clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0: Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. https://www.geeksforgeeks.org/ml-spectral-clustering/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1: Define the Problem and Goal\n",
    "## Problem:\n",
    "The Ultimate Fighting Championship (UFC) is the world's largest mixed martial arts (MMA) promotion company, hosting most of the top-ranked fighters in the sport and producing events worldwide. The UFC features fighters from a wide variety of backgrounds, including boxing, kickboxing, wrestling, Brazilian jiu-jitsu, Muay Thai, karate, judo, and other disciplines. As a result, fighters often have distinct fighting styles, strengths, and weaknesses. For example, some fighters are known for their striking prowess, while others are known for their grappling and submission skills. In addition, fighters may have different physical attributes, such as height, weight, and reach, which can also influence their fighting styles. Given the diversity of fighters in the UFC, it is important to understand the landscape of fighters and their respective skill sets in order to make informed decisions on training strategies, fight planning, and overall career management.\n",
    "\n",
    "## Goal:\n",
    "This analysis is designed to employ clustering techniques on UFC fighter data, aiming to categorize fighters into distinct groups that share similar fighting styles. By amalgamating individual fight statistics encompassing striking, grappling, submission attempts, and other relevant metrics, the primary objective is to uncover latent patterns that shed light on the diverse skill sets, strengths, and weaknesses of UFC fighters. The delineated clusters will serve as valuable tools for coaches, analysts, event organizers, and fighters, offering insights for informed decisions on training strategies, fight planning, and overall career management. The successful execution of this clustering analysis will deepen our understanding of the UFC fighter landscape, facilitating nuanced, data-driven decision-making within the organization.\n",
    "\n",
    "🤖 prompt: Create a problem statement for clustering UFC fighters based on their aggregated career features using the provided dataset: `data.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2: Gather the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/rajeevw/ufcdata/data?select=data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3: Prepare Data for Consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1572,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Model Algorithms\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#Common Helpers\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn import metrics\n",
    "\n",
    "#Visualization\n",
    "import matplotlib as mpl\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Utility\n",
    "import time\n",
    "\n",
    "#Configure Visualization Defaults\n",
    "# %matplotlib inline = show plots in Jupyter Notebook browser\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set_style('white')\n",
    "pylab.rcParams['figure.figsize'] = 12,8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meet and Greet Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1573,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1574,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_value = int(time.time())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preview Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1575,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6012, 144)"
      ]
     },
     "execution_count": 1575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have found that the data has 100+ columns, we will need to tweek the pandas display options to be able to see all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1576,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_info_columns', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1577,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6012 entries, 0 to 6011\n",
      "Data columns (total 144 columns):\n",
      " #    Column                        Non-Null Count  Dtype  \n",
      "---   ------                        --------------  -----  \n",
      " 0    R_fighter                     6012 non-null   object \n",
      " 1    B_fighter                     6012 non-null   object \n",
      " 2    Referee                       5980 non-null   object \n",
      " 3    date                          6012 non-null   object \n",
      " 4    location                      6012 non-null   object \n",
      " 5    Winner                        6012 non-null   object \n",
      " 6    title_bout                    6012 non-null   bool   \n",
      " 7    weight_class                  6012 non-null   object \n",
      " 8    B_avg_KD                      4585 non-null   float64\n",
      " 9    B_avg_opp_KD                  4585 non-null   float64\n",
      " 10   B_avg_SIG_STR_pct             4585 non-null   float64\n",
      " 11   B_avg_opp_SIG_STR_pct         4585 non-null   float64\n",
      " 12   B_avg_TD_pct                  4585 non-null   float64\n",
      " 13   B_avg_opp_TD_pct              4585 non-null   float64\n",
      " 14   B_avg_SUB_ATT                 4585 non-null   float64\n",
      " 15   B_avg_opp_SUB_ATT             4585 non-null   float64\n",
      " 16   B_avg_REV                     4585 non-null   float64\n",
      " 17   B_avg_opp_REV                 4585 non-null   float64\n",
      " 18   B_avg_SIG_STR_att             4585 non-null   float64\n",
      " 19   B_avg_SIG_STR_landed          4585 non-null   float64\n",
      " 20   B_avg_opp_SIG_STR_att         4585 non-null   float64\n",
      " 21   B_avg_opp_SIG_STR_landed      4585 non-null   float64\n",
      " 22   B_avg_TOTAL_STR_att           4585 non-null   float64\n",
      " 23   B_avg_TOTAL_STR_landed        4585 non-null   float64\n",
      " 24   B_avg_opp_TOTAL_STR_att       4585 non-null   float64\n",
      " 25   B_avg_opp_TOTAL_STR_landed    4585 non-null   float64\n",
      " 26   B_avg_TD_att                  4585 non-null   float64\n",
      " 27   B_avg_TD_landed               4585 non-null   float64\n",
      " 28   B_avg_opp_TD_att              4585 non-null   float64\n",
      " 29   B_avg_opp_TD_landed           4585 non-null   float64\n",
      " 30   B_avg_HEAD_att                4585 non-null   float64\n",
      " 31   B_avg_HEAD_landed             4585 non-null   float64\n",
      " 32   B_avg_opp_HEAD_att            4585 non-null   float64\n",
      " 33   B_avg_opp_HEAD_landed         4585 non-null   float64\n",
      " 34   B_avg_BODY_att                4585 non-null   float64\n",
      " 35   B_avg_BODY_landed             4585 non-null   float64\n",
      " 36   B_avg_opp_BODY_att            4585 non-null   float64\n",
      " 37   B_avg_opp_BODY_landed         4585 non-null   float64\n",
      " 38   B_avg_LEG_att                 4585 non-null   float64\n",
      " 39   B_avg_LEG_landed              4585 non-null   float64\n",
      " 40   B_avg_opp_LEG_att             4585 non-null   float64\n",
      " 41   B_avg_opp_LEG_landed          4585 non-null   float64\n",
      " 42   B_avg_DISTANCE_att            4585 non-null   float64\n",
      " 43   B_avg_DISTANCE_landed         4585 non-null   float64\n",
      " 44   B_avg_opp_DISTANCE_att        4585 non-null   float64\n",
      " 45   B_avg_opp_DISTANCE_landed     4585 non-null   float64\n",
      " 46   B_avg_CLINCH_att              4585 non-null   float64\n",
      " 47   B_avg_CLINCH_landed           4585 non-null   float64\n",
      " 48   B_avg_opp_CLINCH_att          4585 non-null   float64\n",
      " 49   B_avg_opp_CLINCH_landed       4585 non-null   float64\n",
      " 50   B_avg_GROUND_att              4585 non-null   float64\n",
      " 51   B_avg_GROUND_landed           4585 non-null   float64\n",
      " 52   B_avg_opp_GROUND_att          4585 non-null   float64\n",
      " 53   B_avg_opp_GROUND_landed       4585 non-null   float64\n",
      " 54   B_avg_CTRL_time(seconds)      4585 non-null   float64\n",
      " 55   B_avg_opp_CTRL_time(seconds)  4585 non-null   float64\n",
      " 56   B_total_time_fought(seconds)  4585 non-null   float64\n",
      " 57   B_total_rounds_fought         6012 non-null   int64  \n",
      " 58   B_total_title_bouts           6012 non-null   int64  \n",
      " 59   B_current_win_streak          6012 non-null   int64  \n",
      " 60   B_current_lose_streak         6012 non-null   int64  \n",
      " 61   B_longest_win_streak          6012 non-null   int64  \n",
      " 62   B_wins                        6012 non-null   int64  \n",
      " 63   B_losses                      6012 non-null   int64  \n",
      " 64   B_draw                        6012 non-null   int64  \n",
      " 65   B_win_by_Decision_Majority    6012 non-null   int64  \n",
      " 66   B_win_by_Decision_Split       6012 non-null   int64  \n",
      " 67   B_win_by_Decision_Unanimous   6012 non-null   int64  \n",
      " 68   B_win_by_KO/TKO               6012 non-null   int64  \n",
      " 69   B_win_by_Submission           6012 non-null   int64  \n",
      " 70   B_win_by_TKO_Doctor_Stoppage  6012 non-null   int64  \n",
      " 71   B_Stance                      5946 non-null   object \n",
      " 72   B_Height_cms                  6002 non-null   float64\n",
      " 73   B_Reach_cms                   5121 non-null   float64\n",
      " 74   B_Weight_lbs                  6004 non-null   float64\n",
      " 75   R_avg_KD                      5300 non-null   float64\n",
      " 76   R_avg_opp_KD                  5300 non-null   float64\n",
      " 77   R_avg_SIG_STR_pct             5300 non-null   float64\n",
      " 78   R_avg_opp_SIG_STR_pct         5300 non-null   float64\n",
      " 79   R_avg_TD_pct                  5300 non-null   float64\n",
      " 80   R_avg_opp_TD_pct              5300 non-null   float64\n",
      " 81   R_avg_SUB_ATT                 5300 non-null   float64\n",
      " 82   R_avg_opp_SUB_ATT             5300 non-null   float64\n",
      " 83   R_avg_REV                     5300 non-null   float64\n",
      " 84   R_avg_opp_REV                 5300 non-null   float64\n",
      " 85   R_avg_SIG_STR_att             5300 non-null   float64\n",
      " 86   R_avg_SIG_STR_landed          5300 non-null   float64\n",
      " 87   R_avg_opp_SIG_STR_att         5300 non-null   float64\n",
      " 88   R_avg_opp_SIG_STR_landed      5300 non-null   float64\n",
      " 89   R_avg_TOTAL_STR_att           5300 non-null   float64\n",
      " 90   R_avg_TOTAL_STR_landed        5300 non-null   float64\n",
      " 91   R_avg_opp_TOTAL_STR_att       5300 non-null   float64\n",
      " 92   R_avg_opp_TOTAL_STR_landed    5300 non-null   float64\n",
      " 93   R_avg_TD_att                  5300 non-null   float64\n",
      " 94   R_avg_TD_landed               5300 non-null   float64\n",
      " 95   R_avg_opp_TD_att              5300 non-null   float64\n",
      " 96   R_avg_opp_TD_landed           5300 non-null   float64\n",
      " 97   R_avg_HEAD_att                5300 non-null   float64\n",
      " 98   R_avg_HEAD_landed             5300 non-null   float64\n",
      " 99   R_avg_opp_HEAD_att            5300 non-null   float64\n",
      " 100  R_avg_opp_HEAD_landed         5300 non-null   float64\n",
      " 101  R_avg_BODY_att                5300 non-null   float64\n",
      " 102  R_avg_BODY_landed             5300 non-null   float64\n",
      " 103  R_avg_opp_BODY_att            5300 non-null   float64\n",
      " 104  R_avg_opp_BODY_landed         5300 non-null   float64\n",
      " 105  R_avg_LEG_att                 5300 non-null   float64\n",
      " 106  R_avg_LEG_landed              5300 non-null   float64\n",
      " 107  R_avg_opp_LEG_att             5300 non-null   float64\n",
      " 108  R_avg_opp_LEG_landed          5300 non-null   float64\n",
      " 109  R_avg_DISTANCE_att            5300 non-null   float64\n",
      " 110  R_avg_DISTANCE_landed         5300 non-null   float64\n",
      " 111  R_avg_opp_DISTANCE_att        5300 non-null   float64\n",
      " 112  R_avg_opp_DISTANCE_landed     5300 non-null   float64\n",
      " 113  R_avg_CLINCH_att              5300 non-null   float64\n",
      " 114  R_avg_CLINCH_landed           5300 non-null   float64\n",
      " 115  R_avg_opp_CLINCH_att          5300 non-null   float64\n",
      " 116  R_avg_opp_CLINCH_landed       5300 non-null   float64\n",
      " 117  R_avg_GROUND_att              5300 non-null   float64\n",
      " 118  R_avg_GROUND_landed           5300 non-null   float64\n",
      " 119  R_avg_opp_GROUND_att          5300 non-null   float64\n",
      " 120  R_avg_opp_GROUND_landed       5300 non-null   float64\n",
      " 121  R_avg_CTRL_time(seconds)      5300 non-null   float64\n",
      " 122  R_avg_opp_CTRL_time(seconds)  5300 non-null   float64\n",
      " 123  R_total_time_fought(seconds)  5300 non-null   float64\n",
      " 124  R_total_rounds_fought         6012 non-null   int64  \n",
      " 125  R_total_title_bouts           6012 non-null   int64  \n",
      " 126  R_current_win_streak          6012 non-null   int64  \n",
      " 127  R_current_lose_streak         6012 non-null   int64  \n",
      " 128  R_longest_win_streak          6012 non-null   int64  \n",
      " 129  R_wins                        6012 non-null   int64  \n",
      " 130  R_losses                      6012 non-null   int64  \n",
      " 131  R_draw                        6012 non-null   int64  \n",
      " 132  R_win_by_Decision_Majority    6012 non-null   int64  \n",
      " 133  R_win_by_Decision_Split       6012 non-null   int64  \n",
      " 134  R_win_by_Decision_Unanimous   6012 non-null   int64  \n",
      " 135  R_win_by_KO/TKO               6012 non-null   int64  \n",
      " 136  R_win_by_Submission           6012 non-null   int64  \n",
      " 137  R_win_by_TKO_Doctor_Stoppage  6012 non-null   int64  \n",
      " 138  R_Stance                      5983 non-null   object \n",
      " 139  R_Height_cms                  6008 non-null   float64\n",
      " 140  R_Reach_cms                   5606 non-null   float64\n",
      " 141  R_Weight_lbs                  6010 non-null   float64\n",
      " 142  B_age                         5840 non-null   float64\n",
      " 143  R_age                         5949 non-null   float64\n",
      "dtypes: bool(1), float64(106), int64(28), object(9)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1578,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_fighter</th>\n",
       "      <th>B_fighter</th>\n",
       "      <th>Referee</th>\n",
       "      <th>date</th>\n",
       "      <th>location</th>\n",
       "      <th>Winner</th>\n",
       "      <th>title_bout</th>\n",
       "      <th>weight_class</th>\n",
       "      <th>B_avg_KD</th>\n",
       "      <th>B_avg_opp_KD</th>\n",
       "      <th>B_avg_SIG_STR_pct</th>\n",
       "      <th>B_avg_opp_SIG_STR_pct</th>\n",
       "      <th>B_avg_TD_pct</th>\n",
       "      <th>B_avg_opp_TD_pct</th>\n",
       "      <th>B_avg_SUB_ATT</th>\n",
       "      <th>B_avg_opp_SUB_ATT</th>\n",
       "      <th>B_avg_REV</th>\n",
       "      <th>B_avg_opp_REV</th>\n",
       "      <th>B_avg_SIG_STR_att</th>\n",
       "      <th>B_avg_SIG_STR_landed</th>\n",
       "      <th>B_avg_opp_SIG_STR_att</th>\n",
       "      <th>B_avg_opp_SIG_STR_landed</th>\n",
       "      <th>B_avg_TOTAL_STR_att</th>\n",
       "      <th>B_avg_TOTAL_STR_landed</th>\n",
       "      <th>B_avg_opp_TOTAL_STR_att</th>\n",
       "      <th>B_avg_opp_TOTAL_STR_landed</th>\n",
       "      <th>B_avg_TD_att</th>\n",
       "      <th>B_avg_TD_landed</th>\n",
       "      <th>B_avg_opp_TD_att</th>\n",
       "      <th>B_avg_opp_TD_landed</th>\n",
       "      <th>B_avg_HEAD_att</th>\n",
       "      <th>B_avg_HEAD_landed</th>\n",
       "      <th>B_avg_opp_HEAD_att</th>\n",
       "      <th>B_avg_opp_HEAD_landed</th>\n",
       "      <th>B_avg_BODY_att</th>\n",
       "      <th>B_avg_BODY_landed</th>\n",
       "      <th>B_avg_opp_BODY_att</th>\n",
       "      <th>B_avg_opp_BODY_landed</th>\n",
       "      <th>B_avg_LEG_att</th>\n",
       "      <th>B_avg_LEG_landed</th>\n",
       "      <th>B_avg_opp_LEG_att</th>\n",
       "      <th>B_avg_opp_LEG_landed</th>\n",
       "      <th>B_avg_DISTANCE_att</th>\n",
       "      <th>B_avg_DISTANCE_landed</th>\n",
       "      <th>B_avg_opp_DISTANCE_att</th>\n",
       "      <th>B_avg_opp_DISTANCE_landed</th>\n",
       "      <th>B_avg_CLINCH_att</th>\n",
       "      <th>B_avg_CLINCH_landed</th>\n",
       "      <th>B_avg_opp_CLINCH_att</th>\n",
       "      <th>B_avg_opp_CLINCH_landed</th>\n",
       "      <th>B_avg_GROUND_att</th>\n",
       "      <th>B_avg_GROUND_landed</th>\n",
       "      <th>B_avg_opp_GROUND_att</th>\n",
       "      <th>B_avg_opp_GROUND_landed</th>\n",
       "      <th>B_avg_CTRL_time(seconds)</th>\n",
       "      <th>B_avg_opp_CTRL_time(seconds)</th>\n",
       "      <th>B_total_time_fought(seconds)</th>\n",
       "      <th>B_total_rounds_fought</th>\n",
       "      <th>B_total_title_bouts</th>\n",
       "      <th>B_current_win_streak</th>\n",
       "      <th>B_current_lose_streak</th>\n",
       "      <th>B_longest_win_streak</th>\n",
       "      <th>B_wins</th>\n",
       "      <th>B_losses</th>\n",
       "      <th>B_draw</th>\n",
       "      <th>B_win_by_Decision_Majority</th>\n",
       "      <th>B_win_by_Decision_Split</th>\n",
       "      <th>B_win_by_Decision_Unanimous</th>\n",
       "      <th>B_win_by_KO/TKO</th>\n",
       "      <th>B_win_by_Submission</th>\n",
       "      <th>B_win_by_TKO_Doctor_Stoppage</th>\n",
       "      <th>B_Stance</th>\n",
       "      <th>B_Height_cms</th>\n",
       "      <th>B_Reach_cms</th>\n",
       "      <th>B_Weight_lbs</th>\n",
       "      <th>R_avg_KD</th>\n",
       "      <th>R_avg_opp_KD</th>\n",
       "      <th>R_avg_SIG_STR_pct</th>\n",
       "      <th>R_avg_opp_SIG_STR_pct</th>\n",
       "      <th>R_avg_TD_pct</th>\n",
       "      <th>R_avg_opp_TD_pct</th>\n",
       "      <th>R_avg_SUB_ATT</th>\n",
       "      <th>R_avg_opp_SUB_ATT</th>\n",
       "      <th>R_avg_REV</th>\n",
       "      <th>R_avg_opp_REV</th>\n",
       "      <th>R_avg_SIG_STR_att</th>\n",
       "      <th>R_avg_SIG_STR_landed</th>\n",
       "      <th>R_avg_opp_SIG_STR_att</th>\n",
       "      <th>R_avg_opp_SIG_STR_landed</th>\n",
       "      <th>R_avg_TOTAL_STR_att</th>\n",
       "      <th>R_avg_TOTAL_STR_landed</th>\n",
       "      <th>R_avg_opp_TOTAL_STR_att</th>\n",
       "      <th>R_avg_opp_TOTAL_STR_landed</th>\n",
       "      <th>R_avg_TD_att</th>\n",
       "      <th>R_avg_TD_landed</th>\n",
       "      <th>R_avg_opp_TD_att</th>\n",
       "      <th>R_avg_opp_TD_landed</th>\n",
       "      <th>R_avg_HEAD_att</th>\n",
       "      <th>R_avg_HEAD_landed</th>\n",
       "      <th>R_avg_opp_HEAD_att</th>\n",
       "      <th>R_avg_opp_HEAD_landed</th>\n",
       "      <th>R_avg_BODY_att</th>\n",
       "      <th>R_avg_BODY_landed</th>\n",
       "      <th>R_avg_opp_BODY_att</th>\n",
       "      <th>R_avg_opp_BODY_landed</th>\n",
       "      <th>R_avg_LEG_att</th>\n",
       "      <th>R_avg_LEG_landed</th>\n",
       "      <th>R_avg_opp_LEG_att</th>\n",
       "      <th>R_avg_opp_LEG_landed</th>\n",
       "      <th>R_avg_DISTANCE_att</th>\n",
       "      <th>R_avg_DISTANCE_landed</th>\n",
       "      <th>R_avg_opp_DISTANCE_att</th>\n",
       "      <th>R_avg_opp_DISTANCE_landed</th>\n",
       "      <th>R_avg_CLINCH_att</th>\n",
       "      <th>R_avg_CLINCH_landed</th>\n",
       "      <th>R_avg_opp_CLINCH_att</th>\n",
       "      <th>R_avg_opp_CLINCH_landed</th>\n",
       "      <th>R_avg_GROUND_att</th>\n",
       "      <th>R_avg_GROUND_landed</th>\n",
       "      <th>R_avg_opp_GROUND_att</th>\n",
       "      <th>R_avg_opp_GROUND_landed</th>\n",
       "      <th>R_avg_CTRL_time(seconds)</th>\n",
       "      <th>R_avg_opp_CTRL_time(seconds)</th>\n",
       "      <th>R_total_time_fought(seconds)</th>\n",
       "      <th>R_total_rounds_fought</th>\n",
       "      <th>R_total_title_bouts</th>\n",
       "      <th>R_current_win_streak</th>\n",
       "      <th>R_current_lose_streak</th>\n",
       "      <th>R_longest_win_streak</th>\n",
       "      <th>R_wins</th>\n",
       "      <th>R_losses</th>\n",
       "      <th>R_draw</th>\n",
       "      <th>R_win_by_Decision_Majority</th>\n",
       "      <th>R_win_by_Decision_Split</th>\n",
       "      <th>R_win_by_Decision_Unanimous</th>\n",
       "      <th>R_win_by_KO/TKO</th>\n",
       "      <th>R_win_by_Submission</th>\n",
       "      <th>R_win_by_TKO_Doctor_Stoppage</th>\n",
       "      <th>R_Stance</th>\n",
       "      <th>R_Height_cms</th>\n",
       "      <th>R_Reach_cms</th>\n",
       "      <th>R_Weight_lbs</th>\n",
       "      <th>B_age</th>\n",
       "      <th>R_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6012</td>\n",
       "      <td>6012</td>\n",
       "      <td>5980</td>\n",
       "      <td>6012</td>\n",
       "      <td>6012</td>\n",
       "      <td>6012</td>\n",
       "      <td>6012</td>\n",
       "      <td>6012</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.00000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>4585.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>5946</td>\n",
       "      <td>6002.000000</td>\n",
       "      <td>5121.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.00000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>6012.000000</td>\n",
       "      <td>5983</td>\n",
       "      <td>6008.000000</td>\n",
       "      <td>5606.000000</td>\n",
       "      <td>6010.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5949.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1514</td>\n",
       "      <td>1987</td>\n",
       "      <td>204</td>\n",
       "      <td>550</td>\n",
       "      <td>166</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Jim Miller</td>\n",
       "      <td>Jeremy Stephens</td>\n",
       "      <td>Herb Dean</td>\n",
       "      <td>2016-11-19</td>\n",
       "      <td>Las Vegas, Nevada, USA</td>\n",
       "      <td>Red</td>\n",
       "      <td>False</td>\n",
       "      <td>Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>858</td>\n",
       "      <td>25</td>\n",
       "      <td>1565</td>\n",
       "      <td>3979</td>\n",
       "      <td>5647</td>\n",
       "      <td>1091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.247476</td>\n",
       "      <td>0.176818</td>\n",
       "      <td>0.453310</td>\n",
       "      <td>0.434290</td>\n",
       "      <td>0.292650</td>\n",
       "      <td>0.268742</td>\n",
       "      <td>0.478884</td>\n",
       "      <td>0.409276</td>\n",
       "      <td>0.15673</td>\n",
       "      <td>0.141306</td>\n",
       "      <td>76.288358</td>\n",
       "      <td>33.337168</td>\n",
       "      <td>73.092895</td>\n",
       "      <td>31.053464</td>\n",
       "      <td>97.635001</td>\n",
       "      <td>52.281925</td>\n",
       "      <td>92.716135</td>\n",
       "      <td>48.504853</td>\n",
       "      <td>2.726783</td>\n",
       "      <td>1.109138</td>\n",
       "      <td>2.861216</td>\n",
       "      <td>1.073168</td>\n",
       "      <td>59.984433</td>\n",
       "      <td>21.387261</td>\n",
       "      <td>57.055890</td>\n",
       "      <td>19.403349</td>\n",
       "      <td>9.684013</td>\n",
       "      <td>6.681643</td>\n",
       "      <td>9.370116</td>\n",
       "      <td>6.352129</td>\n",
       "      <td>6.619912</td>\n",
       "      <td>5.268264</td>\n",
       "      <td>6.666888</td>\n",
       "      <td>5.297986</td>\n",
       "      <td>60.059833</td>\n",
       "      <td>22.313082</td>\n",
       "      <td>58.574160</td>\n",
       "      <td>21.288060</td>\n",
       "      <td>8.069538</td>\n",
       "      <td>5.514441</td>\n",
       "      <td>7.476618</td>\n",
       "      <td>5.045857</td>\n",
       "      <td>8.158988</td>\n",
       "      <td>5.509645</td>\n",
       "      <td>7.042118</td>\n",
       "      <td>4.719547</td>\n",
       "      <td>137.277722</td>\n",
       "      <td>132.591148</td>\n",
       "      <td>611.409924</td>\n",
       "      <td>9.441783</td>\n",
       "      <td>0.277279</td>\n",
       "      <td>0.947272</td>\n",
       "      <td>0.397206</td>\n",
       "      <td>1.630905</td>\n",
       "      <td>2.584997</td>\n",
       "      <td>1.540419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016134</td>\n",
       "      <td>0.218397</td>\n",
       "      <td>0.838157</td>\n",
       "      <td>0.895709</td>\n",
       "      <td>0.569528</td>\n",
       "      <td>0.042083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.951083</td>\n",
       "      <td>183.228686</td>\n",
       "      <td>170.590773</td>\n",
       "      <td>0.243484</td>\n",
       "      <td>0.188045</td>\n",
       "      <td>0.457171</td>\n",
       "      <td>0.425338</td>\n",
       "      <td>0.300901</td>\n",
       "      <td>0.254409</td>\n",
       "      <td>0.463514</td>\n",
       "      <td>0.393863</td>\n",
       "      <td>0.141426</td>\n",
       "      <td>0.143164</td>\n",
       "      <td>79.346284</td>\n",
       "      <td>34.808436</td>\n",
       "      <td>76.069556</td>\n",
       "      <td>31.890675</td>\n",
       "      <td>100.586308</td>\n",
       "      <td>53.693005</td>\n",
       "      <td>95.553843</td>\n",
       "      <td>49.258481</td>\n",
       "      <td>2.943604</td>\n",
       "      <td>1.184075</td>\n",
       "      <td>2.822920</td>\n",
       "      <td>1.018747</td>\n",
       "      <td>62.281073</td>\n",
       "      <td>22.344055</td>\n",
       "      <td>59.780497</td>\n",
       "      <td>20.158009</td>\n",
       "      <td>10.019317</td>\n",
       "      <td>6.871403</td>\n",
       "      <td>9.551659</td>\n",
       "      <td>6.442013</td>\n",
       "      <td>7.045894</td>\n",
       "      <td>5.592977</td>\n",
       "      <td>6.737400</td>\n",
       "      <td>5.290654</td>\n",
       "      <td>62.677000</td>\n",
       "      <td>23.457244</td>\n",
       "      <td>61.612832</td>\n",
       "      <td>22.189858</td>\n",
       "      <td>7.923476</td>\n",
       "      <td>5.409125</td>\n",
       "      <td>7.545115</td>\n",
       "      <td>5.060535</td>\n",
       "      <td>8.745808</td>\n",
       "      <td>5.942066</td>\n",
       "      <td>6.91161</td>\n",
       "      <td>4.640281</td>\n",
       "      <td>144.215233</td>\n",
       "      <td>125.120348</td>\n",
       "      <td>633.322933</td>\n",
       "      <td>13.669993</td>\n",
       "      <td>0.582169</td>\n",
       "      <td>1.314704</td>\n",
       "      <td>0.389055</td>\n",
       "      <td>2.313207</td>\n",
       "      <td>3.756986</td>\n",
       "      <td>2.071357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027944</td>\n",
       "      <td>0.306886</td>\n",
       "      <td>1.251331</td>\n",
       "      <td>1.297572</td>\n",
       "      <td>0.796241</td>\n",
       "      <td>0.069195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.991788</td>\n",
       "      <td>183.562347</td>\n",
       "      <td>170.686356</td>\n",
       "      <td>29.293151</td>\n",
       "      <td>29.598252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.378509</td>\n",
       "      <td>0.324633</td>\n",
       "      <td>0.130458</td>\n",
       "      <td>0.132618</td>\n",
       "      <td>0.273628</td>\n",
       "      <td>0.267178</td>\n",
       "      <td>0.724229</td>\n",
       "      <td>0.653826</td>\n",
       "      <td>0.34199</td>\n",
