got_prjt_v5.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](2022-12-15-14-05-51.png)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HDSC December '22 Premiere Project: Game of Thrones Analysis\n",
    "\n",
    "## A Project by Team Gitlab\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Game of Thrones, popularly known as GOT, is a hit fantasy film series based on the equally famous book series \"A Song of Fire and Ice\" by George RR Martin. GOT is well-known for its vastly complicated political landscape, large cast of characters, and compelling storyline. However, the film is well-known for a number of character deaths.\n",
    "\n",
    "## Table of Content\n",
    "\n",
    "1.<a href=\"#PLP\"> Project Process</a></li>\n",
    "\n",
    "2.<a href=\"#DC\"> Data Collection</a>\n",
    "\n",
    "3.<a href=\"#DEW\"> Data Exploration & Wrangling</a>\n",
    "\n",
    "4.<a href=\"#FE\"> Feature Engineering</a>\n",
    "- <a href=\"#feclen\">Cleaning Feature</a>\n",
    "- <a href=\"#felnk\">Creating links to other data (Variables for creating features)</a>\n",
    "- <a href=\"#fecre\">Creating Feature</a>\n",
    "- <a href=\"#fesel\">Feature Selection</a>\n",
    "- <a href=\"#undsamdata\">Under sample data for model training</a>\n",
    "\n",
    "5.<a href=\"#bldtrnmd\"> Build and Train a Model</a>\n",
    "- <a href=\"#mdscores\">Model Test, Train and ROU-AUC Scores</a>\n",
    "- <a href=\"#mdrecscor\">Model Visualization of Recall Scores</a>\n",
    "- <a href=\"#mdcnfmat\">Model Confusion Matrices</a>\n",
    "- <a href=\"#bmrocurv\">Best Model ROC Curves</a>\n",
    "- <a href=\"#clsrep\">Classification Report of Best Models</a>\n",
    "\n",
    "6.<a href=\"#HP1\"> Hyper Parameter Tuning 1</a>\n",
    "- <a href=\"#rndsecv\"> Random Search with Cross Validation</a>\n",
    "\n",
    "7.<a href=\"#EVPER\"> Evaluate Performance of the Model</a>\n",
    "- <a href=\"#bsper\">Base Model Performance</a>\n",
    "- <a href=\"#rndper\">Performance of Best Random Search Model</a>\n",
    "\n",
    "8.<a href=\"#HP2\"> Hyper Parameter Tuning 2</a>\n",
    "- <a href=\"#grdse\">Grid Search with Cross Validation</a>\n",
    "- <a href=\"#grdper1\">Performance of Best Grid Search Model 1</a>\n",
    "\n",
    "9.<a href=\"#HP3\"> Hyper Parameter Tuning 3</a>\n",
    "- <a href=\"#anogrdse\">Another Grid Search</a>\n",
    "- <a href=\"#grdper2\">Performance of Best Grid Search Model 2</a>\n",
    "\n",
    "10.<a href=\"#FM\"> Final Model</a>\n",
    "- <a href=\"#modpredin\">Model Prediction and Insight</a>\n",
    "- <a href=\"#errchar\">Error Ratio by Character Features</a>\n",
    "\n",
    "11.<a href=\"#SL\"> Summary/Limitations</a>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aims and Objectives\n",
    "The project's goal is to use a carefully curated dataset to predict a character's death.\n",
    "In this project, we wanted to find features that are shared by all previously deceased characters and use these features to predict character deaths based on certain features.\n",
    "\n",
    "## Data Description \n",
    "\n",
    "Three datasets were used for this analysis:\n",
    "\n",
    "The first file is battles.csv, which contains Chris Albon's \"The War of the Five Kings\" Dataset which can be found at [here](https://github.com/chrisalbon/war_of_the_five_kings_dataset). It's an excellent collection of all of the series' battles.\n",
    "\n",
    "Then there's Erin Pierce's and Ben Kahle's character-deaths.csv. This dataset was developed as part of Allen Downey's Bayesian Survival Analysis, which can be found at [here](http://allendowney.blogspot.com/2015/03/bayesian-survival-analysis-for-game-of-life.html).\n",
    "\n",
    "Finally, character-predictions.csv contains a more detailed character dataset. This was scraped from [Westeros site](http://awoiaf.westeros.org/) by the team at A Song of Ice and Data.It also includes their predictions for which characters will die, the methodology of which can be found [here](https://got.show/machine-learning-algorithm-predicts-death-game-of-thrones)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='PLP'></a>\n",
    "## Project Flow Process\n",
    "![](2022-12-15-16-15-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data manipulation and wrangling package\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "# ML package\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",
    "\n",
    "# Data processing package\n",
    "import sklearn.utils \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV, 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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='DC'></a>\n",
    "## Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "battles = pd.read_csv('battles.csv')\n",
    "char_deaths = pd.read_csv('character-deaths.csv')\n",
    "char_pred = pd.read_csv('character-predictions.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>battle_number</th>\n",
       "      <th>attacker_king</th>\n",
       "      <th>defender_king</th>\n",
       "      <th>attacker_1</th>\n",
       "      <th>attacker_2</th>\n",
       "      <th>attacker_3</th>\n",
       "      <th>attacker_4</th>\n",
       "      <th>defender_1</th>\n",
       "      <th>defender_2</th>\n",
       "      <th>defender_3</th>\n",
       "      <th>defender_4</th>\n",
       "      <th>attacker_outcome</th>\n",
       "      <th>battle_type</th>\n",
       "      <th>major_death</th>\n",
       "      <th>major_capture</th>\n",
       "      <th>attacker_size</th>\n",
       "      <th>defender_size</th>\n",
       "      <th>attacker_commander</th>\n",
       "      <th>defender_commander</th>\n",
       "      <th>summer</th>\n",
       "      <th>location</th>\n",
       "      <th>region</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Battle of the Golden Tooth</td>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>win</td>\n",
       "      <td>pitched battle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Jaime Lannister</td>\n",
       "      <td>Clement Piper, Vance</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Golden Tooth</td>\n",
       "      <td>The Westerlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Battle at the Mummer's Ford</td>\n",
       "      <td>298</td>\n",
       "      <td>2</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>win</td>\n",
       "      <td>ambush</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Gregor Clegane</td>\n",
       "      <td>Beric Dondarrion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mummer's Ford</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of Riverrun</td>\n",
       "      <td>298</td>\n",
       "      <td>3</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>win</td>\n",
       "      <td>pitched battle</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Jaime Lannister, Andros Brax</td>\n",
       "      <td>Edmure Tully, Tytos Blackwood</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Riverrun</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  year  battle_number             attacker_king  \\\n",
       "0   Battle of the Golden Tooth   298              1  Joffrey/Tommen Baratheon   \n",
       "1  Battle at the Mummer's Ford   298              2  Joffrey/Tommen Baratheon   \n",
       "2           Battle of Riverrun   298              3  Joffrey/Tommen Baratheon   \n",
       "\n",
       "  defender_king attacker_1 attacker_2 attacker_3 attacker_4 defender_1  \\\n",
       "0    Robb Stark  Lannister        NaN        NaN        NaN      Tully   \n",
       "1    Robb Stark  Lannister        NaN        NaN        NaN  Baratheon   \n",
       "2    Robb Stark  Lannister        NaN        NaN        NaN      Tully   \n",
       "\n",
       "  defender_2  defender_3  defender_4 attacker_outcome     battle_type  \\\n",
       "0        NaN         NaN         NaN              win  pitched battle   \n",
       "1        NaN         NaN         NaN              win          ambush   \n",
       "2        NaN         NaN         NaN              win  pitched battle   \n",
       "\n",
       "   major_death  major_capture  attacker_size  defender_size  \\\n",
       "0          1.0            0.0        15000.0         4000.0   \n",
       "1          1.0            0.0            NaN          120.0   \n",
       "2          0.0            1.0        15000.0        10000.0   \n",
       "\n",
       "             attacker_commander             defender_commander  summer  \\\n",
       "0               Jaime Lannister           Clement Piper, Vance     1.0   \n",
       "1                Gregor Clegane               Beric Dondarrion     1.0   \n",
       "2  Jaime Lannister, Andros Brax  Edmure Tully, Tytos Blackwood     1.0   \n",
       "\n",
       "        location           region note  \n",
       "0   Golden Tooth  The Westerlands  NaN  \n",
       "1  Mummer's Ford   The Riverlands  NaN  \n",
       "2       Riverrun   The Riverlands  NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "battles.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Allegiances</th>\n",
       "      <th>Death Year</th>\n",
       "      <th>Book of Death</th>\n",
       "      <th>Death Chapter</th>\n",
       "      <th>Book Intro Chapter</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nobility</th>\n",
       "      <th>GoT</th>\n",
       "      <th>CoK</th>\n",
       "      <th>SoS</th>\n",
       "      <th>FfC</th>\n",
       "      <th>DwD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Addam Marbrand</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aegon Frey (Jinglebell)</td>\n",
       "      <td>None</td>\n",
