GOT-PT3_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from xgboost import XGBClassifier as XGBC\n",
    "import sklearn.utils \n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV, KFold, train_test_split, StratifiedKFold, RandomizedSearchCV, cross_val_score\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score, roc_curve, confusion_matrix, classification_report, mean_absolute_error\n",
    "\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_data = pd.read_csv('cleaned_got_for_ml1.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>male</th>\n",
       "      <th>book1</th>\n",
       "      <th>book4</th>\n",
       "      <th>isAliveHeir</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>inHouseOfAllegnc</th>\n",
       "      <th>mother_Alicent Hightower</th>\n",
       "      <th>mother_Alyssa Targaryen</th>\n",
       "      <th>mother_Alyssa Velaryon</th>\n",
       "      <th>mother_Betha Blackwood</th>\n",
       "      <th>mother_Cassana Estermont</th>\n",
       "      <th>mother_Catelyn Stark</th>\n",
       "      <th>mother_Cersei Lannister</th>\n",
       "      <th>mother_Daenaera Velaryon</th>\n",
       "      <th>mother_Dyanna Dayne</th>\n",
       "      <th>mother_Lady of House Sunderly</th>\n",
       "      <th>mother_Mariah Martell</th>\n",
       "      <th>mother_Naerys Targaryen</th>\n",
       "      <th>mother_Rhaenyra Targaryen</th>\n",
       "      <th>mother_Rhaenys Targaryen</th>\n",
       "      <th>...</th>\n",
       "      <th>father_Viserys I Targaryen</th>\n",
       "      <th>father_unknown</th>\n",
       "      <th>heir_Aegon IV Targaryen</th>\n",
       "      <th>heir_Aegon Targaryen</th>\n",
       "      <th>heir_Aemon Targaryen</th>\n",
       "      <th>heir_Aenys Targaryen</th>\n",
       "      <th>heir_Aerys II Targaryen</th>\n",
       "      <th>heir_Arlan V Durrandon</th>\n",
       "      <th>heir_Baelor 'Breakspear' Targaryen</th>\n",
       "      <th>heir_Baelor I Targaryen</th>\n",
       "      <th>heir_Bran Stark</th>\n",
       "      <th>heir_Daeron I Targaryen</th>\n",
       "      <th>heir_Daeron II Targaryen</th>\n",
       "      <th>heir_Duncan Targaryen</th>\n",
       "      <th>heir_Durran the Devout</th>\n",
       "      <th>heir_Halleck Hoare</th>\n",
       "      <th>heir_Jaehaerys Targaryen</th>\n",
       "      <th>heir_Myrcella Baratheon</th>\n",
       "      <th>heir_Rhaegar Targaryen</th>\n",
       "      <th>heir_Rhaegel Targaryen</th>\n",
       "      <th>heir_Rhaenyra Targaryen</th>\n",
       "      <th>heir_Shireen Baratheon</th>\n",
       "      <th>heir_Theon Greyjoy</th>\n",
       "      <th>heir_Tommen Baratheon</th>\n",
       "      <th>heir_unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Viserys II Targaryen</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walder Frey</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addison Hill</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aemma Arryn</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylva Santagar</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      male  book1  book4  isAliveHeir   age  numDeadRelations  \\\n",
       "name                                                                            \n",
       "Viserys II Targaryen     1      0      0          0.0  37.0                11   \n",
       "Walder Frey              1      1      1         -1.0  97.0                 1   \n",
       "Addison Hill             1      0      1         -1.0  37.0                 0   \n",
       "Aemma Arryn              0      0      0         -1.0  23.0                 0   \n",
       "Sylva Santagar           0      0      1         -1.0  29.0                 0   \n",
       "\n",
       "                      boolDeadRelations  isPopular  popularity  isAlive  \\\n",
       "name                                                                      \n",
       "Viserys II Targaryen                  1          1    0.605351        0   \n",
       "Walder Frey                           1          1    0.896321        1   \n",
       "Addison Hill                          0          0    0.267559        1   \n",
       "Aemma Arryn                           0          0    0.183946        0   \n",
       "Sylva Santagar                        0          0    0.043478        1   \n",
       "\n",
       "                      inHouseOfAllegnc  mother_Alicent Hightower  \\\n",
       "name                                                               \n",
       "Viserys II Targaryen                 0                         0   \n",
       "Walder Frey                          0                         0   \n",
       "Addison Hill                         0                         0   \n",
       "Aemma Arryn                          1                         0   \n",
       "Sylva Santagar                       0                         0   \n",
       "\n",
       "                      mother_Alyssa Targaryen  mother_Alyssa Velaryon  \\\n",
       "name                                                                    \n",
       "Viserys II Targaryen                        0                       0   \n",
       "Walder Frey                                 0                       0   \n",
       "Addison Hill                                0                       0   \n",
       "Aemma Arryn                                 0                       0   \n",
       "Sylva Santagar                              0                       0   \n",
       "\n",
       "                      mother_Betha Blackwood  mother_Cassana Estermont  \\\n",
       "name                                                                     \n",
       "Viserys II Targaryen                       0                         0   \n",
       "Walder Frey                                0                         0   \n",
       "Addison Hill                               0                         0   \n",
       "Aemma Arryn                                0                         0   \n",
       "Sylva Santagar                             0                         0   \n",
       "\n",
       "                      mother_Catelyn Stark  mother_Cersei Lannister  \\\n",
       "name                                                                  \n",
       "Viserys II Targaryen                     0                        0   \n",
       "Walder Frey                              0                        0   \n",
       "Addison Hill                             0                        0   \n",
       "Aemma Arryn                              0                        0   \n",
       "Sylva Santagar                           0                        0   \n",
       "\n",
       "                      mother_Daenaera Velaryon  mother_Dyanna Dayne  \\\n",
       "name                                                                  \n",
       "Viserys II Targaryen                         0                    0   \n",
       "Walder Frey                                  0                    0   \n",
       "Addison Hill                                 0                    0   \n",
       "Aemma Arryn                                  0                    0   \n",
       "Sylva Santagar                               0                    0   \n",
