got_prjt_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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\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": 3,
   "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')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration and Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Battles Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Battle of the Green Fork</td>\n",
       "      <td>298</td>\n",
       "      <td>4</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>loss</td>\n",
       "      <td>pitched battle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Roose Bolton, Wylis Manderly, Medger Cerwyn, H...</td>\n",
       "      <td>Tywin Lannister, Gregor Clegane, Kevan Lannist...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Green Fork</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Battle of the Whispering Wood</td>\n",
       "      <td>298</td>\n",
       "      <td>5</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</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>1.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>Robb Stark, Brynden Tully</td>\n",
       "      <td>Jaime Lannister</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whispering Wood</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name  year  battle_number  \\\n",
       "0     Battle of the Golden Tooth   298              1   \n",
       "1    Battle at the Mummer's Ford   298              2   \n",
       "2             Battle of Riverrun   298              3   \n",
       "3       Battle of the Green Fork   298              4   \n",
       "4  Battle of the Whispering Wood   298              5   \n",
       "\n",
       "              attacker_king             defender_king attacker_1 attacker_2  \\\n",
       "0  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
       "1  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
       "2  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
       "3                Robb Stark  Joffrey/Tommen Baratheon      Stark        NaN   \n",
       "4                Robb Stark  Joffrey/Tommen Baratheon      Stark      Tully   \n",
       "\n",
       "  attacker_3 attacker_4 defender_1 defender_2  defender_3  defender_4  \\\n",
       "0        NaN        NaN      Tully        NaN         NaN         NaN   \n",
       "1        NaN        NaN  Baratheon        NaN         NaN         NaN   \n",
       "2        NaN        NaN      Tully        NaN         NaN         NaN   \n",
       "3        NaN        NaN  Lannister        NaN         NaN         NaN   \n",
       "4        NaN        NaN  Lannister        NaN         NaN         NaN   \n",
       "\n",
       "  attacker_outcome     battle_type  major_death  major_capture  attacker_size  \\\n",
       "0              win  pitched battle          1.0            0.0        15000.0   \n",
       "1              win          ambush          1.0            0.0            NaN   \n",
       "2              win  pitched battle          0.0            1.0        15000.0   \n",
       "3             loss  pitched battle          1.0            1.0        18000.0   \n",
       "4              win          ambush          1.0            1.0         1875.0   \n",
       "\n",
       "   defender_size                                 attacker_commander  \\\n",
       "0         4000.0                                    Jaime Lannister   \n",
       "1          120.0                                     Gregor Clegane   \n",
       "2        10000.0                       Jaime Lannister, Andros Brax   \n",
       "3        20000.0  Roose Bolton, Wylis Manderly, Medger Cerwyn, H...   \n",
       "4         6000.0                          Robb Stark, Brynden Tully   \n",
       "\n",
       "                                  defender_commander  summer         location  \\\n",
       "0                               Clement Piper, Vance     1.0     Golden Tooth   \n",
       "1                                   Beric Dondarrion     1.0    Mummer's Ford   \n",
       "2                      Edmure Tully, Tytos Blackwood     1.0         Riverrun   \n",
       "3  Tywin Lannister, Gregor Clegane, Kevan Lannist...     1.0       Green Fork   \n",
       "4                                    Jaime Lannister     1.0  Whispering Wood   \n",
       "\n",
       "            region note  \n",
       "0  The Westerlands  NaN  \n",
       "1   The Riverlands  NaN  \n",
       "2   The Riverlands  NaN  \n",
       "3   The Riverlands  NaN  \n",
       "4   The Riverlands  NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Battle Preview\n",
    "battles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 38 entries, 0 to 37\n",
      "Data columns (total 25 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   name                38 non-null     object \n",
      " 1   year                38 non-null     int64  \n",
      " 2   battle_number       38 non-null     int64  \n",
      " 3   attacker_king       36 non-null     object \n",
      " 4   defender_king       35 non-null     object \n",
      " 5   attacker_1          38 non-null     object \n",
      " 6   attacker_2          10 non-null     object \n",
      " 7   attacker_3          3 non-null      object \n",
      " 8   attacker_4          2 non-null      object \n",
      " 9   defender_1          37 non-null     object \n",
      " 10  defender_2          2 non-null      object \n",
      " 11  defender_3          0 non-null      float64\n",
      " 12  defender_4          0 non-null      float64\n",
      " 13  attacker_outcome    37 non-null     object \n",
      " 14  battle_type         37 non-null     object \n",
      " 15  major_death         37 non-null     float64\n",
      " 16  major_capture       37 non-null     float64\n",
      " 17  attacker_size       24 non-null     float64\n",
      " 18  defender_size       19 non-null     float64\n",
      " 19  attacker_commander  37 non-null     object \n",
      " 20  defender_commander  28 non-null     object \n",
      " 21  summer              37 non-null     float64\n",
      " 22  location            37 non-null     object \n",
      " 23  region              38 non-null     object \n",
      " 24  note                5 non-null      object \n",
      "dtypes: float64(7), int64(2), object(16)\n",
      "memory usage: 7.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Battle info\n",
    "battles.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a lot of missing values, let's look at the percentage for each columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: 0.0\n",
      "year: 0.0\n",
      "battle_number: 0.0\n",
      "attacker_king: 5.263157894736842\n",
      "defender_king: 7.894736842105263\n",
      "attacker_1: 0.0\n",
      "attacker_2: 73.68421052631578\n",
      "attacker_3: 92.10526315789474\n",
      "attacker_4: 94.73684210526315\n",
      "defender_1: 2.631578947368421\n",
      "defender_2: 94.73684210526315\n",
      "defender_3: 100.0\n",
      "defender_4: 100.0\n",
      "attacker_outcome: 2.631578947368421\n",
      "battle_type: 2.631578947368421\n",
      "major_death: 2.631578947368421\n",
      "major_capture: 2.631578947368421\n",
      "attacker_size: 36.84210526315789\n",
      "defender_size: 50.0\n",
      "attacker_commander: 2.631578947368421\n",
      "defender_commander: 26.31578947368421\n",
      "summer: 2.631578947368421\n",
      "location: 2.631578947368421\n",
      "region: 0.0\n",
      "note: 86.8421052631579\n"
     ]
    }
   ],
   "source": [
    "for col in battles.columns:\n",
    "    # missing = battles.shape[0] - battles[col].isna().sum()\n",