       "      <td>299.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aegon Targaryen</td>\n",
       "      <td>House Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name      Allegiances  Death Year  Book of Death  \\\n",
       "0           Addam Marbrand        Lannister         NaN            NaN   \n",
       "1  Aegon Frey (Jinglebell)             None       299.0            3.0   \n",
       "2          Aegon Targaryen  House Targaryen         NaN            NaN   \n",
       "\n",
       "   Death Chapter  Book Intro Chapter  Gender  Nobility  GoT  CoK  SoS  FfC  \\\n",
       "0            NaN                56.0       1         1    1    1    1    1   \n",
       "1           51.0                49.0       1         1    0    0    1    0   \n",
       "2            NaN                 5.0       1         1    0    0    0    0   \n",
       "\n",
       "   DwD  \n",
       "0    0  \n",
       "1    0  \n",
       "2    1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_deaths.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>DateoFdeath</th>\n",
       "      <th>mother</th>\n",
       "      <th>father</th>\n",
       "      <th>heir</th>\n",
       "      <th>house</th>\n",
       "      <th>spouse</th>\n",
       "      <th>book1</th>\n",
       "      <th>book2</th>\n",
       "      <th>book3</th>\n",
       "      <th>book4</th>\n",
       "      <th>book5</th>\n",
       "      <th>isAliveMother</th>\n",
       "      <th>isAliveFather</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.946</td>\n",
       "      <td>Viserys II Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhaenyra Targaryen</td>\n",
       "      <td>Daemon Targaryen</td>\n",
       "      <td>Aegon IV Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.613</td>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Frey</td>\n",
       "      <td>Perra Royce</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.507</td>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Swyft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S.No  actual  pred  alive   plod                  name  \\\n",
       "0     1       0     0  0.054  0.946  Viserys II Targaryen   \n",
       "1     2       1     0  0.387  0.613           Walder Frey   \n",
       "2     3       1     0  0.493  0.507          Addison Hill   \n",
       "\n",
       "                  title  male   culture  dateOfBirth  DateoFdeath  \\\n",
       "0                   NaN     1       NaN          NaN          NaN   \n",
       "1  Lord of the Crossing     1  Rivermen        208.0          NaN   \n",
       "2                   Ser     1       NaN          NaN          NaN   \n",
       "\n",
       "               mother            father                heir        house  \\\n",
       "0  Rhaenyra Targaryen  Daemon Targaryen  Aegon IV Targaryen          NaN   \n",
       "1                 NaN               NaN                 NaN   House Frey   \n",
       "2                 NaN               NaN                 NaN  House Swyft   \n",
       "\n",
       "        spouse  book1  book2  book3  book4  book5  isAliveMother  \\\n",
       "0          NaN      0      0      0      0      0            1.0   \n",
       "1  Perra Royce      1      1      1      1      1            NaN   \n",
       "2          NaN      0      0      0      1      0            NaN   \n",
       "\n",
       "   isAliveFather  isAliveHeir  isAliveSpouse  isMarried  isNoble   age  \\\n",
       "0            0.0          0.0            NaN          0        0   NaN   \n",
       "1            NaN          NaN            1.0          1        1  97.0   \n",
       "2            NaN          NaN            NaN          0        1   NaN   \n",
       "\n",
       "   numDeadRelations  boolDeadRelations  isPopular  popularity  isAlive  \n",
       "0                11                  1          1    0.605351        0  \n",
       "1                 1                  1          1    0.896321        1  \n",
       "2                 0                  0          0    0.267559        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_pred.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='DEW'></a>\n",
    "## Data Exploration & Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "battles[\"name\"].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(298, 300)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "battles[\"year\"].min(), battles[\"year\"].max()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There were 38 battles fought within the 3-year context of our data.\n",
    "* Which of these years had the most battles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "battles[\"year\"].value_counts().plot(kind=\"bar\")\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most battles occured in 299BC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "win     32\n",
       "loss     5\n",
       "Name: attacker_outcome, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total wins vs losses\n",
    "battles[\"attacker_outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The number of commanders in each battle\n",
    "battles_df = battles.copy()\n",
    "battles_df[\"attacking_commanders_count\"] = [len(x) if type(x) == list else np.nan for x in battles_df.attacker_commander.str.split(\",\")]\n",
    "#More wins with less commanders...\n",
    "\n",
    "plt.subplots(figsize=(8, 6))\n",
    "sb.barplot(x=\"attacker_king\", y=\"attacking_commanders_count\", hue=\"attacker_outcome\", data=battles_df, palette=\"rainbow\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_death_df = char_deaths.copy()\n",
    "char_death_df.Allegiances.value_counts(ascending=True).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Lannister', 'None', 'House Targaryen', 'House Greyjoy',\n",
       "       'Baratheon', \"Night's Watch\", 'Arryn', 'House Stark',\n",
       "       'House Tyrell', 'Tyrell', 'Stark', 'Greyjoy', 'House Lannister',\n",
       "       'Martell', 'House Martell', 'Wildling', 'Targaryen', 'House Arryn',\n",
       "       'House Tully', 'Tully', 'House Baratheon'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alleg = char_death_df.Allegiances.unique()\n",
    "alleg"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The presence of house causes repeated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_death_df['Allegiances'] = char_death_df['Allegiances'].str.replace('House ', '',)\n",
    "char_death_df.Allegiances.value_counts(ascending=True).plot(kind=\"barh\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "char_death_df[\"Gender\"].value_counts().sort_values().plot.barh()\n",
    "plt.yticks([1, 0], ['Male', 'Female'])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were more male recorded deaths than female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       433.000000\n",
       "mean      -1293.563510\n",
       "std       19564.340993\n",
       "min     -298001.000000\n",
       "25%          18.000000\n",
       "50%          27.000000\n",
       "75%          50.000000\n",
       "max         100.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing age properties\n",
    "char_pred['age'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are negative value for date of birth. Negative mean for age.\n",
    "Let's investigate that."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research was made and according to [Fandom.com](https://gameofthrones.fandom.com/wiki/Doreah) Doreah is 25 years old.\n",
    "\n",
    "According to [Fanthom.com](https://gameofthrones.fandom.com/wiki/Rhaego) Rhaego was never born."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "char = char_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the age\n",
    "char.loc[1684, 'age'] = 25.0\n",
    "char.loc[1868, 'age'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(data=char, x='age', hue='isAlive', multiple='stack')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have more of young characters compared to adults. Also the alive density plot seems to be unimodal, while dead density plot seems to be multimodal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>isAlive</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>367</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "isAlive    0    1\n",
       "male             \n",
       "0        128  613\n",
       "1        367  838"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(char.male, char.isAlive)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 128 dead women, 613 alive women\n",
    "\n",
    "367 dead men and 838 alive men\n",
    "\n",
    "Generally (128/741) * 100 = 17%. That is there is a 17% chance that a female record turns out dead.\n",
    "\n",