       "\n",
       "                      mother_Lady of House Sunderly  mother_Mariah Martell  \\\n",
       "name                                                                         \n",
       "Viserys II Targaryen                              0                      0   \n",
       "Walder Frey                                       0                      0   \n",
       "Addison Hill                                      0                      0   \n",
       "Aemma Arryn                                       0                      0   \n",
       "Sylva Santagar                                    0                      0   \n",
       "\n",
       "                      mother_Naerys Targaryen  mother_Rhaenyra Targaryen  \\\n",
       "name                                                                       \n",
       "Viserys II Targaryen                        0                          1   \n",
       "Walder Frey                                 0                          0   \n",
       "Addison Hill                                0                          0   \n",
       "Aemma Arryn                                 0                          0   \n",
       "Sylva Santagar                              0                          0   \n",
       "\n",
       "                      mother_Rhaenys Targaryen  ...  \\\n",
       "name                                            ...   \n",
       "Viserys II Targaryen                         0  ...   \n",
       "Walder Frey                                  0  ...   \n",
       "Addison Hill                                 0  ...   \n",
       "Aemma Arryn                                  0  ...   \n",
       "Sylva Santagar                               0  ...   \n",
       "\n",
       "                      father_Viserys I Targaryen  father_unknown  \\\n",
       "name                                                               \n",
       "Viserys II Targaryen                           0               0   \n",
       "Walder Frey                                    0               1   \n",
       "Addison Hill                                   0               1   \n",
       "Aemma Arryn                                    0               1   \n",
       "Sylva Santagar                                 0               1   \n",
       "\n",
       "                      heir_Aegon IV Targaryen  heir_Aegon Targaryen  \\\n",
       "name                                                                  \n",
       "Viserys II Targaryen                        1                     0   \n",
       "Walder Frey                                 0                     0   \n",
       "Addison Hill                                0                     0   \n",
       "Aemma Arryn                                 0                     0   \n",
       "Sylva Santagar                              0                     0   \n",
       "\n",
       "                      heir_Aemon Targaryen  heir_Aenys Targaryen  \\\n",
       "name                                                               \n",
       "Viserys II Targaryen                     0                     0   \n",
       "Walder Frey                              0                     0   \n",
       "Addison Hill                             0                     0   \n",
       "Aemma Arryn                              0                     0   \n",
       "Sylva Santagar                           0                     0   \n",
       "\n",
       "                      heir_Aerys II Targaryen  heir_Arlan V Durrandon  \\\n",
       "name                                                                    \n",
       "Viserys II Targaryen                        0                       0   \n",
       "Walder Frey                                 0                       0   \n",
       "Addison Hill                                0                       0   \n",
       "Aemma Arryn                                 0                       0   \n",
       "Sylva Santagar                              0                       0   \n",
       "\n",
       "                      heir_Baelor 'Breakspear' Targaryen  \\\n",
       "name                                                       \n",
       "Viserys II Targaryen                                   0   \n",
       "Walder Frey                                            0   \n",
       "Addison Hill                                           0   \n",
       "Aemma Arryn                                            0   \n",
       "Sylva Santagar                                         0   \n",
       "\n",
       "                      heir_Baelor I Targaryen  heir_Bran Stark  \\\n",
       "name                                                             \n",
       "Viserys II Targaryen                        0                0   \n",
       "Walder Frey                                 0                0   \n",
       "Addison Hill                                0                0   \n",
       "Aemma Arryn                                 0                0   \n",
       "Sylva Santagar                              0                0   \n",
       "\n",
       "                      heir_Daeron I Targaryen  heir_Daeron II Targaryen  \\\n",
       "name                                                                      \n",
       "Viserys II Targaryen                        0                         0   \n",
       "Walder Frey                                 0                         0   \n",
       "Addison Hill                                0                         0   \n",
       "Aemma Arryn                                 0                         0   \n",
       "Sylva Santagar                              0                         0   \n",
       "\n",
       "                      heir_Duncan Targaryen  heir_Durran the Devout  \\\n",
       "name                                                                  \n",
       "Viserys II Targaryen                      0                       0   \n",
       "Walder Frey                               0                       0   \n",
       "Addison Hill                              0                       0   \n",
       "Aemma Arryn                               0                       0   \n",
       "Sylva Santagar                            0                       0   \n",
       "\n",
       "                      heir_Halleck Hoare  heir_Jaehaerys Targaryen  \\\n",
       "name                                                                 \n",
       "Viserys II Targaryen                   0                         0   \n",
       "Walder Frey                            0                         0   \n",
       "Addison Hill                           0                         0   \n",
       "Aemma Arryn                            0                         0   \n",
       "Sylva Santagar                         0                         0   \n",
       "\n",
       "                      heir_Myrcella Baratheon  heir_Rhaegar Targaryen  \\\n",
       "name                                                                    \n",
       "Viserys II Targaryen                        0                       0   \n",
       "Walder Frey                                 0                       0   \n",