    "    percent = (battles[col].isna().sum()/battles.shape[0]) * 100\n",
    "    print(f'{col}: {percent}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at unique attacker king and defender king, then look at the unique commander on both end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Joffrey/Tommen Baratheon' 'Robb Stark' 'Balon/Euron Greyjoy'\n",
      " 'Stannis Baratheon' nan]\n",
      "['Robb Stark' 'Joffrey/Tommen Baratheon' 'Balon/Euron Greyjoy'\n",
      " 'Renly Baratheon' nan 'Mance Rayder' 'Stannis Baratheon']\n",
      "['Lannister' 'Stark' 'Greyjoy' 'Bolton' 'Baratheon' 'Darry'\n",
      " 'Brotherhood without Banners' 'Frey' 'Free folk' 'Brave Companions'\n",
      " 'Bracken']\n",
      "['Tully' 'Baratheon' 'Lannister' 'Darry' 'Stark' 'Greyjoy'\n",
      " 'Brave Companions' 'Mallister' \"Night's Watch\" nan 'Tyrell' 'Blackwood'\n",
      " 'Bolton']\n"
     ]
    }
   ],
   "source": [
    "print(battles.attacker_king.unique())\n",
    "print(battles.defender_king.unique())\n",
    "print(battles.attacker_1.unique())\n",
    "print(battles.defender_1.unique())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the count of each attacker king and defender king"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "att_kg = battles['attacker_king'].value_counts(ascending=True)\n",
    "att_kg.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "att_kg = battles['defender_king'].value_counts(ascending=True)\n",
    "att_kg.plot(kind='barh')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many times did each commander start an attack and how many times did each defend an attack using attacker 1 and defender 1 respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles['attacker_1'].value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles['defender_1'].value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the frequency of battle outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles['attacker_outcome'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many battles was won during summer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles['summer'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which type of battle is mostly fought?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles['battle_type'].value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the distribution of regions in battles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles['region'].value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relationship between attacker size and defender size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2339be960e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(battles.attacker_size, battles.defender_size)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Characters Death Exploration"
   ]
  },
  {
   "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>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",
       "    </tr>\n",
       "  </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",
       "      <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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_deaths.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 917 entries, 0 to 916\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Name                917 non-null    object \n",
      " 1   Allegiances         917 non-null    object \n",
      " 2   Death Year          305 non-null    float64\n",
      " 3   Book of Death       307 non-null    float64\n",
      " 4   Death Chapter       299 non-null    float64\n",
      " 5   Book Intro Chapter  905 non-null    float64\n",
      " 6   Gender              917 non-null    int64  \n",
      " 7   Nobility            917 non-null    int64  \n",
      " 8   GoT                 917 non-null    int64  \n",
      " 9   CoK                 917 non-null    int64  \n",
      " 10  SoS                 917 non-null    int64  \n",
      " 11  FfC                 917 non-null    int64  \n",
      " 12  DwD                 917 non-null    int64  \n",
      "dtypes: float64(4), int64(7), object(2)\n",
      "memory usage: 93.3+ KB\n"
     ]
    }
   ],
   "source": [
    "char_deaths.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "House of Allegiance frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_deaths.Allegiances.value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dead count based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGYCAYAAABoLxltAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgJklEQVR4nO3df2yV9d3/8RctnJPYnVNQ23NoQYJTcEDVUKAcdOBtqdSsTF2CLmik6JSK3zER19moDJCsgY3WrW0wKBsVmT9GmboiFMvcFHt2HGViZ6Eaw7qt9JxSS845K66n0H7/MFy7z4BbTyk9n5bnI7kivT6f0/O+IqXPHE4vhknqFQAAgEES4j0AAADAfyNQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABhneLwH6Ku0tDSFw+F4jwEAAGLgcDh09OjRL903KAMlLS1NLS0t8R4DAAD0QXp6+pdGyqAMlNOvnKSnp/MqCgAAg4TD4VBLS8tX+t49KAPltHA4TKAAADAE8SZZAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYZ3i8B0BsNjR44z0CBtCKDE+8RwCAuOAVFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMaJKVCOHDmi3t7eM47y8nJJkt1uV3l5udrb2xUOh7V9+3alpqZGfY6xY8equrpanZ2dCgQCWr9+vRITE/vvigAAwKAXU6BMnz5dbrfbOubOnStJ+s1vfiNJKi0t1fz587VgwQLNmTNHaWlp2rFjx3+eLCFBO3fulM1m06xZs7Ro0SLl5+drzZo1/XhJAABgsBsmqbevDy4tLVVeXp6uvvpqOZ1OHTt2TAsXLlRVVZUkaeLEiTp8+LBmzpwpn8+n3NxcVVdXKy0tTW1tbZKkJUuWaN26dUpJSVF3d/dXel6Hw6FQKCSn06lwONzX8QelDQ3eeI+AAbQiwxPvEQCg38Ty/bvP70EZMWKE7rnnHv3yl7+UJGVmZspms6m2ttba09TUpObmZnk8X/wh6/F41NDQYMWJJNXU1Cg5OVmTJ08+53PZbDY5HI6oAwAADF19DpTbb79dI0eO1JYtWyRJbrdbXV1dCgaDUfsCgYDcbre1JxAInLF+eu1cioqKFAqFrKOlpaWvYwMAgEGgz4Fy//33a9euXWptbe3Pec6quLhYTqfTOtLT0y/4cwIAgPgZ3pcHXXHFFZo7d66+853vWOf8fr/sdruSk5OjXkVxuVzy+/3WnhkzZkR9LpfLZa2dSyQSUSQS6cuoAABgEOrTKyiLFy9WW1ubdu7caZ2rr69XJBJRdna2dW7ChAkaN26cvN4v3tjp9XqVkZGhlJQUa09OTo6CwaAaGxv7eg0AAGCIifkVlGHDhmnx4sWqrKzUqVOnrPOhUEibN29WSUmJOjo6FAqFVFZWprq6Ovl8PknSnj171NjYqK1bt6qwsFBut1tr165VRUUFr5AAAABLzIEyd+5cjRs3zvrpnf9t+fLl6unpUVVVlex2u2pqarR06VJrvaenR3l5edq4caO8Xq86OztVWVmplStXnt9VAACAIeW87oMSL9wHBRcL7oMCYCgZkPugAAAAXCgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIwTc6CkpaVp69atam9v14kTJ/Thhx8qMzMzas/q1at19OhRnThxQm+99ZauuuqqqPVRo0bpxRdfVDAY1PHjx/X8888rKSnp/K4EAAAMGTEFysiRI/Xee++pu7tbt956qyZNmqQVK1bo+PHj1p7CwkItW7ZMBQUFysrKUmdnp2pqamS3260927Zt0+TJk5WTk6O8vDzNnj1bmzZt6r+rAgAAg9owSb1fdXNxcbFuuOEGzZ49+5x7jh49qg0bNmjDhg2SJKfTqUAgoPz8fL3yyiu65pprdOjQIU2bNk319fWSpHnz5unNN9/UmDFj1Nra+qVzOBwOhUIhOZ1OhcPhrzr+kLChwRvvETCAVmR44j0CAPSbWL5/x/QKyre//W3t379fr776qgKBgA4cOKDvfe971vr48eM1evRo1dbWWudCoZB8Pp88ni/+oPV4PDp+/LgVJ5JUW1urnp4eZWVlxTIOAAAYomIKlCuvvFIPPfSQPvnkE82bN08bN27UL37xC917772SJLfbLUkKBAJRjwsEAtaa2+1WW1tb1PqpU6fU0dFh7flvNptNDocj6gAAAEPX8Fg2JyQkaP/+/XriiSckSR988IGmTJmigoICvfDCCxdkQEkqKirSqlWrLtjnBwAAZonpFZTW1lY1NjZGnTt06JCuuOIKSZLf75ckuVyuqD0ul8ta8/v9Sk1NjVpPTEzUpZdeau35b8XFxXI6ndaRnp4ey9gAAGCQiSlQ3nvvPU2cODHq3IQJE9Tc3CxJOnLkiFpbW5WdnW2tOxwOZWVlyev94s2dXq9Xo0aN0tSpU609N998sxISEuTz+c76vJFIROFwOOoAAABDV0x/xVNaWqq6ujoVFRXp1Vdf1YwZM/Tggw/qwQcftPY888wzevLJJ/XJJ5/oyJEjevrpp3X06FG99tprkqTDhw9r165deu6551RQUKARI0aovLxcL7/88lf6CR4AADD0xRQo+/fv1x133KHi4mKtXLlSR44c0SOPPKJf//rX1p7169crKSlJmzZt0siRI7Vv3z7l5uaqq6vL2nP33XervLxce/fuVU9Pj6qqqrRs2bL+uyoAADCoxXQfFFNwHxRcLLgPCoCh5ILdBwUAAGAgECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACME1Og/PjHP1Zvb2/UcejQIWvdbrervLxc7e3tCofD2r59u1JTU6M+x9ixY1VdXa3Ozk4FAgGtX79eiYmJ/XM1AABgSBge6wP++te/au7cudbHJ0+etH5dWlqqb33rW1qwYIGCwaDKy8u1Y8cO3XjjjZKkhIQE7dy5U36/X7NmzdLo0aP1wgsvqLu7W0888UQ/XA4AABgKYg6UkydPKhAInHHe6XTq/vvv18KFC/X2229LkhYvXqzDhw8rKytLPp9Pt9xyiyZNmqS5c+eqra1NBw8e1FNPPaV169Zp1apV6u7uPv8rAgAAg17M70G5+uqr1dLSok8//VQvvviixo4dK0nKzMyUzWZTbW2ttbepqUnNzc3yeDySJI/Ho4aGBrW1tVl7ampqlJycrMmTJ5/zOW02mxwOR9QBAACGrpgCxefzKT8/X7m5uXrooYc0fvx4vfvuu/