    "And (367/1205) * 100 = 30.5% chance that a male record turns out dead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAF1CAYAAABGc/YfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAu6UlEQVR4nO3df1iUdb7/8dcAIpI/IBHJAcW6wJDa1Bpys806ukR5juj6Y8fcopaw02WWW6eatXNWd93cfme7retGZuTRCDVX3EpHSdM8KZNgYkhIgoI4IGmumkXo5/uHpzkaiPRdhxvw+biuz3Uxn/tzf+Z9z8T46nPfc2OTZAQAAADLBFhdAAAAwMWOQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZcBFZuHChZs+eLUm68cYbVVJS4vfnHD58uCorK32Pd+7cqeHDh1+Que+44w6tWbPG99gYoyuuuOKCzC1JR48eVf/+/S/YfN8pLy/XiBEjLvi8/pSWlqZNmzZZXQbQYRHIgA5o/fr1OnTokIKDg8855sMPP9SVV17ZilWddtVVV+mDDz5odky/fv1kjFFgYGCz45YsWaJbb731gtS1fv16paenn9XXrVs3lZeXX5D5AaA5BDKgg+nXr59+8pOfyBij0aNHW12O35wvrKHtCAjgnxrgfPgtATqYu+66S1u2bNHrr7+utLS0c44781Ti448/rqVLl561fe7cuXrppZckSd27d9err76q6upqVVVVafbs2ef8RzYkJEQLFy7UoUOH9Omnn8rhcJy1/czTdQ6HQx6PR0eOHJHX69Xzzz8vSdq4caMk6csvv9TRo0c1dOhQpaWl6cMPP9QLL7ygL774QrNmzWryNNrtt9+uzz//XAcPHtQzzzwjm80mSZo5c6YWLVrkG3fmKtzvf/97/eQnP9HLL7+so0eP6k9/+pOks0+Bdu/eXVlZWaqtrVVFRYWeeOIJ39zf1fHss8/q0KFD2rNnj1JSUs752n937J9++qkOHTqk1157TZ07d5YkFRUV6V//9V9944KCgnTw4EFdc801Tc7z6KOPqrq6Wvv371d6evpZNQcHB+vZZ5/V3r175fV69Ze//EUhISGS/u/9f/jhh1VTU6Pq6mrdfffdvnkvvfRSrVy5UkeOHNHWrVsbnQoeMGCA3G63vvjiC5WUlGjChAm+bQsXLtS8efP0zjvv6NixY7rllluafS0AnGZoNFrHabt37zb333+/GTJkiKmvrzeRkZG+bQsXLjSzZ882kszw4cNNZWWlkWT69u1rjh8/brp162YkmYCAAFNdXW2uv/56I8msWLHCzJ8/34SGhppevXqZrVu3milTpjT5/H/4wx/Mxo0bTXh4uImOjjZFRUW+55FkysvLzYgRI4wk8z//8z/mF7/4hZFkLrnkEt/z9evXzxhjTGBgoG+/tLQ08+2335oHHnjABAYGmpCQEJOWlmY2bdrkG2OMMe+//74JDw83MTEx5rPPPjPp6elGkpk5c6ZZtGiRb+z3n2P9+vW+sWfOd8UVVxhJJisry/ztb38zXbt2Nf369TOfffaZ+eUvf+mrrb6+3tx7770mICDA/Pu//7vZv3//Od+j8vJyU1RUZKKjo014eLj58MMPfe/Lo48+arKzs31jR48ebXbs2NHkPLfeeqs5cOCAGThwoOnSpYt54403zqr5xRdfNCtXrjTh4eGma9euJjc318yZM8f3/n/77bfmt7/9rQkKCjK33XabOX78uAkLCzOSzJtvvmneeustExoaahITE01VVZXvtQ4NDTX79u0zd999twkMDDSDBw82Bw8eNAMHDvT9d/bll1+aG264wdhsNtO5c2fLfy9otHbQLC+ARqNdoDZs2DBTX19vevbsaSSZXbt2menTp/u2nyuQSTKbNm0yd955p5FkRo4cacrKyowkExkZab7++msTEhLiG+t0Os3777/fZA2ff/65ufXWW32PMzIyzhnIPvjgAzNr1ixfvd+1cwWyvXv3njWuqUB25nPff//9Zt26dUb65wJZQECA+frrr01CQoJv25QpU8z69et9dezevdu3rUuXLsYYY3r37t3ka1ReXm7uu+8+3+PbbrvN93pfdtll5h//+IcvHC9dutQ8+uijTc6zYMECX8CSZK644oqzAtmxY8fM5Zdf7ts+dOhQs2fPHt/7/9VXX531GtfU1Jjrr7/eBAQEmPr6ejNgwADftieffNL3Wk+cONFs3LjxrFrmz59vfvOb3/j+O8vKyrL894FGa0+NU5ZAB5KWluY7jSSdvui9udOWZ1qyZIkmTZok6fS3F5csWSLp9Km9Tp066cCBAzp8+LAOHz6sv/71r4qMjGxynj59+pz1rcq9e/ee8znT09MVHx+vkpIS5efna9SoUc3WeOa8LRmzd+9e9enT57z7nE9ERIQ6d+581rHs3btXdrvd99jr9fp+PnHihCSpa9euP7jOAwcOaPPmzRo3bpx69Oih2267TYsXL25yju+/1mf+3KtXL11yySXatm2b731bvXq1evXq5RvzxRdf6OTJk77HX331lbp27apevXqpU6dO53wf+/Xrp+uvv9437+HDhzV58mRFRUU1WQuA8wuyugAAF0ZISIgmTpyowMBAHThwQJLUuXNnhYeH60c/+pF27NjR7P5Lly7V888/L7vdrrFjx+rHP/6xpNP/sH7zzTeKiIg46x/vczlw4IBiYmJUXFwsSerbt+85x5aVlemOO+6QzWbTz372My1btkw9e/aUMabJ8efqP9P3n7u6ulqSdPz4cYWGhvrGnRkezjd3XV2d6uvr1a9fP+3atcs39/79+89bT3N1fufMOiUpKytL9957r4KCgvTRRx+dte1MBw4cUHR0dJNz1tXV6auvvlJiYuI59z+XgwcP6ttvv1VMTIw+++wzX43fqays1AcffKDk5ORzztGS9wrA/2GFDOggxowZo5MnT2rgwIEaNGiQBg0apISEBG3cuFF33XXXefevq6vThg0btHDhQpWXl/vuUeb1euV2u/X888+rW7dustlsuvzyy3XTTTc1OU9OTo5+/etfKywsTHa7XdOmTTvnc06ePFkREREyxujLL7+UJJ08eVIHDx7UyZMndfnll//g1+HRRx9VWFiYoqOj9dBDD+mtt96SJG3fvl033XSTYmJi1L17d/36178+a7+amppzPt+pU6eUk5OjJ598Ul27dlXfvn318MMP67//+79/cH3fmTp1qux2u8LDwzVjxgxfnZL0t7/9TUOGDNFDDz2kN95445xz5OTk6J577tGVV16pLl266De/+Y1vmzFGmZmZevHFF32rYn369Gk2RJ15vG+//bZmzZqlLl26KCEh4ayV1r///e+Kj4/XL37xCwUFBSkoKEjXXXedJbdRAToKAhnQQaSlpWnhwoWqrKxUTU2Nr7388suaPHlyi24TsWTJEv30pz/1na78zl133aXg4GAVFxfr8OHDWrZsmS677LIm5/jtb3+rvXv3qry8XG63+6xvNn5fSkqKPv30Ux09elQvvfSSnE6nvvnmG504cUJPPvmkNm/erMOHD+v6669v8euwcuVKbdu2Tdu3b9c777yjBQsWSJLWrVunt956Szt27NC2bdv097///az9XnrpJY0fP16HDh3yfbv0TNOmTdPx48e1Z88effjhh1qyZIlee+21Ftf1fUuWLJHb7daePXu0Z88e/f73v/dt+/rrr7V8+XL1799fb7/99jnnWL16tf74xz9q/fr1Kisr00cffSRJ+uabbySd/vZsWVmZtmzZoiNHjmjdunUaMGBAi+p74IEH1LVrV3m9Xr3++utauHChb9uxY8eUnJwsp9Op6upqeb1ePf30075vigL44Ww6fTEZAKAN+a//+i/Fx8frzjvvbPE+V155pXbu3KnOnTu36PQygLaDFTIAaGPCw8OVnp6uV1555bxjx4wZo06dOiksLExPP/20Vq1aRRgD2iECGQC0Iffee68qKyv13nvvtehvR9533306ePCgPv/8c508eVL3339/K1QJ4ELjlCUAAIDFWCEDAACwGIEMAADAYu36xrC1tbXN3gUcAACgrejXr985/8pJuw5ke/fulcPhsLoMAACA8/J4POfcxilLAAAAixHIAAAALEYgAwAAsFi7voasKeHh4Zo+fbpiY2Nls9msLsdSxhhVVFRo7ty5Onz4sNXlAACAc+hwgWz69On6+OOP9bvf/e6i//MhgYGBGjVqlKZPn66ZM2daXQ4AADiHDnfKMjY2Vu++++5FH8Yk6eTJk3rnnXcUGxtrdSkAAKAZHS6Q2Ww2wtgZTp48edGfugUAoK3rcIGsLWhoaFBhYaF27typ7du361e/+tUFC0UzZ87UI488ckHmAgAAbUOHu4bs++pnPXxB5wue9cJ5x5w4cUKDBw+WJPXq1UtLlixRjx49NGvWrAtaCwAA6BhYIfOzgwcPasqUKXrggQckSQEBAXrmmWeUn5+vTz75RFOmTJEkXXLJJVq3bp22bdumHTt2aPTo0b45ZsyYoZKSEq1du1YDBgyw5DgAAID/dPgVsragvLxcAQEBioyMVGpqqo4cOaKkpCQFBwdr8+bNcrvdqqys1NixY3X06FH17NlTW7ZsUW5uroYMGSKn06nBgwcrKChIBQUF2rZtm9WHBAAALiACWSv57hqy5ORk/ehHP9L48eMlST169FBcXJyqqqo0Z84c3XTTTTp16pTsdrt69+6tn/zkJ1qxYoVOnDghScrNzbXsGAAAgH8QyFpB//79dfLkSdXW1spms2natGlyu91njUlLS1OvXr107bXXqqGhQeXl5QoJCZF0+gavAACg4+IaMj+LiIjQ/Pnz9fLLL0uS1qxZo/vvv19BQaezcFxcnEJDQ9WjRw/V1taqoaFBN998s+/eYRs3btTYsWMVEhKirl276t/+7d+sOhQAQFOMoTXV8IOwQuYHXbp0UWFhoTp16qSGhgYtWrRIL7xw+tuZr776qmJjY1VQUCCbzaaDBw9qzJgxWrx4sVatWiWPx6Pt27dr165dkqTCwkK99dZb2r59u/bu3atNmzZZeWgAAMBPTHttHo+nUd8bb7xheV1trfGa0Gg0mh+bMbSmmtXvSxtsTeWW7xqnLAEAACxGIAMAALAYgQwAAMBiBDIAAACLEcgAAAAsRiADAACwGIHMD44ePXreMYMGDZIxRsnJyU3ue9lll2np0qV+qQ8AALQtHT+QtdE7D0+aNEmbNm3SpEmTmtx+4MABTZgw4YI9HwAAaLv8GsimT5+unTt3qqioSEuWLFHnzp0VHh4ut9ut0tJSud1uhYWF+ca7XC7t3r1bJSUljVaO2qOoqCh98MEHKiwsVFFRkW688UbftvHjx+vuu+9WcnKyOnfu3Gjffv36qaioSJK0ZcsWDRw40Ldt/fr1GjJkiEJDQ7VgwQLl5+eroKBAo0eP9v9BAQCAC85vgaxPnz568MEHdd111+nqq69WYGCgnE6nXC6X8vLyFB8fr7y8PLlcLklSQkKCnE6nEhMTlZKSonnz5ikgoH0v4N1xxx1as2aNBg8erGuuuUbbt2+XJA0bNkzl5eXas2ePNmzYoNtvv73ZebKzszVx4kRJp0Nenz59VFBQoCeeeELvv/++kpKSdMstt+jZZ59VaGiovw8LAABcYH5NPEFBQerSpYsCAwMVGhqq6upqpaamKisrS5KUlZWlMWPGSJJSU1OVnZ2t+vp6VVRUqKysTElJSf4sz+88Ho/uuecezZw5U1dffbWOHTsm6fTpyuzsbEmnw9a5Tlt+Jycnx3f6cuLEib5ry5KTk+VyuVRYWKgNGzYoJCREffv29eMRAQAAf/DbHxevrq7Wc889p3379unEiRNyu91au3atevfuLa/XK0nyer2KjIyUJNntdm3ZssW3f1VVlex2e6N5MzIyNGXKFElSRESEv8q/IDZt2qSbbrpJo0aN0qJFi/Tss89q8eLFGjdunEaPHq0nnnhCNptNPXv2VNeuXX2B7fuqq6v1xRdf6Oqrr9bPf/5z3XfffZIkm82mcePGqbS0tDUPCwAAXGB+WyELCwtTamqq+vfvrz59+uiSSy7R5MmTzzneZrM16jNNXESfmZkph8Mhh8Ohurq6C1rzhda3b1/V1tbq1Vdf1YIFCzRkyBCNHDlSn3zyifr27av+/fsrNjZWy5cv960Unkt2drYee+wx9ejRQzt37pQkrVmzRtOmTfONGTRokB+PBgAA+IvfAtnIkSNVXl6uuro6NTQ06O2339YNN9ygmpoaRUVFSTp9PVRtba2k0ytiMTExvv2jo6NVXV3tr/Jaxc0336zt27eroKBA48aN00svvaRJkyZpxYoVZ41bvny57rjjjmbnWrZsmZxOp3Jycnx9s2fPVqdOnbRjxw4VFRVp9uzZfjkOAADgf8YfLSkpyezcudN06dLFSDKvv/66eeCBB8wzzzxjHn/8cSPJPP744+bpp582kszAgQPN9u3bTXBwsImNjTWff/65CQgIaPY5PB5Po7433njDL8fTnhuvCY1Go/mxGUNrqln9vrTB1lRu+a757Rqy/Px8LVu2TAUFBWpoaFBhYaFeeeUVde3aVTk5OUpPT9e+fft8F6sXFxcrJydHxcXFamho0NSpU3Xq1Cl/lQcAANCmWJ4Y/38bK2Qta7wmNBqN5sdm9UpUW21Wvy9tsDW3Qta+b/QFAADQAXS4QGaMUWBgoNVltBmBgYH/+z8qAACgrepwgayiokKjRo0ilOl0GBs1apQqKiqsLgUAADTDbxf1W2Xu3LmaPn26xo0b1+S9zS4mxhhVVFRo7ty5VpcCAACa0eEC2eHDhzVz5kyrywAAAGixDnfKEgAAoL0hkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDG/BbL4+HgVFhb62pEjR/TQQw8pPDxcbrdbpaWlcrvdCgsL8+3jcrm0e/dulZSUKDk52V+lAQAAtCl+C2SlpaUaPHiwBg8erGuvvVZfffWVVqxYIZfLpby8PMXHxysvL08ul0uSlJCQIKfTqcTERKWkpGjevHkKCGABDwAAdHytknhGjBihzz//XPv27VNqaqqysrIkSVlZWRozZowkKTU1VdnZ2aqvr1dFRYXKysqUlJTUGuUBAABYqlUCmdPp1JtvvilJ6t27t7xeryTJ6/UqMjJSkmS321VZWenbp6qqSna7vdFcGRkZ8ng88ng8ioiIaIXqAQAA/MvvgaxTp04aPXq0li5d2uw4m83WqM8Y06gvMzNTDodDDodDdXV1F6xOAAAAq/g9kN12220qKChQbW2tJKmmpkZRUVGSpKioKF9/VVWVYmJifPtFR0erurra3+UBAABYzu+BbNKkSb7TlZKUm5urtLQ0SVJaWppWrlzp63c6nQoODlZsbKzi4uKUn5/v7/IAAADaBOOv1qVLF1NXV2e6d+/u67v00kvNunXrTGlpqVm3bp0JDw/3bZsxY4YpKyszJSUlJiUl5bzzezwev9VOo9FoNFqLmjG0pprV70sbbM3lFtv//tAueTweORwOq8sAAFzMmrjeGZKauDb8YtdcbuFGXwAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGCxIKsLAFqNMVZX0DbZbFZXAAAXPb+ukPXo0UNLly7Vrl27VFxcrKFDhyo8PFxut1ulpaVyu90KCwvzjXe5XNq9e7dKSkqUnJzsz9IAAADaDL8GspdeekmrV69WQkKCrrnmGu3atUsul0t5eXmKj49XXl6eXC6XJCkhIUFOp1OJiYlKSUnRvHnzFBDAGVUAANDx+S3xdOvWTTfddJMWLFggSfr222915MgRpaamKisrS5KUlZWlMWPGSJJSU1OVnZ2t+vp6VVRUqKysTElJSf4qDwAAoM3wWyC7/PLLdfDgQS1cuFAFBQXKzMxUaGioevfuLa/XK0nyer2KjIyUJNntdlVWVvr2r6qqkt1ubzRvRkaGPB6PPB6PIiIi/FU+AABAq/FbIAsKCtKQIUP0l7/8RUOGDNHx48d9pyebYmviwmLTxEXYmZmZcjgccjgcqquru6A1AwAAWMFvgayqqkpVVVXKz8+XJC1btkxDhgxRTU2NoqKiJElRUVGqra31jY+JifHtHx0drerqan+VBwAA0Gb4LZDV1NSosrJS8fHxkqQRI0aouLhYubm5SktLkySlpaVp5cqVkqTc3Fw5nU4FBwcrNjZWcXFxvjAHAADQkfn1PmTTpk3T4sWLFRwcrD179uiee+5RQECAcnJylJ6ern379mnChAmSpOLiYuXk5Ki4uFgNDQ2aOnWqTp065c/yAAAA2gSbpHZ7t0yPxyOHw2F1GWgvuDFs07gxLPDP4bOlaXy2NNJcbuFGXwAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDF/BrIysvLtWPHDhUWFsrj8UiSwsPD5Xa7VVpaKrfbrbCwMN94l8ul3bt3q6SkRMnJyf4sDQAAoM3w+wrZLbfcosGDB8vhcEg6Hbry8vIUHx+vvLw8uVwuSVJCQoKcTqcSExOVkpKiefPmKSCABTwAANDxtXriSU1NVVZWliQpKytLY8aM8fVnZ2ervr5eFRUVKisrU1JSUmuXBwAA0Or8GsiMMXK73fr444+VkZEhSerdu7e8Xq8kyev1KjIyUpJkt9tVWVnp27eqqkp2u73RnBkZGfJ4PPJ4PIqIiPBn+QAAAK0iyJ+TDxs2TAcOHFCvXr20du1alZSUnHOszWZr1GeMadSXmZmpzMxMSfJdlwYAANCe+XWF7MCBA5KkgwcPasWKFUpKSlJNTY2ioqIkSVFRUaqtrZV0ekUsJibGt290dLSqq6v9WR4AAECb4LdAFhoaqq5du/p+Tk5O1s6dO5Wbm6u0tDRJUlpamlauXClJys3NldPpVHBwsGJjYxUXF6f8/Hx/lQcAANBm+O2UZe/evbVixYrTTxIUpCVLlmjNmjXyeDzKyclRenq69u3bpwkTJkiSiouLlZOTo+LiYjU0NGjq1Kk6deqUv8oDAABoM2ySGl+o1U54PB7f7TSA82rimkRIauL6TQA/AJ8tTeOzpZHmcgs3+gIAALAYgQwAAMBiBDIAAACLEcgAAAAs1qJAdsMNN7SoDwAAAD9ciwLZn/70pxb1AQAA4Idr9j5kQ4cO1Q033KBevXrpV7/6la+/e/fuCgwM9HtxAAAAF4NmA1lwcLC6du2qoKAgdevWzdf/j3/8Q+PHj/d7cQAAABeDZgPZxo0btXHjRr3++uvat29fa9UEAABwUWnRn07q3Lmz/vrXvyo2NlZBQf+3y4gRI/xWGAAAwMWiRYFs6dKlmj9/vl599VWdPHnS3zUBAABcVFoUyBoaGjR//nx/1wIAAHBRatFtL1atWqX7779fUVFRCg8P9zUAAAD881q0QpaWliZJevTRR319xhhdccUV/qkKAADgItKiQHb55Zf7uw4AAICLVosC2Z133tlk/6JFiy5oMQAAABejFgUyh8Ph+zkkJEQjRoxQQUEBgQwAAOACaFEge/DBB8963L17d8IYAADABdKib1l+31dffaW4uLgLXQsAAMBFqUUrZLm5uTLGSJICAwOVkJCgnJwcvxYGAABwsWhRIHvuued8Pzc0NGjv3r3av3+/34oCAAC4mLTolOXGjRtVUlKibt26KTw8XPX19f6uCwAA4KLRokA2YcIE5efna8KECZo4caK2bt2qcePG+bs2AACAi0KLTlk+8cQTcjgcOnjwoCQpIiJC69at0/Lly/1aHAAAwMWgRStkAQEBvjAmSV988YUCAv6/vqAJAACA72nRCtnq1au1evVqvfnmm5Kkn//853r33Xf9WhgAAMDFotlAdsUVV6h379567LHHNHbsWN14442y2Wz66KOPtHjx4taqEQAAoENr9rzj3LlzdfToUUnSihUr9Mgjj+jhhx/Wu+++q7lz57ZGfQAAAB1es4EsNjZWRUVFjfq3bdum2NjYlj1BQIAKCgq0atUqSVJ4eLjcbrdKS0vldrsVFhbmG+tyubR7926VlJQoOTm55UcBAADQjjUbyEJCQs65rUuXLi16goceeki7du3yPXa5XMrLy1N8fLzy8vLkcrkkSQkJCXI6nUpMTFRKSormzZvHFwcAAMBFodnE4/F4dO+99zbq/+Uvf6lt27add3K73a5Ro0bp1Vdf9fWlpqYqKytLkpSVlaUxY8b4+rOzs1VfX6+KigqVlZUpKSnphxwLAABAu9TsRf3Tp0/XihUrNHnyZF8Au+666xQcHKyxY8eed/K5c+fqscceU7du3Xx9vXv3ltfrlSR5vV5FRkZKOh3etmzZ4htXVVUlu93+w48IAACgnWk2kNXW1mrYsGG6+eabddVVV0mS3nnnHa1fv/68E48aNUq1tbUqKCjQ8OHDzzveZrM16vvuD5qfKSMjQ1OmTJF0+ga1AAAA7V2L7kO2YcMGbdiw4QdNPGzYMI0ePVq33367QkJC1L17dy1atEg1NTWKioqS1+tVVFSUamtrJZ1eEYuJifHtHx0drerq6kbzZmZmKjMzU9LpU6oAAADtnd+ump8xY4ZiYmLUv39/OZ1