",
    "models = [LogisticRegression(max_iter=500, solver='liblinear'), RandomForestClassifier(n_estimators=400, random_state=seed), \n",
    "          DecisionTreeClassifier(random_state=seed), KNeighborsClassifier(n_neighbors=5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validate each model using K-fold cross validation\n",
    "mean=[]\n",
    "std=[]\n",
    "for model in models:\n",
    "    result = cross_val_score(model, x, y, cv=kfold, scoring=\"accuracy\", n_jobs=-1)\n",
    "    mean.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifiers=['Logistic Regression', 'Random Forest', 'Decision Tree', 'SVM', 'KNN']\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for x in range(len(mean)):\n",
    "    sb.kdeplot(mean[x], shade=True)\n",
    "    \n",
    "plt.title(\"Distribution of each classifier's Accuracy\", fontsize=15)\n",
    "plt.legend(classifiers)\n",
    "plt.xlabel(\"Accuracy\", labelpad=20)\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop = [\"S.No\", \"pred\", \"alive\", \"plod\", \"name\", \"isAlive\", \"DateoFdeath\"]\n",
    "# cols_dlt = ['actual', 'alive', 'name', 'plod', 'pred', 'isAlive', 'DateoFdeath', 'age', 'spouse', 'dateOfBirth', 'episodes', 'S.No']\n",
    "\n",
    "# preds_partial = preds[['S.No',\n",
    "#                       'male',\n",
    "#                       'dateOfBirth',\n",
    "#                       'book1',\n",
    "#                       'book4',\n",
    "#                       'numDeadRelations',\n",
    "#                       'boolDeadRelations',\n",
    "#                       'popularity',\n",
    "#                       'showOnly',\n",
    "#                       'houseDeathRate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>DateoFdeath</th>\n",
       "      <th>mother</th>\n",
       "      <th>father</th>\n",
       "      <th>heir</th>\n",
       "      <th>house</th>\n",
       "      <th>spouse</th>\n",
       "      <th>book1</th>\n",
       "      <th>book2</th>\n",
       "      <th>book3</th>\n",
       "      <th>book4</th>\n",
       "      <th>book5</th>\n",
       "      <th>isAliveMother</th>\n",
       "      <th>isAliveFather</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.946</td>\n",
       "      <td>Viserys II Targaryen</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhaenyra Targaryen</td>\n",
       "      <td>Daemon Targaryen</td>\n",
       "      <td>Aegon IV Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.613</td>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Frey</td>\n",
       "      <td>Perra Royce</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.507</td>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Swyft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.924</td>\n",
       "      <td>Aemma Arryn</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Arryn</td>\n",
       "      <td>Viserys I Targaryen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.183946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.383</td>\n",
       "      <td>Sylva Santagar</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Dornish</td>\n",
       "      <td>276.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Santagar</td>\n",
       "      <td>Eldon Estermont</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>1942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.197</td>\n",
       "      <td>Luwin</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>Westeros</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Stark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>1943</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.013</td>\n",
       "      <td>Reek</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Bolton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.096990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.480</td>\n",
       "      <td>Symeon Star-Eyes</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>1945</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.363</td>\n",
       "      <td>Coldhands</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Three-eyed crow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>1946</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Tytos Lannister</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Lannister</td>\n",
       "      <td>Jeyne Marbrand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S.No  actual  pred  alive   plod                  name  title  male  \\\n",
       "0        1       0     0  0.054  0.946  Viserys II Targaryen     -1     1   \n",
       "1        2       1     0  0.387  0.613           Walder Frey      0     1   \n",
       "2        3       1     0  0.493  0.507          Addison Hill      1     1   \n",
       "3        4       0     0  0.076  0.924           Aemma Arryn      2     0   \n",
       "4        5       1     1  0.617  0.383        Sylva Santagar      3     0   \n",
       "...    ...     ...   ...    ...    ...                   ...    ...   ...   \n",
       "1941  1942       0     1  0.803  0.197                 Luwin     -1     1   \n",
       "1942  1943       0     1  0.987  0.013                  Reek     -1     1   \n",
       "1943  1944       1     1  0.520  0.480      Symeon Star-Eyes     -1     1   \n",
       "1944  1945       1     1  0.637  0.363             Coldhands     -1     1   \n",
       "1945  1946       0     0  0.093  0.907       Tytos Lannister     37     1   \n",
       "\n",
       "       culture  dateOfBirth  DateoFdeath              mother  \\\n",
       "0          NaN          NaN          NaN  Rhaenyra Targaryen   \n",