ra174mt9utrq4uBYPBqMcEAgG53W5JktvtPuPVl9Mfn95zNkVFRQqFQtbR0tISy9gAAGCQiemveHbv3m39uqGhQT6fT83Nzbrzzjv1+eef9/twpxUXF6ukpMT62OFwECkAAAxh5/VjxsFgUB9//LGuuuoq+f1+2e12JScnR+1xuVzy+/2SJL/fL5fLdcb66bVziUQiCofDUQcAABi6zitQkpKS9PWvf12tra2qr69XJBJRdna2tT5hwgSNGzdOXq9XkuT1epWRkaGUlBRrT05OjoLBoBobG89nFAAAMITE9Fc8P/3pT/W73/1Ozc3NSktL0+rVq3Xq1Cm99NJLCoVC2rx5s0pKStTR0aFQKKSysjLV1dXJ5/NJkvbs2aPGxkZt3bpVhYWFcrvdWrt2rSoqKhSJRC7IBQIAgMEnpkAZM2aMXnrpJV122WU6duyY9u3bp5kzZ6q9vV2StHz5cvX09Kiqqkp2u101NTVaunSp9fienh7l5eVp48aN8nq96uzsVGVlpVauXNm/VwUAAAa1YZJ64z1ErBwOh0KhkJxO50X3fpQNDd54j4ABtCLDE+8RAKDfxPL9m3+LBwAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHHOK1B+9KMfqbe3V6WlpdY5u92u8vJytbe3KxwOa/v27UpNTY163NixY1VdXa3Ozk4FAgGtX79eiYmJ5zMKAAAYQvocKNOmTdOSJUt08ODBqPOlpaWaP3++FixYoDlz5igtLU07duz4zxMmJGjnzp2y2WyaNWuWFi1apPz8fK1Zs6bvVwEAAIaUPgVKUlKStm3bpgceeEDHjx+3zjudTt1///169NFH9fbbb+vAgQNavHixbrjhBmVlZUmSbrnlFk2aNEn33HOPDh48qN27d+upp57Sww8/rBEjRvTPVQEAgEGtT4FSUVGhnTt3au/evVHnMzMzZbPZVFtba51rampSc3OzPB6PJMnj8aihoUFtbW3WnpqaGiUnJ2vy5MlnfT6bzSaHwxF1AACAoWt4rA+46667NHXqVE2fPv2MNbfbra6uLgWDwajzgUBAbrfb2hMIBM5YP712NkVFRVq1alWsowIAgEEqpldQxowZo5///Oe6++671dXVdaFmOkNxcbGcTqd1pKenD9hzAwCAgRdToGRmZsrlcunAgQPq7u5Wd3e3brrpJi1btkzd3d0KBAKy2+1KTk6OepzL5ZLf75ck+f1+uVyuM9ZPr51NJBJROByOOgAAwNAVU6Ds3btXU6ZM0fXXX28df/7zn7Vt2zZdf/312r9/vyKRiLKzs63HTJgwQePGjZPX65Ukeb1eZWRkKCUlxdqTk5OjYDCoxsbGfrosAAAwmMX0HpR//etf+uijj6LOdXZ26rPPPrPOb968WSUlJero6FAoFFJZWZnq6urk8/kkSXv27FFjY6O2bt2qwsJCud1urV27VhUVFYpEIv10WQAAYDCL+U2yX2b58uXq6elRVVWV7Ha7ampqtHTpUmu9p6dHeXl52rhxo7xerzo7O1VZWamVK1f29ygAAGCQGiapN95DxMrhcCgUCsnpdF5070fZ0OCN9wgYQCsyPPEeAQD6TSzfv/m3eAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABgnpkApKCjQwYMHFQwGFQwGVVdXp9zcXGvdbrervLxc7e3tCofD2r59u1JTU6M+x9ixY1VdXa3Ozk4FAgGtX79eiYmJ/XM1AABgSIgpUP75z3/q8ccfV2ZmpqZNm6bf//73ev311zVp0iRJUmlpqebPn68FCxZozpw5SktL044dO/7zZAkJ2rlzp2w2m2bNmqVFixYpPz9fa9as6d+rAgAAg9owSb3n8wk+++wz/fCHP9T27dt17NgxLVy4UFVVVZKkiRMn6vDhw5o5c6Z8Pp9yc3NVXV2ttLQ0tbW1SZKWLFmidevWKSUlRd3d3V/pOR0Oh0KhkJxOp8Lh8PmMP+hsaPDGewQMoBUZnniPAAD9Jpbv331+D0pCQoLuuusuJSUlyev1KjMzUzabTbW1tdaepqYmNTc3y+P54g9Zj8ejhoYGK04kqaamRsnJyZo8efI5n8tms8nhcEQdAABg6Io5UKZMmaJwOKyuri49++yzuuOOO3To0CG53W51dXUpGAxG7Q8EAnK73ZIkt9utQCBwxvrptXMpKipSKBSyjpaWlljHBgAAg0jMgdLU1KTrr79eWVlZ2rhxoyorK/WNb3zjQsxmKS4ultPptI709PQL+nwAACC+hsf6gO7ubn366aeSpAMHDmj69On6wQ9+oFdeeUV2u13JyclRr6K4XC75/X5Jkt/v14wZM6I+n8vlstbOJRKJKBKJxDoqAAAYpM77PigJCQmy2+2qr69XJBJRdna2tTZhwgSNGzdOXu8Xb+z0er3KyMhQSkqKtScnJ0fBYFCNjY3nOwoAABgiYnoF5Sc/+Yl27dqlv//973I4HFq4cKFuuukmzZs3T6FQSJs3b1ZJSYk6OjoUCoVUVlamuro6+Xw+SdKePXvU2NiorVu3qrCwUG63W2vXrlVFRQWvkAAAAEtMgZKamqoXXnhBo0ePVjAY1Icffqh58+ZZP7mzfPly9fT0qKqqSna7XTU1NVq6dKn1+J6eHuXl5Wnjxo3yer3q7OxUZWWlVq5c2b9XBQAABrXzvg9KPHAfFFwsuA8KgKFkQO6DAgAAcKEQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBOTIHy+OOP6/3331coFFIgENBvf/tbTZgwIWqP3W5XeXm52tvbFQ6HtX37dqWmpkbtGTt2rKqrq9XZ2alAIKD169crMTHx/K8GAAAMCTEFypw5c1RRUaGZM2cqJydHI0aM0J49e3TJJZdYe0pLSzV//nwtWLBAc+bMUVpamnbs2PGfJ0xI0M6dO2Wz2TRr1iwtWrRI+fn5WrNmTf9dFQAAGNSGSert64Mvv/xyHTt2TLNnz9a7774rp9OpY8eOaeHChaqqqpIkTZw4UYcPH9bMmTPl8/mUm5ur6upqpaWlqa2tTZK0ZMkSrVu3TikpKeru7v7S53U4HAqFQnI6nQqHw30df1Da0OCN9wgYQCsyPPEeAQD6TSzfv8/rPSjJycmSpI6ODklSZmambDabamtrrT1NTU1qbm6Wx/PFH7Qej0cNDQ1WnEhSTU2NkpOTNXny5PMZBwAADBHD+/rAYcOG6ZlnntG+ffv00UcfSZLcbre6uroUDAaj9gYCAbndbmtPIBA4Y/302tnYbDbZ7XbrY4fD0dexAQDAINDnV1AqKio0ZcoUffe73+3Pec6qqKhIoVDIOlpaWi74cwIAgPjpU6CUlZUpLy9P//M//xMVC36/X3a73fqrn9NcLpf8fr+1x+VynbF+eu1siouL5XQ6rSM9Pb0vYwMAgEEi5kApKyvTHXfcoZtvvll/+9vfotbq6+sViUSUnZ1tnZswYYLGjRsnr/eLN3d6vV5lZGQoJSXF2pOTk6NgMKjGxsazPmckElE4HI46AADA0BXTe1AqKiq0cOFC3XbbbQqHw9YrH8FgUP/+978VCoW0efNmlZSUqKOjQ6FQSGVlZaqrq5PP55Mk7dmzR42Njdq6dasKCwvldru1du1aVVRUKBKJ9P8VAgCAQSemQFm6dKkk6Y9//GPU+fz8fFVWVkqSli9frp6eHlVVVclut6umpsZ6nCT19PQoLy9PGzdulNfrVWdnpyorK7Vy5crzvRYAADBEnNd9UOKF+6DgYsF9UAAMJQN2HxQAAIALgUABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGGR7vAQAAX9jQ4I33CBhAKzI88R7BaLyCAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIwTc6B885vf1BtvvKGWlhb19vbqtttuO2PP6tWrdfToUZ04cUJvvfWWrrrqqqj1UaNG6cUXX1QwGNTx48f1/PPPKykpqe9XAQAAhpSYAyUpKUkHDx7Uww8/fNb1wsJCLVu2TAUFBcrKylJnZ6dqampkt9utPdu2bdPkyZOVk5OjvLw8zZ49W5s2ber7VQAAgCFleKwP2L17t3bv3n3O9UceeURr167VG2+8IUm69957FQgEdPvtt+uVV17RNddco1tvvVXTpk1TfX29JOn73/++3nzzTT322GNqbW3t46UAAIChol/fgzJ+/HiNHj1atbW11rlQKCSfzyePxyNJ8ng8On78uBUnklRbW6uenh5lZWWd9fPabDY5HI6oAwAADF39Gihut1uSFAgEos4HAgFrze12q62tLWr91KlT6ujosPb8t6KiIoVCIetoaWnpz7EBAIBhBsVP8RQXF8vpdFpHenp6vEcCAAAXUL8Git/vlyS5XK6o8y6Xy1rz+/1KTU2NWk9MTNSll15q7flvkUhE4XA46gAAAENXvwbKkSNH1NraquzsbOucw+FQVlaWvF6vJMnr9WrUqFGaOnWqtefmm29WQkKCfD5ff44DAAAGqZh/iicpKSnqvibjx4/Xddddp46ODv3jH//QM888oyeffFKffPKJjhw5oqefflpHjx7Va6+9Jkk6fPiwdu3apeeee04FBQUaMWKEysvL9fLLL/MTPAAAQFIfAmXatGn6wx/+YH1cWloqSdqyZYsWL16s9evXKykpSZs2bdLIkSO1b98+5ebmqqury3rM3XffrfLycu3du1c9PT2qqqrSsmXLzv9qAADAkDBMUm+8h4iVw+FQKBSS0+m86N6PsqHBG+8RMIBWZHjiPQIGEF/fF5eL8es7lu/fg+KneAAAwMWFQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBx4hooS5cu1ZEjR/T555/rT3/6k6ZPnx7PcQAAgCHiFih33nmnSkpKtHr1ak2dOlUHDx5UTU2NUlJS4jUSAAAwRNwC5dFHH9Vzzz2nLVu26NChQyooKNCJEyd03333xWskAABgiOHxeNIRI0YoMzNTxcXF1rne3l7V1tbK4/Gcsd9ms8lut1sfOxyOqP9eTGwJifEeAQPoYvw9fjHj6/vicjF+fcdyzXEJlMsvv1zDhw9XIBCIOh8IBHTNNdecsb+oqEirVq0643xLS8uFGhEwwv8LheI9AoAL5GL++nY4HAqHw//nnrgESqyKi4tVUlISde7SSy9VR0dHnCbCQHI4HGppaVF6evqX/oYGMLjw9X3xcTgcOnr06Jfui0ugtLe36+TJk3K5XFHnXS6X/H7/GfsjkYgikUjUOX4jX3zC4TD/34Ehiq/vi8dX/f8clzfJdnd3q76+XtnZ2da5YcOGKTs7W16vNx4jAQAAg8Ttr3hKSkpUWVmp/fv36/3339cjjzyipKQk/epXv4rXSAAAwBBxC5RXX31VKSkpWrNmjdxutz744APl5uaqra0tXiPBUF1dXVq1apW6urriPQqAfsbXN85lmKTeeA8BAADwv/Fv8QAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4wyKW90DAIaGyy67TPfdd588Ho/cbrckye/3q66uTlu2bFF7e3ucJ4QpeAUFg86YMWO0efPmeI8BIEbTpk3Txx9/rGXLlikYDOqdd97RO++8o2AwqGXLlunw4cPKzMyM95gwBPdBwaBz7bXX6sCBAxo+nBcAgcHE6/Xq4MGDKigoOOv6s88+q2uvvVazZs0a4MlgIv6Eh3Hmz5//f65feeWVAzQJgP503XXXKT8//5zrpaWl+stf/jJwA8FoBAqM89prr6m3t1fDhg07557eXl74AwYbv9+vGTNmqKmp6azrM2bMUCAQGOCpYCoCBcZpbW3V0qVL9cYbb5x1/brrrlN9ff0ATwXgfP3sZz/Tpk2blJmZqb1791ox4nK5lJ2drQceeECPPfZYnKeESXo5OEw6Xn/99d7Vq1efc/3aa6/tPXXqVNzn5ODgiP248847e71eb28kEuk9depU76lTp3ojkUiv1+vtXbBgQdzn4zDn4E2yMM6NN96opKQk1dTUnHX9kksu0bRp0/TOO+8M8GQA+svw4cN1+eWXS5La29t18uTJOE8E0xAoAADAONwHBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGCc/w9hhpQZvCHTFgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_deaths.Gender.value_counts().plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Characters Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1946 entries, 0 to 1945\n",
      "Data columns (total 33 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   S.No               1946 non-null   int64  \n",
      " 1   actual             1946 non-null   int64  \n",
      " 2   pred               1946 non-null   int64  \n",
      " 3   alive              1946 non-null   float64\n",
      " 4   plod               1946 non-null   float64\n",
      " 5   name               1946 non-null   object \n",
      " 6   title              938 non-null    object \n",
      " 7   male               1946 non-null   int64  \n",
      " 8   culture            677 non-null    object \n",
      " 9   dateOfBirth        433 non-null    float64\n",
      " 10  DateoFdeath        444 non-null    float64\n",
      " 11  mother             21 non-null     object \n",
      " 12  father             26 non-null     object \n",
      " 13  heir               23 non-null     object \n",
      " 14  house              1519 non-null   object \n",
      " 15  spouse             276 non-null    object \n",
      " 16  book1              1946 non-null   int64  \n",
      " 17  book2              1946 non-null   int64  \n",
      " 18  book3              1946 non-null   int64  \n",