Ovf/++7rzzjuVm5urtLQ0SVJaWppWrlwpScrNzZXT6VRwcLBiY2MVFxen/Px8f5UHAADQZrRohexCeuqpp5STk6P09HTt27dPEyZMkCQVFxcrJydHxcXFamho0NSpU3Xq1KnWLg8AAKDV2SQ1vlCrnfB4PHI4HFaXgfaiiWsSIamJ6zcB/AB8tjSNz5ZGmsst3OgLAADAYgQyAAAAixHIAAAALEYgAwAAsBiBDAAAwGIEMgAAAIsRyAAAACzW6jeGBQC0T/WzHra6hDYp2OoC0CGwQgYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWMxvgaxz587aunWrtm/frp07d2rWrFmSpPDwcLndbpWWlsrtdissLMy3j8vl0u7du1VSUqLk5GR/lQYAANCm+C2QffPNN/qXf/kXDRo0SIMGDVJKSoquv/56uVwu5eXlKT4+Xnl5eXK5XJKkhIQEOZ1OJSYmKiUlRfPmzVNAAAt4AACg4/Nr4jl+/LgkqVOnTurUqZOMMUpNTVVWVpYkKSsrS2PGjJEkpaamKjs7W/X19aqoqFBZWZmSkpL8WR4AAECb4NdAFhAQoMLCQtXW1mrt2rXKz89X79695fV6JUler1eRkZGSJLvdrsrKSt++VVVVstvt/iwPAACgTfBrIDt16pQGDx6s6OhoJSUlKTEx8ZxjbTZboz5jTKO+jIwMeTweeTweRUREXNB6AQAArNAqF2kdOXJEGzZsUEpKimpqahQVFSVJioqKUm1traTTK2IxMTG+faKjo1VdXd1orszMTDkcDjkcDtXV1bVG+QAAAH7lt0AWERGhHj16SJJCQkI0cuRIlZSUKDc3V2lpaZKktLQ0rVy5UpKUm5srp9Op4OBgxcbGKi4uTvn5+f4qDwAAoM0I8tfEl112mbKyshQYGKiAgADl5OTonXfe0UcffaScnBylp6dr3759mjBhgiSpuLhYOTk5Ki4uVkNDg6ZOnapTp075qzwAAIA2wyap8YVa7YTH45HD4bC6DLQXTVyTCElNXL8JNKV+1sNWl9AmBc983uoS2iY+WxppLrdwoy8AAACLEcgAAAAsRiADAACwGIEMAADAYgQyAAAAixHIAAAALEYgAwAAsBiBDAAAwGIEMgAAAIsRyAAAACxGIAMAALAYgQwAAMBiBDIAAACLEcgAAAAsRiADAACwGIEMAADAYgQyAAAAixHIAAAALEYgAwAAsBiBDAAAwGIEMgAAAIsRyAAAACxGIAMAALAYgQwAAMBiBDIAAACLEcgAAAAsRiADAACwGIEMAADAYn4LZNHR0Xr//fdVXFysnTt36sEHH5QkhYeHy+12q7S0VG63W2FhYb59XC6Xdu/erZKSEiUnJ/urNAAAgDbFb4GsoaFBjzzyiAYOHKihQ4dq6tSpSkhIkMvlUl5enuLj45WXlyeXyyVJSkhIkNPpVGJiolJSUjRv3jwFBLCABwAAOj6/JR6v16vCwkJJ0rFjx7Rr1y7Z7XalpqYqKytLkpSVlaUxY8ZIklJTU5Wdna36+npVVFSorKxMSUlJ/ioPAACgzWiVJah+/fpp8ODB2rp1q3r37i2v1yvpdGiLjIyUJNntdlVWVvr2qaqqkt1ub43yAAAALBXk7ye45JJLtHz5ck2fPl1Hjx495zibzdaozxjTqC8jI0NTpkyRJEVERFy4QgEAACzi1xWyoKAgLV++XIsXL9aKFSskSTU1NYqKipIkRUVFqba2VtLpFbGYmBjfvtHR0aqurm40Z2ZmphwOhxwOh+rq6vxZPgAAQKvwayBbsGCBdu3apRdffNHXl5ubq7S0NElSWlqaVq5c6et3Op0KDg5WbGys4uLilJ+f78/yAAAA2gS/nbIcNmyY7rrrLu3YscN3cf+MGTP01FNPKScnR+np6dq3b58mTJggSSouLlZOTo6Ki4vV0NCgqVOn6tSpU/4qDwAAoM3wWyDbvHlzk9eFSdLIkSOb7J8zZ47mzJnjr5IAAADaJG70BQAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABYjkAEAAFiMQAYAAGAxAhkAAIDFCGQAAAAWI5ABAABYjEAGAABgsSCrC8CFVz/rYatLaJOCrS4AAIBzYIUMAADAYgQyAAAAixHIAAAALEYgAwAAsBiBDAAAwGIEMgAAAIv5LZAtWLBANTU1Kioq8vWFh4fL7XartLRUbrdbYWFhvm0ul0u7d+9WSUmJkpOT/VUWAABAm+O3QPb6668rJSXlrD6Xy6W8vDzFx8crLy9PLpdLkpSQkCCn06nExESlpKRo3rx5Cghg8Q4AAFwc/JZ6Nm3apEOHDp3Vl5qaqqysLElSVlaWxowZ4+vPzs5WfX29KioqVFZWpqSkJH+VBgAA0Ka06jJU79695fV6JUler1eRkZGSJLvdrsrKSt+4qqoq2e321iwNAADAMm3iTyfZbLZGfcaYJsdmZGRoypQpkqSIiAi/1gUAANAaWnWFrKamRlFRUZKkqKgo1dbWSjq9IhYTE+MbFx0drerq6ibnyMzMlMPhkMPhUF1dnf+LBgAA8LNWDWS5ublKS0uTJKWlpWnlypW+fqfTqeDgYMXGxiouLk75+fmtWRoAAIBl/HbKcsmSJbr55psVERGhyspKzZw5U0899ZRycnKUnp6uffv2acKECZKk4uJi5eTkqLi4WA0NDZo6dapOnTrlr9IAAADaFL8FsjvuuKPJ/pEjRzbZP2fOHM2ZM8df5QAAALRZ3OwLAADAYgQyAAAAixHIAAAALEYgAwAAsBiBDAAAwGIEMgAAAIsRyAAAACxGIAMAALAYgQwAAMBiBDIAAACLEcgAAAAsRiADAACwGIEMAADAYgQyAAAAixHIAAAALEYgAwAAsBiBDAAAwGIEMgAAAIsRyAAAACxGIAMAALAYgQwAAMBiBDIAAACLEcgAAAAsRiADAACwGIEMAADAYgQyAAAAixHIAAAALEYgAwAAsFibC2S33nqrSkpKtHv3bj3++ONWlwMAAOB3bSqQBQQE6M9//rNuu+02DRw4UJMmTVJCQoLVZQEAAPhVmwpkSUlJKisrU3l5ub799ltlZ2crNTXV6rIAAAD8qk0FMrvdrsrKSt/jqqoq2e12CysCAADwvyCrCziTzWZr1GeMOetxRkaGpkyZIkkaMGCAPB5Pq9TWnnxidQFtVHlFherq6qwuo+3hdwgtxGdL0/hsOQc+Wxrp16/fObe1qUBWVVWlmJgY3+Po6GhVV1efNSYzM1OZmZmtXRo6AI/HI4fDYXUZADoYPltwIbSpU5Yej0dxcXGKjY1Vp06d5HQ6lZuba3VZAAAAftWmVshOnjypBx54QGvWrFFgYKBee+01FRcXW10WAACAX7WpQCZJ7733nt577z2ry0AH9Morr1hdAoAOiM8WXAg2Sea8owAAAOA3beoaMgAAgIsRgQztQkNDgwoLC32tua8O/7PKy8vVs2dPv80PoO0zxuiNN97wPQ4MDFRtba1WrVrV7H7Dhw8/7xigKW3uGjKgKSdOnNDgwYOtLgPAReLYsWO66qqrFBISoq+//lo//elPtX//fqvLQgfGChnarSFDhmjDhg36+OOPtXr1akVFRUmS1q9frxdeeEEffPCBiouLdd1112n58uUqLS3V7NmzffuvWLFCH3/8sXbu3KmMjIwmn2Py5MnaunWrCgsLNX/+fAUE8CsDXCzee+89jRo1SpI0adIkvfnmm75tDodDmzdvVkFBgTZv3qz4+PhG+4eGhmrBggXKz89XQUGBRo8e3Wq1o30yNFpbbw0NDaawsNAUFhaat99+2wQFBZnNmzebiIgII8lMnDjRLFiwwEgy69evN0899ZSRZB588EGzf/9+ExUVZYKDg01lZaW59NJLjSQTHh5uJJmQkBBTVFTk6y8vLzc9e/Y0V155pcnNzTVBQUFGkvnzn/9s7rzzTstfCxqN5v929OhRc/XVV5ulS5eazp07m8LCQjN8+HCzatUqI8l069bNBAYGGklmxIgRZtmyZUbSWWOefPJJM3nyZCPJ9OjRw3z22WcmNDTU8mOjtc3GKUu0C98/ZZmYmKirrrpKa9eulXT6+o4DBw74tn93Q+GioiJ9+umn8nq9kqQ9e/YoJiZGhw4d0oMPPqixY8dKkmJiYhQXF6etW7f65hgxYoSuvfZa35/n6tKli2pra/17oADajKKiIsXGxmrSpEl69913z9rWo0cPZWVlKS4uTsYYderUqdH+ycnJGj16tP7jP/5DkhQSEqK+ffuqpKSkVepH+0IgQ7tks9n06aef6oYbbmhy+zfffCNJOnXqlO/n7x4HBQVp+PDhGjlypH784x/rxIkTWr9+vUJCQho9R1ZWlmbMmOG/AwHQpuXm5uq5557TzTfffNaXfWbPnq3169frZz/7mfr166cNGzY02tdms2ncuHEqLS1txYrRXnFBDNqlzz77TL169dLQoUMlSUFBQRo4cGCL9+/Ro4cOHz6sEydOaMCAAb55zpSXl6fx48erV69ekqTw8HD17dv3whwAgHbhtdde0+9+9zvt3LnzrP4ePXr4LvK/++67m9x3zZo1mjZtmu/xoEGD/FUmOgACGdqlb7/9VuPHj9fTTz+t7du3a/v27edcLWvK6tWrFRQUpE8++USzZ8/Wli1bGo3ZtWuX/vM//1Nut1uffPKJ1q5dq8suu+xCHgaANm7//v364x//2Kj/mWee0R/+8Ad9+OGHCgwMbHLf2bNnq1OnTtqxY4eKiorO+lIR8H3cqR8AAMBirJABAABYjEAGAABgMQIZAACAxQhkAAAAFiOQAQAAWIxABgAAYDECGQAAgMUIZAAAABb7f4fRfMlmjD3PAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(char.male, char.isAlive).plot(kind='bar', figsize=(10,6), color=[\"salmon\", \"cyan\"])\n",
    "\n",
    "plt.title(\"Alive distribution by gender\")\n",
    "plt.xlabel('')\n",
    "plt.xticks([0,1], ['Female' , 'Male'], rotation='horizontal')\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend([\"Dead\", \"IsAlive\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>isAlive</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isPopular</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>435</td>\n",
       "      <td>1396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "isAlive      0     1\n",
       "isPopular           \n",
       "0          435  1396\n",
       "1           60    55"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(char.isPopular, char.isAlive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(char.isPopular, char.isAlive).plot(kind='bar', figsize=(10,6), color=[\"salmon\", \"cyan\"])\n",
    "\n",
    "plt.title(\"Alive distribution by popularity\")\n",
    "plt.xlabel(\"\")\n",
    "plt.xticks([0,1], ['Not Popular' , 'Popular'], rotation='horizontal')\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend([\"Dead\", \"IsAlive\"])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I guess that makes watching GOT interesting!!!\n",
    "\n",
    "Who cares when unknown men die in battle?\n",
    "\n",
    "But when (60/115) * 100 = 55% of people you find interesting die, Thats something(lol)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a low chance of a commander to die in the battle. This is so because sometimes they could be at the castle giving out commands without being physically present in the battle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(char.isMarried, char.isAlive).plot(kind='bar', figsize=(10,6), color=[\"salmon\", \"cyan\"])\n",
    "\n",
    "plt.title(\"Alive distribution by marriage\")\n",
    "plt.xlabel(\"\")\n",
    "plt.xticks([0,1], ['Not Married' , 'Married'], rotation='horizontal')\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend([\"Dead\", \"IsAlive\"])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can confirm that characters not married are more likely to die. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='FE'></a>\n",
    "## Feature Engineering\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='feclen'></a>\n",
    "### Cleaning Feature"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title            1008\n",
       "culture          1269\n",
       "dateOfBirth      1513\n",
       "DateoFdeath      1502\n",
       "mother           1925\n",
       "father           1920\n",
       "heir             1923\n",
       "house             427\n",
       "spouse           1670\n",
       "isAliveMother    1925\n",
       "isAliveFather    1920\n",
       "isAliveHeir      1923\n",
       "isAliveSpouse    1670\n",
       "age              1513\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss = char.isna().sum()\n",
    "miss[miss > 0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 14 columns with NaN vales. We will replace that of age with the average age value.\n",
    "\n",
    "Replace date of birth and date of death with the median value.\n",
    "\n",
    "Replace other categorical variable with unknown\n",
    "\n",
    "Replace other numerical variable with -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing age with average age\n",
    "char[\"age\"].fillna(round(char[\"age\"].mean()), inplace=True)\n",
    "char['dateOfBirth'].fillna(char['dateOfBirth'].median(), inplace=True)\n",
    "char['DateoFdeath'].fillna(char['DateoFdeath'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling missing values in text columns\n",
    "cat = ['title', 'culture', 'mother', 'father', 'heir', 'house', 'spouse']\n",
    "for col in cat:\n",
    "    char[col].fillna('unknown', inplace=True)\n",
    "char.fillna(-1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duplicate cultures\n",
    "\n",
    "Some culture are spelt wrongly thus making duplicate. E.g. 'Westermen', 'Westerman', and 'westermen' are all the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unknown', 'Rivermen', 'Dornish', 'Valyrian', 'Ironborn',\n",
       "       'Free Folk', 'Northmen', 'Summer Isles', 'Braavosi', 'Dothraki',\n",
       "       'Ghiscari', 'Vale mountain clans', 'Reach', 'Tyroshi', 'Lhazarene',\n",
       "       'Free folk', 'Ironmen', 'Qartheen', 'Lysene', 'westermen',\n",
       "       'Westerman', 'Qarth', 'Lyseni', 'northmen', 'Qohor', 'Westeros',\n",
       "       'Norvoshi', 'First Men', 'Meereenese', 'Andal', 'Astapori',\n",
       "       'Westermen', 'ironborn', 'Ghiscaricari', 'Braavos', 'Stormlands',\n",
       "       'Valemen', 'Myrish', 'Lhazareen', 'Dornishmen', 'Sistermen',\n",