       "Addison Hill                                0                       0   \n",
       "Aemma Arryn                                 0                       0   \n",
       "Sylva Santagar                              0                       0   \n",
       "\n",
       "                      heir_Rhaegel Targaryen  heir_Rhaenyra Targaryen  \\\n",
       "name                                                                    \n",
       "Viserys II Targaryen                       0                        0   \n",
       "Walder Frey                                0                        0   \n",
       "Addison Hill                               0                        0   \n",
       "Aemma Arryn                                0                        0   \n",
       "Sylva Santagar                             0                        0   \n",
       "\n",
       "                      heir_Shireen Baratheon  heir_Theon Greyjoy  \\\n",
       "name                                                               \n",
       "Viserys II Targaryen                       0                   0   \n",
       "Walder Frey                                0                   0   \n",
       "Addison Hill                               0                   0   \n",
       "Aemma Arryn                                0                   0   \n",
       "Sylva Santagar                             0                   0   \n",
       "\n",
       "                      heir_Tommen Baratheon  heir_unknown  \n",
       "name                                                       \n",
       "Viserys II Targaryen                      0             0  \n",
       "Walder Frey                               0             1  \n",
       "Addison Hill                              0             1  \n",
       "Aemma Arryn                               0             1  \n",
       "Sylva Santagar                            0             1  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Under Sampling Data\n",
    "dead = cleaned_data[cleaned_data['isAlive'] == 0]\n",
    "alive = cleaned_data[cleaned_data['isAlive'] == 1].sample(500, random_state=42)\n",
    "und_samp_df = pd.concat([dead, alive])\n",
    "\n",
    "und_samp_df = sklearn.utils.shuffle(und_samp_df, random_state=42)\n",
    "und_samp_df1 = und_samp_df.copy()\n",
    "# und_samp_df = und_samp_df.reset_index(drop=True)\n",
    "\n",
    "# Scaling our cleaned_data\n",
    "scaler = StandardScaler()\n",
    "norm_df = scaler.fit_transform(und_samp_df)\n",
    "norm_df = pd.DataFrame(norm_df, columns=und_samp_df.columns)\n",
    "norm_df.index = und_samp_df1.index\n",
    "norm_df['isAlive'] = und_samp_df['isAlive']\n",
    "\n",
    "\n",
    "# Creating Features and Target\n",
    "x = norm_df.drop(columns='isAlive')\n",
    "y = norm_df['isAlive']\n",
    "\n",
    "# Splitting the cleaned_data for model training \n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=.3, shuffle=True, random_state=5, stratify=y)\n",
    "\n",
    "# creating a seed\n",
    "fixed_seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>Train Score</th>\n",
       "      <th>Test_Score</th>\n",
       "      <th>ROC_AUC Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.709770</td>\n",
       "      <td>0.648829</td>\n",
       "      <td>0.724318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <td>0.784483</td>\n",
       "      <td>0.682274</td>\n",
       "      <td>0.737987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.746711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.675585</td>\n",
       "      <td>0.697673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBC</th>\n",
       "      <td>0.873563</td>\n",
       "      <td>0.688963</td>\n",
       "      <td>0.762461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Train Score  Test_Score  ROC_AUC Score\n",
       "LogisticRegression         0.709770    0.648829       0.724318\n",
       "KNeighborsClassifier       0.784483    0.682274       0.737987\n",
       "RandomForestClassifier     0.896552    0.692308       0.746711\n",
       "DecisionTreeClassifier     0.896552    0.675585       0.697673\n",
       "XGBC                       0.873563    0.688963       0.762461"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def train_and_score(x, y):\n",
    "    # Models for training\n",
    "    models = [LogisticRegression(random_state=fixed_seed), KNeighborsClassifier(), RandomForestClassifier(random_state=fixed_seed),\n",
    "            DecisionTreeClassifier(random_state=fixed_seed),  XGBC(seed=fixed_seed)]\n",
    "\n",
    "    test_score = []\n",
    "    train_score = []\n",
    "    roc_auc_lst = []\n",
    "\n",
    "    # Loops through each mosel and get their score\n",
    "    for model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        proba = model.predict_proba(x_test)[:, 1]\n",
    "        test = model.score(x_test, y_test)\n",
    "        train = model.score(x_train, y_train)\n",
    "        roc_auc = roc_auc_score(y_test, proba)\n",
    "        test_score.append(test)\n",
    "        train_score.append(train)\n",
    "        roc_auc_lst.append(roc_auc)\n",
    "    # Score dataframe   \n",
    "    models_score = {\n",
    "        'Train Score': train_score,\n",
    "        'Test_Score': test_score,\n",
    "        \"ROC_AUC Score\": roc_auc_lst\n",
    "    }\n",
    "    df= pd.DataFrame(models_score, index=['LogisticRegression','KNeighborsClassifier', 'RandomForestClassifier', \n",
    "                                    'DecisionTreeClassifier', 'XGBC'])\n",
    "    \n",
    "    # Using stratified kfold to get the recall of each model\n",
    "    skf = StratifiedKFold(n_splits= 5 , shuffle= True , random_state= fixed_seed ) \n",
    "    recall=[]\n",
    "    for model in models:\n",
    "        result = cross_val_score(model, x, y, cv=skf, scoring=\"recall\", n_jobs=-1)\n",
    "        recall.append(result)\n",
    "    classifiers=['Logistic Regression', 'KNeighborsClassifier', 'RandomForest', 'DescisionTree', 'XGB']\n",
    "\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for x in range(len(recall)):\n",
    "        sb.kdeplot(recall[x], shade=True)\n",
    "        \n",
    "    plt.title(\"Distribution of each classifier's Recall\", fontsize=15)\n",
    "    plt.legend(classifiers)\n",
    "    plt.xlabel(\"Recall\", labelpad=20)\n",
    "    plt.yticks([])\n",
    "    return df\n",
    "    plt.show()\n",
    "    \n",
    "train_and_score(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def model_confusion_matrix():\n",
    "    # Models with AUC-ROC score > 0.7\n",
    "    sub_models = [LogisticRegression(random_state=fixed_seed), KNeighborsClassifier(), RandomForestClassifier(random_state=fixed_seed), XGBC(seed=fixed_seed)]\n",
    "    # Getting the confusion metric for each model \n",
    "    cm = []\n",
    "    for model in sub_models:\n",
    "        model.fit(x_train, y_train)\n",
    "        pred = model.predict(x_test)\n",
    "        model_cm = confusion_matrix(y_test, pred)\n",
    "        cm.append(model_cm)\n",
    "        \n",
    "    # Function to plot heatmap\n",
    "    def cm_heatmap(cm, ax, r, c, model):\n",
    "        title = str(sub_models[model]).split('(')[0]\n",