      " 19  book4              1946 non-null   int64  \n",
      " 20  book5              1946 non-null   int64  \n",
      " 21  isAliveMother      21 non-null     float64\n",
      " 22  isAliveFather      26 non-null     float64\n",
      " 23  isAliveHeir        23 non-null     float64\n",
      " 24  isAliveSpouse      276 non-null    float64\n",
      " 25  isMarried          1946 non-null   int64  \n",
      " 26  isNoble            1946 non-null   int64  \n",
      " 27  age                433 non-null    float64\n",
      " 28  numDeadRelations   1946 non-null   int64  \n",
      " 29  boolDeadRelations  1946 non-null   int64  \n",
      " 30  isPopular          1946 non-null   int64  \n",
      " 31  popularity         1946 non-null   float64\n",
      " 32  isAlive            1946 non-null   int64  \n",
      "dtypes: float64(10), int64(15), object(8)\n",
      "memory usage: 501.8+ KB\n"
     ]
    }
   ],
   "source": [
    "char_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>male</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>DateoFdeath</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>count</th>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>973.500000</td>\n",
       "      <td>0.745632</td>\n",
       "      <td>0.687050</td>\n",
       "      <td>0.634470</td>\n",
       "      <td>0.365530</td>\n",
       "      <td>0.619219</td>\n",
       "      <td>1577.364896</td>\n",
       "      <td>2950.193694</td>\n",
       "      <td>0.198356</td>\n",
       "      <td>0.374615</td>\n",
       "      <td>0.480473</td>\n",
       "      <td>0.591984</td>\n",
       "      <td>0.395170</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.778986</td>\n",
       "      <td>0.141829</td>\n",
       "      <td>0.460946</td>\n",
       "      <td>-1293.563510</td>\n",
       "      <td>0.305755</td>\n",
       "      <td>0.074512</td>\n",
       "      <td>0.059096</td>\n",
       "      <td>0.089584</td>\n",
       "      <td>0.745632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>561.906131</td>\n",
       "      <td>0.435617</td>\n",
       "      <td>0.463813</td>\n",
       "      <td>0.312637</td>\n",
       "      <td>0.312637</td>\n",
       "      <td>0.485704</td>\n",
       "      <td>19565.414460</td>\n",
       "      <td>28192.245529</td>\n",
       "      <td>0.398864</td>\n",
       "      <td>0.484148</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.491593</td>\n",
       "      <td>0.489013</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.401918</td>\n",
       "      <td>0.486985</td>\n",
       "      <td>0.415684</td>\n",
       "      <td>0.348965</td>\n",
       "      <td>0.498601</td>\n",
       "      <td>19564.340993</td>\n",
       "      <td>1.383910</td>\n",
       "      <td>0.262669</td>\n",
       "      <td>0.235864</td>\n",
       "      <td>0.160568</td>\n",
       "      <td>0.435617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-298001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>487.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.391250</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013378</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>973.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735500</td>\n",
       "      <td>0.264500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033445</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1459.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.899000</td>\n",
       "      <td>0.608750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>298299.000000</td>\n",
       "      <td>298299.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              S.No       actual         pred        alive         plod  \\\n",
       "count  1946.000000  1946.000000  1946.000000  1946.000000  1946.000000   \n",
       "mean    973.500000     0.745632     0.687050     0.634470     0.365530   \n",
       "std     561.906131     0.435617     0.463813     0.312637     0.312637   \n",
       "min       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%     487.250000     0.000000     0.000000     0.391250     0.101000   \n",
       "50%     973.500000     1.000000     1.000000     0.735500     0.264500   \n",
       "75%    1459.750000     1.000000     1.000000     0.899000     0.608750   \n",
       "max    1946.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "              male    dateOfBirth    DateoFdeath        book1        book2  \\\n",
       "count  1946.000000     433.000000     444.000000  1946.000000  1946.000000   \n",
       "mean      0.619219    1577.364896    2950.193694     0.198356     0.374615   \n",
       "std       0.485704   19565.414460   28192.245529     0.398864     0.484148   \n",
       "min       0.000000     -28.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     240.000000     282.000000     0.000000     0.000000   \n",
       "50%       1.000000     268.000000     299.000000     0.000000     0.000000   \n",
       "75%       1.000000     285.000000     299.000000     0.000000     1.000000   \n",
       "max       1.000000  298299.000000  298299.000000     1.000000     1.000000   \n",
       "\n",
       "             book3        book4        book5  isAliveMother  isAliveFather  \\\n",
       "count  1946.000000  1946.000000  1946.000000      21.000000      26.000000   \n",
       "mean      0.480473     0.591984     0.395170       0.714286       0.192308   \n",
       "std       0.499747     0.491593     0.489013       0.462910       0.401918   \n",
       "min       0.000000     0.000000     0.000000       0.000000       0.000000   \n",
       "25%       0.000000     0.000000     0.000000       0.000000       0.000000   \n",
       "50%       0.000000     1.000000     0.000000       1.000000       0.000000   \n",
       "75%       1.000000     1.000000     1.000000       1.000000       0.000000   \n",
       "max       1.000000     1.000000     1.000000       1.000000       1.000000   \n",
       "\n",
       "       isAliveHeir  isAliveSpouse    isMarried      isNoble            age  \\\n",
       "count    23.000000     276.000000  1946.000000  1946.000000     433.000000   \n",
       "mean      0.652174       0.778986     0.141829     0.460946   -1293.563510   \n",
       "std       0.486985       0.415684     0.348965     0.498601   19564.340993   \n",
       "min       0.000000       0.000000     0.000000     0.000000 -298001.000000   \n",
       "25%       0.000000       1.000000     0.000000     0.000000      18.000000   \n",
       "50%       1.000000       1.000000     0.000000     0.000000      27.000000   \n",
       "75%       1.000000       1.000000     0.000000     1.000000      50.000000   \n",
       "max       1.000000       1.000000     1.000000     1.000000     100.000000   \n",
       "\n",
       "       numDeadRelations  boolDeadRelations    isPopular   popularity  \\\n",
       "count       1946.000000        1946.000000  1946.000000  1946.000000   \n",
       "mean           0.305755           0.074512     0.059096     0.089584   \n",
       "std            1.383910           0.262669     0.235864     0.160568   \n",
       "min            0.000000           0.000000     0.000000     0.000000   \n",
       "25%            0.000000           0.000000     0.000000     0.013378   \n",
       "50%            0.000000           0.000000     0.000000     0.033445   \n",
       "75%            0.000000           0.000000     0.000000     0.086957   \n",
       "max           15.000000           1.000000     1.000000     1.000000   \n",
       "\n",
       "           isAlive  \n",
       "count  1946.000000  \n",
       "mean      0.745632  \n",