       "      <td>0.322623</td>\n",
       "      <td>51.175465</td>\n",
       "      <td>22.030474</td>\n",
       "      <td>49.911897</td>\n",
       "      <td>21.371161</td>\n",
       "      <td>57.524779</td>\n",
       "      <td>31.771066</td>\n",
       "      <td>54.871771</td>\n",
       "      <td>29.863232</td>\n",
       "      <td>2.836065</td>\n",
       "      <td>1.328047</td>\n",
       "      <td>2.738086</td>\n",
       "      <td>1.261016</td>\n",
       "      <td>42.085146</td>\n",
       "      <td>15.314792</td>\n",
       "      <td>40.686905</td>\n",
       "      <td>14.609626</td>\n",
       "      <td>8.377459</td>\n",
       "      <td>5.920334</td>\n",
       "      <td>7.906397</td>\n",
       "      <td>5.672195</td>\n",
       "      <td>6.852332</td>\n",
       "      <td>5.496281</td>\n",
       "      <td>6.582663</td>\n",
       "      <td>5.342577</td>\n",
       "      <td>48.453182</td>\n",
       "      <td>19.216578</td>\n",
       "      <td>47.497037</td>\n",
       "      <td>18.552735</td>\n",
       "      <td>8.069062</td>\n",
       "      <td>5.826976</td>\n",
       "      <td>7.595332</td>\n",
       "      <td>5.530920</td>\n",
       "      <td>9.347720</td>\n",
       "      <td>6.277867</td>\n",
       "      <td>9.462104</td>\n",
       "      <td>6.447007</td>\n",
       "      <td>133.688630</td>\n",
       "      <td>129.213709</td>\n",
       "      <td>260.707940</td>\n",
       "      <td>11.875922</td>\n",
       "      <td>1.125673</td>\n",
       "      <td>1.489959</td>\n",
       "      <td>0.664907</td>\n",
       "      <td>1.808590</td>\n",
       "      <td>3.317241</td>\n",
       "      <td>1.940694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.127316</td>\n",
       "      <td>0.525232</td>\n",
       "      <td>1.370454</td>\n",
       "      <td>1.580829</td>\n",
       "      <td>1.161257</td>\n",
       "      <td>0.211290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.709866</td>\n",
       "      <td>10.344758</td>\n",
       "      <td>36.879171</td>\n",
       "      <td>0.364901</td>\n",
       "      <td>0.320757</td>\n",
       "      <td>0.128835</td>\n",
       "      <td>0.124878</td>\n",
       "      <td>0.261993</td>\n",
       "      <td>0.247180</td>\n",
       "      <td>0.669272</td>\n",
       "      <td>0.623378</td>\n",
       "      <td>0.306744</td>\n",
       "      <td>0.318342</td>\n",
       "      <td>51.753004</td>\n",
       "      <td>22.568121</td>\n",
       "      <td>50.799409</td>\n",
       "      <td>21.709018</td>\n",
       "      <td>57.657913</td>\n",
       "      <td>32.046382</td>\n",
       "      <td>55.811293</td>\n",
       "      <td>30.033210</td>\n",
       "      <td>3.006234</td>\n",
       "      <td>1.322230</td>\n",
       "      <td>2.641710</td>\n",
       "      <td>1.178708</td>\n",
       "      <td>42.130661</td>\n",
       "      <td>15.582960</td>\n",
       "      <td>41.767256</td>\n",
       "      <td>15.313832</td>\n",
       "      <td>8.549729</td>\n",
       "      <td>5.988029</td>\n",
       "      <td>7.764812</td>\n",
       "      <td>5.381532</td>\n",
       "      <td>7.309739</td>\n",
       "      <td>5.772675</td>\n",
       "      <td>6.513925</td>\n",
       "      <td>5.142570</td>\n",
       "      <td>49.645270</td>\n",
       "      <td>19.964855</td>\n",
       "      <td>48.243271</td>\n",
       "      <td>18.992482</td>\n",
       "      <td>7.852318</td>\n",
       "      <td>5.705212</td>\n",
       "      <td>7.460350</td>\n",
       "      <td>5.285806</td>\n",
       "      <td>10.300208</td>\n",
       "      <td>7.000114</td>\n",
       "      <td>8.92240</td>\n",
       "      <td>5.997152</td>\n",
       "      <td>136.696848</td>\n",
       "      <td>121.675716</td>\n",
       "      <td>267.906601</td>\n",
       "      <td>14.197512</td>\n",
       "      <td>1.551233</td>\n",
       "      <td>1.729916</td>\n",
       "      <td>0.649405</td>\n",
       "      <td>2.094238</td>\n",
       "      <td>3.861044</td>\n",
       "      <td>2.265830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166833</td>\n",
       "      <td>0.619485</td>\n",
       "      <td>1.681376</td>\n",
       "      <td>1.859955</td>\n",
       "      <td>1.355801</td>\n",
       "      <td>0.273980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.812985</td>\n",
       "      <td>10.585313</td>\n",
       "      <td>35.724626</td>\n",
       "      <td>4.063297</td>\n",
       "      <td>4.167755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.400000</td>\n",
       "      <td>147.320000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.400000</td>\n",
       "      <td>152.400000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.376489</td>\n",
       "      <td>0.351045</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.033203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.261230</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>36.405273</td>\n",
       "      <td>15.562500</td>\n",
       "      <td>55.484375</td>\n",
       "      <td>28.937500</td>\n",
       "      <td>52.985352</td>\n",
       "      <td>26.875000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>29.296875</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>27.875000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.585938</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.875870</td>\n",
       "      <td>1.437500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.515625</td>\n",
       "      <td>2.456055</td>\n",
       "      <td>1.445312</td>\n",
       "      <td>1.959669</td>\n",
       "      <td>1.140625</td>\n",
       "      <td>1.093750</td>\n",
       "      <td>0.753906</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>35.093750</td>\n",
       "      <td>419.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.720000</td>\n",
       "      <td>177.800000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.376250</td>\n",
       "      <td>0.347203</td>\n",
       "      <td>0.066589</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.875000</td>\n",
       "      <td>18.932042</td>\n",
       "      <td>38.663879</td>\n",
       "      <td>16.030273</td>\n",
       "      <td>58.273438</td>\n",
       "      <td>30.943359</td>\n",
       "      <td>54.249573</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>32.078125</td>\n",
       "      <td>11.608887</td>\n",
       "      <td>29.109661</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.717140</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.147461</td>\n",
       "      <td>1.687500</td>\n",
       "      <td>25.687500</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.974609</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.687500</td>\n",
       "      <td>1.692322</td>\n",
       "      <td>2.620224</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.403694</td>\n",
       "      <td>1.15625</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>38.280029</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>438.458191</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.720000</td>\n",
       "      <td>177.800000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.427500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.148468</td>\n",
       "      <td>0.098389</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>29.687500</td>\n",
       "      <td>64.312305</td>\n",
       "      <td>27.343750</td>\n",
       "      <td>89.750000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>85.531250</td>\n",
       "      <td>44.411522</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.054688</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>51.187500</td>\n",
       "      <td>18.335938</td>\n",
       "      <td>49.612305</td>\n",
       "      <td>16.280411</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>5.076477</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>4.984375</td>\n",
       "      <td>3.937500</td>\n",
       "      <td>48.687500</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>47.115723</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>5.312500</td>\n",
       "      <td>3.437500</td>\n",
       "      <td>5.062500</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>99.211914</td>\n",
       "      <td>94.125000</td>\n",
       "      <td>638.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.340000</td>\n",
       "      <td>182.880000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>0.047168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.421768</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.191738</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>66.984375</td>\n",
       "      <td>27.846680</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>48.544922</td>\n",
       "      <td>88.503906</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>2.015625</td>\n",
       "      <td>0.664062</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>19.015137</td>\n",
       "      <td>51.483437</td>\n",
       "      <td>16.943726</td>\n",
       "      <td>7.917969</td>\n",
       "      <td>5.430634</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>5.062500</td>\n",
       "      <td>4.780975</td>\n",
       "      <td>3.937378</td>\n",
       "      <td>4.899414</td>\n",
       "      <td>3.843750</td>\n",
       "      <td>51.025391</td>\n",
       "      <td>18.400864</td>\n",
       "      <td>50.515625</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5.375000</td>\n",
       "      <td>3.687500</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.656250</td>\n",
       "      <td>104.865234</td>\n",
       "      <td>89.500000</td>\n",
       "      <td>655.844101</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.340000</td>\n",
       "      <td>182.880000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.527500</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.422812</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.12500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>104.750000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>99.875000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>132.470703</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>125.726562</td>\n",
       "      <td>65.187500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>28.815918</td>\n",
       "      <td>77.328781</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>9.296875</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8.781250</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>7.281631</td>\n",
       "      <td>9.125000</td>\n",
       "      <td>7.356445</td>\n",
       "      <td>84.718750</td>\n",
       "      <td>31.574707</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>29.750000</td>\n",
       "      <td>11.078125</td>\n",
       "      <td>7.625000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.003768</td>\n",
       "      <td>7.625000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>200.192383</td>\n",
       "      <td>192.375000</td>\n",
       "      <td>846.406250</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.420000</td>\n",
       "      <td>190.500000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.252541</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.402559</td>\n",
       "      <td>0.726685</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>133.737061</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>128.477032</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>4.271484</td>\n",
       "      <td>1.734375</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>83.004150</td>\n",
       "      <td>29.375000</td>\n",
       "      <td>81.728394</td>\n",
       "      <td>27.148560</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>9.376709</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>8.982079</td>\n",
       "      <td>9.997070</td>\n",
       "      <td>7.947638</td>\n",
       "      <td>9.161275</td>\n",
       "      <td>7.333496</td>\n",
       "      <td>87.806396</td>\n",
       "      <td>32.750000</td>\n",
       "      <td>86.504883</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>7.048340</td>\n",
       "      <td>10.033691</td>\n",
       "      <td>6.675117</td>\n",
       "      <td>11.593750</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.25000</td>\n",
       "      <td>6.224609</td>\n",
       "      <td>211.136719</td>\n",
       "      <td>176.494141</td>\n",
       "      <td>851.088501</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.420000</td>\n",
       "      <td>190.500000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>403.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>524.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.968750</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>62.830322</td>\n",
       "      <td>51.635986</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>422.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>1020.195312</td>\n",
       "      <td>835.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.820000</td>\n",
       "      <td>213.360000</td>\n",
       "      <td>770.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>419.321196</td>\n",
       "      <td>212.142956</td>\n",
       "      <td>454.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>440.342255</td>\n",
       "      <td>248.123047</td>\n",
       "      <td>461.000000</td>\n",
       "      <td>202.859375</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>368.184116</td>\n",
       "      <td>173.163765</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>43.349854</td>\n",
       "      <td>71.570312</td>\n",
       "      <td>62.351562</td>\n",
       "      <td>77.387008</td>\n",
       "      <td>55.426939</td>\n",
       "      <td>396.108599</td>\n",
       "      <td>192.648659</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>104.00000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>996.749573</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.820000</td>\n",
       "      <td>213.360000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         R_fighter        B_fighter    Referee        date  \\\n",
       "count         6012             6012       5980        6012   \n",
       "unique        1514             1987        204         550   \n",
       "top     Jim Miller  Jeremy Stephens  Herb Dean  2016-11-19   \n",
       "freq            27               21        858          25   \n",
       "mean           NaN              NaN        NaN         NaN   \n",
       "std            NaN              NaN        NaN         NaN   \n",
       "min            NaN              NaN        NaN         NaN   \n",
       "25%            NaN              NaN        NaN         NaN   \n",
       "50%            NaN              NaN        NaN         NaN   \n",
       "75%            NaN              NaN        NaN         NaN   \n",
       "max            NaN              NaN        NaN         NaN   \n",
       "\n",
       "                      location Winner title_bout weight_class     B_avg_KD  \\\n",
       "count                     6012   6012       6012         6012  4585.000000   \n",
       "unique                     166      3          2           14          NaN   \n",
       "top     Las Vegas, Nevada, USA    Red      False  Lightweight          NaN   \n",
       "freq                      1565   3979       5647         1091          NaN   \n",
       "mean                       NaN    NaN        NaN          NaN     0.247476   \n",
       "std                        NaN    NaN        NaN          NaN     0.378509   \n",
       "min                        NaN    NaN        NaN          NaN     0.000000   \n",
       "25%                        NaN    NaN        NaN          NaN     0.000000   \n",
       "50%                        NaN    NaN        NaN          NaN     0.015625   \n",
       "75%                        NaN    NaN        NaN          NaN     0.500000   \n",
       "max                        NaN    NaN        NaN          NaN     5.000000   \n",
       "\n",
       "        B_avg_opp_KD  B_avg_SIG_STR_pct  B_avg_opp_SIG_STR_pct  B_avg_TD_pct  \\\n",
       "count    4585.000000        4585.000000            4585.000000   4585.000000   \n",
       "unique           NaN                NaN                    NaN           NaN   \n",
       "top              NaN                NaN                    NaN           NaN   \n",
       "freq             NaN                NaN                    NaN           NaN   \n",
       "mean        0.176818           0.453310               0.434290      0.292650   \n",
       "std         0.324633           0.130458               0.132618      0.273628   \n",
       "min         0.000000           0.000000               0.000000      0.000000   \n",
       "25%         0.000000           0.376489               0.351045      0.031250   \n",
       "50%         0.000000           0.450000               0.427500      0.250000   \n",
       "75%         0.250000           0.527500               0.510000      0.500000   \n",
       "max         3.000000           1.000000               1.000000      1.000000   \n",
       "\n",
       "        B_avg_opp_TD_pct  B_avg_SUB_ATT  B_avg_opp_SUB_ATT   B_avg_REV  \\\n",
       "count        4585.000000    4585.000000        4585.000000  4585.00000   \n",
       "unique               NaN            NaN                NaN         NaN   \n",
       "top                  NaN            NaN                NaN         NaN   \n",
       "freq                 NaN            NaN                NaN         NaN   \n",
       "mean            0.268742       0.478884           0.409276     0.15673   \n",
       "std             0.267178       0.724229           0.653826     0.34199   \n",
       "min             0.000000       0.000000           0.000000     0.00000   \n",
       "25%             0.033203       0.000000           0.000000     0.00000   \n",
       "50%             0.200000       0.148468           0.098389     0.00000   \n",
       "75%             0.422812       0.750000           0.531250     0.12500   \n",
       "max             1.000000       8.000000           7.000000     4.00000   \n",
       "\n",
       "        B_avg_opp_REV  B_avg_SIG_STR_att  B_avg_SIG_STR_landed  \\\n",
       "count     4585.000000        4585.000000           4585.000000   \n",
       "unique            NaN                NaN                   NaN   \n",
       "top               NaN                NaN                   NaN   \n",
       "freq              NaN                NaN                   NaN   \n",
       "mean         0.141306          76.288358             33.337168   \n",
       "std          0.322623          51.175465             22.030474   \n",
       "min          0.000000           0.000000              0.000000   \n",
       "25%          0.000000          38.261230             17.000000   \n",
       "50%          0.000000          66.500000             29.687500   \n",
       "75%          0.125000         104.750000             45.500000   \n",
       "max          3.000000         456.000000            186.000000   \n",
       "\n",
       "        B_avg_opp_SIG_STR_att  B_avg_opp_SIG_STR_landed  B_avg_TOTAL_STR_att  \\\n",
       "count             4585.000000               4585.000000          4585.000000   \n",
       "unique                    NaN                       NaN                  NaN   \n",
       "top                       NaN                       NaN                  NaN   \n",
       "freq                      NaN                       NaN                  NaN   \n",
       "mean                73.092895                 31.053464            97.635001   \n",
       "std                 49.911897                 21.371161            57.524779   \n",
       "min                  0.000000                  0.000000             0.000000   \n",
       "25%                 36.405273                 15.562500            55.484375   \n",
       "50%                 64.312305                 27.343750            89.750000   \n",
       "75%                 99.875000                 42.000000           132.470703   \n",
       "max                403.000000                202.000000           524.000000   \n",
       "\n",
       "        B_avg_TOTAL_STR_landed  B_avg_opp_TOTAL_STR_att  \\\n",
       "count              4585.000000              4585.000000   \n",
       "unique                     NaN                      NaN   \n",
       "top                        NaN                      NaN   \n",
       "freq                       NaN                      NaN   \n",
       "mean                 52.281925                92.716135   \n",
       "std                  31.771066                54.871771   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                  28.937500                52.985352   \n",
       "50%                  48.000000                85.531250   \n",
       "75%                  70.000000               125.726562   \n",
       "max                 242.000000               433.000000   \n",
       "\n",
       "        B_avg_opp_TOTAL_STR_landed  B_avg_TD_att  B_avg_TD_landed  \\\n",
       "count                  4585.000000   4585.000000      4585.000000   \n",
       "unique                         NaN           NaN              NaN   \n",
       "top                            NaN           NaN              NaN   \n",
       "freq                           NaN           NaN              NaN   \n",
       "mean                     48.504853      2.726783         1.109138   \n",
       "std                      29.863232      2.836065         1.328047   \n",
       "min                       0.000000      0.000000         0.000000   \n",
       "25%                      26.875000      0.625000         0.062500   \n",
       "50%                      44.411522      2.000000         0.750000   \n",
       "75%                      65.187500      4.000000         1.562500   \n",
       "max                     232.000000     19.000000        10.968750   \n",
       "\n",
       "        B_avg_opp_TD_att  B_avg_opp_TD_landed  B_avg_HEAD_att  \\\n",
       "count        4585.000000          4585.000000     4585.000000   \n",
       "unique               NaN                  NaN             NaN   \n",
       "top                  NaN                  NaN             NaN   \n",
       "freq                 NaN                  NaN             NaN   \n",
       "mean            2.861216             1.073168       59.984433   \n",
       "std             2.738086             1.261016       42.085146   \n",
       "min             0.000000             0.000000        0.000000   \n",
       "25%             1.000000             0.125000       29.296875   \n",
       "50%             2.054688             0.750000       51.187500   \n",
       "75%             4.000000             1.500000       81.500000   \n",
       "max            20.000000            11.500000      397.000000   \n",
       "\n",
       "        B_avg_HEAD_landed  B_avg_opp_HEAD_att  B_avg_opp_HEAD_landed  \\\n",
       "count         4585.000000         4585.000000            4585.000000   \n",
       "unique                NaN                 NaN                    NaN   \n",
       "top                   NaN                 NaN                    NaN   \n",
       "freq                  NaN                 NaN                    NaN   \n",
       "mean            21.387261           57.055890              19.403349   \n",
       "std             15.314792           40.686905              14.609626   \n",
       "min              0.000000            0.000000               0.000000   \n",
       "25%             10.500000           27.875000               9.000000   \n",
       "50%             18.335938           49.612305              16.280411   \n",
       "75%             28.815918           77.328781              26.500000   \n",
       "max            138.000000          373.000000             126.000000   \n",
       "\n",
       "        B_avg_BODY_att  B_avg_BODY_landed  B_avg_opp_BODY_att  \\\n",
       "count      4585.000000        4585.000000         4585.000000   \n",
       "unique             NaN                NaN                 NaN   \n",
       "top                NaN                NaN                 NaN   \n",
       "freq               NaN                NaN                 NaN   \n",
       "mean          9.684013           6.681643            9.370116   \n",
       "std           8.377459           5.920334            7.906397   \n",
       "min           0.000000           0.000000            0.000000   \n",
       "25%           3.585938           2.375000            3.500000   \n",
       "50%           7.500000           5.076477            7.500000   \n",
       "75%          13.500000           9.296875           13.000000   \n",
       "max          71.000000          64.000000           71.000000   \n",
       "\n",
       "        B_avg_opp_BODY_landed  B_avg_LEG_att  B_avg_LEG_landed  \\\n",
       "count             4585.000000    4585.000000       4585.000000   \n",
       "unique                    NaN            NaN               NaN   \n",
       "top                       NaN            NaN               NaN   \n",
       "freq                      NaN            NaN               NaN   \n",
       "mean                 6.352129       6.619912          5.268264   \n",
       "std                  5.672195       6.852332          5.496281   \n",
       "min                  0.000000       0.000000          0.000000   \n",
       "25%                  2.250000       1.875870          1.437500   \n",
       "50%                  5.000000       4.500000          3.625000   \n",
       "75%                  8.781250       9.250000          7.281631   \n",
       "max                 64.000000      62.830322         51.635986   \n",
       "\n",
       "        B_avg_opp_LEG_att  B_avg_opp_LEG_landed  B_avg_DISTANCE_att  \\\n",
       "count         4585.000000           4585.000000         4585.000000   \n",
       "unique                NaN                   NaN                 NaN   \n",
       "top                   NaN                   NaN                 NaN   \n",
       "freq                  NaN                   NaN                 NaN   \n",
       "mean             6.666888              5.297986           60.059833   \n",
       "std              6.582663              5.342577           48.453182   \n",
       "min              0.000000              0.000000            0.000000   \n",
       "25%              2.000000              1.625000           23.000000   \n",
       "50%              4.984375              3.937500           48.687500   \n",
       "75%              9.125000              7.356445           84.718750   \n",
       "max             69.000000             58.000000          422.000000   \n",
       "\n",
       "        B_avg_DISTANCE_landed  B_avg_opp_DISTANCE_att  \\\n",
       "count             4585.000000             4585.000000   \n",
       "unique                    NaN                     NaN   \n",
       "top                       NaN                     NaN   \n",
       "freq                      NaN                     NaN   \n",
       "mean                22.313082               58.574160   \n",
       "std                 19.216578               47.497037   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  8.000000               22.500000   \n",
       "50%                 17.000000               47.115723   \n",
       "75%                 31.574707               83.000000   \n",
       "max                161.000000              391.000000   \n",