    "        ax[r,c].set_title(f\"{title} Confusion Matrix\", fontsize=15.)\n",
    "        fig = sb.heatmap(cm, square = True, xticklabels = [\"Deceased\", \"Alive\"], annot = True, ax=ax[r,c],\n",
    "                        annot_kws = {\"fontsize\": 13}, yticklabels = [\"Deceased\", \"Alive\"],\n",
    "                        cbar = True, cbar_kws = {\"orientation\": \"vertical\"}, cmap = \"BrBG\", fmt='d').set(\n",
    "            xlabel = \"Predicted Class\", ylabel = \"Actual Class\",)\n",
    "        return fig\n",
    "    \n",
    "    # Displaying confusion metrics\n",
    "    f, ax = plt.subplots(2, 2, figsize=(15, 15))\n",
    "    cm_heatmap(cm[0], ax, 0, 0, 0)\n",
    "    cm_heatmap(cm[1], ax, 0, 1, 1)\n",
    "    cm_heatmap(cm[2], ax, 1, 0, 2)\n",
    "    cm_heatmap(cm[3], ax, 1, 1, 3)\n",
    "    plt.show()\n",
    "\n",
    "model_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def model_ROC_curve():\n",
    "    # Two best model based on AUC-ROC score and confusion matrix observation.\n",
    "    best_model = [RandomForestClassifier(random_state=fixed_seed), XGBC(seed=fixed_seed)]\n",
    "    # ROC curve for each model\n",
    "    roc = []\n",
    "    for model in best_model:\n",
    "        model.fit(x_train, y_train)\n",
    "        proba = model.predict_proba(x_test)[:, 1]\n",
    "        roc_cur = roc_curve(y_test, proba)\n",
    "        roc.append(roc_cur)\n",
    "    \n",
    "    # Plotting ROC curve for the models\n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "    fpr1, tpr1, thresholds1 = roc[0]\n",
    "    fpr2, tpr2, thresholds2 = roc[1]\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "\n",
    "    # Plot tpr against fpr\n",
    "    plt.plot(fpr1, tpr1, color='r')\n",
    "    plt.plot(fpr2, tpr2, color='g')\n",
    "    plt.legend(['RandomForestClassifier', 'XGBC'], loc=\"lower right\")\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve for Character Death Prediction')\n",
    "    plt.show()\n",
    "    \n",
    "model_ROC_curve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Dead       0.68      0.71      0.70       149\n",
      "       Alive       0.70      0.67      0.69       150\n",
      "\n",
      "    accuracy                           0.69       299\n",
      "   macro avg       0.69      0.69      0.69       299\n",
      "weighted avg       0.69      0.69      0.69       299\n",
      "\n",
      "========================================\n",
      "XGBClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Dead       0.67      0.73      0.70       149\n",
      "       Alive       0.71      0.65      0.68       150\n",
      "\n",
      "    accuracy                           0.69       299\n",
      "   macro avg       0.69      0.69      0.69       299\n",
      "weighted avg       0.69      0.69      0.69       299\n",
      "\n",
      "========================================\n"
     ]
    }
   ],
   "source": [
    "# Two best model based on AUC-ROC score and confusion matrix observation.\n",
    "best_model = [RandomForestClassifier(random_state=fixed_seed), XGBC(seed=fixed_seed)]\n",
    "\n",
    "for model in best_model:\n",
    "    model.fit(x_train, y_train)\n",
    "    pred = model.predict(x_test)\n",
    "    print(str(model).split('(')[0])\n",
    "    cls_rep = classification_report(y_test, pred, target_names=['Dead', 'Alive'])\n",
    "    print(cls_rep)\n",
    "    print('='*40)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the test performance above, we can see that both models share lots of similarity, such as having the same accuracy, same f1 score for dead category, same macro avg, and weighted avg.\n",
    "\n",
    "Comparing the precision and recall score the maximum difference they have is 2. \n",
    "\n",
    "we will proceed to tuning Random Forest Classifier for better performance. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper Parameter Tuning 1\n",
    "\n",
    "We are going to tune Random Forest Classifier model to get it's best performance. Some ranges of parameters will be giving, then a random search will be done on those parameters for the best."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Search with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid = {'bootstrap': [True, False],\n",
    "                'max_depth': [20, 40, 60, 80,],\n",
    "                'max_features': ['auto', 'sqrt'],\n",
    "                'min_samples_leaf': [1, 2, 4],\n",
    "                'min_samples_split': [2, 5, 10],\n",
    "                'criterion': [\"gini\", \"entropy\"],\n",
    "                'n_estimators': [200, 400, 600, 800,],\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [20, 40, 60, 80],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800]},\n",
       "                   random_state=42, scoring='neg_mean_absolute_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rfc = RandomForestClassifier(random_state=fixed_seed)\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rfc_random = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, \n",
    "                                random_state=fixed_seed, n_jobs = -1, scoring='neg_mean_absolute_error')\n",
    "# Fit the random search model\n",
    "rfc_random.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 200,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 80,\n",
       " 'criterion': 'gini',\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best parameters from random search \n",
    "rfc_random.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Performance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate model average error and accuracy\n",
    "def evaluate(model, test_features, test_labels):\n",
    "    predictions = model.predict(test_features)\n",
    "    errors = abs(predictions - test_labels)\n",
    "    print('Model Performance')\n",
    "    print('='*30)\n",
    "    avg_error = 'Average Error: {:0.4f} degrees.'.format(np.mean(errors))\n",
    "    print(avg_error)\n",
    "    accuracy = accuracy_score(y_test, predictions)\n",
    "    print('Accuracy: {:0.2f}%.'.format(100 * accuracy))\n",
    "    \n",
    "    return [np.mean(errors), accuracy]\n",
    "\n",
    "# Function to show model performance difference\n",
    "def improvement(new_score, base):\n",
    "    print('Improvement Error: {:0.2f}%.'.format( 100 * (new_score[0] - base[0]) / base[0]))\n",
    "    print('Improvement Accuracy: {:0.2f}%.'.format( 100 * (new_score[1] - base[1]) / base[1]))\n",
    "    print('=' * 30)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='bsper'></a>\n",
    "#### Base Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "==============================\n",
      "Average Error: 0.3077 degrees.\n",
      "Accuracy: 69.23%.\n"
     ]
    }
   ],
   "source": [
    "base_model = RandomForestClassifier(n_estimators = 100, random_state = fixed_seed)\n",
    "base_model.fit(x_train, y_train)\n",
    "base_performance = evaluate(base_model, x_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance of Best Random Search Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "==============================\n",
      "Average Error: 0.3010 degrees.\n",
      "Accuracy: 69.90%.\n",
      "Improvement Error: -2.17%.\n",