       "std       0.435617  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       1.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are negative value for date of birth. Negative mean for age.\n",
    "Let's investigate that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
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       "      <th>isAlive</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>1685</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.747</td>\n",
       "      <td>Doreah</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Lysene</td>\n",
       "      <td>278279.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>-277980.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>1869</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.640</td>\n",
       "      <td>Rhaego</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298299.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>-298001.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.167224</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      S.No  actual  pred  alive   plod    name title  male culture  \\\n",
       "1684  1685       0     0  0.253  0.747  Doreah   NaN     0  Lysene   \n",
       "1868  1869       0     0  0.360  0.640  Rhaego   NaN     0     NaN   \n",
       "\n",
       "      dateOfBirth  DateoFdeath mother father heir            house spouse  \\\n",
       "1684     278279.0        299.0    NaN    NaN  NaN  House Targaryen    NaN   \n",
       "1868     298299.0        298.0    NaN    NaN  NaN  House Targaryen    NaN   \n",
       "\n",
       "      book1  book2  book3  book4  book5  isAliveMother  isAliveFather  \\\n",
       "1684      1      1      0      0      1            NaN            NaN   \n",
       "1868      1      1      1      1      0            NaN            NaN   \n",
       "\n",
       "      isAliveHeir  isAliveSpouse  isMarried  isNoble       age  \\\n",
       "1684          NaN            NaN          0        0 -277980.0   \n",
       "1868          NaN            NaN          0        0 -298001.0   \n",
       "\n",
       "      numDeadRelations  boolDeadRelations  isPopular  popularity  isAlive  \n",
       "1684                 0                  0          0    0.110368        0  \n",
       "1868                 6                  1          0    0.167224        0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred[char_pred['age'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_pred.male.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(char_pred.culture.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_pred.isAlive.value_counts().plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling/Cleaning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning Allegiance column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_deaths['Allegiances'] = char_deaths['Allegiances'].str.replace('House ', '',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_deaths['Allegiances'].value_counts(normalize=True, ascending=True).plot(kind='barh')\n"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "## Feature engineering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables For Creating Feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "renam_warlead = []\n",
    "for x in all_warlead:\n",
    "    house = 'House ' + x\n",
    "    renam_warlead.append(house)\n",
    "    \n",
    "# renam_warlead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Stark',\n",
       " 'Lannister',\n",
       " 'Baratheon',\n",
       " 'Free folk',\n",
       " 'Tully',\n",
       " 'Brotherhood without Banners',\n",
       " 'Glover',\n",
       " 'Frey',\n",
       " 'Mormont',\n",
       " 'Mallister',\n",
       " 'Bracken',\n",
       " 'Tyrell',\n",
       " \"Night's Watch\",\n",
       " 'Greyjoy',\n",
       " 'Karstark',\n",
       " 'Blackwood',\n",
       " 'Darry',\n",
       " 'Giants',\n",
       " 'Thenns',\n",
       " 'Brave Companions',\n",
       " 'Bolton']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_warlead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "battles.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_commanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Charcters who died\n",
    "a = char_pred[char_pred['name'].isin(char_deaths['Name'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# characters who swore allegiance\n",
    "b = char_deaths[char_deaths['Allegiances'] != 'None']\n",
    "c = char_pred[char_pred['name'].isin(b['Name'])]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Characters whose house swore allegiance\n",
    "d = char_pred[char_pred['house'].isin(c['house'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "hus_char_with_allegnc = d['house']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "husOfAllgnc = list(set(char_deaths['Allegiances']))\n",
    "husOfAllgnc.remove('None')\n",
    "renam_husOfAllgnc = []\n",
    "for x in husOfAllgnc:\n",
    "    house = 'House ' + x\n",
    "    renam_husOfAllgnc.append(house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['House Stark',\n",
       " 'House Lannister',\n",
       " 'House Baratheon',\n",
       " 'House Arryn',\n",
       " 'House Tully',\n",
       " 'House Wildling',\n",
       " 'House Martell',\n",
       " 'House Targaryen',\n",
       " \"House Night's Watch\",\n",
       " 'House Greyjoy',\n",
       " 'House Tyrell']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "renam_husOfAllgnc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Tyrell': 0.028353326063249727,\n",
       " 'Arryn': 0.03271537622682661,\n",
       " 'Tully': 0.03271537622682661,\n",
       " 'Targaryen': 0.03925845147219193,\n",
       " 'Martell': 0.04034896401308615,\n",
       " 'Wildling': 0.04362050163576881,\n",
       " 'Baratheon': 0.0697928026172301,\n",
       " 'Greyjoy': 0.08178844056706652,\n",
       " 'Lannister': 0.11123227917121047,\n",
       " 'Stark': 0.11777535441657579,\n",
       " \"Night's Watch\": 0.12649945474372956,\n",
       " 'None': 0.27589967284623773}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_prop = char_deaths['Allegiances'].value_counts(normalize=True, ascending=True).to_dict() \n",
    "death_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.028353326063249727,\n",
       " 0.03271537622682661,\n",
       " 0.03271537622682661,\n",
       " 0.03925845147219193,\n",
       " 0.04034896401308615,\n",
       " 0.04362050163576881,\n",
       " 0.0697928026172301,\n",
       " 0.08178844056706652,\n",
       " 0.11123227917121047,\n",
       " 0.11777535441657579,\n",
       " 0.12649945474372956,\n",
       " 0.27589967284623773]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_perct_list = list(death_prop.values())\n",
    "death_perct_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "char = char_pred.copy()\n",
    "orig = char_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_pred = char.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'culture', 'dateOfBirth', 'DateoFdeath', 'mother', 'father',\n",
       "       'heir', 'house', 'spouse', 'isAliveMother', 'isAliveFather',\n",
       "       'isAliveHeir', 'isAliveSpouse'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping column with 5% or more missing values\n",
    "\n",
    "# Columns with less than 5% missing value except age\n",
    "five_pct = char_pred.shape[0] * 0.05\n",
    "missing = char_pred.isna().sum()\n",
    "missing = missing[missing > five_pct]\n",
    "missing.index[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_pred = char_pred.drop(missing.index[:-1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill age with mean value\n",