       "\n",
       "        B_avg_opp_DISTANCE_landed  B_avg_CLINCH_att  B_avg_CLINCH_landed  \\\n",
       "count                 4585.000000       4585.000000          4585.000000   \n",
       "unique                        NaN               NaN                  NaN   \n",
       "top                           NaN               NaN                  NaN   \n",
       "freq                          NaN               NaN                  NaN   \n",
       "mean                    21.288060          8.069538             5.514441   \n",
       "std                     18.552735          8.069062             5.826976   \n",
       "min                      0.000000          0.000000             0.000000   \n",
       "25%                      7.750000          2.500000             1.515625   \n",
       "50%                     16.500000          5.750000             3.750000   \n",
       "75%                     29.750000         11.078125             7.625000   \n",
       "max                    159.000000         87.000000            68.000000   \n",
       "\n",
       "        B_avg_opp_CLINCH_att  B_avg_opp_CLINCH_landed  B_avg_GROUND_att  \\\n",
       "count            4585.000000              4585.000000       4585.000000   \n",
       "unique                   NaN                      NaN               NaN   \n",
       "top                      NaN                      NaN               NaN   \n",
       "freq                     NaN                      NaN               NaN   \n",
       "mean                7.476618                 5.045857          8.158988   \n",
       "std                 7.595332                 5.530920          9.347720   \n",
       "min                 0.000000                 0.000000          0.000000   \n",
       "25%                 2.456055                 1.445312          1.959669   \n",
       "50%                 5.312500                 3.437500          5.062500   \n",
       "75%                10.250000                 7.000000         11.003768   \n",
       "max               105.000000                84.000000         88.000000   \n",
       "\n",
       "        B_avg_GROUND_landed  B_avg_opp_GROUND_att  B_avg_opp_GROUND_landed  \\\n",
       "count           4585.000000           4585.000000              4585.000000   \n",
       "unique                  NaN                   NaN                      NaN   \n",
       "top                     NaN                   NaN                      NaN   \n",
       "freq                    NaN                   NaN                      NaN   \n",
       "mean               5.509645              7.042118                 4.719547   \n",
       "std                6.277867              9.462104                 6.447007   \n",
       "min                0.000000              0.000000                 0.000000   \n",
       "25%                1.140625              1.093750                 0.753906   \n",
       "50%                3.500000              3.875000                 2.500000   \n",
       "75%                7.625000              9.000000                 6.000000   \n",
       "max               48.000000            141.000000                84.000000   \n",
       "\n",
       "        B_avg_CTRL_time(seconds)  B_avg_opp_CTRL_time(seconds)  \\\n",
       "count                4585.000000                   4585.000000   \n",
       "unique                       NaN                           NaN   \n",
       "top                          NaN                           NaN   \n",
       "freq                         NaN                           NaN   \n",
       "mean                  137.277722                    132.591148   \n",
       "std                   133.688630                    129.213709   \n",
       "min                     0.000000                      0.000000   \n",
       "25%                    36.000000                     35.093750   \n",
       "50%                    99.211914                     94.125000   \n",
       "75%                   200.192383                    192.375000   \n",
       "max                  1020.195312                    835.000000   \n",
       "\n",
       "        B_total_time_fought(seconds)  B_total_rounds_fought  \\\n",
       "count                    4585.000000            6012.000000   \n",
       "unique                           NaN                    NaN   \n",
       "top                              NaN                    NaN   \n",
       "freq                             NaN                    NaN   \n",
       "mean                      611.409924               9.441783   \n",
       "std                       260.707940              11.875922   \n",
       "min                         7.000000               0.000000   \n",
       "25%                       419.000000               1.000000   \n",
       "50%                       638.500000               5.000000   \n",
       "75%                       846.406250              13.000000   \n",
       "max                      1500.000000              94.000000   \n",
       "\n",
       "        B_total_title_bouts  B_current_win_streak  B_current_lose_streak  \\\n",
       "count           6012.000000           6012.000000            6012.000000   \n",
       "unique                  NaN                   NaN                    NaN   \n",
       "top                     NaN                   NaN                    NaN   \n",
       "freq                    NaN                   NaN                    NaN   \n",
       "mean               0.277279              0.947272               0.397206   \n",
       "std                1.125673              1.489959               0.664907   \n",
       "min                0.000000              0.000000               0.000000   \n",
       "25%                0.000000              0.000000               0.000000   \n",
       "50%                0.000000              0.000000               0.000000   \n",
       "75%                0.000000              1.000000               1.000000   \n",
       "max               16.000000             16.000000               5.000000   \n",
       "\n",
       "        B_longest_win_streak       B_wins     B_losses  B_draw  \\\n",
       "count            6012.000000  6012.000000  6012.000000  6012.0   \n",
       "unique                   NaN          NaN          NaN     NaN   \n",
       "top                      NaN          NaN          NaN     NaN   \n",
       "freq                     NaN          NaN          NaN     NaN   \n",
       "mean                1.630905     2.584997     1.540419     0.0   \n",
       "std                 1.808590     3.317241     1.940694     0.0   \n",
       "min                 0.000000     0.000000     0.000000     0.0   \n",
       "25%                 0.000000     0.000000     0.000000     0.0   \n",
       "50%                 1.000000     1.000000     1.000000     0.0   \n",
       "75%                 3.000000     4.000000     2.000000     0.0   \n",
       "max                16.000000    23.000000    16.000000     0.0   \n",
       "\n",
       "        B_win_by_Decision_Majority  B_win_by_Decision_Split  \\\n",
       "count                  6012.000000              6012.000000   \n",
       "unique                         NaN                      NaN   \n",
       "top                            NaN                      NaN   \n",
       "freq                           NaN                      NaN   \n",
       "mean                      0.016134                 0.218397   \n",
       "std                       0.127316                 0.525232   \n",
       "min                       0.000000                 0.000000   \n",
       "25%                       0.000000                 0.000000   \n",
       "50%                       0.000000                 0.000000   \n",
       "75%                       0.000000                 0.000000   \n",
       "max                       2.000000                 5.000000   \n",
       "\n",
       "        B_win_by_Decision_Unanimous  B_win_by_KO/TKO  B_win_by_Submission  \\\n",
       "count                   6012.000000      6012.000000          6012.000000   \n",
       "unique                          NaN              NaN                  NaN   \n",
       "top                             NaN              NaN                  NaN   \n",
       "freq                            NaN              NaN                  NaN   \n",
       "mean                       0.838157         0.895709             0.569528   \n",
       "std                        1.370454         1.580829             1.161257   \n",
       "min                        0.000000         0.000000             0.000000   \n",
       "25%                        0.000000         0.000000             0.000000   \n",
       "50%                        0.000000         0.000000             0.000000   \n",
       "75%                        1.000000         1.000000             1.000000   \n",
       "max                       10.000000        11.000000            14.000000   \n",
       "\n",
       "        B_win_by_TKO_Doctor_Stoppage  B_Stance  B_Height_cms  B_Reach_cms  \\\n",
       "count                    6012.000000      5946   6002.000000  5121.000000   \n",
       "unique                           NaN         5           NaN          NaN   \n",
       "top                              NaN  Orthodox           NaN          NaN   \n",
       "freq                             NaN      4530           NaN          NaN   \n",
       "mean                        0.042083       NaN    178.951083   183.228686   \n",
       "std                         0.211290       NaN      8.709866    10.344758   \n",
       "min                         0.000000       NaN    152.400000   147.320000   \n",
       "25%                         0.000000       NaN    172.720000   177.800000   \n",
       "50%                         0.000000       NaN    180.340000   182.880000   \n",
       "75%                         0.000000       NaN    185.420000   190.500000   \n",
       "max                         2.000000       NaN    210.820000   213.360000   \n",
       "\n",
       "        B_Weight_lbs     R_avg_KD  R_avg_opp_KD  R_avg_SIG_STR_pct  \\\n",
       "count    6004.000000  5300.000000   5300.000000        5300.000000   \n",
       "unique           NaN          NaN           NaN                NaN   \n",
       "top              NaN          NaN           NaN                NaN   \n",
       "freq             NaN          NaN           NaN                NaN   \n",
       "mean      170.590773     0.243484      0.188045           0.457171   \n",
       "std        36.879171     0.364901      0.320757           0.128835   \n",
       "min       115.000000     0.000000      0.000000           0.000000   \n",
       "25%       145.000000     0.000000      0.000000           0.376250   \n",
       "50%       170.000000     0.047168      0.000000           0.450000   \n",
       "75%       185.000000     0.437500      0.252541           0.530000   \n",
       "max       770.000000     4.000000      3.000000           1.000000   \n",
       "\n",
       "        R_avg_opp_SIG_STR_pct  R_avg_TD_pct  R_avg_opp_TD_pct  R_avg_SUB_ATT  \\\n",
       "count             5300.000000   5300.000000       5300.000000    5300.000000   \n",
       "unique                    NaN           NaN               NaN            NaN   \n",
       "top                       NaN           NaN               NaN            NaN   \n",
       "freq                      NaN           NaN               NaN            NaN   \n",
       "mean                 0.425338      0.300901          0.254409       0.463514   \n",
       "std                  0.124878      0.261993          0.247180       0.669272   \n",
       "min                  0.000000      0.000000          0.000000       0.000000   \n",
       "25%                  0.347203      0.066589          0.037109       0.000000   \n",
       "50%                  0.421768      0.250000          0.191738       0.187500   \n",
       "75%                  0.500000      0.500000          0.402559       0.726685   \n",
       "max                  1.000000      1.000000          1.000000       9.000000   \n",
       "\n",
       "        R_avg_opp_SUB_ATT    R_avg_REV  R_avg_opp_REV  R_avg_SIG_STR_att  \\\n",
       "count         5300.000000  5300.000000    5300.000000        5300.000000   \n",
       "unique                NaN          NaN            NaN                NaN   \n",
       "top                   NaN          NaN            NaN                NaN   \n",
       "freq                  NaN          NaN            NaN                NaN   \n",
       "mean             0.393863     0.141426       0.143164          79.346284   \n",
       "std              0.623378     0.306744       0.318342          51.753004   \n",
       "min              0.000000     0.000000       0.000000           0.000000   \n",
       "25%              0.000000     0.000000       0.000000          41.875000   \n",
       "50%              0.125000     0.000000       0.000000          69.500000   \n",
       "75%              0.515625     0.125000       0.125000         106.000000   \n",
       "max              8.000000     3.000000       4.000000         419.321196   \n",
       "\n",
       "        R_avg_SIG_STR_landed  R_avg_opp_SIG_STR_att  R_avg_opp_SIG_STR_landed  \\\n",
       "count            5300.000000            5300.000000               5300.000000   \n",
       "unique                   NaN                    NaN                       NaN   \n",
       "top                      NaN                    NaN                       NaN   \n",
       "freq                     NaN                    NaN                       NaN   \n",
       "mean               34.808436              76.069556                 31.890675   \n",
       "std                22.568121              50.799409                 21.709018   \n",
       "min                 0.000000               0.000000                  0.000000   \n",
       "25%                18.932042              38.663879                 16.030273   \n",
       "50%                30.500000              66.984375                 27.846680   \n",
       "75%                46.000000             103.500000                 43.000000   \n",
       "max               212.142956             454.000000                175.000000   \n",
       "\n",
       "        R_avg_TOTAL_STR_att  R_avg_TOTAL_STR_landed  R_avg_opp_TOTAL_STR_att  \\\n",
       "count           5300.000000             5300.000000              5300.000000   \n",
       "unique                  NaN                     NaN                      NaN   \n",
       "top                     NaN                     NaN                      NaN   \n",
       "freq                    NaN                     NaN                      NaN   \n",
       "mean             100.586308               53.693005                95.553843   \n",
       "std               57.657913               32.046382                55.811293   \n",
       "min                0.000000                0.000000                 0.000000   \n",
       "25%               58.273438               30.943359                54.249573   \n",
       "50%               94.000000               48.544922                88.503906   \n",
       "75%              133.737061               70.000000               128.477032   \n",
       "max              440.342255              248.123047               461.000000   \n",
       "\n",
       "        R_avg_opp_TOTAL_STR_landed  R_avg_TD_att  R_avg_TD_landed  \\\n",
       "count                  5300.000000   5300.000000      5300.000000   \n",
       "unique                         NaN           NaN              NaN   \n",
       "top                            NaN           NaN              NaN   \n",
       "freq                           NaN           NaN              NaN   \n",
       "mean                     49.258481      2.943604         1.184075   \n",
       "std                      30.033210      3.006234         1.322230   \n",
       "min                       0.000000      0.000000         0.000000   \n",
       "25%                      27.500000      0.750000         0.187500   \n",
       "50%                      44.750000      2.000000         0.812500   \n",
       "75%                      65.500000      4.271484         1.734375   \n",
       "max                     202.859375     30.000000        11.000000   \n",
       "\n",
       "        R_avg_opp_TD_att  R_avg_opp_TD_landed  R_avg_HEAD_att  \\\n",
       "count        5300.000000          5300.000000     5300.000000   \n",
       "unique               NaN                  NaN             NaN   \n",
       "top                  NaN                  NaN             NaN   \n",
       "freq                 NaN                  NaN             NaN   \n",
       "mean            2.822920             1.018747       62.281073   \n",
       "std             2.641710             1.178708       42.130661   \n",
       "min             0.000000             0.000000        0.000000   \n",
       "25%             1.000000             0.132812       32.078125   \n",
       "50%             2.015625             0.664062       54.000000   \n",
       "75%             4.000000             1.500000       83.004150   \n",
       "max            22.000000            13.000000      368.184116   \n",
       "\n",
       "        R_avg_HEAD_landed  R_avg_opp_HEAD_att  R_avg_opp_HEAD_landed  \\\n",
       "count         5300.000000         5300.000000            5300.000000   \n",
       "unique                NaN                 NaN                    NaN   \n",
       "top                   NaN                 NaN                    NaN   \n",
       "freq                  NaN                 NaN                    NaN   \n",
       "mean            22.344055           59.780497              20.158009   \n",
       "std             15.582960           41.767256              15.313832   \n",
       "min              0.000000            0.000000               0.000000   \n",
       "25%             11.608887           29.109661               9.500000   \n",
       "50%             19.015137           51.483437              16.943726   \n",
       "75%             29.375000           81.728394              27.148560   \n",
       "max            173.163765          400.000000             139.000000   \n",
       "\n",
       "        R_avg_BODY_att  R_avg_BODY_landed  R_avg_opp_BODY_att  \\\n",
       "count      5300.000000        5300.000000         5300.000000   \n",
       "unique             NaN                NaN                 NaN   \n",
       "top                NaN                NaN                 NaN   \n",
       "freq               NaN                NaN                 NaN   \n",
       "mean         10.019317           6.871403            9.551659   \n",
       "std           8.549729           5.988029            7.764812   \n",
       "min           0.000000           0.000000            0.000000   \n",
       "25%           4.000000           2.717140            3.750000   \n",
       "50%           7.917969           5.430634            7.750000   \n",
       "75%          13.750000           9.376709           13.500000   \n",
       "max          71.000000          54.000000           75.000000   \n",
       "\n",
       "        R_avg_opp_BODY_landed  R_avg_LEG_att  R_avg_LEG_landed  \\\n",
       "count             5300.000000    5300.000000       5300.000000   \n",
       "unique                    NaN            NaN               NaN   \n",
       "top                       NaN            NaN               NaN   \n",
       "freq                      NaN            NaN               NaN   \n",
       "mean                 6.442013       7.045894          5.592977   \n",
       "std                  5.381532       7.309739          5.772675   \n",
       "min                  0.000000       0.000000          0.000000   \n",
       "25%                  2.500000       2.000000          1.500000   \n",
       "50%                  5.062500       4.780975          3.937378   \n",
       "75%                  8.982079       9.997070          7.947638   \n",
       "max                 43.349854      71.570312         62.351562   \n",
       "\n",
       "        R_avg_opp_LEG_att  R_avg_opp_LEG_landed  R_avg_DISTANCE_att  \\\n",
       "count         5300.000000           5300.000000         5300.000000   \n",
       "unique                NaN                   NaN                 NaN   \n",
       "top                   NaN                   NaN                 NaN   \n",
       "freq                  NaN                   NaN                 NaN   \n",
       "mean             6.737400              5.290654           62.677000   \n",
       "std              6.513925              5.142570           49.645270   \n",
       "min              0.000000              0.000000            0.000000   \n",
       "25%              2.147461              1.687500           25.687500   \n",
       "50%              4.899414              3.843750           51.025391   \n",
       "75%              9.161275              7.333496           87.806396   \n",
       "max             77.387008             55.426939          396.108599   \n",
       "\n",
       "        R_avg_DISTANCE_landed  R_avg_opp_DISTANCE_att  \\\n",
       "count             5300.000000             5300.000000   \n",
       "unique                    NaN                     NaN   \n",
       "top                       NaN                     NaN   \n",
       "freq                      NaN                     NaN   \n",
       "mean                23.457244               61.612832   \n",
       "std                 19.964855               48.243271   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  9.000000               24.974609   \n",
       "50%                 18.400864               50.515625   \n",
       "75%                 32.750000               86.504883   \n",
       "max                192.648659              440.000000   \n",
       "\n",
       "        R_avg_opp_DISTANCE_landed  R_avg_CLINCH_att  R_avg_CLINCH_landed  \\\n",
       "count                 5300.000000       5300.000000          5300.000000   \n",
       "unique                        NaN               NaN                  NaN   \n",
       "top                           NaN               NaN                  NaN   \n",
       "freq                          NaN               NaN                  NaN   \n",
       "mean                    22.189858          7.923476             5.409125   \n",
       "std                     18.992482          7.852318             5.705212   \n",
       "min                      0.000000          0.000000             0.000000   \n",
       "25%                      8.000000          2.687500             1.692322   \n",
       "50%                     17.500000          5.750000             3.750000   \n",
       "75%                     31.500000         10.750000             7.048340   \n",
       "max                    169.000000         82.000000            61.000000   \n",
       "\n",
       "        R_avg_opp_CLINCH_att  R_avg_opp_CLINCH_landed  R_avg_GROUND_att  \\\n",
       "count            5300.000000              5300.000000       5300.000000   \n",
       "unique                   NaN                      NaN               NaN   \n",
       "top                      NaN                      NaN               NaN   \n",
       "freq                     NaN                      NaN               NaN   \n",
       "mean                7.545115                 5.060535          8.745808   \n",
       "std                 7.460350                 5.285806         10.300208   \n",
       "min                 0.000000                 0.000000          0.000000   \n",
       "25%                 2.620224                 1.562500          2.000000   \n",
       "50%                 5.500000                 3.500000          5.375000   \n",
       "75%                10.033691                 6.675117         11.593750   \n",
       "max                82.000000                51.000000        141.000000   \n",
       "\n",
       "        R_avg_GROUND_landed  R_avg_opp_GROUND_att  R_avg_opp_GROUND_landed  \\\n",
       "count           5300.000000            5300.00000              5300.000000   \n",
       "unique                  NaN                   NaN                      NaN   \n",
       "top                     NaN                   NaN                      NaN   \n",
       "freq                    NaN                   NaN                      NaN   \n",
       "mean               5.942066               6.91161                 4.640281   \n",
       "std                7.000114               8.92240                 5.997152   \n",
       "min                0.000000               0.00000                 0.000000   \n",
       "25%                1.403694               1.15625                 0.750000   \n",
       "50%                3.687500               4.00000                 2.656250   \n",
       "75%                8.000000               9.25000                 6.224609   \n",
       "max               68.000000             104.00000                68.000000   \n",
       "\n",
       "        R_avg_CTRL_time(seconds)  R_avg_opp_CTRL_time(seconds)  \\\n",
       "count                5300.000000                   5300.000000   \n",
       "unique                       NaN                           NaN   \n",
       "top                          NaN                           NaN   \n",
       "freq                         NaN                           NaN   \n",
       "mean                  144.215233                    125.120348   \n",
       "std                   136.696848                    121.675716   \n",
       "min                     0.000000                      0.000000   \n",
       "25%                    38.280029                     34.000000   \n",
       "50%                   104.865234                     89.500000   \n",
       "75%                   211.136719                    176.494141   \n",
       "max                   996.749573                    812.000000   \n",
       "\n",
       "        R_total_time_fought(seconds)  R_total_rounds_fought  \\\n",
       "count                    5300.000000            6012.000000   \n",
       "unique                           NaN                    NaN   \n",
       "top                              NaN                    NaN   \n",
       "freq                             NaN                    NaN   \n",
       "mean                      633.322933              13.669993   \n",
       "std                       267.906601              14.197512   \n",
       "min                         7.000000               0.000000   \n",
       "25%                       438.458191               3.000000   \n",
       "50%                       655.844101               9.000000   \n",
       "75%                       851.088501              20.000000   \n",
       "max                      1500.000000              88.000000   \n",
       "\n",
       "        R_total_title_bouts  R_current_win_streak  R_current_lose_streak  \\\n",
       "count           6012.000000           6012.000000            6012.000000   \n",
       "unique                  NaN                   NaN                    NaN   \n",
       "top                     NaN                   NaN                    NaN   \n",
       "freq                    NaN                   NaN                    NaN   \n",
       "mean               0.582169              1.314704               0.389055   \n",
       "std                1.551233              1.729916               0.649405   \n",