      "Improvement Accuracy: 0.97%.\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "best_random = rfc_random.best_estimator_\n",
    "random_performance = evaluate(best_random, x_test, y_test)\n",
    "improvement(random_performance, base_performance)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper Parameter Tuning 2\n",
    "\n",
    "Now let's do a grid search around the best parameter gotten from the random search"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [False], 'criterion': ['gini'],\n",
       "                         'max_depth': [50, 60], 'max_features': ['auto'],\n",
       "                         'min_samples_leaf': [3, 4, 5],\n",
       "                         'min_samples_split': [2, 3, 4],\n",
       "                         'n_estimators': [200, 400, 600]},\n",
       "             scoring='neg_mean_absolute_error', verbose=2)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'bootstrap': [False],\n",
    "    'max_depth': [50, 60],\n",
    "    'max_features': ['auto'],\n",
    "    'min_samples_leaf': [3, 4, 5],\n",
    "    'min_samples_split': [2, 3, 4],\n",
    "    'n_estimators': [200, 400, 600],\n",
    "    'criterion': [\"gini\"]\n",
    "}\n",
    "# Create a based model\n",
    "rfc = RandomForestClassifier(random_state=fixed_seed)\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rfc, param_grid = param_grid, scoring='neg_mean_absolute_error',\n",
    "                          cv = 3, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 50,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 5,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='grdper1'></a>\n",
    "#### Performance of Best Grid Search Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "==============================\n",
      "Average Error: 0.3077 degrees.\n",
      "Accuracy: 69.23%.\n",
      "Improvement Error: 0.00%.\n",
      "Improvement Accuracy: 0.00%.\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "best_grid = grid_search.best_estimator_\n",
    "grid_performance = evaluate(best_grid, x_test, y_test)\n",
    "improvement(grid_performance, base_performance)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems we have about maxed out performance given the fact that ther is no improvement on error and acccuracy, but we can give it one more try with a grid further refined from our previous results."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper Parameter Tuning 3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [False], 'criterion': ['gini'],\n",
       "                         'max_depth': [40, 50], 'max_features': ['auto'],\n",
       "                         'min_samples_leaf': [5, 6, 7],\n",
       "                         'min_samples_split': [2, 3],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             scoring='neg_mean_absolute_error', verbose=2)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'bootstrap': [False],\n",
    "    'max_depth': [40, 50,],\n",
    "    'max_features': ['auto'],\n",
    "    'min_samples_leaf': [5, 6, 7],\n",
    "    'min_samples_split': [2, 3],\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'criterion': [\"gini\"]\n",
    "}\n",
    "# Create a based model\n",
    "rfc = RandomForestClassifier(random_state=fixed_seed)\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rfc, param_grid = param_grid, scoring='neg_mean_absolute_error',\n",
    "                          cv = 3, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 40,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 5,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best parameters from grid seach\n",
    "grid_search.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='grdper2'></a>\n",
    "#### Performance of Best Grid Search Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "==============================\n",
      "Average Error: 0.2910 degrees.\n",
      "Accuracy: 70.90%.\n",
      "Improvement Error: -5.43%.\n",
      "Improvement Accuracy: 2.42%.\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "best_grid = grid_search.best_estimator_\n",
    "grid_performance2 = evaluate(best_grid, x_test, y_test)\n",
    "improvement(grid_performance2, base_performance)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A big decrease in performance, that is, the error has decrease by 5.43%, yet the same best parameters was given. This indicates we have reached diminishing returns for hyperparameter tuning. We could continue, but the returns would be the same output in terms of parameters."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='FM'></a>\n",
    "## Final Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model Parameter:\n",
      "==============================\n",
      "{'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 40, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 5, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}\n",
      "==============================\n",
      "Model Performance\n",
      "==============================\n",
      "Average Error: 0.2910 degrees.\n",
      "Accuracy: 70.90%.\n"
     ]
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "print('Final Model Parameter:')\n",
    "print('='*30)\n",
    "print(final_model.get_params())\n",
    "print('='*30)\n",
    "grid_final_accuracy = evaluate(final_model, x_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Prediction and Insight\n",
    "Below we create a pipeline to create a model using the best parameter from the final model above, then make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.2842809364548495\n"
     ]
    }
   ],
   "source": [
    "# SPlitting under sample data\n",
    "train = und_samp_df[:696].copy()\n",
    "test = und_samp_df[696:].copy()\n",
    "# Creating features\n",
    "features = train.columns.drop('isAlive')\n",
    "# Instantiate random forest model\n",
    "rndfrst = RandomForestClassifier(bootstrap=False, criterion='gini',\n",
    "                                 max_depth=40, max_features='auto', \n",
    "                                 min_samples_leaf=5, min_samples_split=2,\n",
    "                                 n_estimators=100, random_state=42, \n",
    "                                 )\n",
    "\n",
    "# Build the steps\n",
    "steps = [(\"scaler\", StandardScaler()),\n",
    "         (\"rndfrst\", rndfrst)]\n",
    "\n",
    "# create the pipeline\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipeline.fit(train[features], train['isAlive'])\n",
    "\n",
    "# Make predictions on the test set\n",
    "rndfrst_pred1 = pipeline.predict(test[features])\n",
    "# Add predictions to test data\n",
    "test['predictions'] = rndfrst_pred1\n",
    "# Checking mean absolute error\n",
    "mae = mean_absolute_error(test['isAlive'], test['predictions'])\n",
    "print(f'Mean Absolute Error: {mae}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we check visually how the model is performing for some character with acertain feature, by comparing the main labels with the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "import plotly.graph_objects as gob\n",
    "import plotly.offline as po\n",
    "po.init_notebook_mode(connected=True)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_dt = pd.read_csv('GOT_Char_details.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_copy = test.copy()\n",