       "1     Rivermen        208.0          NaN                 NaN   \n",
       "2          NaN          NaN          NaN                 NaN   \n",
       "3          NaN         82.0        105.0                 NaN   \n",
       "4      Dornish        276.0          NaN                 NaN   \n",
       "...        ...          ...          ...                 ...   \n",
       "1941  Westeros          NaN        299.0                 NaN   \n",
       "1942       NaN          NaN        299.0                 NaN   \n",
       "1943       NaN          NaN          NaN                 NaN   \n",
       "1944       NaN          NaN          NaN                 NaN   \n",
       "1945       NaN        220.0        267.0                 NaN   \n",
       "\n",
       "                father                heir            house  \\\n",
       "0     Daemon Targaryen  Aegon IV Targaryen              NaN   \n",
       "1                  NaN                 NaN       House Frey   \n",
       "2                  NaN                 NaN      House Swyft   \n",
       "3                  NaN                 NaN      House Arryn   \n",
       "4                  NaN                 NaN   House Santagar   \n",
       "...                ...                 ...              ...   \n",
       "1941               NaN                 NaN      House Stark   \n",
       "1942               NaN                 NaN     House Bolton   \n",
       "1943               NaN                 NaN              NaN   \n",
       "1944               NaN                 NaN  Three-eyed crow   \n",
       "1945               NaN                 NaN  House Lannister   \n",
       "\n",
       "                   spouse  book1  book2  book3  book4  book5  isAliveMother  \\\n",
       "0                     NaN      0      0      0      0      0            1.0   \n",
       "1             Perra Royce      1      1      1      1      1            NaN   \n",
       "2                     NaN      0      0      0      1      0            NaN   \n",
       "3     Viserys I Targaryen      0      0      0      0      0            NaN   \n",
       "4         Eldon Estermont      0      0      0      1      0            NaN   \n",
       "...                   ...    ...    ...    ...    ...    ...            ...   \n",
       "1941                  NaN      1      1      1      1      1            NaN   \n",
       "1942                  NaN      0      1      0      1      1            NaN   \n",
       "1943                  NaN      1      1      1      1      1            NaN   \n",
       "1944                  NaN      0      0      1      1      1            NaN   \n",
       "1945       Jeyne Marbrand      0      0      1      1      1            NaN   \n",
       "\n",
       "      isAliveFather  isAliveHeir  isAliveSpouse  isMarried  isNoble   age  \\\n",
       "0               0.0          0.0            NaN          0        0   NaN   \n",
       "1               NaN          NaN            1.0          1        1  97.0   \n",
       "2               NaN          NaN            NaN          0        1   NaN   \n",
       "3               NaN          NaN            0.0          1        1  23.0   \n",
       "4               NaN          NaN            1.0          1        1  29.0   \n",
       "...             ...          ...            ...        ...      ...   ...   \n",
       "1941            NaN          NaN            NaN          0        0   NaN   \n",
       "1942            NaN          NaN            NaN          0        0   NaN   \n",
       "1943            NaN          NaN            NaN          0        0   NaN   \n",
       "1944            NaN          NaN            NaN          0        0   NaN   \n",
       "1945            NaN          NaN            1.0          1        1  47.0   \n",
       "\n",
       "      numDeadRelations  boolDeadRelations  isPopular  popularity  isAlive  \n",
       "0                   11                  1          1    0.605351        0  \n",
       "1                    1                  1          1    0.896321        1  \n",
       "2                    0                  0          0    0.267559        1  \n",
       "3                    0                  0          0    0.183946        0  \n",
       "4                    0                  0          0    0.043478        1  \n",
       "...                ...                ...        ...         ...      ...  \n",
       "1941                 0                  0          1    0.351171        0  \n",
       "1942                 0                  0          0    0.096990        0  \n",
       "1943                 0                  0          0    0.030100        1  \n",
       "1944                 0                  0          0    0.130435        1  \n",
       "1945                 4                  1          0    0.210702        0  \n",
       "\n",
       "[1946 rows x 33 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "char1 = char_pred.copy()\n",
    "# Factorize our categorical features\n",
    "char1.loc[:, \"title\"] = pd.factorize(char1[\"title\"])[0]\n",
    "\n",
    "# char1.loc[:, \"culture\"] = pd.factorize(char1[\"culture\"])[0]\n",
    "# char1.loc[:, \"mother\"] = pd.factorize(char1[\"mother\"])[0]\n",
    "# char1.loc[:, \"father\"] = pd.factorize(char1[\"father\"])[0]\n",
    "# char1.loc[:, \"heir\"] = pd.factorize(char1[\"heir\"])[0]\n",
    "# char1.loc[:, \"house\"] = pd.factorize(char1[\"house\"])[0]\n",
    "# char1.loc[:, \"spouse\"] = pd.factorize(char1[\"spouse\"])[0]\n",
    "char1"
   ]
  }
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