       "min                0.000000              0.000000               0.000000   \n",
       "25%                0.000000              0.000000               0.000000   \n",
       "50%                0.000000              1.000000               0.000000   \n",
       "75%                1.000000              2.000000               1.000000   \n",
       "max               16.000000             16.000000               4.000000   \n",
       "\n",
       "        R_longest_win_streak       R_wins     R_losses  R_draw  \\\n",
       "count            6012.000000  6012.000000  6012.000000  6012.0   \n",
       "unique                   NaN          NaN          NaN     NaN   \n",
       "top                      NaN          NaN          NaN     NaN   \n",
       "freq                     NaN          NaN          NaN     NaN   \n",
       "mean                2.313207     3.756986     2.071357     0.0   \n",
       "std                 2.094238     3.861044     2.265830     0.0   \n",
       "min                 0.000000     0.000000     0.000000     0.0   \n",
       "25%                 1.000000     1.000000     0.000000     0.0   \n",
       "50%                 2.000000     3.000000     1.000000     0.0   \n",
       "75%                 3.000000     6.000000     3.000000     0.0   \n",
       "max                16.000000    23.000000    17.000000     0.0   \n",
       "\n",
       "        R_win_by_Decision_Majority  R_win_by_Decision_Split  \\\n",
       "count                  6012.000000              6012.000000   \n",
       "unique                         NaN                      NaN   \n",
       "top                            NaN                      NaN   \n",
       "freq                           NaN                      NaN   \n",
       "mean                      0.027944                 0.306886   \n",
       "std                       0.166833                 0.619485   \n",
       "min                       0.000000                 0.000000   \n",
       "25%                       0.000000                 0.000000   \n",
       "50%                       0.000000                 0.000000   \n",
       "75%                       0.000000                 0.000000   \n",
       "max                       2.000000                 5.000000   \n",
       "\n",
       "        R_win_by_Decision_Unanimous  R_win_by_KO/TKO  R_win_by_Submission  \\\n",
       "count                   6012.000000      6012.000000          6012.000000   \n",
       "unique                          NaN              NaN                  NaN   \n",
       "top                             NaN              NaN                  NaN   \n",
       "freq                            NaN              NaN                  NaN   \n",
       "mean                       1.251331         1.297572             0.796241   \n",
       "std                        1.681376         1.859955             1.355801   \n",
       "min                        0.000000         0.000000             0.000000   \n",
       "25%                        0.000000         0.000000             0.000000   \n",
       "50%                        1.000000         1.000000             0.000000   \n",
       "75%                        2.000000         2.000000             1.000000   \n",
       "max                       10.000000        11.000000            13.000000   \n",
       "\n",
       "        R_win_by_TKO_Doctor_Stoppage  R_Stance  R_Height_cms  R_Reach_cms  \\\n",
       "count                    6012.000000      5983   6008.000000  5606.000000   \n",
       "unique                           NaN         5           NaN          NaN   \n",
       "top                              NaN  Orthodox           NaN          NaN   \n",
       "freq                             NaN      4538           NaN          NaN   \n",
       "mean                        0.069195       NaN    178.991788   183.562347   \n",
       "std                         0.273980       NaN      8.812985    10.585313   \n",
       "min                         0.000000       NaN    152.400000   152.400000   \n",
       "25%                         0.000000       NaN    172.720000   177.800000   \n",
       "50%                         0.000000       NaN    180.340000   182.880000   \n",
       "75%                         0.000000       NaN    185.420000   190.500000   \n",
       "max                         2.000000       NaN    210.820000   213.360000   \n",
       "\n",
       "        R_Weight_lbs        B_age        R_age  \n",
       "count    6010.000000  5840.000000  5949.000000  \n",
       "unique           NaN          NaN          NaN  \n",
       "top              NaN          NaN          NaN  \n",
       "freq             NaN          NaN          NaN  \n",
       "mean      170.686356    29.293151    29.598252  \n",
       "std        35.724626     4.063297     4.167755  \n",
       "min       115.000000    18.000000    19.000000  \n",
       "25%       145.000000    26.000000    27.000000  \n",
       "50%       170.000000    29.000000    29.000000  \n",
       "75%       185.000000    32.000000    32.000000  \n",
       "max       345.000000    51.000000    47.000000  "
      ]
     },
     "execution_count": 1578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1579,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_fighter</th>\n",
       "      <th>B_fighter</th>\n",
       "      <th>Referee</th>\n",
       "      <th>date</th>\n",
       "      <th>location</th>\n",
       "      <th>Winner</th>\n",
       "      <th>title_bout</th>\n",
       "      <th>weight_class</th>\n",
       "      <th>B_avg_KD</th>\n",
       "      <th>B_avg_opp_KD</th>\n",
       "      <th>B_avg_SIG_STR_pct</th>\n",
       "      <th>B_avg_opp_SIG_STR_pct</th>\n",
       "      <th>B_avg_TD_pct</th>\n",
       "      <th>B_avg_opp_TD_pct</th>\n",
       "      <th>B_avg_SUB_ATT</th>\n",
       "      <th>B_avg_opp_SUB_ATT</th>\n",
       "      <th>B_avg_REV</th>\n",
       "      <th>B_avg_opp_REV</th>\n",
       "      <th>B_avg_SIG_STR_att</th>\n",
       "      <th>B_avg_SIG_STR_landed</th>\n",
       "      <th>B_avg_opp_SIG_STR_att</th>\n",
       "      <th>B_avg_opp_SIG_STR_landed</th>\n",
       "      <th>B_avg_TOTAL_STR_att</th>\n",
       "      <th>B_avg_TOTAL_STR_landed</th>\n",
       "      <th>B_avg_opp_TOTAL_STR_att</th>\n",
       "      <th>B_avg_opp_TOTAL_STR_landed</th>\n",
       "      <th>B_avg_TD_att</th>\n",
       "      <th>B_avg_TD_landed</th>\n",
       "      <th>B_avg_opp_TD_att</th>\n",
       "      <th>B_avg_opp_TD_landed</th>\n",
       "      <th>B_avg_HEAD_att</th>\n",
       "      <th>B_avg_HEAD_landed</th>\n",
       "      <th>B_avg_opp_HEAD_att</th>\n",
       "      <th>B_avg_opp_HEAD_landed</th>\n",
       "      <th>B_avg_BODY_att</th>\n",
       "      <th>B_avg_BODY_landed</th>\n",
       "      <th>B_avg_opp_BODY_att</th>\n",
       "      <th>B_avg_opp_BODY_landed</th>\n",
       "      <th>B_avg_LEG_att</th>\n",
       "      <th>B_avg_LEG_landed</th>\n",
       "      <th>B_avg_opp_LEG_att</th>\n",
       "      <th>B_avg_opp_LEG_landed</th>\n",
       "      <th>B_avg_DISTANCE_att</th>\n",
       "      <th>B_avg_DISTANCE_landed</th>\n",
       "      <th>B_avg_opp_DISTANCE_att</th>\n",
       "      <th>B_avg_opp_DISTANCE_landed</th>\n",
       "      <th>B_avg_CLINCH_att</th>\n",
       "      <th>B_avg_CLINCH_landed</th>\n",
       "      <th>B_avg_opp_CLINCH_att</th>\n",
       "      <th>B_avg_opp_CLINCH_landed</th>\n",
       "      <th>B_avg_GROUND_att</th>\n",
       "      <th>B_avg_GROUND_landed</th>\n",
       "      <th>B_avg_opp_GROUND_att</th>\n",
       "      <th>B_avg_opp_GROUND_landed</th>\n",
       "      <th>B_avg_CTRL_time(seconds)</th>\n",
       "      <th>B_avg_opp_CTRL_time(seconds)</th>\n",
       "      <th>B_total_time_fought(seconds)</th>\n",
       "      <th>B_total_rounds_fought</th>\n",
       "      <th>B_total_title_bouts</th>\n",
       "      <th>B_current_win_streak</th>\n",
       "      <th>B_current_lose_streak</th>\n",
       "      <th>B_longest_win_streak</th>\n",
       "      <th>B_wins</th>\n",
       "      <th>B_losses</th>\n",
       "      <th>B_draw</th>\n",
       "      <th>B_win_by_Decision_Majority</th>\n",
       "      <th>B_win_by_Decision_Split</th>\n",
       "      <th>B_win_by_Decision_Unanimous</th>\n",
       "      <th>B_win_by_KO/TKO</th>\n",
       "      <th>B_win_by_Submission</th>\n",
       "      <th>B_win_by_TKO_Doctor_Stoppage</th>\n",
       "      <th>B_Stance</th>\n",
       "      <th>B_Height_cms</th>\n",
       "      <th>B_Reach_cms</th>\n",
       "      <th>B_Weight_lbs</th>\n",
       "      <th>R_avg_KD</th>\n",
       "      <th>R_avg_opp_KD</th>\n",
       "      <th>R_avg_SIG_STR_pct</th>\n",
       "      <th>R_avg_opp_SIG_STR_pct</th>\n",
       "      <th>R_avg_TD_pct</th>\n",
       "      <th>R_avg_opp_TD_pct</th>\n",
       "      <th>R_avg_SUB_ATT</th>\n",
       "      <th>R_avg_opp_SUB_ATT</th>\n",
       "      <th>R_avg_REV</th>\n",
       "      <th>R_avg_opp_REV</th>\n",
       "      <th>R_avg_SIG_STR_att</th>\n",
       "      <th>R_avg_SIG_STR_landed</th>\n",
       "      <th>R_avg_opp_SIG_STR_att</th>\n",
       "      <th>R_avg_opp_SIG_STR_landed</th>\n",
       "      <th>R_avg_TOTAL_STR_att</th>\n",
       "      <th>R_avg_TOTAL_STR_landed</th>\n",
       "      <th>R_avg_opp_TOTAL_STR_att</th>\n",
       "      <th>R_avg_opp_TOTAL_STR_landed</th>\n",
       "      <th>R_avg_TD_att</th>\n",
       "      <th>R_avg_TD_landed</th>\n",
       "      <th>R_avg_opp_TD_att</th>\n",
       "      <th>R_avg_opp_TD_landed</th>\n",
       "      <th>R_avg_HEAD_att</th>\n",
       "      <th>R_avg_HEAD_landed</th>\n",
       "      <th>R_avg_opp_HEAD_att</th>\n",
       "      <th>R_avg_opp_HEAD_landed</th>\n",
       "      <th>R_avg_BODY_att</th>\n",
       "      <th>R_avg_BODY_landed</th>\n",
       "      <th>R_avg_opp_BODY_att</th>\n",
       "      <th>R_avg_opp_BODY_landed</th>\n",
       "      <th>R_avg_LEG_att</th>\n",
       "      <th>R_avg_LEG_landed</th>\n",
       "      <th>R_avg_opp_LEG_att</th>\n",
       "      <th>R_avg_opp_LEG_landed</th>\n",
       "      <th>R_avg_DISTANCE_att</th>\n",
       "      <th>R_avg_DISTANCE_landed</th>\n",
       "      <th>R_avg_opp_DISTANCE_att</th>\n",
       "      <th>R_avg_opp_DISTANCE_landed</th>\n",
       "      <th>R_avg_CLINCH_att</th>\n",
       "      <th>R_avg_CLINCH_landed</th>\n",
       "      <th>R_avg_opp_CLINCH_att</th>\n",
       "      <th>R_avg_opp_CLINCH_landed</th>\n",
       "      <th>R_avg_GROUND_att</th>\n",
       "      <th>R_avg_GROUND_landed</th>\n",
       "      <th>R_avg_opp_GROUND_att</th>\n",
       "      <th>R_avg_opp_GROUND_landed</th>\n",
       "      <th>R_avg_CTRL_time(seconds)</th>\n",
       "      <th>R_avg_opp_CTRL_time(seconds)</th>\n",
       "      <th>R_total_time_fought(seconds)</th>\n",
       "      <th>R_total_rounds_fought</th>\n",
       "      <th>R_total_title_bouts</th>\n",
       "      <th>R_current_win_streak</th>\n",
       "      <th>R_current_lose_streak</th>\n",
       "      <th>R_longest_win_streak</th>\n",
       "      <th>R_wins</th>\n",
       "      <th>R_losses</th>\n",
       "      <th>R_draw</th>\n",
       "      <th>R_win_by_Decision_Majority</th>\n",
       "      <th>R_win_by_Decision_Split</th>\n",
       "      <th>R_win_by_Decision_Unanimous</th>\n",
       "      <th>R_win_by_KO/TKO</th>\n",
       "      <th>R_win_by_Submission</th>\n",
       "      <th>R_win_by_TKO_Doctor_Stoppage</th>\n",
       "      <th>R_Stance</th>\n",
       "      <th>R_Height_cms</th>\n",
       "      <th>R_Reach_cms</th>\n",
       "      <th>R_Weight_lbs</th>\n",
       "      <th>B_age</th>\n",
       "      <th>R_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>Shamil Abdurakhimov</td>\n",
       "      <td>Timothy Johnson</td>\n",
       "      <td>Mike King</td>\n",
       "      <td>2015-04-04</td>\n",
       "      <td>Fairfax, Virginia, USA</td>\n",
       "      <td>Blue</td>\n",
       "      <td>False</td>\n",
       "      <td>Heavyweight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Southpaw</td>\n",
       "      <td>190.50</td>\n",
       "      <td>195.58</td>\n",
       "      <td>265.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>190.50</td>\n",
       "      <td>193.04</td>\n",
       "      <td>235.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3899</th>\n",
       "      <td>Ben Alloway</td>\n",
       "      <td>Manuel Rodriguez</td>\n",
       "      <td>Steve Perceval</td>\n",
       "      <td>2012-12-14</td>\n",
       "      <td>Gold Coast, Queensland, Australia</td>\n",
       "      <td>Red</td>\n",
       "      <td>False</td>\n",
       "      <td>Welterweight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>175.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>180.34</td>\n",
       "      <td>193.04</td>\n",
       "      <td>170.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5047</th>\n",
       "      <td>Joe Lauzon</td>\n",
       "      <td>Kyle Bradley</td>\n",
       "      <td>Josh Arney</td>\n",
       "      <td>2008-09-17</td>\n",
       "      <td>Omaha, Nebraska, USA</td>\n",
       "      <td>Red</td>\n",
       "      <td>False</td>\n",
       "      <td>Lightweight</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</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",
       "      <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>Orthodox</td>\n",
       "      <td>175.26</td>\n",
       "      <td>180.34</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.52875</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.67375</td>\n",
       "      <td>0.250</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.25</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5.375</td>\n",
       "      <td>40.75</td>\n",
       "      <td>18.5</td>\n",
       "      <td>16.25</td>\n",
       "      <td>11.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>59.25</td>\n",
       "      <td>2.625</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.375</td>\n",
       "      <td>4.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.875</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.625</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.625</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.125</td>\n",
       "      <td>2.125</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.625</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.625</td>\n",
       "      <td>31.5</td>\n",
       "      <td>14.625</td>\n",
       "      <td>103.875</td>\n",
       "      <td>126.125</td>\n",
       "      <td>332.125</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</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",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>177.80</td>\n",
       "      <td>180.34</td>\n",
       "      <td>155.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>Markus Perez</td>\n",
       "      <td>Anthony Hernandez</td>\n",
       "      <td>Eduardo Herdy</td>\n",
       "      <td>2019-02-02</td>\n",
       "      <td>Fortaleza, Ceara, Brazil</td>\n",
       "      <td>Red</td>\n",
       "      <td>False</td>\n",
       "      <td>Middleweight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>182.88</td>\n",
       "      <td>190.50</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.59500</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>35.500</td>\n",
       "      <td>116.25</td>\n",
       "      <td>57.0</td>\n",
       "      <td>90.50</td>\n",
       "      <td>47.0</td>\n",
       "      <td>152.25</td>\n",
       "      <td>86.50</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>50.000</td>\n",
       "      <td>14.75</td>\n",
       "      <td>89.5</td>\n",
       "      <td>37.750</td>\n",
       "      <td>23.500</td>\n",
       "      <td>18.250</td>\n",
       "      <td>19.75</td>\n",
       "      <td>15.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>61.750</td>\n",
       "      <td>24.250</td>\n",
       "      <td>87.0</td>\n",
       "      <td>39.250</td>\n",
       "      <td>12.750</td>\n",
       "      <td>9.500</td>\n",
       "      <td>17.75</td>\n",
       "      <td>10.25</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.750</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7.500</td>\n",
       "      <td>65.000</td>\n",
       "      <td>302.750</td>\n",
       "      <td>742.000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>Orthodox</td>\n",
       "      <td>185.42</td>\n",
       "      <td>185.42</td>\n",
       "      <td>185.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>Ben Rothwell</td>\n",
       "      <td>Mark Hunt</td>\n",
       "      <td>Adam Martinez</td>\n",
       "      <td>2011-09-24</td>\n",
       "      <td>Denver, Colorado, USA</td>\n",
       "      <td>Blue</td>\n",
       "      <td>False</td>\n",
       "      <td>Heavyweight</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>177.80</td>\n",
       "      <td>182.88</td>\n",
       "      <td>265.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.35500</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.50</td>\n",
       "      <td>46.5</td>\n",
       "      <td>23.500</td>\n",
       "      <td>78.00</td>\n",
       "      <td>47.5</td>\n",
       "      <td>94.00</td>\n",
       "      <td>61.5</td>\n",
       "      <td>110.50</td>\n",
       "      <td>76.50</td>\n",
       "      <td>3.500</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>39.500</td>\n",
       "      <td>17.50</td>\n",
       "      <td>72.5</td>\n",
       "      <td>43.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>13.500</td>\n",
       "      <td>3.000</td>\n",
       "      <td>15.5</td>\n",
       "      <td>7.500</td>\n",
       "      <td>6.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>27.00</td>\n",
       "      <td>17.500</td>\n",
       "      <td>54.5</td>\n",
       "      <td>37.500</td>\n",
       "      <td>368.000</td>\n",
       "      <td>187.000</td>\n",
       "      <td>629.000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>Orthodox</td>\n",
       "      <td>193.04</td>\n",
       "      <td>198.12</td>\n",
       "      <td>265.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                R_fighter          B_fighter         Referee        date  \\\n",
       "2880  Shamil Abdurakhimov    Timothy Johnson       Mike King  2015-04-04   \n",
       "3899          Ben Alloway   Manuel Rodriguez  Steve Perceval  2012-12-14   \n",
       "5047           Joe Lauzon       Kyle Bradley      Josh Arney  2008-09-17   \n",
       "1068         Markus Perez  Anthony Hernandez   Eduardo Herdy  2019-02-02   \n",
       "4312         Ben Rothwell          Mark Hunt   Adam Martinez  2011-09-24   \n",
       "\n",
       "                               location Winner  title_bout  weight_class  \\\n",
       "2880             Fairfax, Virginia, USA   Blue       False   Heavyweight   \n",
       "3899  Gold Coast, Queensland, Australia    Red       False  Welterweight   \n",
       "5047               Omaha, Nebraska, USA    Red       False   Lightweight   \n",
       "1068           Fortaleza, Ceara, Brazil    Red       False  Middleweight   \n",
       "4312              Denver, Colorado, USA   Blue       False   Heavyweight   \n",
       "\n",
       "      B_avg_KD  B_avg_opp_KD  B_avg_SIG_STR_pct  B_avg_opp_SIG_STR_pct  \\\n",
       "2880       NaN           NaN                NaN                    NaN   \n",
       "3899       NaN           NaN                NaN                    NaN   \n",
       "5047       0.0           1.0              0.000                   0.70   \n",
       "1068       NaN           NaN                NaN                    NaN   \n",
       "4312       0.5           0.0              0.285                   0.12   \n",
       "\n",
       "      B_avg_TD_pct  B_avg_opp_TD_pct  B_avg_SUB_ATT  B_avg_opp_SUB_ATT  \\\n",
       "2880           NaN               NaN            NaN                NaN   \n",
       "3899           NaN               NaN            NaN                NaN   \n",
       "5047           0.0             0.000            0.0                0.0   \n",
       "1068           NaN               NaN            NaN                NaN   \n",
       "4312           0.5             0.125            0.0                1.0   \n",
       "\n",
       "      B_avg_REV  B_avg_opp_REV  B_avg_SIG_STR_att  B_avg_SIG_STR_landed  \\\n",
       "2880        NaN            NaN                NaN                   NaN   \n",
       "3899        NaN            NaN                NaN                   NaN   \n",
       "5047        0.0            0.0                3.0                   0.0   \n",
       "1068        NaN            NaN                NaN                   NaN   \n",
       "4312        0.0            0.0               25.5                   8.0   \n",
       "\n",
       "      B_avg_opp_SIG_STR_att  B_avg_opp_SIG_STR_landed  B_avg_TOTAL_STR_att  \\\n",
       "2880                    NaN                       NaN                  NaN   \n",
       "3899                    NaN                       NaN                  NaN   \n",
       "5047                   17.0                      12.0                  4.0   \n",
       "1068                    NaN                       NaN                  NaN   \n",
       "4312                   20.5                       4.5                 32.5   \n",
       "\n",
       "      B_avg_TOTAL_STR_landed  B_avg_opp_TOTAL_STR_att  \\\n",
       "2880                     NaN                      NaN   \n",
       "3899                     NaN                      NaN   \n",
       "5047                     1.0                     23.0   \n",
       "1068                     NaN                      NaN   \n",
       "4312                    13.5                     28.0   \n",
       "\n",
       "      B_avg_opp_TOTAL_STR_landed  B_avg_TD_att  B_avg_TD_landed  \\\n",
       "2880                         NaN           NaN              NaN   \n",
       "3899                         NaN           NaN              NaN   \n",
       "5047                        18.0           1.0              0.0   \n",
       "1068                         NaN           NaN              NaN   \n",
       "4312                        11.0           0.5              0.5   \n",
       "\n",
       "      B_avg_opp_TD_att  B_avg_opp_TD_landed  B_avg_HEAD_att  \\\n",
       "2880               NaN                  NaN             NaN   \n",
       "3899               NaN                  NaN             NaN   \n",
       "5047               0.0                  0.0             3.0   \n",
       "1068               NaN                  NaN             NaN   \n",
       "4312               3.0                  0.5            24.5   \n",
       "\n",
       "      B_avg_HEAD_landed  B_avg_opp_HEAD_att  B_avg_opp_HEAD_landed  \\\n",
       "2880                NaN                 NaN                    NaN   \n",
       "3899                NaN                 NaN                    NaN   \n",
       "5047                0.0                16.0                   11.0   \n",
       "1068                NaN                 NaN                    NaN   \n",
       "4312                8.0                20.0                    4.0   \n",
       "\n",
       "      B_avg_BODY_att  B_avg_BODY_landed  B_avg_opp_BODY_att  \\\n",
       "2880             NaN                NaN                 NaN   \n",
       "3899             NaN                NaN                 NaN   \n",
       "5047             0.0                0.0                 0.0   \n",
       "1068             NaN                NaN                 NaN   \n",
       "4312             1.0                0.0                 0.5   \n",
       "\n",
       "      B_avg_opp_BODY_landed  B_avg_LEG_att  B_avg_LEG_landed  \\\n",
       "2880                    NaN            NaN               NaN   \n",
       "3899                    NaN            NaN               NaN   \n",
       "5047                    0.0            0.0               0.0   \n",
       "1068                    NaN            NaN               NaN   \n",
       "4312                    0.5            0.0               0.0   \n",
       "\n",
       "      B_avg_opp_LEG_att  B_avg_opp_LEG_landed  B_avg_DISTANCE_att  \\\n",
       "2880                NaN                   NaN                 NaN   \n",
       "3899                NaN                   NaN                 NaN   \n",
       "5047                1.0                   1.0                 3.0   \n",
       "1068                NaN                   NaN                 NaN   \n",
       "4312                0.0                   0.0                24.0   \n",
       "\n",
       "      B_avg_DISTANCE_landed  B_avg_opp_DISTANCE_att  \\\n",
       "2880                    NaN                     NaN   \n",
       "3899                    NaN                     NaN   \n",
       "5047                    0.0                    10.0   \n",
       "1068                    NaN                     NaN   \n",
       "4312                    7.0                    19.5   \n",
       "\n",
       "      B_avg_opp_DISTANCE_landed  B_avg_CLINCH_att  B_avg_CLINCH_landed  \\\n",
       "2880                        NaN               NaN                  NaN   \n",
       "3899                        NaN               NaN                  NaN   \n",
       "5047                        6.0               0.0                  0.0   \n",
       "1068                        NaN               NaN                  NaN   \n",
       "4312                        3.5               1.0                  0.5   \n",
       "\n",
       "      B_avg_opp_CLINCH_att  B_avg_opp_CLINCH_landed  B_avg_GROUND_att  \\\n",
       "2880                   NaN                      NaN               NaN   \n",
       "3899                   NaN                      NaN               NaN   \n",
       "5047                   3.0                      3.0               0.0   \n",
       "1068                   NaN                      NaN               NaN   \n",
       "4312                   0.5                      0.5               0.5   \n",
       "\n",
       "      B_avg_GROUND_landed  B_avg_opp_GROUND_att  B_avg_opp_GROUND_landed  \\\n",
       "2880                  NaN                   NaN                      NaN   \n",
       "3899                  NaN                   NaN                      NaN   \n",
       "5047                  0.0                   4.0                      3.0   \n",
       "1068                  NaN                   NaN                      NaN   \n",
       "4312                  0.5                   0.5                      0.5   \n",
       "\n",
       "      B_avg_CTRL_time(seconds)  B_avg_opp_CTRL_time(seconds)  \\\n",
       "2880                       NaN                           NaN   \n",
       "3899                       NaN                           NaN   \n",
       "5047                       0.0                           4.0   \n",
       "1068                       NaN                           NaN   \n",
       "4312                      18.0                          52.5   \n",
       "\n",
       "      B_total_time_fought(seconds)  B_total_rounds_fought  \\\n",
       "2880                           NaN                      0   \n",
       "3899                           NaN                      0   \n",
       "5047                          33.0                      1   \n",
       "1068                           NaN                      0   \n",
       "4312                         232.0                      3   \n",
       "\n",
       "      B_total_title_bouts  B_current_win_streak  B_current_lose_streak  \\\n",