       "       'Northern mountain clans', 'Andals', 'Vale', 'Crannogmen',\n",
       "       'Wildling', 'Dorne', 'Pentoshi', 'free folk', 'Summer Islander',\n",
       "       'Westerlands', 'Summer Islands', 'Asshai', 'Riverlands', 'Naathi',\n",
       "       'Rhoynar', 'Meereen', 'Norvos', 'Stormlander', 'Wildlings',\n",
       "       'Astapor', 'Reachmen', \"Asshai'i\", 'Ibbenese', 'The Reach'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char['culture'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From  Shail Daliwala's kernel on Kaggle\n",
    "cult = {\n",
    "    'Summer Islands': ['summer islands', 'summer islander', 'summer isles'],\n",
    "    'Ghiscari': ['ghiscari', 'ghiscaricari',  'ghis'],\n",
    "    'Asshai': [\"asshai'i\", 'asshai'],\n",
    "    'Lysene': ['lysene', 'lyseni'],\n",
    "    'Andal': ['andal', 'andals'],\n",
    "    'Braavosi': ['braavosi', 'braavos'],\n",
    "    'Dornish': ['dornishmen', 'dorne', 'dornish'],\n",
    "    'Myrish': ['myr', 'myrish', 'myrmen'],\n",
    "    'Westermen': ['westermen', 'westerman', 'westerlands'],\n",
    "    'Westerosi': ['westeros', 'westerosi'],\n",
    "    'Stormlander': ['stormlands', 'stormlander'],\n",
    "    'Norvoshi': ['norvos', 'norvoshi'],\n",
    "    'Northmen': ['the north', 'northmen'],\n",
    "    'Free Folk': ['wildling', 'first men', 'free folk'],\n",
    "    'Qartheen': ['qartheen', 'qarth'],\n",
    "    'Reach': ['the reach', 'reach', 'reachmen'],\n",
    "}\n",
    "\n",
    "def get_cult(value):\n",
    "    value = value.lower()\n",
    "    v = [k for (k, v) in cult.items() if value in v]\n",
    "    return v[0] if len(v) > 0 else value.title()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='felnk'></a>\n",
    "### Creating links to other data (Variables for creating features)\n",
    "In the battle csv we have attackers and defenders, it's likely that this attackers and defenders are of powerful house in the country or region, we can create a link to characters who belongs to this house.\n",
    "\n",
    "Also in the battles data are commanders and defenders. Having the names gathered together can help point out who is a commander and who is not. This can also play part in determining if a character, who is a commander will die.\n",
    "\n",
    "In the character death data, it is observed that characters whom never swore allegiance died the most. We can have a column that shows if a character's house swore alliegiance to another. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names of all Attackers\n",
    "all_attackers = set(battles.attacker_1).union(set(battles.attacker_2))\\\n",
    ".union(set(battles.attacker_3)).union(set(battles.attacker_4))\n",
    "# Names of all Defenders\n",
    "all_defenders = set(battles.defender_1).union(set(battles.defender_2))\n",
    "\n",
    "# Unique attackers and defenders combined\n",
    "all_warlead = all_attackers.union(all_defenders)\n",
    "all_warlead = list(all_warlead)\n",
    "all_warlead.remove(np.nan)\n",
    "\n",
    "renam_warlead = []\n",
    "for x in all_warlead:\n",
    "    house = 'House ' + x\n",
    "    renam_warlead.append(house)\n",
    "    \n",
    "# All attacking commanders\n",
    "all = []\n",
    "for i in battles['attacker_commander'].dropna().values:\n",
    "    splitted = i.split(', ')\n",
    "    all +=splitted\n",
    "    \n",
    "all_att_commanders = set(all)\n",
    "\n",
    "# All defending commanders\n",
    "all = []\n",
    "for i in battles['defender_commander'].dropna().values:\n",
    "    splitted = i.split(', ')\n",
    "    all +=splitted\n",
    "    \n",
    "all_def_commanders = set(all)\n",
    "\n",
    "# All commanders \n",
    "all_commanders = all_att_commanders.union(all_def_commanders)\n",
    "# characters who swore allegiance in character death data\n",
    "char_with_allgnc = char_deaths[char_deaths['Allegiances'] != 'None']\n",
    "\n",
    "# Extracting the above character from all character data\n",
    "all_char_with_allgnc = char_pred[char_pred['name'].isin(char_with_allgnc['Name'])]\n",
    "\n",
    "# house of character with allegiance to another house (house in character dead allegiance column)\n",
    "hus_char_with_allegnc = all_char_with_allgnc['house']\n",
    "\n",
    "# Extract unique house of allegiance from charater death data.\n",
    "husOfAllgnc = list(set(char_deaths['Allegiances']))\n",
    "husOfAllgnc.remove('None')\n",
    "\n",
    "renam_husOfAllgnc = []\n",
    "for x in husOfAllgnc:\n",
    "    house = 'House ' + x\n",
    "    renam_husOfAllgnc.append(house)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='fecre'></a>\n",
    "### Creating Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Characters that are commanders.\n",
    "char['isACommander'] = np.where(char['name'].isin(all_commanders), 1, 0)\n",
    "# Characters that belong to the house of either the attacker or defender\n",
    "char['inHouseOfWarLead'] = np.where(char['house'].isin(renam_warlead), 1, 0)\n",
    "# Characters whose house swore alliegiance to another house\n",
    "char['houseSworeAllegiance'] = np.where(char['house'].isin(hus_char_with_allegnc), 1, 0)\n",
    "# House of alliegiance, characters that belong to those house\n",
    "char['inHouseOfAllegnc'] = np.where(char['house'].isin(renam_husOfAllgnc), 1, 0)\n",
    "\n",
    "# Dropping redundant columns\n",
    "char = char.drop(['S.No', 'actual', 'pred', 'alive', 'plod', 'dateOfBirth',\n",
    "                  'DateoFdeath', 'house'], axis=1)\n",
    "char.set_index('name', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save character details\n",
    "char.to_csv('GOT_Char_details.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='fesel'></a>\n",
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = char.copy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book3                   0.006693\n",
       "inHouseOfWarLead        0.029822\n",
       "isAliveSpouse           0.031536\n",
       "book5                   0.032846\n",
       "isNoble                 0.042211\n",
       "isMarried               0.050037\n",
       "houseSworeAllegiance    0.052159\n",
       "isACommander            0.057968\n",
       "book2                   0.067200\n",
       "inHouseOfAllegnc        0.113380\n",
       "isAliveHeir             0.116365\n",
       "isAliveFather           0.123615\n",
       "age                     0.136891\n",
       "isAliveMother           0.140661\n",
       "male                    0.146982\n",
       "book1                   0.147401\n",
       "isPopular               0.153860\n",
       "popularity              0.183223\n",
       "numDeadRelations        0.192444\n",
       "boolDeadRelations       0.193736\n",
       "book4                   0.268975\n",
       "isAlive                 1.000000\n",
       "Name: isAlive, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking correlation of numerical featres with the target variable.\n",
    "num_features = new.select_dtypes(['int', 'float'])\n",
    "corr_mat = num_features.corr()['isAlive'].abs().sort_values()\n",
    "corr_mat"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the correlation ranges from 0.006 t0 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of numerical feature in the training dataset\n",
    "plt.figure(figsize=(8, 8))\n",
    "num_features = new.select_dtypes(['int', 'float'])\n",
    "corr_mat = num_features.corr()\n",
    "sb.heatmap(data=corr_mat)\n",
    "plt.title('Features Relationship')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Multicollinearity"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heatmap above, we can spot some multicollinearity (Multicollinearity refers to the statistical phenomenon where two or more independent variables are strongly correlated) amongst features. \n",
    "\n",
    "Let's view them using the Variance Inflation Factor (VIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>isAliveFather</td>\n",
       "      <td>165.111250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>isAliveMother</td>\n",
       "      <td>93.203589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>isAliveHeir</td>\n",
       "      <td>77.020836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isAliveSpouse</td>\n",
       "      <td>28.026185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>isMarried</td>\n",
       "      <td>15.455632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age</td>\n",
       "      <td>10.098704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>popularity</td>\n",
       "      <td>7.642320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>isPopular</td>\n",
       "      <td>4.347781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>houseSworeAllegiance</td>\n",
       "      <td>3.579165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>book4</td>\n",
       "      <td>3.242501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>numDeadRelations</td>\n",
       "      <td>3.237888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>boolDeadRelations</td>\n",
       "      <td>3.036974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>book3</td>\n",
       "      <td>2.898499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>2.783317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>book2</td>\n",
       "      <td>2.480204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>isNoble</td>\n",
       "      <td>2.156332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>book5</td>\n",
       "      <td>2.125849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>inHouseOfWarLead</td>\n",