    "char_pred['age'] = char_pred['age'].fillna(round(char_pred['age'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred.isna().sum().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_sum = char_pred[['book1', 'book2', 'book3', 'book4', 'book5']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_pred.insert(7, 'books', book_sum)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models Before Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "char = char_pred.copy()\n",
    "char_pred = char.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Redundant columns \n",
    "redundant_col = ['S.No', 'actual', 'pred', 'alive', 'plod', 'book1', \n",
    "                 'book2', 'book3', 'book4', 'book5', 'name']\n",
    "char_pred1 = char_pred.drop(columns=redundant_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>books</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</td>\n",
       "      <td>37.0</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>1</td>\n",
       "      <td>5</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>1</td>\n",
       "      <td>3</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 × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  books  isMarried  isNoble   age  numDeadRelations  \\\n",
       "0        1      0          0        0  37.0                11   \n",
       "1        1      5          1        1  97.0                 1   \n",
       "2        1      1          0        1  37.0                 0   \n",
       "3        0      0          1        1  23.0                 0   \n",
       "4        0      1          1        1  29.0                 0   \n",
       "...    ...    ...        ...      ...   ...               ...   \n",
       "1941     1      5          0        0  37.0                 0   \n",
       "1942     1      3          0        0  37.0                 0   \n",
       "1943     1      5          0        0  37.0                 0   \n",
       "1944     1      3          0        0  37.0                 0   \n",
       "1945     1      3          1        1  47.0                 4   \n",
       "\n",
       "      boolDeadRelations  isPopular  popularity  isAlive  \n",
       "0                     1          1    0.605351        0  \n",
       "1                     1          1    0.896321        1  \n",
       "2                     0          0    0.267559        1  \n",
       "3                     0          0    0.183946        0  \n",
       "4                     0          0    0.043478        1  \n",
       "...                 ...        ...         ...      ...  \n",
       "1941                  0          1    0.351171        0  \n",
       "1942                  0          0    0.096990        0  \n",
       "1943                  0          0    0.030100        1  \n",
       "1944                  0          0    0.130435        1  \n",
       "1945                  1          0    0.210702        0  \n",
       "\n",
       "[1946 rows x 10 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>books</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>0.783978</td>\n",
       "      <td>-1.316946</td>\n",
       "      <td>-0.406429</td>\n",
       "      <td>-0.924479</td>\n",
       "      <td>0.005405</td>\n",
       "      <td>7.727560</td>\n",
       "      <td>3.523394</td>\n",
       "      <td>3.989180</td>\n",
       "      <td>3.212131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.783978</td>\n",
       "      <td>1.909920</td>\n",
       "      <td>2.459190</td>\n",
       "      <td>1.081135</td>\n",
       "      <td>4.935460</td>\n",
       "      <td>0.501655</td>\n",
       "      <td>3.523394</td>\n",
       "      <td>3.989180</td>\n",
       "      <td>5.024255</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.783978</td>\n",
       "      <td>-0.671573</td>\n",
       "      <td>-0.406429</td>\n",
       "      <td>1.081135</td>\n",
       "      <td>0.005405</td>\n",
       "      <td>-0.220936</td>\n",
       "      <td>-0.283671</td>\n",
       "      <td>-0.250549</td>\n",
       "      <td>1.108401</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.274890</td>\n",
       "      <td>-1.316946</td>\n",
       "      <td>2.459190</td>\n",
       "      <td>1.081135</td>\n",
       "      <td>-1.144942</td>\n",
       "      <td>-0.220936</td>\n",
       "      <td>-0.283671</td>\n",
       "      <td>-0.250549</td>\n",
       "      <td>0.587676</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.274890</td>\n",
       "      <td>-0.671573</td>\n",
       "      <td>2.459190</td>\n",
       "      <td>1.081135</td>\n",
       "      <td>-0.651936</td>\n",
       "      <td>-0.220936</td>\n",
       "      <td>-0.283671</td>\n",
       "      <td>-0.250549</td>\n",
       "      <td>-0.287143</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       male     books  isMarried   isNoble       age  numDeadRelations  \\\n",
       "0  0.783978 -1.316946  -0.406429 -0.924479  0.005405          7.727560   \n",
       "1  0.783978  1.909920   2.459190  1.081135  4.935460          0.501655   \n",
       "2  0.783978 -0.671573  -0.406429  1.081135  0.005405         -0.220936   \n",
       "3 -1.274890 -1.316946   2.459190  1.081135 -1.144942         -0.220936   \n",
       "4 -1.274890 -0.671573   2.459190  1.081135 -0.651936         -0.220936   \n",
       "\n",
       "   boolDeadRelations  isPopular  popularity  isAlive  \n",
       "0           3.523394   3.989180    3.212131        0  \n",
       "1           3.523394   3.989180    5.024255        1  \n",
       "2          -0.283671  -0.250549    1.108401        1  \n",
       "3          -0.283671  -0.250549    0.587676        0  \n",
       "4          -0.283671  -0.250549   -0.287143        1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data = (char_pred1 - char_pred1.mean())/(char_pred1.std())\n",
    "normalized_data['isAlive'] = char_pred1['isAlive']\n",
    "normalized_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "973"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(char_pred1.shape[0]/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22762589928057556 0.4771015607609931\n"
     ]
    }
   ],
   "source": [
    "data = normalized_data.copy()\n",
    "\n",
    "train = data[:973]\n",
    "test = data[973:]\n",
    "\n",
    "features = train.columns.tolist()\n",
    "features.remove('isAlive')\n",
    "knn = KNeighborsRegressor(n_neighbors=5, algorithm='brute')\n",
    "\n",
    "knn.fit(train[features], train['isAlive'])\n",
    "pred = knn.predict(test[features])\n",
    "mse = mean_squared_error(test['isAlive'], pred)\n",
    "rmse = np.sqrt(mse)\n",
    "print(mse, rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.29290853031860226, 0.24691675231243576, 0.2386662098892315, 0.2286742034943474, 0.22762589928057556, 0.21249743062692703, 0.21556269270298048, 0.21672262190247804, 0.2138880324311979]\n"
     ]
    }
   ],
   "source": [
    "hyper_params = [1, 2, 3, 4, 5,10, 20, 30, 60]\n",
    "mse_values = list()\n",
    "\n",
    "for hp in hyper_params:\n",
    "    knn = KNeighborsRegressor(n_neighbors=hp, algorithm='brute')\n",
    "\n",
    "    knn.fit(train[features], train['isAlive'])\n",
    "    pred = knn.predict(test[features])\n",
    "    mse = mean_squared_error(test['isAlive'], pred)\n",
    "    # rmse = np.sqrt(mse)\n",
    "    # print(mse, rmse)\n",
    "    mse_values.append(mse)\n",
    "    \n",
    "print(mse_values)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data1 = char_pred1[['male', 'isPopular', 'age', 'isAlive']]\n",
    "# train1 = data1[:1362]\n",
    "# test1 = data1[1362:]\n",
    "\n",
    "\n",
    "# features1 = train1.columns.tolist()\n",