       "2880                    0                     0                      0   \n",
       "3899                    0                     0                      0   \n",
       "5047                    0                     0                      1   \n",
       "1068                    0                     0                      0   \n",
       "4312                    0                     0                      1   \n",
       "\n",
       "      B_longest_win_streak  B_wins  B_losses  B_draw  \\\n",
       "2880                     0       0         0       0   \n",
       "3899                     0       0         0       0   \n",
       "5047                     0       0         1       0   \n",
       "1068                     0       0         0       0   \n",
       "4312                     1       1         1       0   \n",
       "\n",
       "      B_win_by_Decision_Majority  B_win_by_Decision_Split  \\\n",
       "2880                           0                        0   \n",
       "3899                           0                        0   \n",
       "5047                           0                        0   \n",
       "1068                           0                        0   \n",
       "4312                           0                        0   \n",
       "\n",
       "      B_win_by_Decision_Unanimous  B_win_by_KO/TKO  B_win_by_Submission  \\\n",
       "2880                            0                0                    0   \n",
       "3899                            0                0                    0   \n",
       "5047                            0                0                    0   \n",
       "1068                            0                0                    0   \n",
       "4312                            0                1                    0   \n",
       "\n",
       "      B_win_by_TKO_Doctor_Stoppage  B_Stance  B_Height_cms  B_Reach_cms  \\\n",
       "2880                             0  Southpaw        190.50       195.58   \n",
       "3899                             0  Orthodox        175.26          NaN   \n",
       "5047                             0  Orthodox        175.26       180.34   \n",
       "1068                             0  Orthodox        182.88       190.50   \n",
       "4312                             0  Orthodox        177.80       182.88   \n",
       "\n",
       "      B_Weight_lbs  R_avg_KD  R_avg_opp_KD  R_avg_SIG_STR_pct  \\\n",
       "2880         265.0       NaN           NaN                NaN   \n",
       "3899         170.0       NaN           NaN                NaN   \n",
       "5047         155.0     0.125           0.0            0.52875   \n",
       "1068         185.0     0.000           0.0            0.59500   \n",
       "4312         265.0     0.000           0.0            0.37500   \n",
       "\n",
       "      R_avg_opp_SIG_STR_pct  R_avg_TD_pct  R_avg_opp_TD_pct  R_avg_SUB_ATT  \\\n",
       "2880                    NaN           NaN               NaN            NaN   \n",
       "3899                    NaN           NaN               NaN            NaN   \n",
       "5047                  0.350       0.67375             0.250           2.00   \n",
       "1068                  0.490       0.35000             0.125           0.75   \n",
       "4312                  0.595       0.35500             0.500           1.00   \n",
       "\n",
       "      R_avg_opp_SUB_ATT  R_avg_REV  R_avg_opp_REV  R_avg_SIG_STR_att  \\\n",
       "2880                NaN        NaN            NaN                NaN   \n",
       "3899                NaN        NaN            NaN                NaN   \n",
       "5047                0.0      0.125           0.25               10.5   \n",
       "1068                0.0      0.000           0.00               77.0   \n",
       "4312                1.0      0.000           1.50               46.5   \n",
       "\n",
       "      R_avg_SIG_STR_landed  R_avg_opp_SIG_STR_att  R_avg_opp_SIG_STR_landed  \\\n",
       "2880                   NaN                    NaN                       NaN   \n",
       "3899                   NaN                    NaN                       NaN   \n",
       "5047                 5.375                  40.75                      18.5   \n",
       "1068                35.500                 116.25                      57.0   \n",
       "4312                23.500                  78.00                      47.5   \n",
       "\n",
       "      R_avg_TOTAL_STR_att  R_avg_TOTAL_STR_landed  R_avg_opp_TOTAL_STR_att  \\\n",
       "2880                  NaN                     NaN                      NaN   \n",
       "3899                  NaN                     NaN                      NaN   \n",
       "5047                16.25                    11.0                    87.00   \n",
       "1068                90.50                    47.0                   152.25   \n",
       "4312                94.00                    61.5                   110.50   \n",
       "\n",
       "      R_avg_opp_TOTAL_STR_landed  R_avg_TD_att  R_avg_TD_landed  \\\n",
       "2880                         NaN           NaN              NaN   \n",
       "3899                         NaN           NaN              NaN   \n",
       "5047                       59.25         2.625             1.75   \n",
       "1068                       86.50         2.000             0.75   \n",
       "4312                       76.50         3.500             2.50   \n",
       "\n",
       "      R_avg_opp_TD_att  R_avg_opp_TD_landed  R_avg_HEAD_att  \\\n",
       "2880               NaN                  NaN             NaN   \n",
       "3899               NaN                  NaN             NaN   \n",
       "5047              2.00                  1.0           9.375   \n",
       "1068              4.25                  0.5          50.000   \n",
       "4312              3.00                  2.5          39.500   \n",
       "\n",
       "      R_avg_HEAD_landed  R_avg_opp_HEAD_att  R_avg_opp_HEAD_landed  \\\n",
       "2880                NaN                 NaN                    NaN   \n",
       "3899                NaN                 NaN                    NaN   \n",
       "5047               4.75                36.0                 13.875   \n",
       "1068              14.75                89.5                 37.750   \n",
       "4312              17.50                72.5                 43.000   \n",
       "\n",
       "      R_avg_BODY_att  R_avg_BODY_landed  R_avg_opp_BODY_att  \\\n",
       "2880             NaN                NaN                 NaN   \n",
       "3899             NaN                NaN                 NaN   \n",
       "5047           0.625              0.625                3.75   \n",
       "1068          23.500             18.250               19.75   \n",
       "4312           7.000              6.000                3.50   \n",
       "\n",
       "      R_avg_opp_BODY_landed  R_avg_LEG_att  R_avg_LEG_landed  \\\n",
       "2880                    NaN            NaN               NaN   \n",
       "3899                    NaN            NaN               NaN   \n",
       "5047                  3.625            0.5               0.0   \n",
       "1068                 15.000            3.5               2.5   \n",
       "4312                  2.500            0.0               0.0   \n",
       "\n",
       "      R_avg_opp_LEG_att  R_avg_opp_LEG_landed  R_avg_DISTANCE_att  \\\n",
       "2880                NaN                   NaN                 NaN   \n",
       "3899                NaN                   NaN                 NaN   \n",
       "5047                1.0                  1.00               6.125   \n",
       "1068                7.0                  4.25              61.750   \n",
       "4312                2.0                  2.00              13.500   \n",
       "\n",
       "      R_avg_DISTANCE_landed  R_avg_opp_DISTANCE_att  \\\n",
       "2880                    NaN                     NaN   \n",
       "3899                    NaN                     NaN   \n",
       "5047                  2.125                     9.0   \n",
       "1068                 24.250                    87.0   \n",
       "4312                  3.000                    15.5   \n",
       "\n",
       "      R_avg_opp_DISTANCE_landed  R_avg_CLINCH_att  R_avg_CLINCH_landed  \\\n",
       "2880                        NaN               NaN                  NaN   \n",
       "3899                        NaN               NaN                  NaN   \n",
       "5047                      3.625             0.625                0.625   \n",
       "1068                     39.250            12.750                9.500   \n",
       "4312                      7.500             6.000                3.000   \n",
       "\n",
       "      R_avg_opp_CLINCH_att  R_avg_opp_CLINCH_landed  R_avg_GROUND_att  \\\n",
       "2880                   NaN                      NaN               NaN   \n",
       "3899                   NaN                      NaN               NaN   \n",
       "5047                  0.25                     0.25              3.75   \n",
       "1068                 17.75                    10.25              2.50   \n",
       "4312                  8.00                     2.50             27.00   \n",
       "\n",
       "      R_avg_GROUND_landed  R_avg_opp_GROUND_att  R_avg_opp_GROUND_landed  \\\n",
       "2880                  NaN                   NaN                      NaN   \n",
       "3899                  NaN                   NaN                      NaN   \n",
       "5047                2.625                  31.5                   14.625   \n",
       "1068                1.750                  11.5                    7.500   \n",
       "4312               17.500                  54.5                   37.500   \n",
       "\n",
       "      R_avg_CTRL_time(seconds)  R_avg_opp_CTRL_time(seconds)  \\\n",
       "2880                       NaN                           NaN   \n",
       "3899                       NaN                           NaN   \n",
       "5047                   103.875                       126.125   \n",
       "1068                    65.000                       302.750   \n",
       "4312                   368.000                       187.000   \n",
       "\n",
       "      R_total_time_fought(seconds)  R_total_rounds_fought  \\\n",
       "2880                           NaN                      0   \n",
       "3899                           NaN                      0   \n",
       "5047                       332.125                      6   \n",
       "1068                       742.000                      7   \n",
       "4312                       629.000                      5   \n",
       "\n",
       "      R_total_title_bouts  R_current_win_streak  R_current_lose_streak  \\\n",
       "2880                    0                     0                      0   \n",
       "3899                    0                     0                      0   \n",
       "5047                    0                     3                      0   \n",
       "1068                    0                     0                      1   \n",
       "4312                    0                     0                      1   \n",
       "\n",
       "      R_longest_win_streak  R_wins  R_losses  R_draw  \\\n",
       "2880                     0       0         0       0   \n",
       "3899                     0       0         0       0   \n",
       "5047                     3       3         1       0   \n",
       "1068                     1       1         2       0   \n",
       "4312                     1       1         1       0   \n",
       "\n",
       "      R_win_by_Decision_Majority  R_win_by_Decision_Split  \\\n",
       "2880                           0                        0   \n",
       "3899                           0                        0   \n",
       "5047                           0                        0   \n",
       "1068                           0                        0   \n",
       "4312                           0                        0   \n",
       "\n",
       "      R_win_by_Decision_Unanimous  R_win_by_KO/TKO  R_win_by_Submission  \\\n",
       "2880                            0                0                    0   \n",
       "3899                            0                0                    0   \n",
       "5047                            0                1                    2   \n",
       "1068                            0                0                    1   \n",
       "4312                            1                0                    0   \n",
       "\n",
       "      R_win_by_TKO_Doctor_Stoppage  R_Stance  R_Height_cms  R_Reach_cms  \\\n",
       "2880                             0  Orthodox        190.50       193.04   \n",
       "3899                             0  Orthodox        180.34       193.04   \n",
       "5047                             0  Orthodox        177.80       180.34   \n",
       "1068                             0  Orthodox        185.42       185.42   \n",
       "4312                             0  Orthodox        193.04       198.12   \n",
       "\n",
       "      R_Weight_lbs  B_age  R_age  \n",
       "2880         235.0   30.0   33.0  \n",
       "3899         170.0   27.0   31.0  \n",
       "5047         155.0   26.0   24.0  \n",
       "1068         185.0   25.0   30.0  \n",
       "4312         265.0   37.0   29.0  "
      ]
     },
     "execution_count": 1579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5, random_state=seed_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our data is a mix of categorical and numerical data. We will need to handle these differently. We can see that there are a lot of string values in the data, also there are some inconsistencies between the cms/feet and lbs/kgs columns. We can also see that there are some missing values in the data. We will need to handle these as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split our data into two rows for each current row. This is because we want to cluster the fighters not the fights. We will need to split the data into two rows for each fighter. One row will be for the red corner and the other will be for the blue corner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1580,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataframe into two: one for red fighters and one for blue fighters\n",
    "# Remove the R_ and B_ prefixes from the column names, but only the first occurrence (prefix)\n",
    "df_red = data.filter(regex='^R_|^date').rename(columns=lambda x: x.replace('R_', '', 1))\n",
    "df_blue = data.filter(regex='^B_|^date').rename(columns=lambda x: x.replace('B_', '', 1))\n",
    "\n",
    "# Concatenate the two dataframes\n",
    "df_fighters = pd.concat([df_red, df_blue])\n",
    "\n",
    "# Reset the index (to ensure each fighter has a unique index, instead of a shared index)\n",
    "df_fighters = df_fighters.reset_index(drop=True)\n",
    "\n",
    "# Sort by fighter and date and drop duplicates, only keeping the last fight of each fighter (most recent)\n",
    "df_fighters = df_fighters.sort_values(['fighter', 'date']).drop_duplicates(subset='fighter', keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1581,
   "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>fighter</th>\n",
       "      <th>date</th>\n",
       "      <th>avg_KD</th>\n",
       "      <th>avg_opp_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_opp_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_opp_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_opp_SUB_ATT</th>\n",
       "      <th>avg_REV</th>\n",
       "      <th>avg_opp_REV</th>\n",
       "      <th>avg_SIG_STR_att</th>\n",
       "      <th>avg_SIG_STR_landed</th>\n",
       "      <th>avg_opp_SIG_STR_att</th>\n",
       "      <th>avg_opp_SIG_STR_landed</th>\n",
       "      <th>avg_TOTAL_STR_att</th>\n",
       "      <th>avg_TOTAL_STR_landed</th>\n",
       "      <th>avg_opp_TOTAL_STR_att</th>\n",
       "      <th>avg_opp_TOTAL_STR_landed</th>\n",
       "      <th>avg_TD_att</th>\n",
       "      <th>avg_TD_landed</th>\n",
       "      <th>avg_opp_TD_att</th>\n",
       "      <th>avg_opp_TD_landed</th>\n",
       "      <th>avg_HEAD_att</th>\n",
       "      <th>avg_HEAD_landed</th>\n",
       "      <th>avg_opp_HEAD_att</th>\n",
       "      <th>avg_opp_HEAD_landed</th>\n",
       "      <th>avg_BODY_att</th>\n",
       "      <th>avg_BODY_landed</th>\n",
       "      <th>avg_opp_BODY_att</th>\n",
       "      <th>avg_opp_BODY_landed</th>\n",
       "      <th>avg_LEG_att</th>\n",
       "      <th>avg_LEG_landed</th>\n",
       "      <th>avg_opp_LEG_att</th>\n",
       "      <th>avg_opp_LEG_landed</th>\n",
       "      <th>avg_DISTANCE_att</th>\n",
       "      <th>avg_DISTANCE_landed</th>\n",
       "      <th>avg_opp_DISTANCE_att</th>\n",
       "      <th>avg_opp_DISTANCE_landed</th>\n",
       "      <th>avg_CLINCH_att</th>\n",
       "      <th>avg_CLINCH_landed</th>\n",
       "      <th>avg_opp_CLINCH_att</th>\n",
       "      <th>avg_opp_CLINCH_landed</th>\n",
       "      <th>avg_GROUND_att</th>\n",
       "      <th>avg_GROUND_landed</th>\n",
       "      <th>avg_opp_GROUND_att</th>\n",
       "      <th>avg_opp_GROUND_landed</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "      <th>avg_opp_CTRL_time(seconds)</th>\n",
       "      <th>total_time_fought(seconds)</th>\n",
       "      <th>total_rounds_fought</th>\n",
       "      <th>total_title_bouts</th>\n",
       "      <th>current_win_streak</th>\n",
       "      <th>current_lose_streak</th>\n",
       "      <th>longest_win_streak</th>\n",
       "      <th>wins</th>\n",
       "      <th>losses</th>\n",
       "      <th>draw</th>\n",
       "      <th>win_by_Decision_Majority</th>\n",
       "      <th>win_by_Decision_Split</th>\n",
       "      <th>win_by_Decision_Unanimous</th>\n",
       "      <th>win_by_KO/TKO</th>\n",
       "      <th>win_by_Submission</th>\n",
       "      <th>win_by_TKO_Doctor_Stoppage</th>\n",
       "      <th>Stance</th>\n",
       "      <th>Height_cms</th>\n",
       "      <th>Reach_cms</th>\n",
       "      <th>Weight_lbs</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adrian Yanez</td>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>170.18</td>\n",
       "      <td>177.80</td>\n",
       "      <td>135.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Trevin Giles</td>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>1.031250</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.576875</td>\n",
       "      <td>0.381250</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>0.11625</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.18750</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>0.25</td>\n",
       "      <td>77.562500</td>\n",
       "      <td>43.156250</td>\n",
       "      <td>69.15625</td>\n",
       "      <td>27.593750</td>\n",
       "      <td>93.125000</td>\n",
       "      <td>57.218750</td>\n",
       "      <td>98.343750</td>\n",
       "      <td>52.468750</td>\n",
       "      <td>1.281250</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>4.687500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>71.09375</td>\n",
       "      <td>38.062500</td>\n",
       "      <td>50.406250</td>\n",
       "      <td>13.343750</td>\n",
       "      <td>4.687500</td>\n",
       "      <td>3.312500</td>\n",
       "      <td>9.093750</td>\n",
       "      <td>6.156250</td>\n",
       "      <td>1.781250</td>\n",
       "      <td>1.781250</td>\n",
       "      <td>9.656250</td>\n",
       "      <td>8.093750</td>\n",
       "      <td>58.312500</td>\n",
       "      <td>30.437500</td>\n",
       "      <td>63.312500</td>\n",
       "      <td>23.125000</td>\n",
       "      <td>6.968750</td>\n",
       "      <td>3.656250</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>12.281250</td>\n",
       "      <td>9.062500</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>176.156250</td>\n",
       "      <td>154.687500</td>\n",
       "      <td>747.343750</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187.96</td>\n",
       "      <td>185.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tai Tuivasa</td>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.538906</td>\n",
       "      <td>0.598594</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.31250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.234375</td>\n",
       "      <td>30.375000</td>\n",
       "      <td>43.81250</td>\n",
       "      <td>24.796875</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>41.390625</td>\n",
       "      <td>50.234375</td>\n",
       "      <td>30.906250</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.843750</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>42.50000</td>\n",
       "      <td>16.781250</td>\n",
       "      <td>34.296875</td>\n",
       "      <td>17.937500</td>\n",
       "      <td>5.781250</td>\n",
       "      <td>4.546875</td>\n",
       "      <td>6.125000</td>\n",
       "      <td>3.718750</td>\n",
       "      <td>10.953125</td>\n",
       "      <td>9.046875</td>\n",
       "      <td>3.390625</td>\n",
       "      <td>3.140625</td>\n",
       "      <td>45.875000</td>\n",
       "      <td>20.281250</td>\n",
       "      <td>36.343750</td>\n",
       "      <td>18.687500</td>\n",
       "      <td>11.328125</td>\n",
       "      <td>8.562500</td>\n",
       "      <td>1.4375</td>\n",
       "      <td>1.171875</td>\n",
       "      <td>2.031250</td>\n",
       "      <td>1.531250</td>\n",
       "      <td>6.031250</td>\n",
       "      <td>4.937500</td>\n",
       "      <td>82.453125</td>\n",
       "      <td>89.781250</td>\n",
       "      <td>448.203125</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Southpaw</td>\n",
       "      <td>187.96</td>\n",
       "      <td>190.50</td>\n",
       "      <td>264.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheyanne Buys</td>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Switch</td>\n",
       "      <td>160.02</td>\n",
       "      <td>160.02</td>\n",
       "      <td>115.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marion Reneau</td>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.403076</td>\n",
       "      <td>0.554961</td>\n",
       "      <td>0.511719</td>\n",
       "      <td>0.62875</td>\n",
       "      <td>0.231445</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.50</td>\n",
       "      <td>109.331055</td>\n",
       "      <td>44.362305</td>\n",
       "      <td>148.84668</td>\n",
       "      <td>84.554688</td>\n",
       "      <td>137.154297</td>\n",
       "      <td>70.198242</td>\n",
       "      <td>172.499023</td>\n",
       "      <td>106.680664</td>\n",
       "      <td>2.261719</td>\n",
       "      <td>1.261719</td>\n",
       "      <td>3.137695</td>\n",
       "      <td>1.771484</td>\n",
       "      <td>86.37500</td>\n",
       "      <td>26.030273</td>\n",
       "      <td>94.225586</td>\n",
       "      <td>38.693359</td>\n",
       "      <td>17.272461</td>\n",
       "      <td>13.469727</td>\n",
       "      <td>37.717773</td>\n",
       "      <td>31.658203</td>\n",
       "      <td>5.683594</td>\n",
       "      <td>4.862305</td>\n",
       "      <td>16.903320</td>\n",
       "      <td>14.203125</td>\n",
       "      <td>85.845703</td>\n",
       "      <td>27.907227</td>\n",
       "      <td>106.913086</td>\n",
       "      <td>48.479492</td>\n",
       "      <td>18.996094</td>\n",
       "      <td>13.970703</td>\n",
       "      <td>35.3125</td>\n",
       "      <td>32.059570</td>\n",
       "      <td>4.489258</td>\n",
       "      <td>2.484375</td>\n",
       "      <td>6.621094</td>\n",
       "      <td>4.015625</td>\n",
       "      <td>141.285156</td>\n",
       "      <td>310.152344</td>\n",
       "      <td>874.380859</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>167.64</td>\n",
       "      <td>172.72</td>\n",
       "      <td>135.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fighter        date    avg_KD  avg_opp_KD  avg_SIG_STR_pct  \\\n",
       "0   Adrian Yanez  2021-03-20  1.000000    0.000000         0.500000   \n",
       "1   Trevin Giles  2021-03-20  1.031250    0.062500         0.576875   \n",
       "2    Tai Tuivasa  2021-03-20  0.546875    0.187500         0.538906   \n",
       "3  Cheyanne Buys  2021-03-20       NaN         NaN              NaN   \n",
       "4  Marion Reneau  2021-03-20  0.000000    0.000977         0.403076   \n",
       "\n",
       "   avg_opp_SIG_STR_pct  avg_TD_pct  avg_opp_TD_pct  avg_SUB_ATT  \\\n",
       "0             0.460000    0.000000         0.00000     0.000000   \n",
       "1             0.381250    0.406250         0.11625     0.250000   \n",
       "2             0.598594    0.000000         0.31250     0.000000   \n",
       "3                  NaN         NaN             NaN          NaN   \n",
       "4             0.554961    0.511719         0.62875     0.231445   \n",
       "\n",
       "   avg_opp_SUB_ATT  avg_REV  avg_opp_REV  avg_SIG_STR_att  avg_SIG_STR_landed  \\\n",
       "0          0.00000  0.00000         0.00        34.000000           17.000000   \n",
       "1          1.18750  0.37500         0.25        77.562500           43.156250   \n",
       "2          0.25000  0.00000         0.00        59.234375           30.375000   \n",
       "3              NaN      NaN          NaN              NaN                 NaN   \n",
       "4          0.03125  0.03125         0.50       109.331055           44.362305   \n",
       "\n",
       "   avg_opp_SIG_STR_att  avg_opp_SIG_STR_landed  avg_TOTAL_STR_att  \\\n",
       "0             13.00000                6.000000          35.000000   \n",
       "1             69.15625               27.593750          93.125000   \n",
       "2             43.81250               24.796875          70.500000   \n",
       "3                  NaN                     NaN                NaN   \n",
       "4            148.84668               84.554688         137.154297   \n",
       "\n",
       "   avg_TOTAL_STR_landed  avg_opp_TOTAL_STR_att  avg_opp_TOTAL_STR_landed  \\\n",
       "0             18.000000              16.000000                  9.000000   \n",
       "1             57.218750              98.343750                 52.468750   \n",
       "2             41.390625              50.234375                 30.906250   \n",
       "3                   NaN                    NaN                       NaN   \n",
       "4             70.198242             172.499023                106.680664   \n",
       "\n",
       "   avg_TD_att  avg_TD_landed  avg_opp_TD_att  avg_opp_TD_landed  avg_HEAD_att  \\\n",
       "0    0.000000       0.000000        3.000000           0.000000      32.00000   \n",
       "1    1.281250       0.781250        4.687500           0.437500      71.09375   \n",
       "2    0.031250       0.000000        2.843750           1.750000      42.50000   \n",
       "3         NaN            NaN             NaN                NaN           NaN   \n",
       "4    2.261719       1.261719        3.137695           1.771484      86.37500   \n",
       "\n",
       "   avg_HEAD_landed  avg_opp_HEAD_att  avg_opp_HEAD_landed  avg_BODY_att  \\\n",
       "0        15.000000         11.000000             5.000000      2.000000   \n",
       "1        38.062500         50.406250            13.343750      4.687500   \n",
       "2        16.781250         34.296875            17.937500      5.781250   \n",
       "3              NaN               NaN                  NaN           NaN   \n",