       "      <td>2.057726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>inHouseOfAllegnc</td>\n",
       "      <td>2.013128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>book1</td>\n",
       "      <td>1.713839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>isACommander</td>\n",
       "      <td>1.291378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                features         VIF\n",
       "7          isAliveFather  165.111250\n",
       "6          isAliveMother   93.203589\n",
       "8            isAliveHeir   77.020836\n",
       "9          isAliveSpouse   28.026185\n",
       "10             isMarried   15.455632\n",
       "12                   age   10.098704\n",
       "16            popularity    7.642320\n",
       "15             isPopular    4.347781\n",
       "19  houseSworeAllegiance    3.579165\n",
       "4                  book4    3.242501\n",
       "13      numDeadRelations    3.237888\n",
       "14     boolDeadRelations    3.036974\n",
       "3                  book3    2.898499\n",
       "0                   male    2.783317\n",
       "2                  book2    2.480204\n",
       "11               isNoble    2.156332\n",
       "5                  book5    2.125849\n",
       "18      inHouseOfWarLead    2.057726\n",
       "20      inHouseOfAllegnc    2.013128\n",
       "1                  book1    1.713839\n",
       "17          isACommander    1.291378"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "x = num_features.drop(columns='isAlive')\n",
    "vif = pd.DataFrame()\n",
    "vif['features'] = x.columns\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]\n",
    "vif.sort_values(by='VIF', ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop some columns with multicolinearity. \n",
    "\n",
    "Then we will go with features that has a correlation greater than 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns with colinearity\n",
    "new = new.drop(['isAliveFather', 'isAliveMother', 'isAliveSpouse'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book3                   0.006693\n",
       "inHouseOfWarLead        0.029822\n",
       "book5                   0.032846\n",
       "isNoble                 0.042211\n",
       "isMarried               0.050037\n",
       "houseSworeAllegiance    0.052159\n",
       "isACommander            0.057968\n",
       "book2                   0.067200\n",
       "Name: isAlive, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting features with coefficient greater than 0.1\n",
    "num_features = new.select_dtypes(['int', 'float'])\n",
    "corr_mat = num_features.corr()['isAlive'].abs().sort_values()\n",
    "corr_less = corr_mat[corr_mat < 0.1]\n",
    "corr_less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = new.drop(corr_less.index, axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of categorical variable\n",
    "nominal_features = ['title', 'culture', 'mother', 'father', 'heir', 'spouse']\n",
    "\n",
    "# Counting unique values in each column\n",
    "unique_count = new[nominal_features].apply(lambda x: len(x.value_counts())).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arbitrary cutoff of 25 unique count\n",
    "unique_count = unique_count[unique_count > 25].index\n",
    "# Dropping \n",
    "new = new.drop(unique_count, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the remaining text column to categorical type\n",
    "txt_col = new.select_dtypes(include=['object'])\n",
    "for col in txt_col:\n",
    "    new[col] = new[col].astype('category')\n",
    "    \n",
    "# Creating dummy columns from txt columns and add back to dataframe\n",
    "new = pd.concat([\n",
    "    new, pd.get_dummies(new.select_dtypes(include=['category']))\n",
    "], axis=1).drop(txt_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luwin</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>Reek</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>0.096990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>Symeon Star-Eyes</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0.030100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>Coldhands</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>0.130435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>Tytos Lannister</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210702</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946 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",
       "Luwin                    1      1      1         -1.0  37.0                 0   \n",
       "Reek                     1      0      1         -1.0  37.0                 0   \n",
       "Symeon Star-Eyes         1      1      1         -1.0  37.0                 0   \n",
       "Coldhands                1      0      1         -1.0  37.0                 0   \n",
       "Tytos Lannister          1      0      1         -1.0  47.0                 4   \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",
       "Luwin                                 0          1    0.351171        0   \n",
       "Reek                                  0          0    0.096990        0   \n",
       "Symeon Star-Eyes                      0          0    0.030100        1   \n",
       "Coldhands                             0          0    0.130435        1   \n",
       "Tytos Lannister                       1          0    0.210702        0   \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",
       "Luwin                                1                         0   \n",
       "Reek                                 0                         0   \n",
       "Symeon Star-Eyes                     0                         0   \n",
       "Coldhands                            0                         0   \n",
       "Tytos Lannister                      1                         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",
       "Luwin                                       0                       0   \n",
       "Reek                                        0                       0   \n",
       "Symeon Star-Eyes                            0                       0   \n",
       "Coldhands                                   0                       0   \n",
       "Tytos Lannister                             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",
       "Luwin                                      0                         0   \n",
       "Reek                                       0                         0   \n",
       "Symeon Star-Eyes                           0                         0   \n",
       "Coldhands                                  0                         0   \n",
       "Tytos Lannister                            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",
       "Luwin                                    0                        0   \n",
       "Reek                                     0                        0   \n",
       "Symeon Star-Eyes                         0                        0   \n",
       "Coldhands                                0                        0   \n",
       "Tytos Lannister                          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",
       "Luwin                                        0                    0   \n",
       "Reek                                         0                    0   \n",
       "Symeon Star-Eyes                             0                    0   \n",
       "Coldhands                                    0                    0   \n",
       "Tytos Lannister                              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",
       "Luwin                                             0                      0   \n",
       "Reek                                              0                      0   \n",
       "Symeon Star-Eyes                                  0                      0   \n",
       "Coldhands                                         0                      0   \n",
       "Tytos Lannister                                   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",
       "Luwin                                       0                          0   \n",
       "Reek                                        0                          0   \n",
       "Symeon Star-Eyes                            0                          0   \n",
       "Coldhands                                   0                          0   \n",
       "Tytos Lannister                             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",
       "Luwin                                        0  ...   \n",
       "Reek                                         0  ...   \n",
       "Symeon Star-Eyes                             0  ...   \n",
       "Coldhands                                    0  ...   \n",
       "Tytos Lannister                              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",
       "Luwin                                          0               1   \n",
       "Reek                                           0               1   \n",
       "Symeon Star-Eyes                               0               1   \n",
       "Coldhands                                      0               1   \n",
       "Tytos Lannister                                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",
       "Luwin                                       0                     0   \n",
       "Reek                                        0                     0   \n",
       "Symeon Star-Eyes                            0                     0   \n",
       "Coldhands                                   0                     0   \n",