    "test_copy['male'].replace({0:'Female', 1:'Male'}, inplace=True)\n",
    "test_copy['isAlive'].replace({0:'Dead', 1:'Alive'}, inplace=True)\n",
    "test_copy['predictions'].replace({0:'Dead', 1:'Alive'}, inplace=True)\n",
    "test_copy['isPopular'].replace({0:'Not Popular', 1:'Popular'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grouper(col):\n",
    "    orig = test_copy.groupby(col, as_index=False)[['isAlive']].value_counts(normalize=True)\n",
    "    orig['proportion'] = orig['proportion'].round(3)\n",
    "    pred = test_copy.groupby(col, as_index=False)[['predictions']].value_counts(normalize=True)\n",
    "    pred['proportion'] = pred['proportion'].round(3)\n",
    "    return[orig, pred]\n",
    "\n",
    "def sep_plot(org, prd, col, val1, val2):\n",
    "    a = org[org[col] == val1]\n",
    "    c = prd[prd[col] == val1]\n",
    "    b = org[org[col] == val2]\n",
    "    d = prd[prd[col] == val2]\n",
    "    \n",
    "    fig = gob.Figure(gob.Bar(x=a.isAlive, y=a.proportion, name='Main label', text=a.proportion))\n",
    "    fig.add_trace(gob.Bar(x=c.predictions, y=c.proportion, name='Pred label', text=c.proportion))\n",
    "    fig.update_layout(barmode='group', width=500, title=f'{val1} Character Prediction Comparison')\n",
    "    fig.update_yaxes(showticklabels=False)\n",
    "    fig.show()\n",
    "    \n",
    "    fig = gob.Figure(gob.Bar(x=b.isAlive, y=b.proportion, name='Main label', text=b.proportion))\n",
    "    fig.add_trace(gob.Bar(x=d.predictions, y=d.proportion, name='Pred label', text=d.proportion))\n",
    "    fig.update_layout(barmode='group', width=500, title=f'{val2} Character Prediction Comparison')\n",
    "    fig.update_yaxes(showticklabels=False)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Main label",
         "text": [
          0.512,
          0.488
         ],
         "type": "bar",
         "x": [
          "Dead",
          "Alive"
         ],
         "y": [
          0.512,
          0.488
         ]
        },
        {
         "name": "Pred label",
         "text": [
          0.626,
          0.374
         ],
         "type": "bar",
         "x": [
          "Dead",
          "Alive"
         ],
         "y": [
          0.626,
          0.374
         ]
        }
       ],
       "layout": {
        "barmode": "group",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Male Character Prediction Comparison"
        },
        "width": 500,
        "yaxis": {
         "showticklabels": false
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"6c063aa7-a93b-494f-842c-e3b96c551e5a\" class=\"plotly-graph-div\" style=\"height:525px; width:500px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6c063aa7-a93b-494f-842c-e3b96c551e5a\")) {                    Plotly.newPlot(                        \"6c063aa7-a93b-494f-842c-e3b96c551e5a\",                        [{\"name\":\"Main label\",\"text\":[0.512,0.488],\"x\":[\"Dead\",\"Alive\"],\"y\":[0.512,0.488],\"type\":\"bar\"},{\"name\":\"Pred label\",\"text\":[0.626,0.374],\"x\":[\"Dead\",\"Alive\"],\"y\":[0.626,0.374],\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"barmode\":\"group\",\"width\":500,\"title\":{\"text\":\"Male Character Prediction Comparison\"},\"yaxis\":{\"showticklabels\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('6c063aa7-a93b-494f-842c-e3b96c551e5a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Main label",
         "text": [
          0.648,
          0.352
         ],
         "type": "bar",
         "x": [
          "Alive",
          "Dead"
         ],
         "y": [
          0.648,
          0.352
         ]
        },
        {
         "name": "Pred label",
         "text": [
          0.614,
          0.386
         ],
         "type": "bar",
         "x": [
          "Alive",
          "Dead"
         ],
         "y": [
          0.614,
          0.386
         ]
        }
       ],
       "layout": {
        "barmode": "group",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Female Character Prediction Comparison"
        },
        "width": 500,
        "yaxis": {
         "showticklabels": false
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"3780c797-e916-47a8-a03d-368b279f48ec\" class=\"plotly-graph-div\" style=\"height:525px; width:500px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"3780c797-e916-47a8-a03d-368b279f48ec\")) {                    Plotly.newPlot(                        \"3780c797-e916-47a8-a03d-368b279f48ec\",                        [{\"name\":\"Main label\",\"text\":[0.648,0.352],\"x\":[\"Alive\",\"Dead\"],\"y\":[0.648,0.352],\"type\":\"bar\"},{\"name\":\"Pred label\",\"text\":[0.614,0.386],\"x\":[\"Alive\",\"Dead\"],\"y\":[0.614,0.386],\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"barmode\":\"group\",\"width\":500,\"title\":{\"text\":\"Female Character Prediction Comparison\"},\"yaxis\":{\"showticklabels\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3780c797-e916-47a8-a03d-368b279f48ec');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp = grouper('male')\n",
    "sep_plot(gp[0], gp[1], 'male', 'Male', 'Female')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Main label",
         "text": [
          0.727,
          0.273
         ],
         "type": "bar",
         "x": [
          "Dead",
          "Alive"
         ],
         "y": [
          0.727,
          0.273
         ]
        },
        {
         "name": "Pred label",
         "text": [
          1
         ],
         "type": "bar",
         "x": [
          "Dead"
         ],
         "y": [
          1
         ]
        }
       ],
       "layout": {
        "barmode": "group",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Popular Character Prediction Comparison"
        },
        "width": 500,
        "yaxis": {
         "showticklabels": false
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"2ee34d8d-2d05-4dab-943d-41a9e9da66c6\" class=\"plotly-graph-div\" style=\"height:525px; width:500px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"2ee34d8d-2d05-4dab-943d-41a9e9da66c6\")) {                    Plotly.newPlot(                        \"2ee34d8d-2d05-4dab-943d-41a9e9da66c6\",                        [{\"name\":\"Main label\",\"text\":[0.727,0.273],\"x\":[\"Dead\",\"Alive\"],\"y\":[0.727,0.273],\"type\":\"bar\"},{\"name\":\"Pred label\",\"text\":[1.0],\"x\":[\"Dead\"],\"y\":[1.0],\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"barmode\":\"group\",\"width\":500,\"title\":{\"text\":\"Popular Character Prediction Comparison\"},\"yaxis\":{\"showticklabels\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('2ee34d8d-2d05-4dab-943d-41a9e9da66c6');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Main label",
         "text": [
          0.556,
          0.444
         ],
         "type": "bar",
         "x": [
          "Alive",
          "Dead"
         ],
         "y": [
          0.556,
          0.444
         ]
        },
        {
         "name": "Pred label",
         "text": [
          0.52,
          0.48
         ],
         "type": "bar",
         "x": [
          "Dead",
          "Alive"
         ],
         "y": [
          0.52,
          0.48
         ]
        }
       ],
       "layout": {