       "4        26.030273         94.225586            38.693359     17.272461   \n",
       "\n",
       "   avg_BODY_landed  avg_opp_BODY_att  avg_opp_BODY_landed  avg_LEG_att  \\\n",
       "0         2.000000          2.000000             1.000000     0.000000   \n",
       "1         3.312500          9.093750             6.156250     1.781250   \n",
       "2         4.546875          6.125000             3.718750    10.953125   \n",
       "3              NaN               NaN                  NaN          NaN   \n",
       "4        13.469727         37.717773            31.658203     5.683594   \n",
       "\n",
       "   avg_LEG_landed  avg_opp_LEG_att  avg_opp_LEG_landed  avg_DISTANCE_att  \\\n",
       "0        0.000000         0.000000            0.000000         33.000000   \n",
       "1        1.781250         9.656250            8.093750         58.312500   \n",
       "2        9.046875         3.390625            3.140625         45.875000   \n",
       "3             NaN              NaN                 NaN               NaN   \n",
       "4        4.862305        16.903320           14.203125         85.845703   \n",
       "\n",
       "   avg_DISTANCE_landed  avg_opp_DISTANCE_att  avg_opp_DISTANCE_landed  \\\n",
       "0            16.000000             12.000000                 6.000000   \n",
       "1            30.437500             63.312500                23.125000   \n",
       "2            20.281250             36.343750                18.687500   \n",
       "3                  NaN                   NaN                      NaN   \n",
       "4            27.907227            106.913086                48.479492   \n",
       "\n",
       "   avg_CLINCH_att  avg_CLINCH_landed  avg_opp_CLINCH_att  \\\n",
       "0        1.000000           1.000000              1.0000   \n",
       "1        6.968750           3.656250              5.0000   \n",
       "2       11.328125           8.562500              1.4375   \n",
       "3             NaN                NaN                 NaN   \n",
       "4       18.996094          13.970703             35.3125   \n",
       "\n",
       "   avg_opp_CLINCH_landed  avg_GROUND_att  avg_GROUND_landed  \\\n",
       "0               0.000000        0.000000           0.000000   \n",
       "1               4.125000       12.281250           9.062500   \n",
       "2               1.171875        2.031250           1.531250   \n",
       "3                    NaN             NaN                NaN   \n",
       "4              32.059570        4.489258           2.484375   \n",
       "\n",
       "   avg_opp_GROUND_att  avg_opp_GROUND_landed  avg_CTRL_time(seconds)  \\\n",
       "0            0.000000               0.000000                5.000000   \n",
       "1            0.843750               0.343750              176.156250   \n",
       "2            6.031250               4.937500               82.453125   \n",
       "3                 NaN                    NaN                     NaN   \n",
       "4            6.621094               4.015625              141.285156   \n",
       "\n",
       "   avg_opp_CTRL_time(seconds)  total_time_fought(seconds)  \\\n",
       "0                   54.000000                  166.000000   \n",
       "1                  154.687500                  747.343750   \n",
       "2                   89.781250                  448.203125   \n",
       "3                         NaN                         NaN   \n",
       "4                  310.152344                  874.380859   \n",
       "\n",
       "   total_rounds_fought  total_title_bouts  current_win_streak  \\\n",
       "0                    1                  0                   1   \n",
       "1                   17                  0                   2   \n",
       "2                   13                  0                   3   \n",
       "3                    0                  0                   0   \n",
       "4                   30                  0                   2   \n",
       "\n",
       "   current_lose_streak  longest_win_streak  wins  losses  draw  \\\n",
       "0                    0                   1     1       0     0   \n",
       "1                    0                   2     4       2     0   \n",
       "2                    0                   3     4       3     0   \n",
       "3                    0                   0     0       0     0   \n",
       "4                    0                   2     5       6     0   \n",
       "\n",
       "   win_by_Decision_Majority  win_by_Decision_Split  win_by_Decision_Unanimous  \\\n",
       "0                         0                      0                          0   \n",
       "1                         0                      1                          0   \n",
       "2                         0                      0                          1   \n",
       "3                         0                      0                          0   \n",
       "4                         0                      0                          1   \n",
       "\n",
       "   win_by_KO/TKO  win_by_Submission  win_by_TKO_Doctor_Stoppage    Stance  \\\n",
       "0              1                  0                           0  Orthodox   \n",
       "1              3                  0                           0  Orthodox   \n",
       "2              3                  0                           0  Southpaw   \n",
       "3              0                  0                           0    Switch   \n",
       "4              2                  2                           0  Orthodox   \n",
       "\n",
       "   Height_cms  Reach_cms  Weight_lbs   age  \n",
       "0      170.18     177.80       135.0  27.0  \n",
       "1      182.88     187.96       185.0  28.0  \n",
       "2      187.96     190.50       264.0  28.0  \n",
       "3      160.02     160.02       115.0  25.0  \n",
       "4      167.64     172.72       135.0  43.0  "
      ]
     },
     "execution_count": 1581,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n",
    "df_red.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1582,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latest fighting data is being used for each fighter: True\n"
     ]
    }
   ],
   "source": [
    "# Create a 'fighter' column for both 'B_fighter' and 'R_fighter' in the original dataframe\n",
    "df_original_blue = data.copy()\n",
    "df_original_blue['fighter'] = df_original_blue['B_fighter']\n",
    "\n",
    "df_original_red = data.copy()\n",
    "df_original_red['fighter'] = df_original_red['R_fighter']\n",
    "\n",
    "# Concatenate the data\n",
    "df_original = pd.concat([df_original_blue, df_original_red])\n",
    "\n",
    "# Get the maximum date for each fighter in the original dataframe\n",
    "max_dates_original = df_original.groupby('fighter')['date'].max()\n",
    "\n",
    "# Get the date for each fighter in the new dataframe\n",
    "dates_new = df_fighters.set_index('fighter')['date']\n",
    "\n",
    "# Compare the two series\n",
    "is_latest_data = (max_dates_original == dates_new).all()\n",
    "\n",
    "print(f\"Latest fighting data is being used for each fighter: {is_latest_data}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have confirmed that our data is split correctly, we set the original data to the split data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1583,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df_fighters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1584,
   "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>fighter</th>\n",
       "      <th>date</th>\n",
       "      <th>avg_KD</th>\n",
       "      <th>avg_opp_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_opp_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_opp_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_opp_SUB_ATT</th>\n",
       "      <th>avg_REV</th>\n",
       "      <th>avg_opp_REV</th>\n",
       "      <th>avg_SIG_STR_att</th>\n",
       "      <th>avg_SIG_STR_landed</th>\n",
       "      <th>avg_opp_SIG_STR_att</th>\n",
       "      <th>avg_opp_SIG_STR_landed</th>\n",
       "      <th>avg_TOTAL_STR_att</th>\n",
       "      <th>avg_TOTAL_STR_landed</th>\n",
       "      <th>avg_opp_TOTAL_STR_att</th>\n",
       "      <th>avg_opp_TOTAL_STR_landed</th>\n",
       "      <th>avg_TD_att</th>\n",
       "      <th>avg_TD_landed</th>\n",
       "      <th>avg_opp_TD_att</th>\n",
       "      <th>avg_opp_TD_landed</th>\n",
       "      <th>avg_HEAD_att</th>\n",
       "      <th>avg_HEAD_landed</th>\n",
       "      <th>avg_opp_HEAD_att</th>\n",
       "      <th>avg_opp_HEAD_landed</th>\n",
       "      <th>avg_BODY_att</th>\n",
       "      <th>avg_BODY_landed</th>\n",
       "      <th>avg_opp_BODY_att</th>\n",
       "      <th>avg_opp_BODY_landed</th>\n",
       "      <th>avg_LEG_att</th>\n",
       "      <th>avg_LEG_landed</th>\n",
       "      <th>avg_opp_LEG_att</th>\n",
       "      <th>avg_opp_LEG_landed</th>\n",
       "      <th>avg_DISTANCE_att</th>\n",
       "      <th>avg_DISTANCE_landed</th>\n",
       "      <th>avg_opp_DISTANCE_att</th>\n",
       "      <th>avg_opp_DISTANCE_landed</th>\n",
       "      <th>avg_CLINCH_att</th>\n",
       "      <th>avg_CLINCH_landed</th>\n",
       "      <th>avg_opp_CLINCH_att</th>\n",
       "      <th>avg_opp_CLINCH_landed</th>\n",
       "      <th>avg_GROUND_att</th>\n",
       "      <th>avg_GROUND_landed</th>\n",
       "      <th>avg_opp_GROUND_att</th>\n",
       "      <th>avg_opp_GROUND_landed</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "      <th>avg_opp_CTRL_time(seconds)</th>\n",
       "      <th>total_time_fought(seconds)</th>\n",
       "      <th>total_rounds_fought</th>\n",
       "      <th>total_title_bouts</th>\n",
       "      <th>current_win_streak</th>\n",
       "      <th>current_lose_streak</th>\n",
       "      <th>longest_win_streak</th>\n",
       "      <th>wins</th>\n",
       "      <th>losses</th>\n",
       "      <th>draw</th>\n",
       "      <th>win_by_Decision_Majority</th>\n",
       "      <th>win_by_Decision_Split</th>\n",
       "      <th>win_by_Decision_Unanimous</th>\n",
       "      <th>win_by_KO/TKO</th>\n",
       "      <th>win_by_Submission</th>\n",
       "      <th>win_by_TKO_Doctor_Stoppage</th>\n",
       "      <th>Stance</th>\n",
       "      <th>Height_cms</th>\n",
       "      <th>Reach_cms</th>\n",
       "      <th>Weight_lbs</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10036</th>\n",
       "      <td>Dan Stittgen</td>\n",
       "      <td>2012-07-11</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.36000</td>\n",
       "      <td>0.57000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>22.000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>7.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>22.000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>36.000</td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>253.00</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",
       "      <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>Orthodox</td>\n",
       "      <td>185.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6524</th>\n",
       "      <td>Michal Oleksiejczuk</td>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.58625</td>\n",
       "      <td>0.35125</td>\n",
       "      <td>0.0825</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>31.75</td>\n",
       "      <td>52.25</td>\n",
       "      <td>16.125</td>\n",
       "      <td>66.625</td>\n",
       "      <td>37.375</td>\n",
       "      <td>57.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.375</td>\n",
       "      <td>19.75</td>\n",
       "      <td>41.625</td>\n",
       "      <td>9.125</td>\n",
       "      <td>13.625</td>\n",
       "      <td>10.625</td>\n",
       "      <td>9.375</td>\n",
       "      <td>6.25</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>54.125</td>\n",
       "      <td>26.75</td>\n",
       "      <td>49.625</td>\n",
       "      <td>13.625</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.625</td>\n",
       "      <td>2.625</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.625</td>\n",
       "      <td>3.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.25</td>\n",
       "      <td>25.0</td>\n",
       "      <td>368.25</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Southpaw</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187.96</td>\n",
       "      <td>205.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>Marcos Mariano</td>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.33000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>20.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>37.000</td>\n",
       "      <td>18.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>215.0</td>\n",
       "      <td>295.00</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",
       "      <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>Switch</td>\n",
       "      <td>185.42</td>\n",
       "      <td>193.04</td>\n",
       "      <td>155.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788</th>\n",
       "      <td>Jimmy Quinlan</td>\n",
       "      <td>2013-04-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Orthodox</td>\n",
       "      <td>182.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11947</th>\n",
       "      <td>Tai Bowden</td>\n",
       "      <td>1996-05-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   fighter        date  avg_KD  avg_opp_KD  avg_SIG_STR_pct  \\\n",
       "10036         Dan Stittgen  2012-07-11   0.000         1.0          0.36000   \n",
       "6524   Michal Oleksiejczuk  2020-02-22   0.875         0.0          0.58625   \n",
       "6730        Marcos Mariano  2019-09-21   0.000         0.0          0.33000   \n",
       "9788         Jimmy Quinlan  2013-04-13     NaN         NaN              NaN   \n",
       "11947           Tai Bowden  1996-05-17     NaN         NaN              NaN   \n",
       "\n",
       "       avg_opp_SIG_STR_pct  avg_TD_pct  avg_opp_TD_pct  avg_SUB_ATT  \\\n",
       "10036              0.57000      0.0000            0.00          0.0   \n",
       "6524               0.35125      0.0825            0.25          0.0   \n",
       "6730               0.50000      0.0000            1.00          0.0   \n",
       "9788                   NaN         NaN             NaN          NaN   \n",
       "11947                  NaN         NaN             NaN          NaN   \n",
       "\n",
       "       avg_opp_SUB_ATT  avg_REV  avg_opp_REV  avg_SIG_STR_att  \\\n",
       "10036            0.000      0.0          0.0             22.0   \n",
       "6524             0.625      0.0          0.0             60.5   \n",
       "6730             1.000      0.0          0.0              9.0   \n",
       "9788               NaN      NaN          NaN              NaN   \n",
       "11947              NaN      NaN          NaN              NaN   \n",
       "\n",
       "       avg_SIG_STR_landed  avg_opp_SIG_STR_att  avg_opp_SIG_STR_landed  \\\n",
       "10036                8.00                38.00                  22.000   \n",
       "6524                31.75                52.25                  16.125   \n",
       "6730                 3.00                40.00                  20.000   \n",
       "9788                  NaN                  NaN                     NaN   \n",
       "11947                 NaN                  NaN                     NaN   \n",
       "\n",
       "       avg_TOTAL_STR_att  avg_TOTAL_STR_landed  avg_opp_TOTAL_STR_att  \\\n",
       "10036             22.000                 8.000                   38.0   \n",
       "6524              66.625                37.375                   57.5   \n",
       "6730              11.000                 4.000                   64.0   \n",
       "9788                 NaN                   NaN                    NaN   \n",
       "11947                NaN                   NaN                    NaN   \n",
       "\n",
       "       avg_opp_TOTAL_STR_landed  avg_TD_att  avg_TD_landed  avg_opp_TD_att  \\\n",
       "10036                      22.0       0.000           0.00             0.0   \n",
       "6524                       21.0       0.375           0.25             1.5   \n",
       "6730                       39.0       0.000           0.00             1.0   \n",
       "9788                        NaN         NaN            NaN             NaN   \n",
       "11947                       NaN         NaN            NaN             NaN   \n",
       "\n",
       "       avg_opp_TD_landed  avg_HEAD_att  avg_HEAD_landed  avg_opp_HEAD_att  \\\n",
       "10036                0.0         5.000             0.00            23.000   \n",
       "6524                 0.5        45.375            19.75            41.625   \n",
       "6730                 1.0         7.000             2.00            37.000   \n",
       "9788                 NaN           NaN              NaN               NaN   \n",
       "11947                NaN           NaN              NaN               NaN   \n",
       "\n",
       "       avg_opp_HEAD_landed  avg_BODY_att  avg_BODY_landed  avg_opp_BODY_att  \\\n",
       "10036                9.000         5.000            1.000             9.000   \n",
       "6524                 9.125        13.625           10.625             9.375   \n",
       "6730                18.000         0.000            0.000             1.000   \n",
       "9788                   NaN           NaN              NaN               NaN   \n",
       "11947                  NaN           NaN              NaN               NaN   \n",
       "\n",
       "       avg_opp_BODY_landed  avg_LEG_att  avg_LEG_landed  avg_opp_LEG_att  \\\n",
       "10036                 7.00         12.0           7.000             6.00   \n",
       "6524                  6.25          1.5           1.375             1.25   \n",
       "6730                  0.00          2.0           1.000             2.00   \n",
       "9788                   NaN          NaN             NaN              NaN   \n",
       "11947                  NaN          NaN             NaN              NaN   \n",
       "\n",
       "       avg_opp_LEG_landed  avg_DISTANCE_att  avg_DISTANCE_landed  \\\n",
       "10036                6.00            22.000                 8.00   \n",
       "6524                 0.75            54.125                26.75   \n",
       "6730                 2.00             7.000                 1.00   \n",
       "9788                  NaN               NaN                  NaN   \n",
       "11947                 NaN               NaN                  NaN   \n",
       "\n",
       "       avg_opp_DISTANCE_att  avg_opp_DISTANCE_landed  avg_CLINCH_att  \\\n",
       "10036                36.000                   20.000            0.00   \n",
       "6524                 49.625                   13.625            1.75   \n",
       "6730                  4.000                    2.000            0.00   \n",
       "9788                    NaN                      NaN             NaN   \n",
       "11947                   NaN                      NaN             NaN   \n",
       "\n",
       "       avg_CLINCH_landed  avg_opp_CLINCH_att  avg_opp_CLINCH_landed  \\\n",
       "10036              0.000               0.000                    0.0   \n",
       "6524               1.625               2.625                    2.5   \n",
       "6730               0.000               0.000                    0.0   \n",
       "9788                 NaN                 NaN                    NaN   \n",
       "11947                NaN                 NaN                    NaN   \n",
       "\n",
       "       avg_GROUND_att  avg_GROUND_landed  avg_opp_GROUND_att  \\\n",
       "10036           0.000              0.000                 2.0   \n",
       "6524            4.625              3.375                 0.0   \n",
       "6730            2.000              2.000                36.0   \n",
       "9788              NaN                NaN                 NaN   \n",
       "11947             NaN                NaN                 NaN   \n",
       "\n",
       "       avg_opp_GROUND_landed  avg_CTRL_time(seconds)  \\\n",
       "10036                    2.0                    0.00   \n",
       "6524                     0.0                   47.25   \n",
       "6730                    18.0                    0.00   \n",
       "9788                     NaN                     NaN   \n",
       "11947                    NaN                     NaN   \n",
       "\n",
       "       avg_opp_CTRL_time(seconds)  total_time_fought(seconds)  \\\n",
       "10036                         1.0                      253.00   \n",
       "6524                         25.0                      368.25   \n",
       "6730                        215.0                      295.00   \n",
       "9788                          NaN                         NaN   \n",
       "11947                         NaN                         NaN   \n",
       "\n",
       "       total_rounds_fought  total_title_bouts  current_win_streak  \\\n",
       "10036                    1                  0                   0   \n",
       "6524                     7                  0                   0   \n",
       "6730                     1                  0                   0   \n",
       "9788                     0                  0                   0   \n",
       "11947                    0                  0                   0   \n",
       "\n",
       "       current_lose_streak  longest_win_streak  wins  losses  draw  \\\n",
       "10036                    1                   0     0       1     0   \n",
       "6524                     1                   2     2       2     0   \n",
       "6730                     1                   0     0       1     0   \n",
       "9788                     0                   0     0       0     0   \n",
       "11947                    0                   0     0       0     0   \n",
       "\n",
       "       win_by_Decision_Majority  win_by_Decision_Split  \\\n",
       "10036                         0                      0   \n",
       "6524                          0                      0   \n",
       "6730                          0                      0   \n",
       "9788                          0                      0   \n",
       "11947                         0                      0   \n",
       "\n",
       "       win_by_Decision_Unanimous  win_by_KO/TKO  win_by_Submission  \\\n",
       "10036                          0              0                  0   \n",
       "6524                           0              2                  0   \n",
       "6730                           0              0                  0   \n",
       "9788                           0              0                  0   \n",
       "11947                          0              0                  0   \n",
       "\n",
       "       win_by_TKO_Doctor_Stoppage    Stance  Height_cms  Reach_cms  \\\n",
       "10036                           0  Orthodox      185.42        NaN   \n",
       "6524                            0  Southpaw      182.88     187.96   \n",
       "6730                            0    Switch      185.42     193.04   \n",
       "9788                            0  Orthodox      182.88        NaN   \n",
       "11947                           0       NaN      180.34        NaN   \n",
       "\n",
       "       Weight_lbs   age  \n",
       "10036       170.0  31.0  \n",
       "6524        205.0  24.0  \n",
       "6730        155.0  32.0  \n",
       "9788        185.0  26.0  \n",
       "11947       230.0   NaN  "
      ]
     },
     "execution_count": 1584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5, random_state=seed_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see quite some missing values, our assumption is that these are missing because the fighter did have any previous fights. Let's check this assumption before moving on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1585,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fighters with only one fight have NaN values: True\n"
     ]
    }
   ],
   "source": [
    "# Count the number of fights for each fighter in the original dataset\n",
    "fight_counts = df_original['fighter'].value_counts()\n",
    "\n",
    "# Get the fighters who have only one fight\n",
    "one_fight_fighters = fight_counts[fight_counts == 1].index\n",
    "\n",
    "# Check if these fighters have NaN values in the new fighters dataset\n",
    "nan_values = df_fighters[df_fighters['fighter'].isin(one_fight_fighters)].isna().any(axis=1)\n",
    "\n",
    "print(f\"Fighters with only one fight have NaN values: {nan_values.any()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the above, our assumption was correct. We will handle these missing values later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should determine the columns that we want to use for the clustering of the fighters based on their fighting styles. We will need to drop the columns that we will not be using. Out of all of the columns, these columns offer us some insights as to why a fighter may be in a particular cluster:\n",
    "\n",
    "- `avg_KD`: Average Knockdowns Landed   \n",
    "- `avg_SIG_STR_pct`: Average Significant Strike Percentage  \n",
    "- `avg_TD_pct`: Average Takedown Percentage \n",
    "- `avg_SUB_ATT`: Average Submission Attempts    \n",
    "- `avg_CTRL_time(seconds)`: Average Control Time    \n",
    "\n",
    "These columns provide insights into a fighter's striking, grappling, submission, and control abilities. By clustering fighters based on these features, we can potentially identify distinct fighting styles within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1586,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['avg_KD', 'avg_SIG_STR_pct', 'avg_TD_pct', 'avg_SUB_ATT', 'avg_CTRL_time(seconds)']\n",
    "cols = [col for col in data.columns if any(name in col for name in col_names)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have all the necessary columns for our analysis, we can drop the rest of the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1587,
   "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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10036</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.36000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6524</th>\n",
       "      <td>0.875</td>\n",
       "      <td>0.58625</td>\n",
       "      <td>0.0825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.33000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11947</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_KD  avg_SIG_STR_pct  avg_TD_pct  avg_SUB_ATT  \\\n",
       "10036   0.000          0.36000      0.0000          0.0   \n",
       "6524    0.875          0.58625      0.0825          0.0   \n",
       "6730    0.000          0.33000      0.0000          0.0   \n",
       "9788      NaN              NaN         NaN          NaN   \n",
       "11947     NaN              NaN         NaN          NaN   \n",
       "\n",
       "       avg_CTRL_time(seconds)  \n",
       "10036                    0.00  \n",
       "6524                    47.25  \n",
       "6730                     0.00  \n",
       "9788                      NaN  \n",
       "11947                     NaN  "
      ]
     },
     "execution_count": 1587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[cols]\n",