       "Tytos Lannister                             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",
       "Luwin                                    0                     0   \n",
       "Reek                                     0                     0   \n",
       "Symeon Star-Eyes                         0                     0   \n",
       "Coldhands                                0                     0   \n",
       "Tytos Lannister                          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",
       "Luwin                                       0                       0   \n",
       "Reek                                        0                       0   \n",
       "Symeon Star-Eyes                            0                       0   \n",
       "Coldhands                                   0                       0   \n",
       "Tytos Lannister                             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",
       "Luwin                                                  0   \n",
       "Reek                                                   0   \n",
       "Symeon Star-Eyes                                       0   \n",
       "Coldhands                                              0   \n",
       "Tytos Lannister                                        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",
       "Luwin                                       0                0   \n",
       "Reek                                        0                0   \n",
       "Symeon Star-Eyes                            0                0   \n",
       "Coldhands                                   0                0   \n",
       "Tytos Lannister                             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",
       "Luwin                                       0                         0   \n",
       "Reek                                        0                         0   \n",
       "Symeon Star-Eyes                            0                         0   \n",
       "Coldhands                                   0                         0   \n",
       "Tytos Lannister                             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",
       "Luwin                                     0                       0   \n",
       "Reek                                      0                       0   \n",
       "Symeon Star-Eyes                          0                       0   \n",
       "Coldhands                                 0                       0   \n",
       "Tytos Lannister                           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",
       "Luwin                                  0                         0   \n",
       "Reek                                   0                         0   \n",
       "Symeon Star-Eyes                       0                         0   \n",
       "Coldhands                              0                         0   \n",
       "Tytos Lannister                        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",
       "Luwin                                       0                       0   \n",
       "Reek                                        0                       0   \n",
       "Symeon Star-Eyes                            0                       0   \n",
       "Coldhands                                   0                       0   \n",
       "Tytos Lannister                             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",
       "Luwin                                      0                        0   \n",
       "Reek                                       0                        0   \n",
       "Symeon Star-Eyes                           0                        0   \n",
       "Coldhands                                  0                        0   \n",
       "Tytos Lannister                            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",
       "Luwin                                      0                   0   \n",
       "Reek                                       0                   0   \n",
       "Symeon Star-Eyes                           0                   0   \n",
       "Coldhands                                  0                   0   \n",
       "Tytos Lannister                            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",
       "Luwin                                     0             1  \n",
       "Reek                                      0             1  \n",
       "Symeon Star-Eyes                          0             1  \n",
       "Coldhands                                 0             1  \n",
       "Tytos Lannister                           0             1  \n",
       "\n",
       "[1946 rows x 73 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "character = char_pred.copy()\n",
    "def transform_data(data):\n",
    "    # Replacing the age\n",
    "    data.loc[1684, 'age'] = 25.0\n",
    "    data.loc[1868, 'age'] = 0.0\n",
    "    \n",
    "    # Replacing missing values\n",
    "    data[\"age\"].fillna(round(data[\"age\"].mean()), inplace=True)\n",
    "    data['dateOfBirth'].fillna(data['dateOfBirth'].median(), inplace=True)\n",
    "    data['DateoFdeath'].fillna(data['DateoFdeath'].median(), inplace=True)\n",
    "    \n",
    "    data.loc[:, \"culture\"] = [get_cult(x) for x in data.culture.fillna(\"\")]\n",
    "    cat = ['title', 'mother', 'father', 'heir', 'house', 'spouse']\n",
    "    for col in cat:\n",
    "        data[col].fillna('unknown', inplace=True)\n",
    "    data.fillna(-1, inplace=True)\n",
    "    \n",
    "    data['isACommander'] = np.where(data['name'].isin(all_commanders), 1, 0)\n",
    "    data['inHouseOfWarLead'] = np.where(data['house'].isin(renam_warlead), 1, 0)\n",
    "    data['houseSworeAllegiance'] = np.where(data['house'].isin(hus_char_with_allegnc), 1, 0)\n",
    "    data['inHouseOfAllegnc'] = np.where(data['house'].isin(renam_husOfAllgnc), 1, 0)\n",
    "    \n",
    "    data = data.drop(['S.No', 'actual', 'pred', 'alive', 'plod', 'title',\n",
    "                      'dateOfBirth', 'DateoFdeath', 'house'], axis=1)\n",
    "    \n",
    "    return data\n",
    "\n",
    "def select_features(data):\n",
    "    data.set_index('name', inplace=True)\n",
    "    # Heatmap of numerical feature in the training dataset\n",
    "    num_features = data.select_dtypes(['int', 'float'])\n",
    "    corr_mat = num_features.corr()['isAlive'].abs().sort_values()\n",
    "    corr_less = corr_mat[corr_mat < 0.1]\n",
    "    data = data.drop(corr_less.index, axis=1)\n",
    "    \n",
    "    # Dropping columns with colinearity\n",
    "    data = data.drop(['isAliveFather', 'isAliveMother'], axis=1)\n",
    "    \n",
    "    # List of categorical variable\n",
    "    nominal_features = ['culture', 'mother', 'father', 'heir', 'spouse']\n",
    "    # Counting unique values in each column\n",
    "    unique_count = data[nominal_features].apply(lambda x: len(x.value_counts())).sort_values()\n",
    "    unique_count = unique_count[unique_count > 25].index\n",
    "    # Dropping \n",
    "    data = data.drop(unique_count, axis=1)\n",
    "    # Converting the remaining text column to categorical type\n",
    "    txt_col = data.select_dtypes(include=['object'])\n",
    "    for col in txt_col:\n",
    "        data[col] = data[col].astype('category')\n",
    "        \n",
    "    # Creating dummy columns from txt columns and add back to dataframe\n",
    "    data = pd.concat([\n",
    "        data, pd.get_dummies(data.select_dtypes(include=['category']))\n",
    "    ], axis=1).drop(txt_col, axis=1)\n",
    "    \n",
    "    return data\n",
    "    \n",
    "transformed_data = transform_data(character)\n",
    "selected_features = select_features(transformed_data)\n",
    "\n",
    "selected_features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features.to_csv('cleaned_got_for_ml1.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='undsamdata'></a>\n",
    "### Under sample data for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1451\n",
       "0     495\n",
       "Name: isAlive, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features.isAlive.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable is imbalance, there are more people alive than dead. To handle this, we will undersample the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(995, 73)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dead = selected_features[selected_features['isAlive'] == 0]\n",
    "alive = selected_features[selected_features['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",
    "und_samp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    500\n",
       "0    495\n",
       "Name: isAlive, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "und_samp_df.isAlive.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling our 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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Features and Target\n",
    "x = norm_df.drop(columns='isAlive')\n",
    "y = norm_df['isAlive']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    350\n",
       "0    346\n",
       "Name: isAlive, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='bldtrnmd'></a>\n",
    "## Build and Train a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a seed\n",
    "fixed_seed = 42\n",
    "\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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='mdscores'></a>\n",
    "### Models Test, Train and ROU-AUC Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_score = {\n",