        "barmode": "group",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Not Popular Character Prediction Comparison"
        },
        "width": 500,
        "yaxis": {
         "showticklabels": false
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"ffaa8cc5-b066-443c-bb38-eb9b6dc3400a\" class=\"plotly-graph-div\" style=\"height:525px; width:500px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"ffaa8cc5-b066-443c-bb38-eb9b6dc3400a\")) {                    Plotly.newPlot(                        \"ffaa8cc5-b066-443c-bb38-eb9b6dc3400a\",                        [{\"name\":\"Main label\",\"text\":[0.556,0.444],\"x\":[\"Alive\",\"Dead\"],\"y\":[0.556,0.444],\"type\":\"bar\"},{\"name\":\"Pred label\",\"text\":[0.52,0.48],\"x\":[\"Dead\",\"Alive\"],\"y\":[0.52,0.48],\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"barmode\":\"group\",\"width\":500,\"title\":{\"text\":\"Not Popular Character Prediction Comparison\"},\"yaxis\":{\"showticklabels\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ffaa8cc5-b066-443c-bb38-eb9b6dc3400a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp = grouper('isPopular')\n",
    "sep_plot(gp[0], gp[1], 'isPopular', 'Popular', 'Not Popular')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plots show the comparison of main label and prediction in terms of proportion. We can also see the small differnce between the two.\n",
    "\n",
    "Below we will look at some characters with a certain feature and their prediction in a tabuler form, that way we can know if the prediction is quite accurate for each characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "ne = char_dt[char_dt.index.isin(test.index)]\n",
    "ne['pred'] = test['predictions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>isAlive</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Leobald Tallhart</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halys Hornwood</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leo Lefford</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lyle Crakehall</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amory Lorch</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donal Noye</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edmure Tully</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gregor Clegane</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacelyn Bywater</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaime Lannister</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevan Lannister</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victarion Greyjoy</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sandor Clegane</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   isAlive  pred\n",
       "name                            \n",
       "Leobald Tallhart         0     1\n",
       "Halys Hornwood           0     1\n",
       "Leo Lefford              0     1\n",
       "Lyle Crakehall           1     1\n",
       "Amory Lorch              0     0\n",
       "Donal Noye               0     0\n",
       "Edmure Tully             1     0\n",
       "Gregor Clegane           0     0\n",
       "Jacelyn Bywater          0     1\n",
       "Jaime Lannister          1     0\n",
       "Kevan Lannister          0     0\n",
       "Victarion Greyjoy        1     0\n",
       "Sandor Clegane           0     0"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing predictions for characters that are commanders\n",
    "commander = ne[ne['isACommander'] == 1]\n",
    "commander[['isAlive', 'pred']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>isAlive</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aemma Arryn</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilbert</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wynafryd Manderly</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorko Terys</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maegor I Targaryen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Randa</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rast</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raymond Nayland</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redtusk</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Rolfe</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    isAlive  pred\n",
       "name                             \n",
       "Aemma Arryn               0     0\n",
       "Wilbert                   0     1\n",
       "Wynafryd Manderly         1     1\n",
       "Yorko Terys               1     1\n",
       "Maegor I Targaryen        0     0\n",
       "Randa                     1     1\n",
       "Rast                      0     0\n",
       "Raymond Nayland           1     1\n",
       "Redtusk                   1     0\n",
       "Red Rolfe                 0     0"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing predictions for characters that are commanders\n",
    "commander = ne[ne['isACommander'] == 0]\n",
    "commander[['isAlive', 'pred']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>isAlive</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aemma Arryn</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Rolfe</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhaenys Targaryen (daughter of Aemon)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victor Tyrell</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Werlag</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aegon Targaryen (son of Jaehaerys I)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alebelly</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Becca</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belandra</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bors</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       isAlive  pred\n",
       "name                                                \n",
       "Aemma Arryn                                  0     0\n",
       "Red Rolfe                                    0     0\n",
       "Rhaenys Targaryen (daughter of Aemon)        0     0\n",
       "Victor Tyrell                                1     1\n",
       "Werlag                                       1     1\n",
       "Aegon Targaryen (son of Jaehaerys I)         0     0\n",
       "Alebelly                                     0     0\n",
       "Becca                                        1     1\n",
       "Belandra                                     1     1\n",
       "Bors                                         1     1"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing predictions for characters that are in house of allegiance\n",
    "huz_of_allgnc = ne[ne['inHouseOfAllegnc'] == 1]\n",
    "huz_of_allgnc[['isAlive', 'pred']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>isAlive</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wilbert</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorko Terys</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maegor I Targaryen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Randa</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raymond Nayland</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redtusk</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rolfe</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runciter</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stone Thumbs</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sybassion</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    isAlive  pred\n",