    "# features1.remove('isAlive')\n",
    "# lr = LinearRegression()\n",
    "# lr.fit(train1[features1], train1['isAlive'])\n",
    "# pred = knn.predict(test1[features1])\n",
    "# mse = mean_squared_error(test1['isAlive'], pred)\n",
    "# rmse = np.sqrt(mse)\n",
    "# print(mse, rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data1 = char_pred1.copy()\n",
    "# train1 = data1[:1362]\n",
    "# test1 = data1[1362:]\n",
    "\n",
    "\n",
    "# features = train1.columns.tolist()\n",
    "# features.remove('isAlive')\n",
    "\n",
    "\n",
    "# lr = LinearRegression()\n",
    "# lr.fit(train1[features], train1['isAlive'])\n",
    "# pred = knn.predict(test[features])\n",
    "# mse = mean_squared_error(test1['isAlive'], pred)\n",
    "# rmse = np.sqrt(mse)\n",
    "# print(mse, rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8263103802672148"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgr = LogisticRegression()\n",
    "lgr.fit(train[features], train['isAlive'])\n",
    "predlgr = lgr.predict(test[features])\n",
    "lgr.score(train[features], train['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7029804727646455"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgr.score(test[features], test['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2970195272353546 0.5449949790918762\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(test['isAlive'], predlgr)\n",
    "rmse = np.sqrt(mse)\n",
    "print(mse, rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr.score(train[features], train['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15048135451878975"
      ]
     },
     "execution_count": 707,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(train[features], train['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numeric_train = train.select_dtypes(include=['integer', 'float'])\n",
    "# numeric_test = test.select_dtypes(include=['integer', 'float'])\n",
    "\n",
    "# features = numeric_train.columns.drop('isAlive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = char_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "redundant_col = ['S.No', 'actual', 'pred', 'alive', 'plod', 'book1', \n",
    "                 'book2', 'book3', 'book4', 'book5']\n",
    "new = new.drop(columns=redundant_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>name</th>\n",
       "      <th>male</th>\n",
       "      <th>books</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>Viserys II Targaryen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>Walder Frey</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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>Addison Hill</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</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>Aemma Arryn</td>\n",
       "      <td>0</td>\n",
       "      <td>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>Sylva Santagar</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>Luwin</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>Reek</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>Symeon Star-Eyes</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>Coldhands</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>Tytos Lannister</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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 × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  male  books  isMarried  isNoble   age  \\\n",
       "0     Viserys II Targaryen     1      0          0        0  37.0   \n",
       "1              Walder Frey     1      5          1        1  97.0   \n",
       "2             Addison Hill     1      1          0        1  37.0   \n",
       "3              Aemma Arryn     0      0          1        1  23.0   \n",
       "4           Sylva Santagar     0      1          1        1  29.0   \n",
       "...                    ...   ...    ...        ...      ...   ...   \n",
       "1941                 Luwin     1      5          0        0  37.0   \n",
       "1942                  Reek     1      3          0        0  37.0   \n",
       "1943      Symeon Star-Eyes     1      5          0        0  37.0   \n",
       "1944             Coldhands     1      3          0        0  37.0   \n",
       "1945       Tytos Lannister     1      3          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 11 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "new['isACommander'] = np.where(new['name'].isin(all_commanders), 1, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>male</th>\n",
       "      <th>books</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Leobald Tallhart</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110368</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Stafford Lannister</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170569</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Stannis Baratheon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>Rolph Spicer</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.060201</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>Sandor Clegane</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>Robett Glover</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.167224</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>Loras Tyrell</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.665552</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>Walder Rivers</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.080268</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  male  books  isMarried  isNoble   age  \\\n",
       "1            Walder Frey     1      5          1        1  97.0   \n",
       "52      Leobald Tallhart     1      4          1        1  37.0   \n",
       "101           Robb Stark     1      0          0        0  37.0   \n",
       "169   Stafford Lannister     1      5          1        1  53.0   \n",
       "172    Stannis Baratheon     1      0          0        0  37.0   \n",
       "...                  ...   ...    ...        ...      ...   ...   \n",
       "1850        Rolph Spicer     0      2          0        1  37.0   \n",
       "1853      Sandor Clegane     1      5          0        0  30.0   \n",
       "1879       Robett Glover     1      5          1        0  37.0   \n",
       "1927        Loras Tyrell     1      5          0        1  23.0   \n",
       "1936       Walder Rivers     1      5          1        1  37.0   \n",
       "\n",
       "      numDeadRelations  boolDeadRelations  isPopular  popularity  isAlive  \\\n",
       "1                    1                  1          1    0.896321        1   \n",
       "52                   0                  0          0    0.110368        0   \n",
       "101                  7                  1          1    1.000000        0   \n",
       "169                  1                  1          0    0.170569        0   \n",
       "172                  4                  1          1    1.000000        1   \n",
       "...                ...                ...        ...         ...      ...   \n",
       "1850                 0                  0          0    0.060201        1   \n",
       "1853                 1                  1          1    0.695652        0   \n",
       "1879                 0                  0          0    0.167224        1   \n",
       "1927                 2                  1          1    0.665552        1   \n",
       "1936                 0                  0          0    0.080268        1   \n",
       "\n",
       "      isACommander  \n",
       "1                1  \n",
       "52               1  \n",
       "101              1  \n",