    "    'Train Score': train_score,\n",
    "    'Test_Score': test_score,\n",
    "    \"ROC_AUC Score\": roc_auc_lst\n",
    "}\n",
    "pd.DataFrame(models_score, index=['LogisticRegression','KNeighborsClassifier', 'RandomForestClassifier', \n",
    "                              'DecisionTreeClassifier', 'XGBC'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that some feature are performing well than the other, both on the training set and testing set. We will be working with the ROC-AUC score because this shows the ability of the model to distinguish between classes. The higher the score the better the model is at distinguishing between character that are dead and alive.\n",
    "\n",
    "Let's see the recall score for each of the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='mdrecscor'></a>\n",
    "### Models Visualization of Recall Score  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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 Score\", fontsize=15)\n",
    "plt.legend(classifiers)\n",
    "plt.xlabel(\"Recall\", labelpad=20)\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above we can see the probabilty density of recall score for each model. Random forest classifier has a unimodal shape, and has the longest peak. \n",
    "\n",
    "XGBClassifier and Random forest classifier seems to have the highest recall score though low density compared to Logistic regression and Random forest classifier.\n",
    "\n",
    "We will proceed with the models with higher AUC-ROC score, and check their confusion metrics"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='mdcnfmat'></a>\n",
    "### Models Confusion Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see the confusion matrix for each models.\n",
    "\n",
    "Logistic Regression has the highest False Positive (60) and lowest True Negative (90) compared to other models. \n",
    "\n",
    "KNeighbors Classifier is performing well in detecting characters that are dead (True Positive 110) compared to other models. It also has the lowest False Negative (39).\n",
    "\n",
    "Amongst the four model, Random Forest Classifier is the only model with the highest True negative (101). Its False Positive (49) is lower than the aforementioned two models.\n",
    "\n",
    "XGBClassifier is the second model with the highest True Positive (109), a difference of 1 compared to KNeighbors Classifier. Its also the second model with the highest True Negative (97).\n",
    "\n",
    "Moving on, we will only consider two models from the above four. It's very clear that Logistic Regression model is not performing well with our data. \n",
    "\n",
    "We will proceed with Random Forest Classifier and XGBClassifier because, comparing the TP and TN amognst all four models, both has the highest number if joined together. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='bmrocurv'></a>\n",
    "### Best Model ROC Curve"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen that Random Forest Classifier and XGBC are perfoming well with our data. We'll choose the two models and proceed.\n",
    "\n",
    "Let's start by looking at the ROC curve of the two models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "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",
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen how the curve look like. Both models seems to be competing. Random Forest Classifier seems to cover more area than XGBClassifier.\n",
    "\n",
    "Let's see their classification report."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clsrep'></a>\n",
    "### Classification Report of Best Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier Test Performance\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 Test Performance\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": [
    "for model in best_model:\n",
    "    model.fit(x_train, y_train)\n",
    "    pred = model.predict(x_test)\n",
    "    print(str(model).split('(')[0] +' Test Performance')\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": [
    "<a id='HP1'></a>\n",
    "## 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": [
    "<a id='rndsecv'></a>\n",
    "### Random Search with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 61,
   "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": 61,
     "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": 62,
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best Parameters from random search\n",
    "rfc_random.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EVPER'></a>\n",
    "## Evaluate Performance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 64,
   "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": [
    "<a id='rndper'></a>\n",
    "#### Performance of Best Random Search Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": [
    "<a id='HP2'></a>\n",
    "## Hyper Parameter Tuning 2\n",
    "Now let's do a grid search around the best parameter gotten from the random search"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='grdse'></a>\n",
    "### Grid Search with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "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, cv = 3, \n",
    "                           n_jobs = -1, verbose = 2, scoring='neg_mean_absolute_error')\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best parameter from grid search \n",
    "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": 68,
   "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": [
    "<a id='HP3'></a>\n",
    "## Hyper Parameter Tuning 3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='anogrdse'></a>\n",
    "#### Another Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 69,
     "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, cv = 3, \n",
    "                           n_jobs = -1, verbose = 2, scoring='neg_mean_absolute_error')\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best performance from second grid search\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": 71,
   "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, 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": 72,
   "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": [
    "<a id='modpredin'></a>\n",
    "### 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": 73,
   "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": 74,
   "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": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "char_dt = pd.read_csv('GOT_Char_details.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "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": 78,
   "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=\"72e32b69-7276-425d-aad5-c5028735c13a\" 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(\"72e32b69-7276-425d-aad5-c5028735c13a\")) {                    Plotly.newPlot(                        \"72e32b69-7276-425d-aad5-c5028735c13a\",                        [{\"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('72e32b69-7276-425d-aad5-c5028735c13a');\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=\"b8893ff8-f217-4a6e-9088-06abc9877ada\" 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(\"b8893ff8-f217-4a6e-9088-06abc9877ada\")) {                    Plotly.newPlot(                        \"b8893ff8-f217-4a6e-9088-06abc9877ada\",                        [{\"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('b8893ff8-f217-4a6e-9088-06abc9877ada');\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": 79,
   "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=\"cf085c97-a030-4e34-911b-52f4432d6243\" 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(\"cf085c97-a030-4e34-911b-52f4432d6243\")) {                    Plotly.newPlot(                        \"cf085c97-a030-4e34-911b-52f4432d6243\",                        [{\"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('cf085c97-a030-4e34-911b-52f4432d6243');\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=\"35ca89cb-4cec-42f1-bd9b-0eeca41d1e09\" 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(\"35ca89cb-4cec-42f1-bd9b-0eeca41d1e09\")) {                    Plotly.newPlot(                        \"35ca89cb-4cec-42f1-bd9b-0eeca41d1e09\",                        [{\"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('35ca89cb-4cec-42f1-bd9b-0eeca41d1e09');\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, also in popular character, the model wasn't performing well\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": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "ne = char_dt[char_dt.index.isin(test.index)]\n",
    "ne['pred'] = test['predictions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 81,
     "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": 82,
   "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": 82,
     "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": 83,
   "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": 83,
     "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": 84,
   "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": 84,
     "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": [
    "<a id='errchar'></a>\n",
    "### 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": 85,
   "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": [
    "<a id='SL'></a>\n",
    "## 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",
    ">\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",
    "\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."
   ]
  }
 ],
 "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
}