       "name                             \n",
       "Wilbert                   0     1\n",
       "Yorko Terys               1     1\n",
       "Maegor I Targaryen        0     0\n",
       "Randa                     1     1\n",
       "Raymond Nayland           1     1\n",
       "Redtusk                   1     0\n",
       "Rolfe                     1     1\n",
       "Runciter                  0     0\n",
       "Stone Thumbs              0     1\n",
       "Sybassion                 1     0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing predictions for characters that their house swore allegiance\n",
    "swore_alleg = ne[ne['houseSworeAllegiance'] == 0]\n",
    "swore_alleg[['isAlive', 'pred']][:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error Ratio by Character Features\n",
    "It's difficult to tell the number of error by looking at the table as it is above, so we will look at the error ratio for sets of character with a particular feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isACommander\n",
      "0    0.50\n",
      "1    1.75\n",
      "dtype: float64\n",
      "====================\n",
      "inHouseOfAllegnc\n",
      "0    0.517730\n",
      "1    0.631579\n",
      "dtype: float64\n",
      "====================\n",
      "inHouseOfWarLead\n",
      "0    0.514925\n",
      "1    0.615385\n",
      "dtype: float64\n",
      "====================\n",
      "isPopular\n",
      "0    0.512987\n",
      "1    1.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "errors = (ne['isAlive'] - ne['pred']).abs()\n",
    "def error_ratio(col):\n",
    "    # error by feature \n",
    "    er = errors.groupby(ne[col]).mean()\n",
    "    # average people by feature\n",
    "    avg = ne['isAlive'].groupby(ne[col]).mean()\n",
    "    err_ratio = er/avg\n",
    "    return err_ratio\n",
    "print(error_ratio('isACommander'))\n",
    "print('='*20)\n",
    "print(error_ratio('inHouseOfAllegnc'))\n",
    "print('='*20)\n",
    "print(error_ratio('inHouseOfWarLead'))\n",
    "print('='*20)\n",
    "print(error_ratio('isPopular'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from above that the error ratio is high for characters that are commanders, and low for those that are not. It is the same for characters with popularity and that are not, high and low error ratio respectively."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "1. There were 38 battles fought within 3-year context of our data\n",
    "2. Characters without alleigiance to any house has the highest death count, followed by the nights watch.\n",
    "3. From the characters data, we have 128 dead women, 618 alive women, 367 dead men, and 838 alive men.\n",
    "4. There is a 17% chance that a female record turns out dead, and there is 30.5% chance a male record turns out dead.\n",
    "5. In other to link the battle and character death deatails to the characters data, 4 features were created.\n",
    "    * Feature to show characters who were commanders.\n",
    "    * Feature to show characters who belong to house of war attackers an defenders.\n",
    "    * Feature to show characters who belong to one of the house of allegiance.\n",
    "    * Feature to show characters who house swore alliegiance to another house.\n",
    "6. The data was under sampled because of the label feature was imbalance 1,451 and 495 for alive and dead charaters respectively. After undersampling we had 500 and 495 for alive and dead characters respectively.\n",
    "7. The data was trained on five (5) classification model, and the following score was gotten for each:\n",
    "\n",
    "    |Models|Train Score| Test_Score\t|ROC_AUC Score|\n",
    "    |---|:---:|:---:|:--:|\n",
    "    LogisticRegression\t| 0.709770\t| 0.648829 |\t0.724318\n",
    "    KNeighborsClassifier|\t0.784483 |\t0.682274 |\t0.737987\n",
    "    RandomForestClassifier|\t0.896552 |\t0.692308 |\t0.746711\n",
    "    DecisionTreeClassifier|\t0.896552 | 0.675585 |0.697673\n",
    "    XGBC\t| 0.873563 | 0.688963 |\t0.762461\n",
    ">\n",
    "8. Out of the five model, two of them (Random Forest Classifier and XGBClassifier) was performing better than the other, below is their performance on the test data:\n",
    "* Random Forest Classifier Test Performance: Accuracy 69%  \n",
    "\n",
    "    | Character | precision | recall | f1-score |\n",
    "    |---|---|---|---|\n",
    "    |Dead       |0.68  |    0.71  |    0.70   |    149\n",
    "    |Alive       |0.70|      0.67  |    0.69 |      150\n",
    ">\n",
    "* XGBClassifier Test Performance: Accuracy 69%\n",
    "\n",
    "    |Character| precision | recall |  f1-score |\n",
    "    |---|---|---|---|\n",
    "    |Dead   |    0.67   |   0.73   |   0.70    \n",
    "    |Alive |       0.71  |    0.65   |   0.68  \n",
    ">\n",
    "9. Random Forest classifier was chosen as the model of interest, and its parameter was tuned using Random Search Cross Validation and Grid Search Cross Validaton, for best results. Below are the performance after tuning:\n",
    "* Base Model Performance\n",
    ">\n",
    ">Average Error: 0.3077 degrees.\n",
    ">\n",
    ">Accuracy: 69.23%.\n",
    "\n",
    "* Fine-Tuning Model 1 Performance (RandomSearchCV)\n",
    ">\n",
    ">Average Error: 0.3010 degrees.\n",
    ">\n",
    ">Accuracy: 69.90%.\n",
    ">\n",
    ">Improvement Error: -2.17%.\n",
    ">\n",
    ">Improvement Accuracy: 0.97%.\n",
    "\n",
    "* Fine-Tuning Model 2 Performance (GridSearchCV)\n",
    ">\n",
    ">Average Error: 0.3077 degrees.\n",
    ">\n",
    ">Accuracy: 69.23%.\n",
    ">\n",
    ">Improvement Error: 0.00%.\n",
    ">\n",
    ">Improvement Accuracy: 0.00%.\n",
    "\n",
    "* Fine-Tuning Model 3 Performance (GridSearchCV)\n",
    ">\n",
    ">Average Error: 0.2910 degrees.\n",
    ">\n",
    ">Accuracy: 70.90%.\n",
    ">\n",
    ">Improvement Error: -5.43%.\n",
    ">\n",
    ">Improvement Accuracy: 2.42%.\n",
    "\n",
    "10. Using the best parameters from the last model tuning, we made some predictions and found out, based on the first ten rows, the model was performing well on characters that belongs to one of the house of allegiance.\n",
    "\n",
    "* The error ratio for characters for characters who are commander is high (1.75) compared to characters who are not commander (0.50)\n",
    "\n",
    "### Limitations\n",
    "- Limited data. After feature selection, we found out that more than 50% of the features were dropped because some were leaaking data of the target variable, some had low correlation, and some had multicollinearity.\n",
    "- We know most of of the characters died because of the different battles fought, but the link between the character data and battle data is not sufficient enough.\n",
    "- Few people had an understanding of how to do EDA and build a model\n",
    "- More commitment from members to push themselves to learn as well as to attend the group meetings.\n",
    " \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b68a3c467d07aa8b20c71071b0b7606f948be72d30c76b97a14d11803ebfb6c4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}