       "169              1  \n",
       "172              1  \n",
       "...            ...  \n",
       "1850             1  \n",
       "1853             1  \n",
       "1879             1  \n",
       "1927             1  \n",
       "1936             1  \n",
       "\n",
       "[67 rows x 12 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new[new['name'].isin(all_commanders)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "new['house'] = orig['house']\n",
    "new['inHouseOfWarLead'] = np.where(new['house'].isin(all_warlead), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "new['houseSworeAllegiance'] = np.where(new['house'].isin(hus_char_with_allegnc), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "new['inHouseOfAllegnc'] = np.where(new['house'].isin(renam_husOfAllgnc), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cond_list = [new['house'] == 'House Tyrell']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "new1 = new.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>name</th>\n",
       "      <th>male</th>\n",
       "      <th>books</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>house</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>Viserys II Targaryen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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>House Frey</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</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>House Swyft</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aemma Arryn</td>\n",
       "      <td>0</td>\n",
       "      <td>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>House Arryn</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sylva Santagar</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>House Santagar</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>Luwin</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>House Stark</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>Reek</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>House Bolton</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>Symeon Star-Eyes</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>Coldhands</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</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>Three-eyed crow</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>Tytos Lannister</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>House Lannister</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  male  books  isMarried  isNoble   age  \\\n",
       "0     Viserys II Targaryen     1      0          0        0  37.0   \n",
       "1              Walder Frey     1      5          1        1  97.0   \n",
       "2             Addison Hill     1      1          0        1  37.0   \n",
       "3              Aemma Arryn     0      0          1        1  23.0   \n",
       "4           Sylva Santagar     0      1          1        1  29.0   \n",
       "...                    ...   ...    ...        ...      ...   ...   \n",
       "1941                 Luwin     1      5          0        0  37.0   \n",
       "1942                  Reek     1      3          0        0  37.0   \n",
       "1943      Symeon Star-Eyes     1      5          0        0  37.0   \n",
       "1944             Coldhands     1      3          0        0  37.0   \n",
       "1945       Tytos Lannister     1      3          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",
       "      isACommander            house  inHouseOfWarLead  houseSworeAllegiance  \\\n",
       "0                0              NaN                 0                     1   \n",
       "1                1       House Frey                 0                     1   \n",
       "2                0      House Swyft                 0                     1   \n",
       "3                0      House Arryn                 0                     1   \n",
       "4                0   House Santagar                 0                     1   \n",
       "...            ...              ...               ...                   ...   \n",
       "1941             0      House Stark                 0                     1   \n",
       "1942             0     House Bolton                 0                     1   \n",
       "1943             0              NaN                 0                     1   \n",
       "1944             0  Three-eyed crow                 0                     0   \n",
       "1945             0  House Lannister                 0                     1   \n",
       "\n",
       "      inHouseOfAllegnc  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    1  \n",
       "4                    0  \n",
       "...                ...  \n",
       "1941                 1  \n",
       "1942                 0  \n",
       "1943                 0  \n",
       "1944                 0  \n",
       "1945                 1  \n",
       "\n",
       "[1946 rows x 16 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new1['inHouseOfWarLead'].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1946 entries, 0 to 1945\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   male                  1946 non-null   int64  \n",
      " 1   books                 1946 non-null   int64  \n",
      " 2   isMarried             1946 non-null   int64  \n",
      " 3   isNoble               1946 non-null   int64  \n",
      " 4   age                   1946 non-null   float64\n",
      " 5   numDeadRelations      1946 non-null   int64  \n",
      " 6   boolDeadRelations     1946 non-null   int64  \n",
      " 7   isPopular             1946 non-null   int64  \n",
      " 8   popularity            1946 non-null   float64\n",
      " 9   isAlive               1946 non-null   int64  \n",
      " 10  isACommander          1946 non-null   int32  \n",
      " 11  inHouseOfWarLead      1946 non-null   int32  \n",
      " 12  houseSworeAllegiance  1946 non-null   int32  \n",
      " 13  inHouseOfAllegnc      1946 non-null   int32  \n",
      "dtypes: float64(2), int32(4), int64(8)\n",
      "memory usage: 182.6 KB\n"
     ]
    }
   ],
   "source": [
    "new1 = new1.drop(columns=['name', 'house'])\n",
    "new1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8091042584434655"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = new1.copy()\n",
    "train1 = data1[:1362]\n",
    "test1 = data1[1362:]\n",
    "\n",
    "features = train1.columns.tolist()\n",
    "features.remove('isAlive')\n",
    "\n",
    "lgr = LogisticRegression(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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6712328767123288"
      ]
     },
     "execution_count": 724,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgr.score(test1[features], test1['isAlive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male                   -0.146982\n",
       "books                   0.038919\n",
       "isMarried              -0.050037\n",
       "isNoble                -0.042211\n",
       "age                    -0.136891\n",
       "numDeadRelations       -0.192444\n",
       "boolDeadRelations      -0.193736\n",
       "isPopular              -0.153860\n",
       "popularity             -0.183223\n",
       "isAlive                 1.000000\n",
       "isACommander           -0.057968\n",
       "inHouseOfWarLead       -0.113636\n",
       "houseSworeAllegiance   -0.024843\n",
       "inHouseOfAllegnc       -0.113380\n",
       "Name: isAlive, dtype: float64"
      ]
     },
     "execution_count": 727,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.corr()['isAlive']"
   ]
  }
 ],
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