    "data.sample(5, random_state=seed_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Distribution of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(20,20), bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the data in this data set does not seem to be following the Gaussian distribution. We will take this into consideration when we are making decisions in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for Duplicate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1589,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461"
      ]
     },
     "execution_count": 1589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the duplicated rows\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have quite some duplicate data in our data set, let's investigate further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1590,
   "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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6156</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10897</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9345</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11594</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_KD  avg_SIG_STR_pct  avg_TD_pct  avg_SUB_ATT  \\\n",
       "6156      NaN              NaN         NaN          NaN   \n",
       "10897     NaN              NaN         NaN          NaN   \n",
       "9345      0.0             0.33         0.0          0.0   \n",
       "11594     NaN              NaN         NaN          NaN   \n",
       "12001     NaN              NaN         NaN          NaN   \n",
       "\n",
       "       avg_CTRL_time(seconds)  \n",
       "6156                      NaN  \n",
       "10897                     NaN  \n",
       "9345                      0.0  \n",
       "11594                     NaN  \n",
       "12001                     NaN  "
      ]
     },
     "execution_count": 1590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated(keep=False)].sample(5, random_state=seed_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, these are just the rows with missing data, it appears that these are fighters with no fights in the UFC, and thus they have NaN values for all of their performance metrics. We will need to handle these rows, before we can move on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check & Handle Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1591,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_KD                    18.56\n",
       "avg_SIG_STR_pct           18.56\n",
       "avg_TD_pct                18.56\n",
       "avg_SUB_ATT               18.56\n",
       "avg_CTRL_time(seconds)    18.56\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().mean().round(4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we are clustering the fighters based on fighting styles, we will need to drop the rows that have missing data. These rows do not provide us any information about the fighters fighting style, thus we will not be able to cluster them. The origin of the missing data is that the fighter has not fought in the UFC yet, and thus they do not have any average statistics. We will need to drop these rows, because we are clustering the fighters based on their average statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1592,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d2/b1g6zx2s0xj74b70lpyzqjlr0000gn/T/ipykernel_13840/1368182302.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now perform a final check to check how many missing values are left in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1593,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_KD                    0.0\n",
       "avg_SIG_STR_pct           0.0\n",
       "avg_TD_pct                0.0\n",
       "avg_SUB_ATT               0.0\n",
       "avg_CTRL_time(seconds)    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1593,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the percentage of missing values again\n",
    "data.isnull().mean().round(4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see there are no more missing values in the data set. We can now move on to the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1594,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List to store columns with outliers\n",
    "cols_with_outliers = []\n",
    "\n",
    "# Loop through each column in the dataframe\n",
    "for col in data.columns:\n",
    "    Q1 = data[col].quantile(0.25)\n",
    "    Q3 = data[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    # Check if any outliers in this column\n",
    "    if ((data[col] < (Q1 - 1.5 * IQR)) | (data[col] > (Q3 + 1.5 * IQR))).any():\n",
    "        cols_with_outliers.append(col)\n",
    "\n",
    "# Determine the number of rows needed for the plots\n",
    "n_rows = len(cols_with_outliers) // 4\n",
    "if len(cols_with_outliers) % 4:\n",
    "    n_rows += 1\n",
    "\n",
    "# Create a grid of subplots\n",
    "fig, axes = plt.subplots(n_rows, 4, figsize=(20, 5*n_rows))\n",
    "\n",
    "# Flatten the axes array and remove extra subplots\n",
    "axes = axes.flatten()\n",
    "for ax in axes[len(cols_with_outliers):]:\n",
    "    fig.delaxes(ax)\n",
    "\n",
    "# Plot boxplots for columns with outliers\n",
    "for col, ax in zip(cols_with_outliers, axes):\n",
    "    sns.boxplot(y=data[col], ax=ax)\n",
    "    ax.set_title(col)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we can see a lot of outliers in the data, most of them can be justified by the fact that the UFC has a very diverse roster of fighters. There are many fighters that have just started their career and have not had many fights. There are also many fighters that have been fighting for a long time and have had many fights. Also due to the fact that MMA has many different fighting styles, there are many different ways to win a fight. Some fighters are better at striking, some are better at grappling, and some are better at submissions. These are the justifiable outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the data needs to be transformed before we can use it in our analysis. We will transform the data in the following ways:\n",
    "- Scale the numerical data, to ensure that the data is on the same scale. This is necessary for clustering algorithms, because they are distance based."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.170707</td>\n",
       "      <td>0.436045</td>\n",
       "      <td>0.247810</td>\n",
       "      <td>0.372936</td>\n",
       "      <td>121.208329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.303589</td>\n",
       "      <td>0.137061</td>\n",
       "      <td>0.257392</td>\n",
       "      <td>0.625476</td>\n",
       "      <td>125.302872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.402832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.438672</td>\n",
       "      <td>0.183255</td>\n",
       "      <td>0.046631</td>\n",
       "      <td>81.742188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.510295</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>183.269531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>749.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_KD  avg_SIG_STR_pct   avg_TD_pct  avg_SUB_ATT  \\\n",
       "count  1742.000000      1742.000000  1742.000000  1742.000000   \n",
       "mean      0.170707         0.436045     0.247810     0.372936   \n",
       "std       0.303589         0.137061     0.257392     0.625476   \n",
       "min       0.000000         0.000000     0.000000     0.000000   \n",
       "25%       0.000000         0.360000     0.000000     0.000000   \n",
       "50%       0.000000         0.438672     0.183255     0.046631   \n",
       "75%       0.250000         0.510295     0.400000     0.500000   \n",
       "max       2.000000         1.000000     1.000000     5.000000   \n",
       "\n",
       "       avg_CTRL_time(seconds)  \n",
       "count             1742.000000  \n",
       "mean               121.208329  \n",
       "std                125.302872  \n",
       "min                  0.000000  \n",
       "25%                 25.402832  \n",
       "50%                 81.742188  \n",
       "75%                183.269531  \n",
       "max                749.000000  "
      ]
     },
     "execution_count": 1595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scale Numerical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because clustering is distance based, we need to scale the data so that the distance between the data points is not skewed by the magnitude of the data. As our data is not Gaussian distributed, and does contain outliers (that are justifiable), we will use the `RobustScaler` to scale our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1596,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the RobustScaler\n",
    "scaler = RobustScaler()\n",
    "\n",
    "# fit the scaler and transform the data\n",
    "data_scaled = scaler.fit_transform(data)\n",
    "\n",
    "# create a pandas DataFrame\n",
    "data_scaled = pd.DataFrame(data_scaled, columns=data.columns)\n",
    "\n",
    "# replace the original data with the scaled data\n",
    "data = data_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Final Transformed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1597,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>-0.614156</td>\n",
       "      <td>-0.038313</td>\n",
       "      <td>-0.062012</td>\n",
       "      <td>0.071931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.257306</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.517792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.324882</td>\n",
       "      <td>0.657488</td>\n",
       "      <td>1.156738</td>\n",
       "      <td>1.654927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.323842</td>\n",
       "      <td>0.210613</td>\n",
       "      <td>1.406738</td>\n",
       "      <td>0.817195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>0.523499</td>\n",
       "      <td>-1.395900</td>\n",
       "      <td>0.984939</td>\n",
       "      <td>-0.092987</td>\n",
       "      <td>-0.067343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        avg_KD  avg_SIG_STR_pct  avg_TD_pct  avg_SUB_ATT  \\\n",
       "448   0.625000        -0.614156   -0.038313    -0.062012   \n",
       "1349  0.000000        -0.257306   -0.458137    -0.093262   \n",
       "1707  0.500000         0.324882    0.657488     1.156738   \n",
       "984   0.000000        -0.323842    0.210613     1.406738   \n",
       "949   0.523499        -1.395900    0.984939    -0.092987   \n",
       "\n",
       "      avg_CTRL_time(seconds)  \n",
       "448                 0.071931  \n",
       "1349               -0.517792  \n",
       "1707                1.654927  \n",
       "984                 0.817195  \n",
       "949                -0.067343  "
      ]
     },
     "execution_count": 1597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5, random_state=seed_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1598,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1.742000e+03</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>1742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.682830</td>\n",
       "      <td>-0.017481</td>\n",
       "      <td>1.613887e-01</td>\n",
       "      <td>0.652611</td>\n",
       "      <td>0.249997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.214355</td>\n",
       "      <td>0.911947</td>\n",
       "      <td>6.434806e-01</td>\n",
       "      <td>1.250952</td>\n",
       "      <td>0.793726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.918738</td>\n",
       "      <td>-4.581367e-01</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.517792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.523449</td>\n",
       "      <td>-4.581367e-01</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.356879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.469447e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.476551</td>\n",
       "      <td>5.418633e-01</td>\n",
       "      <td>0.906738</td>\n",
       "      <td>0.643121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.734841</td>\n",
       "      <td>2.041863e+00</td>\n",
       "      <td>9.906738</td>\n",
       "      <td>4.226717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_KD  avg_SIG_STR_pct    avg_TD_pct  avg_SUB_ATT  \\\n",
       "count  1742.000000      1742.000000  1.742000e+03  1742.000000   \n",
       "mean      0.682830        -0.017481  1.613887e-01     0.652611   \n",
       "std       1.214355         0.911947  6.434806e-01     1.250952   \n",
       "min       0.000000        -2.918738 -4.581367e-01    -0.093262   \n",
       "25%       0.000000        -0.523449 -4.581367e-01    -0.093262   \n",
       "50%       0.000000         0.000000  3.469447e-17     0.000000   \n",
       "75%       1.000000         0.476551  5.418633e-01     0.906738   \n",
       "max       8.000000         3.734841  2.041863e+00     9.906738   \n",
       "\n",
       "       avg_CTRL_time(seconds)  \n",
       "count             1742.000000  \n",
       "mean                 0.249997  \n",
       "std                  0.793726  \n",
       "min                 -0.517792  \n",
       "25%                 -0.356879  \n",
       "50%                  0.000000  \n",
       "75%                  0.643121  \n",
       "max                  4.226717  "
      ]
     },
     "execution_count": 1598,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the data has been scaled properly, the data now has different means and standard deviations values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Model Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our data has been prepared, we can now move on to the modeling phase. Since we will be clustering without labels, we will be using unsupervised learning algorithms. There is no real way to determine which algorithm will perform the best based on metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1599,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_k(X, model, min_k=2, max_k=10):\n",
    "    silhouette_scores = {}\n",
    "    best_k = None\n",
    "    best_score = -1\n",
    "\n",
    "    for k in range(min_k, max_k+1):\n",
    "        model.n_clusters = k\n",
    "        model.fit(X)\n",
    "        cluster_labels = model.labels_\n",
    "        silhouette_score = metrics.silhouette_score(X, cluster_labels)\n",
    "        silhouette_scores[k] = silhouette_score\n",
    "\n",
    "        if silhouette_score > best_score:\n",
    "            best_score = silhouette_score\n",
    "            best_k = k\n",
    "\n",
    "    plt.plot(list(silhouette_scores.keys()), list(silhouette_scores.values()), 'bx-')\n",
    "    plt.xlabel('Values of K')\n",
    "    plt.ylabel('Silhouette score')\n",
    "    plt.title('Silhouette analysis For Optimal k')\n",
    "    plt.show()\n",
    "\n",
    "    return best_k, best_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Means Clustering is a clustering algorithm that aims to partition n observations into k clusters. This is done by assigning each observation to the cluster with the nearest mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1600,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = data.copy()\n",
    "best_k, best_score = find_best_k(X, KMeans(random_state=seed_value, n_init=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best K-Means model is the one with 3 clusters. This model has the highest Silhouette Score, thus it is the best model. The silhouette score is a measure of how similar an object is to its own cluster compared to other clusters. The silhouette scores range from -1 to 1, where a higher score indicates that the object is better matched to its own cluster and worse matched to neighboring clusters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1601,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the model\n",
    "kmeans = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n",
    "\n",
    "# Fit and predict clusters\n",
    "X['Cluster'] = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34843682181770314"
      ]
     },
     "execution_count": 1602,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1603,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RFi1alOSJFfKJEyemubk5b3/72/3lFRg06uvrM3v27DQ1Ne2yZd2jHAGg/ynk0As7T1mfNWvWbu8hv/feezNv3ry0tLTkmGOOKTouwB6rr6/PlClTdvlc8+UiAPQ/hRx64cmnrFdUVOxSup2yDgxmu/tcAxjMOjo6fNHIgKSQQy88+ZT1SZMm7TLulHUAgIGhubk5TU1NaWtr675WXV2dhoYGt+JQOIe6QS/sPI14yZIl6ezs7DHW2dmZpUuXOo0YAKBgzc3NaWxsTE1NTebPn5/Fixdn/vz5qampSWNjY5qbm4uOyH5OIYdeePJpxAsWLEhra2u2bt2a1tbWLFiwwGnEAAAF6+joSFNTU+rq6jJ37txMmjQpw4cPz6RJkzJ37tzU1dWlqakpHR0dRUdlP6aQQy/tPI14zZo1mTdvXs4888zMmzcva9ascRoxAEDBdh7CO3369JSX96w95eXlOe2009LW1paWlpaCEoJ7yKFPnEYMADAwPfkQ3t1xCC8DgUIOfeQ0YqDUbNu2LU1NTfmf//mfPP3pT09DQ0MOOOCAomMBPCVPPoR3d4+pdQgvA4FCDgB0+/jHP54VK1Z0v/7tb3+bH/3oRznhhBNy0UUXFZgM4KnZeQjvtddem02bNmXDhg3dY1VVVRk1apRDeCmce8gBgCS7lvEnW7FiRT7+8Y/v40QAvVdRUZH6+vqsXr0627dvz3ve85584QtfyHve855s3749q1evTn19vVsNKZQVcgAg27Zt6y7jxx9/fN70pjdl/PjxWbt2ba6//vr8+te/zooVK7Jt2zbb14FBoaOjI83NzZk4cWI2bdqUz3/+891j1dXVmThxYpqbm/P2t79dKacwCjkAkK9+9atJknHjxmXOnDlpbW3NypUrU1lZmTlz5uSCCy7IX/7yl3z1q1/Nu9/97oLTAvxjO09ZnzVr1m7vIb/33nszb968tLS0OA+IwijkAEDuvffeJMmUKVPyvve9L21tbd1j1dXVecELXpBly5Z1zwMY6J58yvruDuF1yjoDgUIOAOTggw9Oknz/+9/PCSeckFmzZnVvWV+yZEmWLVvWYx7AQPfkU9YnTZq0y7hT1hkIHOoGAOS1r31t9+9nzZqVSZMmZfjw4Zk0aVJmzZq123kAA9nOU9aXLFmSzs7OHmOdnZ1ZunSpU9YpnEIOAGTIkP/dNHfWWWdl8eLFuf/++7N48eKcddZZu50HMJBVVFSkoaEhK1euzIIFC9La2pqtW7emtbU1CxYsyMqVK9PQ0OBANwrl/1UBgGzatKn79x0dHfnud7+b7373u393HsBAV19fn9mzZ6epqSnz5s3rvl5dXZ3Zs2envr6+wHSgkAMA+d97KE8++eT8/Oc/77G9s7y8PCeeeGJuvfVW91oCg059fX2mTJmyyynrVsYZCBRy6KOOjg4f8MCgV1tbm1GjRuXWW2/N8ccfn+OPPz7Dhg3L9u3b8+tf/zq33nprRo8e7V5LYFDa3SnrMBAo5NAHzc3NaWpq2uXxQA0NDbZAAYNaV1dXj1//+vcAQN8p5NBLzc3NaWxsTF1d3S6PB2psbHRfEjCotLS0ZNOmTd1b1n/96193j5WXl+fkk0/OrbfempaWFqtMANBPFHLohY6OjjQ1NaWuri5z585NefkTDyyYNGlS5s6dmwULFqSpqSlTpkyxfR0YFNrb25Mkt912W44//vg8//nPzwEHHJBt27blN7/5TW677bYe8wCAvvPYM+iFlpaWtLW1Zfr06d1lfKfy8vKcdtppaWtrS0tLS0EJAZ6aUaNGJUmOOuqoXHjhhXnVq16VU045Ja961aty4YUX5qijjuoxDwDoO4UcemHnCtH48eN3O15TU9NjHgAAwF+zZR16Yedjf9auXZtJkybtMr5mzZoe8wAGup3PF1+1alU+8YlP7LJlfdWqVT3mAQB9p5BDL9TW1qa6ujpLlizpcQ95knR2dmbp0qWprq72eCBg0Nj5BeJJJ52Un//851m5cmX3WFlZWU466aTcdtttvmgEgH5kyzr0QkVFRRoaGrJy5cosWLAgra2t2bp1a1pbW7NgwYKsXLkyDQ0NDnQDBo2dzyG/7bbbMnTo0B5jQ4cOzW233eY55ADQz6yQQy/V19dn9uzZaWpqyrx587qvV1dXe+QZMCjt2LEjSTJ8+PCcffbZOeGEE7JixYp885vfzPbt27N9+/aCEwJAaVHIoQ/q6+szZcqUtLS0pL29PZWVlamtrbUyDgw6d999d7Zu3ZrDDz8827dvz6JFi7Jo0aIkSVVVVQ4//PD8+c9/zt13351jjz224LQAUBoUcuijioqKHHPMMUXHAOiTu+++O0nyzne+M895znN2+aLx7rvvzkc+8hGFHAD6kUIOAPTgi0YA2Dcc6gYAZPLkyUmSb33rW+ns7Owx1tnZmW9/+9s95gEAfWeFHADI5MmTM2rUqO7nkE+fPj01NTVZs2ZNlixZklWrVmX06NEKOZSI9evXZ/PmzUXHYB8ZMWJExo4dW3QMdkMhBwBSUVGRd7/73bniiivyu9/9rsdzyIcNG5Ykede73uXQSigBmzZtynnnnbfLbhhKV3l5ea655pqMGjWq6Cj8FYUcAEjyxJMj3v/+9+erX/1qNmzY0H29srIyDQ0NHucIJWLUqFG58sor97sV8nXr1mXhwoU5//zzc8QRRxQdZ58aMWKEMj5AKeQAQLf6+vpMnjw5l1xySTZu3JgxY8bkIx/5SEaOHFl0NKAf7c/bl4844ohMmDCh6BiQRCEHAJ7k3HPPzfr167tfb968OWeffXbGjh2bq6++usBkAFB6nLIOACTpWcaPO+64XH755TnuuOOSPHEA1LnnnltkPAAoOVbIAYBs3ry5u4w3NTXloIMOSpLMmzcvW7ZsSUNDQ/epzCNGjCgyKgCUDCvkAEDmz5+f5ImV8Z1lfKeDDjooz3ve83rMAwD6TiEHALpPVX/Tm9602/HTTz+9xzwAoO8UcgAgVVVVSZLrr79+t+M33HBDj3kAQN8N+EK+atWqnH322XnBC16QE088MXPnzs3GjRuLjgUAJeXiiy9Oktx5553ZsmVLj7EtW7bkrrvu6jEPAOi7AV3IH3vssbzzne/Mcccdl9tuuy3Lli1Le3u7vwwAQD8bMWJE93OJGxoactlll+UPf/hDLrvssjQ0NCR54rnFDnQDgP4zoAv5/fffn6OPPjrnnntuhg0blkMOOSRnnHFGfvWrXxUdDQBKztVXX91dyu+6665ccskl3SvjnkMOAP1vQD/2bMKECbnmmmt6XPvRj36UyZMnF5QIAErb1Vdfnc2bN2f+/PnZsGFDqqqqcvHFF1sZB4C9YEAX8ifr6urKZz7zmfz0pz/N1772taLjAEDJOvDAAzNjxoy0t7ensrIyBx54YNGRAKAkDYpC/uijj+YDH/hA7r777nzta1/LUUcdVXQkAChJzc3NaWpqSltbW/e16urqNDQ0pL6+vsBkAFB6BvQ95EmyZs2anH766Xn00Udz/fXXK+MAsJc0NzensbExNTU1mT9/fhYvXpz58+enpqYmjY2NaW5uLjoiAJSUAV3IH3744Zx11lk5/vjjc+2112bMmDFFRwKAktTR0ZGmpqbU1dVl7ty5mTRpUoYPH55JkyZl7ty5qaurS1NTUzo6OoqOCgAlY0AX8iVLluT+++/PD3/4w9TV1eW4447r/gcA6D8tLS1pa2vL9OnTU17e868H5eXlOe2009LW1paWlpaCEgJA6RnQ95CfffbZOfvss4uOAQAlr729PUkyfvz43Y7X1NT0mAcA9N2AXiEHAPaNysrKJMnatWt3O75mzZoe8wCAvhvQK+QAMBCsX78+mzdvLjrGXnXggQfmkEMOyeLFi3PWWWf12Lbe2dmZr33taxkzZkwOPPDA/PGPfyww6d43YsSIjB07tugYAOwHFHIA+Ds2bdqU8847L52dnUVH2SceeuihXHTRRX9z/O+NlYry8vJcc801GTVqVNFRAChxCjkA/B2jRo3KlVdeWfIr5Dv97ne/y7Jly/LQQw91XxszZkxe85rX5LnPfW6ByfadESNGKOMA7BMKOQD8A/vT9uUJEybkta99bX7yk59k0aJFmTlzZk455ZRUVFQUHQ0ASo5D3QCAHioqKjJx4sQkycSJE5VxANhLFHIAAAAogEIOAAAABXAPOf1uf3g8EP/L44EAAKB3FHL61f72eCA8HggAAHpLIadf7W+PB9pp3bp1WbhwYc4///wcccQRRcfZpzweCAAAekchp9/tz9uXjzjiiEyYMKHoGAAAwCDgUDcAAAAogEIOAAAABVDIAQAAoAAKOQAAABRAIQcAAIACKOQAAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAijkAAAAUACFHAAAAAqgkAMAAEABFHIAAAAogEIOAAAABVDIAQAAoAAKOQAAABRAIQcAAIACKOQAAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAijkAAAAUACFHAAAAAqgkAMAAEABelXIP/CBD2Tt2rW7HfvjH/+Y97znPX0KBQAAAKVuyJ5OvP/++7t//53vfCfTpk1LRUXFLvN+9rOf5Re/+EX/pAMAAIAStceF/CMf+UhuueWW7tfvfe97dzuvq6srJ554Yt+TAQAAQAnb40J+6aWX5he/+EW6urpy8cUX51/+5V9SU1PTY055eXlGjRqVqVOn9ntQAAAAKCV7XMjHjh2b0047LUlSVlaWl770pTn44IMzdOjQJMnWrVuzbdu2VFZW7pWgAAAAUEp6dajba17zmjQ2NuYtb3lL97U777wzJ510Ui6//PJ0dHT0W0AAAAAoRb0q5AsXLsyNN96YN77xjd3XJk+enAsvvDBLly7NF7/4xf7KBwAAACVpj7esP9kPfvCDXHjhhTnjjDO6r40ePTpnnnlmysvL85WvfMWjzwAAAODv6NUK+UMPPZQjjjhit2PPetazsn79+j6FAgAAgFLXq0I+ceLE/OhHP9rt2E033ZRnPOMZfQoFAAAApa5XW9b/+Z//ObNnz057e3umTZuWpz3tadm4cWNuvvnm/PjHP87HPvax/s4JAAAAJaVXhfw1r3lNHnnkkVx11VX58Y9/3H39kEMOyYc+9KEeh70BAAAAu+pVIU+St771rTnjjDPypz/9Ke3t7Rk1alQmTJiQ8vJe7YIHAACA/UqvC3mS/PGPf8yKFSvy8MMP52lPe1qGDx+eww8/vL+yAQAAQMnqVSHftm1b5syZk5tuuildXV3d18vLy3PGGWfkkksuSVlZWb+F3Gnjxo0544wz8tGPfjRTp07t958PAAAA+0qvCvknP/nJ3HLLLbnooovyT//0TxkzZkwefPDBLF++PJ/5zGcybty4zJw5s1+Drly5MhdddFHWrFnTrz8XAAAAitCrG75/+MMf5oILLshZZ52VcePGZdiwYXn605+es88+O+edd16+9a1v9WvIpUuX5v3vf38uuOCCfv25AAAAUJReFfItW7ZkwoQJux07+uij89BDD/Up1F876aSTctNNN+XVr351v/5cAAAAKEqvCvk//dM/5Wtf+1o6Ozt3Gfvud7+bl73sZX0O9mRVVVUZMqRP588BAADAgNKrlvvc5z43n/3sZ/Oa17wmr3nNa1JdXZ329vb85Cc/yW9/+9ucddZZueqqq5IkZWVlOffcc/s1NAAAAAx2vSrkl112WZJk06ZN3cX7yb785S93/14hBwAAgF31qpCvWrWqv3MAAADAfqVX95DvqY6OjtTW1ubuu+/em28DAAAAg85ePymtq6urX39ea2trv/48AAAAKMJeXSEHAAAAdk8hBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAXYK4X8scceS5KUlZVlypQpGTFixN54GwAAABi0nnIhX716dVavXv03x2+88caceuqpT/zw8vIsXrw4z3zmM3sdEAAAAErRkD2d+OCDD+bcc8/NXXfdlSQ59thj8/nPfz6HHHJIkuTee+/NZZddljvuuMOKOAAAAPwDe7xC3tjYmLvvvjvvete7csEFF+S+++7LFVdckST54he/mNNOOy133HFHXv/612f58uV7LTAAAACUgj1eIb/99tvz7ne/O+edd16SZOLEibn44oszbty4XH311XnOc56Tf/u3f8vznve8vRYWAAAASsVT2rJ+wgkndL+eMmVKHn744SxatCjnn39+3vOe96S83KHtT7Zhw4Y88sgjRcdgH1i3bl2PXyl9I0eOTFVVVdExAAAYxPa4kG/fvr3HveE7f//P//zPOeecc/o/2SC3YcOGvO/887N9x46io7APLVy4sOgI7CPDhg7NZxcuVMoBAOi1PS7kf8u0adP6I0fJeeSRR7J9x468bsi2HFrWWXQcoB890FWe7+944r9zhRwAgN7qcyGvqKjojxwl69Cyzowr7yo6BtCfOn3JBgBA3z2lQv6HP/wh27ZtS5J0dHSkrKwsf/jDH7Jly5Zd5k6ZMqV/EgIAAEAJekqF/NJLL+3xuqurKx/60IdSVlbW41pZWVlaWlr6JyEAAACUoD0u5E1NTXszBwAAAOxX9riQH3HEEamqqsrQoUP3Zh4AAADYL+zxg8Nf/vKX24YOAAAA/WSPC3lXl5PCAQAAoL/scSEHAAAA+s9TOmV9w4YNuf/++/do7mGHHdarQAAAALA/eEqF/L3vfe8ez3W/OQAAAPxtT6mQv+c970lNTc3eygIAAAD7jadUyF/2spfl2GOP3VtZAAAAYL/hUDcAAAAogEIOAAAABdjjQv6xj30s48eP35tZAAAAYL+xx/eQn3baaX9z7N577829996bZz/72Zk4cWK/BAMAgL1tw4YNeeSRR4qOwT6wbt26Hr9S+kaOHJmqqqqiY/xdT+lQt5tvvjmf/exn85a3vCVnnnlmkuQTn/hEvvKVr6SrqytlZWWZMWNGLrnkkr0SFgAA+suGDRvyvvPPz/YdO4qOwj60cOHCoiOwjwwbOjSfXbhwQJfyPS7kv/zlL3PeeeflOc95To488sgkyc9//vN8+ctfzpQpUzJv3rz88Y9/zAc/+MFMnjw5p59++l4LDQAAffXII49k+44ded2QbTm0rLPoOEA/eqCrPN/f8cR/5yVRyL/85S/npJNOyqJFi1Je/sSt59/85jdTVlaW+fPnZ/z48Tn66KNzzz335Nvf/rZCDgDAoHBoWWfGlXcVHQPoT52D40u2PT7U7a677sqb3/zm7jLe2dmZ22+/PUceeWSPw95e8IIX5J577un/pAAAAFBC9riQP/LIIxkzZkz369bW1jz66KOZOnVqzx9YXp7OQfJtBAAAABRljwv5oYcemvvvv7/79e23356ysrLU19f3mNfS0jKg9+gDAADAQLDHhfzEE0/MV7/61WzevDmPPPJIvvWtb+Xggw/OySef3D2nvb09TU1Nu6yaAwAAAD3t8aFu5557bt7ylrfkxBNPTFlZWbZu3Zp/+7d/ywEHHJAkueqqq3LDDTdk06ZNmTlz5l4LDAAAAKVgjwv5YYcdlu985zv51re+lQcffDAvfelL8+IXv7h7fMmSJRk3blyuuuqqHoe8AQAAALva40KePHEf+bnnnrvbsZtvvrn7BPYn+9WvfpXJkyfnoIMO6l1CAAAAKEF7fA/5P/xBuynjHR0daWhoyJ/+9Kf+ehsAAAAoCf1WyP+Wrq6uvf0WAAAAMOg8pS3rAOzfNmzYkEceeaToGOwD69at6/ErpW/kyJEeXQuwjynkAOyRDRs25H3nn5/tO3YUHYV9aOHChUVHYB8ZNnRoPrtwoVIOsA8p5ADskUceeSTbd+zI64Zsy6FlnUXHAfrRA13l+f6OJ/47V8gB9h2FHICn5NCyzowrdz4IlJROX7IBFGGvH+oGAAAA7EohBwAAgAIo5AAAAFCAvVrIy8vL8973vjfV1dV7820AAABg0HnKh7r95je/ydatW/PCF74wnZ2decc73tFj/FWvelVmzJiRJCkrK8t73/vefgkKAAAApeQprZB/5CMfyYwZM/L1r389SdLV1ZU77rgjjz76aLq6urJ+/fosWLAgbW1teyUsAAAAlIo9LuTf//73c91112Xu3Ln57Gc/22Pssssuy+LFi/Ptb387w4YNy7e//e1+DwoAAAClZI+3rF9//fV5wxvekLPPPvtvzhk9enSmT5+eW265xVb1/++BrrLEoz2hpDzQVVZ0BAAASsAeF/JVq1blrLPO+ofz6uvr8+///u99ClVKvv/4gUVHAAAAYADa40L+2GOPZdSoUT2uVVRU5Mtf/nKe9axndV8bPnx4Ojo6+i/hIPe6IY/l0LKuomMA/eiBrjJftgEA0Gd7XMirqqqybt26nHDCCT2uv/CFL+zx+k9/+lPGjh3bP+lKwKFlXRlXrpBDSXEbCgAA/WCPD3WbMmVKvvOd7/zdOV1dXbnhhhty0kkn9TUXAAAAlLQ9LuQzZszIr371q3z0ox/N9u3bdxnv6urKxz72saxatar7OeQAAADA7u3xlvVjjz02c+bMySc+8Yn85Cc/ySte8Yo84xnPSJL8+c9/zs0335x169blQx/6UCZOnLjXAgMAAEAp2ONCniTveMc7Ultbm6uvvjqLFy9OZ+f/3kh5/PHH58Mf/vAu95QDAAAAu3pKhTxJpk6dmqlTp+aRRx7JunXr0tnZmcMOOyyHHHLI3sgHAAAAJekpF/KdRo4cmdra2v7MAgAAAPuNPS7k/+iE9b/2xje+8SlGAQAAgP3HHhfyiy66aI9/aFlZmUIOAAAAf8ceF/L/+I//2Js5AAAAYL+yx4X88MMP35s5AAAAYL/ylA916+rqyvbt23PAAQd0X7vlllty77335qijjspJJ53UrwEBAACgFD2lQt7U1JQrr7wy55xzTs4+++wkyaxZs/KjH/0oXV1dKSsry0te8pJcddVVGTKk1we4AwAAQMkr39OJP/7xjzN//vxMnTo1U6ZMSZLceOONWb58eV75ylfmV7/6Vb75zW/mt7/9bRYvXrzXAgMAAEAp2ONC/o1vfCOve93rctVVV+WYY45JkixZsiQVFRX50Ic+lJEjR+Z5z3tezj777Hz3u9/da4EBAACgFOxxIW9pacmrXvWq7tePP/54VqxYkdra2hx66KHd14899tjcd999/ZsSAAAASsweF/ItW7Zk5MiR3a/vvvvuPPbYY3nBC17QY15nZ2f/pQMAAIAStceFfNy4cfnv//7v7te33nprysrKcuKJJ/aYd+edd+bpT396vwVMkgcffDDnnHNOTjjhhEydOjWXX355Hn/88X59DwAAANiX9riQn3LKKbnmmmuyZs2a/Pd//3e+/e1v52lPe1rq6+u756xduzZNTU39/uizWbNm5aCDDsqtt96a66+/Prfffnu+8pWv9Ot7AAAAwL60x88m+5d/+Zfceuut+ad/+qckSUVFRT7zmc+koqIiSXLxxRdn+fLlOfjggzNz5sx+C3jffffljjvuyM9+9rMMHz4848ePzznnnJNPfvKTeec739lv7wMAwP7pga6yxF2XUFIe6CorOsIe2eNCXllZmaVLl+aHP/xhHnzwwZx88smZNGlS9/gf//jHnHLKKbngggvytKc9rd8C3nPPPamsrMzYsWO7r02cODH3339/Nm3alFGjRvXbewEAsP/5/uMHFh0B2E/tcSFPkgMOOCBvfOMbdzv2zW9+c7fXv/Od7+RlL3tZRo8e/ZTDJcnmzZszfPjwHtd2vt6yZYtCDgBAn7xuyGM5tKyr6BhAP3qgq2xQfNn2lAr5U9XR0ZEPfOADuf7663tdyA866KBs3bq1x7Wdr0eMGNHnjAAA7N8OLevKuHKFHErKILkNZY8Pdeutrq6+fbg9+9nPTnt7ex544IHua6tXr864ceN6PIYNAAAABpO9Xsj76pnPfGbq6uoyf/78PProo1m7dm0+97nP5U1velPR0QAAAKDXBnwhT5KFCxfm8ccfz8tf/vK85S1vycknn5xzzjmn6FgAAADQa3v1HvL+cuihh2bhwoVFxwAAAIB+MyhWyAEAAKDUKOQAAABQAIUcAAAACqCQAwAAQAH26qFuFRUV+djHPpYjjjhib74NAPvQA11lSWfRKYD+9EBXWdERAPZLvSrkZ555ZsrKdv/BXV5enoMOOijPeMYz8uY3vzmnnXZanwICMLB8//EDi44AAFASelXIx48fn2XLlqWrqyvHHXdcqqqq8uCDD+Y3v/lNduzYkec+97n53e9+l29+85v5+te/nuc85zn9nXvQeKCrPOm0lASl5IGu/ftun9cNeSyHlnUVHQPoRw90lfmyDaAAvSrkVVVVOeyww/KlL30phx12WPf1DRs25J3vfGde/OIXZ+bMmTn33HPzmc98Jl/4whf6LfBgMXLkyAwbOjTf31F0EmBvGDZ0aEaOHFl0jEIcWtaVceUKOZQUawcAhehVIb/hhhsyb968HmU8eaKon3POObnssstyzjnn5K1vfWsuvPDCfgk62FRVVeWzCxfmkUceKToK+8C6deuycOHCnH/++c5M2E+MHDkyVVVVRccAAGAQ61Uh37p1a4YOHbrbsbKysmzevDlJctBBB2X79u29TzfIVVVV+Qv7fuaII47IhAkTio4BAAAMAr26EfL444/PZz/72WzYsKHH9QcffDBXX311jjvuuCTJHXfckZqamr6nBAAAgBLTqxXyD3zgA3n729+eV7ziFTnuuOMyZsyYbNy4MXfeeWdGjBiRT33qU/nZz36Wq6++Oh/+8If7OTIAAAAMfr1aIZ8wYUJuvPHGnH322dm2bVvuvvvudHV15V3veleWL1+eiRMnprKyMp/+9Kdzxhln9HdmAAAAGPR6tUL++9//Psccc0ze9773/c05xx57bI499theBwMAAIBS1qsV8je96U159atfnS984Qv5n//5n/7OBAAAACWvV4V80aJFmTx5chYtWpSXv/zlOfPMM3PDDTfk0Ucf7e98AAAAUJJ6Vchf8pKX5JOf/GR+8Ytf5IorrsjIkSPz4Q9/OCeeeGL+9V//Nf/5n//ZzzEBAACgtPTqHvKdDjjggLz61a/Oq1/96mzatCkLFy7Mddddlx/+8IdpaWnpr4wAAABQcvpUyJPkt7/9bW688cYsX748f/nLXzJ58uS84Q1v6I9sAAAAULJ6VchbW1tz44035sYbb8y6desybty4vP71r88b3vCGTJw4sb8zAgAAQMnpVSF/wxvekBEjRuSVr3xlLrvsstTX1/d3LgAAAChpvSrkV1xxRV75yldm06ZN2bFjR+6///4kSWdnZ7Zu3ZoVK1ZkxowZ/RoUAAAASkmvCvmRRx6ZN77xjfnTn/602/GysjKFHAAAAP6OXhXyBQsWZNOmTbnwwgvz05/+NMOGDcvLXvay/OxnP8vPfvazNDU19XdOAAAAKCm9eg75XXfdlfe97315xzvekde85jXZsmVL3va2t+Xzn/98pk2blsWLF/d3TgAAACgpvSrk27dvz7Oe9awkyYQJE9La2to9Nn369PzmN7/pl3AAAABQqnpVyA877LCsXbs2SfKMZzwjjz76aNatW5ckGTZsWB5++OH+SwgAAAAlqFeF/JWvfGWuuOKKLF++PFVVVZkwYUI+/elPp7W1NV/60pcyfvz4/s4JAAAAJaVXhfy9731v6urqcsMNNyRJPvCBD+Tmm2/OG9/4xjQ3N+e8887r15AAAABQanp1yvoBBxyQhQsXZseOHUmSk08+OcuWLcvvf//7TJ48OTU1Nf0aEgAAAEpNrwr5TkOHDu3+/fjx421VBwAAgD3Uqy3rAAAAQN8o5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAgwpOgAAABTpga7ypLOz6BhAP3qga3CsPSvkAADsl0aOHJlhQ4fm+zuKTgLsDcOGDs3IkSOLjvF3KeQAAOyXqqqq8tmFC/PII48UHYV9YN26dVm4cGHOP//8HHHEEUXHYR8YOXJkqqqqio7xdynkADwltnZC6RksWzv3hqqqqgH/F3b61xFHHJEJEyYUHQOSKOQA7CFbO6G0DYatnQClRiEHYI/Y2rl/sbVz/zMYtnYClBqFHIA9Zmvn/sfWTgDYe/bfG4YAAACgQAo5AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAijkAAAAUACFHAAAAAqgkAMAAEABFHIAAAAogEIOAAAABVDIAQAAoAAKOQAAABRAIQcAAIACKOQAAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIMmkK+devWnHHGGVmyZEnRUQAAAKDPBkUhv+eee/L2t789v/nNb4qOAgAAAP1iwBfy22+/PWeddVZOO+20HHbYYUXHAQAAgH4xpOgAjz32WNavX7/bsaqqqhx99NH56U9/mgMOOCBf/vKX93E6AAAA2DsKL+R33XVXGhoadjt29dVXZ9q0afs4EQAAAOx9hRfyqVOnprW1tegYAAAAsE8N+HvIAQAAoBQp5AAAAFAAhRwAAAAKUPg95E/FT37yk6IjAAAAQL+wQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAijkAAAAUACFHAAAAAqgkAMAAEABFHIAAAAogEIOAAAABVDIAQAAoAAKOQAAABRAIQcAAIACKOQAAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAijk0EcdHR1ZvXp1kmT16tXp6OgoOBEAADAYDCk6AAxmzc3NaWpqSltbW5Jk0aJFWbp0aRoaGlJfX19wOgAAYCCzQg691NzcnMbGxtTU1OTcc89Nkpx77rmpqalJY2NjmpubC04IAAAMZFbI6Xfr16/P5s2bi46xV3V2dubaa6/N0UcfnenTp2fFihVJkv/5n//J9OnTs3nz5nzpS1/KoYcemvLy0v7ea8SIERk7dmzRMQAAYNBRyOlXmzZtynnnnZfOzs6io+wTDz30UC6++OLu10uWLMmSJUu6X1900UVFxNqnysvLc80112TUqFFFRwEAgEFFIadfjRo1KldeeWXJr5Dfeeedue6665IktbW1OeWUUzJu3Lj85S9/yU9+8pO0tLQkSWbMmJHjjjuuyKh73YgRI5RxAADoBYWcfrc/bF9+9NFHkyRHH310Lr300u5t6bW1tXnJS16SSy65JKtWrcqzn/3sTJgwocioAADAAFXaN7cCAADAAKWQQy9s2rQpSdLa2poFCxaktbU1W7du7fH6yfMAAAD+mi3r0AuVlZVJnrhH/Oabb868efO6x6qrqzNjxox84xvf6J4HAADw1xRy6IXa2tpUV1fnv/7rv7Jw4cKsWrUq7e3tqayszNFHH50rrrgi1dXVqa2tLToqAAAwQNmyDr1QUVGRhoaGrFy5MldccUWGDh2aurq6DB06NFdccUVWrlyZhoaGVFRUFB0VAAAYoKyQQy/V19dn9uzZaWpq2mXL+uzZs1NfX19gOgAAYKBTyKEP6uvrM2XKlLS0tHRvWa+trbUyDgAA/EMKOfRRRUVFjjnmmKJjAAAAg4x7yAEAAKAACjkAAAAUQCEHAACAAijkAAAAUACFHAAAAArglHXoo46ODo89AwAAnjKFHPqgubk5TU1NaWtr675WXV2dhoaG1NfXF5gMAAAY6GxZh15qbm5OY2NjampqMn/+/CxevDjz589PTU1NGhsb09zcXHREAABgAFPIoRc6OjrS1NSUurq6zJ07N5MmTcrw4cMzadKkzJ07N3V1dWlqakpHR0fRUQEAgAFKIYdeaGlpSVtbW6ZPn57y8p7/GZWXl+e0005LW1tbWlpaCkoIAAAMdAo59EJ7e3uSZPz48bsdr6mp6TEPAADgrynk0AuVlZVJkrVr1+52fM2aNT3mAQAA/DWFHHqhtrY21dXVWbJkSTo7O3uMdXZ2ZunSpamurk5tbW1BCQEAgIFOIYdeqKioSENDQ1auXJkFCxaktbU1W7duTWtraxYsWJCVK1emoaHB88gBAIC/yXPIoZfq6+sze/bsNDU1Zd68ed3Xq6urM3v2bM8hBwAA/i6FHPqgvr4+U6ZMSUtLS9rb21NZWZna2lor4wAAwD+kkEMfVVRU5Jhjjik6BgAAMMi4hxwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUYMAX8nXr1uW9731v6uvrM3Xq1JxzzjlZu3Zt0bEAAACgTwZ8IT/33HMzevTo/OQnP8lPfvKTVFZW5pxzzik6FgAAAPTJgC7kDz/8cA499NC8733vy0EHHZQRI0akoaEh//Vf/5WHH3646HgAAADQa0OKDvDYY49l/fr1ux2rqqrKtdde2+Paj370oxx++OEZPXr0vogHAAAAe0Xhhfyuu+5KQ0PDbseuvvrqTJs2rfv1ddddly996Uv5v//3/+6reAAAALBXFF7Ip06dmtbW1r87Z/v27fnYxz6WG2+8MYsWLUp9ff0+SgcAAAB7R+GF/B/ZuHFj/uVf/iXbt2/P9ddfn/HjxxcdCQAAAPpsQB/qtmPHjrzzne/MwQcfnOuuu04ZBwAAoGQM6BXyn/70p7n77rtzwAEH5IUvfGGPsR/84Ac57LDDCkoGAAAAfTOgC/krX/nKf3h/OQDsbevXr8/mzZuLjrFPrVu3rsev+5MRI0Zk7NixRccAYD8woAs5ABRt06ZNOe+889LZ2Vl0lEIsXLiw6Aj7XHl5ea655pqMGjWq6CgAlDiFHAD+jlGjRuXKK6/cb1bIf/CDH+TWW2/t8QVEeXl5Tj755LzmNa8pMNm+M2LECGUcgH1CIQeAf2B/2b7c1NSUW265JaNHj86MGTNSV1eXlStX5rrrruu+3tDQUHRMACgZA/qUdQBg39ixY0eWLVuW0aNHZ9GiRZk2bVoOOeSQTJs2LYsWLcro0aOzbNmy7Nixo+ioAFAyrJADAFm+fHk6OzszY8aMDBnS868HQ4YMyVvf+tYsWrQoy5cvz+te97qCUgL9xWGV+xeHVQ5cCjkAkPXr1ydJ6urq0tHRkZaWlrS3t6eysjK1tbWpq6vrMQ8YvBxW6bBKBg6FHADoXjn593//9/zmN79JW1tb91h1dXWe97zn9ZgHDF7722GVOKxyIFPIAYCceuqpaWpqyo9//OMcf/zxmTVrVsaPH5+1a9fm+uuvz0033ZSysrKceuqpRUcF+oEv12BgcKgbAJDy8vIceOCBSZLVq1fnvvvuy9atW3Pfffdl9erVSZIDDzww5eX+6gAA/cUKOQCQlpaWbN26NSeffHJ+/vOfZ9GiRd1jO59Dfuutt6alpSXHHHNMgUkBoHQo5ABA2tvbkyTvfve7c84552T58uVZv359xo4dm1NPPTWPP/54br311u55AEDfKeQAQCorK5Mka9euzcSJE/OsZz0rhxxySCorK1NeXp41a9b0mAcA9J1CDgCktrY21dXVufbaa7Np06Zs2LChe6yqqiqjRo1KdXV1amtrC0wJAKXFySwAQCoqKlJfX5/Vq1dn+/btmTlzZr74xS9m5syZ2b59e1avXp36+vpUVFQUHRUASoZCDgCko6Mjzc3NmThxYoYNG5ZFixblXe96VxYtWpRhw4Zl4sSJaW5uTkdHR9FRAaBk2LIOAKSlpSVtbW2ZNWtWJk6cmJaWlrS3t6eysjK1tbW59957M2/ePKesA0A/UsgBgO7T08ePH5+KiopdSndNTU2PeQBA39myDgD0OGV9d5yyDgD9TyEHALpPWV+yZEk6Ozt7jHV2dmbp0qVOWQeAfqaQAwCpqKhIQ0NDVq5cmQULFqS1tTVbt25Na2trFixYkJUrV6ahocEp6wDQj9xDDgAkSerr6zN79uw0NTVl3rx53derq6sze/bs1NfXF5gOAEqPQg4AdKuvr8+UKVN2OWXdyjgA9D+FHADoYXenrAMMZh0dHb5oZEBSyAEAgJLV3NycpqamtLW1dV+rrq5OQ0ODW3EonEPdAACAktTc3JzGxsbU1NRk/vz5Wbx4cebPn5+ampo0Njamubm56Ijs5xRyAACg5HR0dKSpqSl1dXWZO3duJk2alOHDh2fSpEmZO3du6urq0tTUlI6OjqKjsh9TyAEAgJLT0tKStra2TJ8+PeXlPWtPeXl5TjvttLS1taWlpaWghKCQAwAAJai9vT1JMn78+N2O19TU9JgHRXCoGwAAUHIqKyuTJGvXrs3EiRN3OWV9zZo1PeZBERRyAACg5NTW1qa6ujrXXnttNm3alA0bNnSPVVVVZdSoUamurk5tbW2BKdnf2bIOAACUnIqKitTX12f16tXZvn17Zs6cmS9+8YuZOXNmtm/fntWrV6e+vt7zyCmUQg4AAJScjo6ONDc3Z+LEiRk2bFgWLVqUd73rXVm0aFGGDRuWiRMnprm52SnrFMqWdQAAoOTsPGV91qxZu72H/N577828efPS0tKSY445pui47KcUcgAAoOQ8+ZT1ioqKXUq3U9YZCGxZBwAASs6TT1nfHaesMxAo5AAAQMnZecr6kiVL0tnZ2WOss7MzS5cudco6hbNlHQAAKDkVFRVpaGhIY2NjPv7xj2fcuHHZsWNHhg4dmr/85S+58847M3v2bKesUyiFHAAAKEn19fWpq6vLihUrdhk74YQTUl9fX0Aq+F8KOQAAUJKampqyYsWKjB49Oi95yUsyduzYrF+/PrfccktWrFiRpqamNDQ0FB2T/ZhCDgAAlJwdO3Zk2bJlGT16dBYtWpQhQ/63+rztbW/LzJkzs2zZssyYMSNDhw4tMCn7M4e6AQAAJWf58uXp7OzMjBkzepTxJBkyZEje+ta3prOzM8uXLy8oISjkAABACVq/fn2SpK6ubrfjO6/vnAdFUMgBAICSM3bs2CTJypUrdzu+8/rOeVAEhRwAACg5p556asrLy3Pdddfl8ccf7zH2+OOP55vf/GbKy8tz6qmnFpQQFHIAAKAEDR06NK997Wvz8MMPZ+bMmbnpppuycePG3HTTTZk5c2YefvjhvPa1r3WgG4VyyjoAAFCSdj7SbNmyZVm0aFH39fLy8rz+9a/3yDMKp5ADAAAlq6GhITNmzMjy5cuzfv36jB07NqeeeqqVcQYEhRwAAChpQ4cOzete97qiY8Au3EMOAAAABVDIAQAAoAAKOQAAABRAIQcAAIACKOQAAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUYEjRAQCAgaWlpSUf+tCHul9fdtllqa2tLTARQN9s3rw58+fPz4YNG1JVVZWLL744I0aMKDoWKOQAwP9605vetMu1neX8+uuv39dxAPrs3HPPzfr167tfb9y4MWeddVbGjh2bq6++usBkYMs6APD//XUZP/HEE//uOMBA9+Qyftxxx+Xyyy/PcccdlyRZv359zj333CLjgRVyAOCJbeo7ffSjH83RRx+dJLnggguyatWqfPCDH+yeZ/s6MBhs3ry5u4w3NTXloIMOSpLMmzcvW7ZsSUNDQ9avX5/Nmzfbvk5hrJADAD3uGd9Zxnf3+snzAAay+fPnJ3liZXxnGd/poIMOyvOe97we86AICjkA0O2vt6nvNHXq1H2cBKBvNmzYkORv325z+umn95gHRVDIAYBuP//5z3d7/Ze//OU+TgLQN1VVVUn+9oGUN9xwQ495UIQBX8hbWlrS0NCQurq6TJ06NXPmzMlDDz1UdCwAKCmXXXZZ9+9XrVrVY+zJr588D2Agu/jii5Mkd955Z7Zs2dJjbMuWLbnrrrt6zIMiDOhD3bZv3553vetdmTFjRr70pS9ly5YtOf/88/Pxj388n/jEJ4qOBwAl48kHte08wG3q1Km7rIw70A0YLEaMGJGxY8dm/fr1aWhoyPOe97ycfvrpueGGG7rL+NixYx3oRqEGdCEfNmxYfvzjH+fAAw9MeXl5Hn744WzdujVjxowpOhoAlJzrr7++x72Wf13GPYccGGyuvvrq7kef3XXXXd1FPInnkDMgFF7IH3vsse7HEfy1qqqq7hMR3/rWt+bOO+/MkUcemf/zf/7PvowIAPuN66+/Pi0tLT1OU7/sssusjAOD1tVXX53Nmzdn/vz52bBhQ6qqqnLxxRdbGWdAKLyQ33XXXWloaNjt2NVXX51p06YlSb7yla9k27Zt+fCHP5yzzz473/nOd1JRUbEvowLAfqG2ttZqOFBSRowYkcsvv7zoGLCLwgv51KlT09ra+g/nHXjggTnwwAPzwQ9+MC960YvS2tqa5zznOfsgIQAAAPS/AX3K+rp163LKKaekra2t+9r27duTJKNHjy4qFgAAAPTZgC7khx9+eCorK/Oxj30smzdvzsaNG3PppZfmxS9+cQ4//PCi4wEAAECvDehCXlZWls997nN5/PHHc8opp+QNb3hDnv70p+dTn/pU0dEAAACgTwq/h/wfGTduXK688sqiYwAAAEC/GtAr5AAAAFCqFHIAAAAogEIOAAAABVDIAQAAoAAKOQAAABRAIQcAAIACKOQAAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwAAgAIo5AAAAFCAIUUH2Ju2bduWJFm9enXBSQAAANgf7OyfO/vo31PShXzdunVJkjlz5hScBAAAgP3JunXrcvzxx//dOWVdXV1d+yjPPrdx48bcdtttOeKII3LAAQcUHQcAAIASt23btqxbty4nnXRSxowZ83fnlnQhBwAAgIHKoW4AAABQAIUcAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhhz568MEHc8455+SEE07I1KlTc/nll+fxxx8vOhZAn23cuDGveMUr8stf/rLoKAB9smrVqpx99tl5wQtekBNPPDFz587Nxo0bi44FCjn01axZs3LQQQfl1ltvzfXXX5/bb789X/nKV4qOBdAnK1euzBlnnJE1a9YUHQWgTx577LG8853vzHHHHZfbbrsty5YtS3t7ey6++OKio4FCDn1x33335Y477sicOXMyfPjwjB8/Puecc06+/vWvFx0NoNeWLl2a97///bnggguKjgLQZ/fff3+OPvronHvuuRk2bFgOOeSQnHHGGfnVr35VdDRQyKEv7rnnnlRWVmbs2LHd1yZOnJj7778/mzZtKjAZQO+ddNJJuemmm/LqV7+66CgAfTZhwoRcc801qaio6L72ox/9KJMnTy4wFTxhSNEBYDDbvHlzhg8f3uPaztdbtmzJqFGjiogF0CdVVVVFRwDYK7q6uvKZz3wmP/3pT/O1r32t6DigkENfHHTQQdm6dWuPaztfjxgxoohIAADsxqOPPpoPfOADufvuu/O1r30tRx11VNGRwJZ16ItnP/vZaW9vzwMPPNB9bfXq1Rk3blxGjhxZYDIAAHZas2ZNTj/99Dz66KO5/vrrlXEGDIUc+uCZz3xm6urqMn/+/Dz66KNZu3ZtPve5z+VNb3pT0dEAAEjy8MMP56yzzsrxxx+fa6+9NmPGjCk6EnSzZR36aOHChfnIRz6Sl7/85SkvL88b3/jGnHPOOUXHAgAgyZIlS3L//ffnhz/8YZYvX95j7M477ywoFTyhrKurq6voEAAAALC/sWUdAAAACqCQAwAAQAEUcgAAACiAQg4AAAAFUMgBAACgAAo5AAAAFEAhBwD6xBNUAaB3FHIAGOR+97vfZc6cOXnpS1+aY489Ni9/+cvzwQ9+MGvXru2ec+aZZ+bMM8/s1/fdtGlTLrzwwqxYsaJffy4A7C8UcgAYxL7+9a/nrW99ax588MHMnj07X/ziF/Oe97wnv/rVr3L66afn7rvv3mvv3dLSku985zvp7Ozca+8BAKVsSNEBAIDeWblyZS6//PK8/e1vz7x587qvT506NS9/+cszffr0fOADH8j3vve9AlMCAH+LFXIAGKSuvfbajBw5Mv/6r/+6y9iYMWNy0UUX5ZWvfGUeffTRXcaPOuqoXHnllT2uXXnllTnqqKO6X2/cuDHvf//7c+KJJ+a5z31u3vCGN+Q73/lOkuSXv/xlGhoakiQNDQ09tsPffPPNmT59ep773OfmxBNPzEc/+tFs2bKlx/u84hWvyFVXXZWpU6dm2rRpeeihh/r07wIABiMr5AAwCHV1deW2227LKaeckuHDh+92zqmnntqn95gzZ04efPDBXHrppRkxYkS+973v5cILL8zTn/70TJ48OZdcckk+8pGP5JJLLsnUqVOTJN///vfz/ve/P6973esya9as/PnPf86nP/3p3Hvvvfnyl7+csrKyJMn999+fm266KZ/61Kfy0EMP5ZBDDulTVgAYjBRyABiEHnrooWzbti1HHHHEXnuPO+64I+ecc06mTZuW5Imt8JWVlamoqMjBBx+cI488Mkly5JFH5sgjj0xXV1euuOKKnHzyybniiiu6f84zn/nMvOMd78gtt9ySl770pUmSxx9/PBdeeGFe9KIX7bX8ADDQKeQAMAiVlz9x11lHR8dee4+pU6fmyiuvzKpVq/KSl7wkL37xi3PhhRf+zfl//OMf85e//CUzZ87M448/3n19ypQpOfjgg/Pzn/+8u5AnyaRJk/ZadgAYDBRyABiEKisrM2LEiNx///1/c86WLVuyffv2VFZW9uo9Pv3pT+fzn/98fvjDH2b58uUpLy/Pi170onz4wx/O+PHjd5nf3t6eJLn00ktz6aWX7jLe1tbW4/Whhx7aq1wAUCoUcgAYpE466aT88pe/zLZt23LAAQfsMr5kyZJcfvnl+cY3vrHbP//Xq+tPPngtSUaOHJk5c+Zkzpw5+eMf/5j/+I//yOc+97lceumlueaaa3b5eaNGjUqSzJ07Ny94wQt2GR89evQe/28DgP2BU9YBYJD653/+57S3t+fTn/70LmMPPvhgrrnmmjzjGc/I85///F3GDz744PzlL3/pce3Xv/519+///Oc/5yUveUmWL1+eJJkwYULe9a535UUvelH3n6uoqOjx5ydMmJCnPe1pWbduXZ773Od2/zNu3Lg0NjbmD3/4Q1//JwNASbFCDgCD1POf//y8733vy2c+85msXr06p512Wg455JDcc889+dKXvpTNmzfnC1/4QvfJ5k/20pe+ND/4wQ9y7LHH5lnPelaWLl2a++67r3v88MMPz7hx4/LRj340jz76aGpqavL73/8+t9xyS2bOnJnkiRX0JPnP//zPjB49OkcffXQuuOCCXHLJJamoqMjLXvaybNq0KZ/73Oeyfv36TJ48ed/8iwGAQaKsq6urq+gQAEDv3XLLLfn617+elpaWtLe3Z9y4cXnhC1+Y97znPTnssMOSpPs54YsXL06SPPDAA7nsssvys5/9LEOGDMmrX/3qHHPMMfngBz+Y1tbWJMmGDRvyqU99KrfddlseeuihPP3pT8/pp5+ed7/73SkvL09nZ2fmzJmTm266KTU1NVm2bFmS5MYbb8w111yTe+65JwcddFCOP/74zJo1q/sZ51deeWWuuuqq7vcBgP2VQg4AAAAFcA85AAAAFEAhBwAAgAIo5AAAAFAAhRwAAAAKoJADAABAARRyAAAAKIBCDgAAAAVQyAEAAKAACjkAAAAUQCEHAACAAijkAAAAUACFHAAAAArw/wDhp/35pEFEiwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in X.columns:\n",
    "    if feature != 'Cluster':\n",
    "        sns.boxplot(x='Cluster', y=feature, data=X)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1604,
   "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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.840534</td>\n",
       "      <td>1.729363</td>\n",
       "      <td>0.906738</td>\n",
       "      <td>0.185332</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.372820</td>\n",
       "      <td>2.041863</td>\n",
       "      <td>0.406738</td>\n",
       "      <td>0.207503</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.773998</td>\n",
       "      <td>2.041863</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>0.435543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.108640</td>\n",
       "      <td>0.166863</td>\n",
       "      <td>0.406738</td>\n",
       "      <td>-0.462366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>4.020508</td>\n",
       "      <td>-0.373874</td>\n",
       "      <td>0.897344</td>\n",
       "      <td>0.850342</td>\n",
       "      <td>-0.194038</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.256383</td>\n",
       "      <td>0.691863</td>\n",
       "      <td>0.906738</td>\n",
       "      <td>1.097494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.254419</td>\n",
       "      <td>-0.289387</td>\n",
       "      <td>0.906738</td>\n",
       "      <td>-0.194735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.207405</td>\n",
       "      <td>0.791863</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.486120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.797861</td>\n",
       "      <td>-0.384895</td>\n",
       "      <td>0.098145</td>\n",
       "      <td>-0.420273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.158203</td>\n",
       "      <td>-0.419542</td>\n",
       "      <td>0.033356</td>\n",
       "      <td>-0.089076</td>\n",
       "      <td>0.630585</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.258346</td>\n",
       "      <td>-0.433137</td>\n",
       "      <td>5.656738</td>\n",
       "      <td>-0.041520</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.473548</td>\n",
       "      <td>0.479363</td>\n",
       "      <td>1.906738</td>\n",
       "      <td>0.780771</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.922664</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>3.906738</td>\n",
       "      <td>-0.384769</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.232355</td>\n",
       "      <td>0.160613</td>\n",
       "      <td>3.281738</td>\n",
       "      <td>0.095462</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.057699</td>\n",
       "      <td>-0.208137</td>\n",
       "      <td>3.906738</td>\n",
       "      <td>0.220172</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        avg_KD  avg_SIG_STR_pct  avg_TD_pct  avg_SUB_ATT  \\\n",
       "15    2.000000         0.840534    1.729363     0.906738   \n",
       "70    2.000000         1.372820    2.041863     0.406738   \n",
       "188   2.000000         0.773998    2.041863    -0.093262   \n",
       "385   7.000000         0.108640    0.166863     0.406738   \n",
       "1092  4.020508        -0.373874    0.897344     0.850342   \n",
       "37    0.000000         1.256383    0.691863     0.906738   \n",
       "1588  1.000000         2.254419   -0.289387     0.906738   \n",
       "989   0.000000        -0.207405    0.791863    -0.093262   \n",
       "764   0.000000        -0.797861   -0.384895     0.098145   \n",
       "312   0.158203        -0.419542    0.033356    -0.089076   \n",
       "1057  0.250000         0.258346   -0.433137     5.656738   \n",
       "298   0.000000        -0.473548    0.479363     1.906738   \n",
       "146   0.000000        -0.922664   -0.458137     3.906738   \n",
       "931   0.000000        -0.232355    0.160613     3.281738   \n",
       "740   0.000000        -0.057699   -0.208137     3.906738   \n",
       "\n",
       "      avg_CTRL_time(seconds)  Cluster  \n",
       "15                  0.185332        0  \n",
       "70                  0.207503        0  \n",
       "188                 0.435543        0  \n",
       "385                -0.462366        0  \n",
       "1092               -0.194038        0  \n",
       "37                  1.097494        1  \n",
       "1588               -0.194735        1  \n",
       "989                -0.486120        1  \n",
       "764                -0.420273        1  \n",
       "312                 0.630585        1  \n",
       "1057               -0.041520        2  \n",
       "298                 0.780771        2  \n",
       "146                -0.384769        2  \n",
       "931                 0.095462        2  \n",
       "740                 0.220172        2  "
      ]
     },
     "execution_count": 1604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take 5 samples with cluster 0 and 5 samples with cluster 1\n",
    "pd.concat([X[X['Cluster'] == 0].sample(5, random_state=seed_value),\n",
    "                     X[X['Cluster'] == 1].sample(5, random_state=seed_value), \n",
    "                                    X[X['Cluster'] == 2].sample(5, random_state=seed_value)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these plots, we can possibly infer the following clusters:\n",
    "- `0` is the cluster of fighters that are good at striking, due to the high `avg_SIG_STR_pct` and `avg_KD` values.\n",
    "- `1` is the cluster of fighters that are balanced, due to the balance of all the features with no clear values that stand out.\n",
    "- `2` is the cluster of fighters that are good at submissions, due to the high `avg_SUB_ATT` and `avg_TD_pct` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster\n",
       "1    1154\n",
       "0     308\n",
       "2     280\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 1605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check cluster sizes\n",
    "X['Cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the fighters have been put into the balanced cluster, this could be because most fighters are balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agglomerative Clustering is a hierarchical clustering algorithm. It starts with each data point in its own cluster. Then it merges the two most similar clusters, and repeats until there is only one cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1606,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "X = data.copy()\n",
    "best_k, best_score = find_best_k(X, AgglomerativeClustering())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1607,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=best_k)\n",
    "\n",
    "# Fit the model and assign clusters\n",
    "X['Cluster'] = agg.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28958042548841184"
      ]
     },
     "execution_count": 1608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in X.columns:\n",
    "    if feature != 'Cluster':\n",
    "        sns.boxplot(x='Cluster', y=feature, data=X)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1610,
   "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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408051</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>0.906738</td>\n",
       "      <td>-0.479786</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.323842</td>\n",
       "      <td>1.541863</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>0.571734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.423646</td>\n",
       "      <td>0.791863</td>\n",
       "      <td>0.906738</td>\n",
       "      <td>0.248677</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.721094</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.517792</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.454950</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>1.906738</td>\n",
       "      <td>-0.239076</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>5.851562</td>\n",
       "      <td>-0.000520</td>\n",
       "      <td>0.168475</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.148637</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.273017</td>\n",
       "      <td>0.579363</td>\n",
       "      <td>0.406738</td>\n",
       "      <td>0.617659</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>4.203125</td>\n",
       "      <td>0.597523</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>0.172363</td>\n",
       "      <td>-0.463207</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.773998</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>0.302520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.398175</td>\n",
       "      <td>0.668426</td>\n",
       "      <td>0.594238</td>\n",
       "      <td>1.265407</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        avg_KD  avg_SIG_STR_pct  avg_TD_pct  avg_SUB_ATT  \\\n",
       "1716  0.000000         0.408051   -0.458137     0.906738   \n",
       "474   0.000000        -0.323842    1.541863    -0.093262   \n",
       "533   0.000000        -0.423646    0.791863     0.906738   \n",
       "1339  0.000000        -1.721094   -0.458137    -0.093262   \n",
       "640   0.000000        -1.454950   -0.458137     1.906738   \n",
       "1480  5.851562        -0.000520    0.168475    -0.093262   \n",
       "1226  2.000000         1.273017    0.579363     0.406738   \n",
       "669   4.203125         0.597523   -0.458137     0.172363   \n",
       "1723  4.000000         0.773998   -0.458137    -0.093262   \n",
       "956   3.000000         0.398175    0.668426     0.594238   \n",
       "\n",
       "      avg_CTRL_time(seconds)  Cluster  \n",
       "1716               -0.479786        0  \n",
       "474                 0.571734        0  \n",
       "533                 0.248677        0  \n",
       "1339               -0.517792        0  \n",
       "640                -0.239076        0  \n",
       "1480               -0.148637        1  \n",
       "1226                0.617659        1  \n",
       "669                -0.463207        1  \n",
       "1723                0.302520        1  \n",
       "956                 1.265407        1  "
      ]
     },
     "execution_count": 1610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take 5 samples with cluster 0 and 5 samples with cluster 1\n",
    "pd.concat([X[X['Cluster'] == 0].sample(5, random_state=seed_value),\n",
    "                     X[X['Cluster'] == 1].sample(5, random_state=seed_value)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these plots, we can possibly infer the following clusters:\n",
    "- `0` is the cluster of fighters that are good at striking, due to the high `avg_SIG_STR_pct` and `avg_KD` values.\n",
    "- `1` is the cluster of fighters that are good at submissions, due to the high `avg_SUB_ATT` and `avg_TD_pct` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1611,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster\n",
       "0    1406\n",
       "1     336\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 1611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check cluster sizes\n",
    "X['Cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the fighters have been put into the striking cluster. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectral Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spectral Clustering is a clustering algorithm that uses the graph laplacian to perform dimensionality reduction before clustering in fewer dimensions. Graph laplacian is a matrix representation of a graph. The graph laplacian is used to find the number of connected components in a graph. The number of connected components in a graph is the number of clusters in the data. This model is a lot more complex than the other models, and has a lot more parameters to tune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "\n",
    "X = data.copy()\n",
    "best_k, best_score = find_best_k(X, SpectralClustering())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1613,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec = SpectralClustering(n_clusters=best_k, assign_labels=\"discretize\", random_state=seed_value)\n",
    "\n",
    "X['Cluster'] = spec.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7068311208476614"
      ]
     },
     "execution_count": 1614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in X.columns:\n",
    "    if feature != 'Cluster':\n",
    "        sns.boxplot(x='Cluster', y=feature, data=X)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1616,
   "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>avg_KD</th>\n",
       "      <th>avg_SIG_STR_pct</th>\n",
       "      <th>avg_TD_pct</th>\n",
       "      <th>avg_SUB_ATT</th>\n",
       "      <th>avg_CTRL_time(seconds)</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>0.314453</td>\n",
       "      <td>-1.372008</td>\n",
       "      <td>-0.375129</td>\n",
       "      <td>-0.093262</td>\n",
       "      <td>-0.407076</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.637924</td>\n",
       "      <td>-0.370637</td>\n",
       "      <td>0.406738</td>\n",
       "      <td>-0.110804</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.505892</td>\n",
       "      <td>0.166863</td>\n",
       "      <td>3.906738</td>\n",
       "      <td>0.438711</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008837</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>1.906738</td>\n",
       "      <td>-0.429110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.203246</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>-0.062012</td>\n",
       "      <td>-0.060771</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.607659</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>9.906738</td>\n",
       "      <td>2.750788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.139945</td>\n",
       "      <td>-0.158137</td>\n",
       "      <td>7.906738</td>\n",
       "      <td>1.325535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.323842</td>\n",
       "      <td>0.916863</td>\n",
       "      <td>9.906738</td>\n",
       "      <td>2.535416</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.206481</td>\n",
       "      <td>-0.458137</td>\n",
       "      <td>9.906738</td>\n",
       "      <td>0.888457</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.597379</td>\n",
       "      <td>0.635613</td>\n",
       "      <td>7.531738</td>\n",
       "      <td>0.665563</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        avg_KD  avg_SIG_STR_pct  avg_TD_pct  avg_SUB_ATT  \\\n",
       "945   0.314453        -1.372008   -0.375129    -0.093262   \n",
       "294   0.000000        -1.637924   -0.370637     0.406738   \n",
       "191   0.000000         1.505892    0.166863     3.906738   \n",
       "644   0.000000         0.008837   -0.458137     1.906738   \n",
       "424   0.750000        -0.203246    0.489129    -0.062012   \n",
       "1233  0.000000         0.607659   -0.458137     9.906738   \n",
       "1236  0.000000         1.139945   -0.158137     7.906738   \n",
       "658   0.000000        -0.323842    0.916863     9.906738   \n",
       "1133  0.000000         1.206481   -0.458137     9.906738   \n",
       "486   0.000000         1.597379    0.635613     7.531738   \n",
       "\n",
       "      avg_CTRL_time(seconds)  Cluster  \n",
       "945                -0.407076        0  \n",
       "294                -0.110804        0  \n",
       "191                 0.438711        0  \n",
       "644                -0.429110        0  \n",
       "424                -0.060771        0  \n",
       "1233                2.750788        1  \n",
       "1236                1.325535        1  \n",
       "658                 2.535416        1  \n",
       "1133                0.888457        1  \n",
       "486                 0.665563        1  "
      ]
     },
     "execution_count": 1616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take 5 samples with cluster 0 and 5 samples with cluster 1\n",
    "pd.concat([X[X['Cluster'] == 0].sample(5, random_state=seed_value),\n",
    "                     X[X['Cluster'] == 1].sample(5, random_state=seed_value)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these plots, it's very hard to infer any clusters. We can see that the clusters are very mixed, and there is no clear separation between the clusters. \n",
    "This is probably due to the fact that the data is not separable by a linear boundary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no real better/worse model, because we are clustering without labels. If we would measure the clustering accuracy based on the silhouette score, then the Spectral Clustering model would be the best model. However, we can see that the clusters are not very clear, and there is no clear separation between the clusters. If we would measure the clustering accuracy based on the interpretability of the clusters, then the Hierarchical Clustering or the K-Means Clustering model would be the best model. In the K-Means Clustering model, we have 2 clear clusters, with one cluster that has some mixed data. In the Hierarchical Clustering model, we have 2 clear clusters, both with somewhat mixed data. We can also see this in the silhouette score, the K-Means Clustering model has a higher silhouette score than the Hierarchical Clustering model. Meaning that the K-Means Clustering model has better defined clusters than the Hierarchical Clustering model, and thus is the best model."
   ]
  }
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