{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<img src=\"http://i.imgur.com/Vu37PDA.jpg\" style=\"PADDING-RIGHT: 85px\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UFC MMA Predictor Workflow\n",
    "## by Jason Chan Jin An"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "[**Github**](https://www.github.com/jasonchanhku) || [**LinkedIn**](https://www.linkedin.com/in/jason-chan-jin-an-45a76a76/) || [**Email**](mailto:jasonchanhku@gmail.com) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This is the workflow and backend process of the **UFC MMA Predictor Webapp** i built https://ufcmmapredictor.herokuapp.com/ .This Jupyter Notebook highlights the following:\n",
    "\n",
    "* Introduction\n",
    "    * Background\n",
    "    * Objective\n",
    "* Data Requirements\n",
    "    * Web Scraping\n",
    "    * Data Cleansing and Blending\n",
    "    * Finalized Dataset\n",
    "* Libraries Used\n",
    "* Exploratory Data Analysis (EDA)\n",
    "    * Statistical Overview\n",
    "    * Heatmap and Correlation\n",
    "    * Statistical Tests (T-test)\n",
    "    * Distribution Plots\n",
    "* Feature Selection\n",
    "    * Feature Importance\n",
    "* Modelling the Data\n",
    "    * Logistic Regression\n",
    "    * Random Forest\n",
    "    * Neural Network\n",
    "* Conclusion\n",
    "* Improvements\n",
    "* Citation\n",
    "* Collaboration & Sponsorship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "This web app is the outcome of being a full time Data Scientist and a hardcore UFC fan. As a hardcore UFC fan, it has always been a challenge to predict the winner of a fight. It is either the **Favourite** or **Underdog**. However, there are times where my predictions would go horribly wrong. Being the curious person I am, I found myself asking questions such as:\n",
    "\n",
    "* How often do favourites triumph over underdogs?\n",
    "* Do fighters with better fighting stats always win?\n",
    "* What are the most important skills that determines the winner? Is it striking? Wrestling? BJJ?\n",
    "* How has the MMA sport evolved? Do fights go the distance more?\n",
    "\n",
    "All these questions then lead me to build a web app that utilizes machine learning to predict the winner of a fight. This is essentially shaped into a **binary Classification** problem with label **Favourite or Underdog**. This app will then contribute as a validation point to my predictions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "The objective of this data science project is to build a model that:\n",
    "* Predicts better than 50% accuracy (better than randomly selecting any of the two fighter as the winner)\n",
    "* Predicts better than choosing all favourite only (roughly 60%)\n",
    "\n",
    "By satisfying these two objectives, I believe my web app can add some serious value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Data Requirements\n",
    "\n",
    "For this projects, there the following two datasets are needed and scraped from public sources:\n",
    "\n",
    "## UFC Fighters Database\n",
    "\n",
    "Dataset that contains fight stats of all fighters in the UFC\n",
    "\n",
    "Source(s):\n",
    "* http://www.fightmetric.com/statistics/fighters\n",
    "\n",
    "Web scraping code(s):\n",
    "* https://github.com/jasonchanhku/web_scraping/blob/master/MMA%20Project/MMA%20fighters%20database.R\n",
    "\n",
    "### Dataset Preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>Weight</th>\n",
       "      <th>WeightClass</th>\n",
       "      <th>REACH</th>\n",
       "      <th>SLPM</th>\n",
       "      <th>SAPM</th>\n",
       "      <th>STRA</th>\n",
       "      <th>STRD</th>\n",
       "      <th>TD</th>\n",
       "      <th>TDA</th>\n",
       "      <th>TDD</th>\n",
       "      <th>SUBA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom Aaron</td>\n",
       "      <td>155</td>\n",
       "      <td>lightweight</td>\n",
       "      <td>71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Danny Abbadi</td>\n",
       "      <td>155</td>\n",
       "      <td>lightweight</td>\n",
       "      <td>71</td>\n",
       "      <td>3.29</td>\n",
       "      <td>4.41</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Abbott</td>\n",
       "      <td>265</td>\n",
       "      <td>heavyweight</td>\n",
       "      <td>77</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shamil Abdurakhimov</td>\n",
       "      <td>235</td>\n",
       "      <td>heavyweight</td>\n",
       "      <td>76</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hiroyuki Abe</td>\n",
       "      <td>145</td>\n",
       "      <td>featherweight</td>\n",
       "      <td>70</td>\n",
       "      <td>1.71</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  NAME  Weight    WeightClass  REACH  SLPM  SAPM  STRA  STRD  \\\n",
       "0            Tom Aaron     155    lightweight     71  0.00  0.00  0.00  0.00   \n",
       "1         Danny Abbadi     155    lightweight     71  3.29  4.41  0.38  0.57   \n",
       "2         David Abbott     265    heavyweight     77  1.35  3.55  0.30  0.38   \n",
       "3  Shamil Abdurakhimov     235    heavyweight     76  2.48  2.50  0.45  0.58   \n",
       "4         Hiroyuki Abe     145  featherweight     70  1.71  3.11  0.36  0.63   \n",
       "\n",
       "     TD   TDA   TDD  SUBA  \n",
       "0  0.00  0.00  0.00   0.0  \n",
       "1  0.00  0.00  0.77   0.0  \n",
       "2  1.07  0.33  0.66   0.0  \n",
       "3  1.40  0.22  0.77   0.3  \n",
       "4  0.00  0.00  0.33   0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "fighter_db = pd.read_csv('https://raw.githubusercontent.com/jasonchanhku/UFC-MMA-Predictor/master/Datasets/UFC_Fighters_Database.csv')\n",
    "fighter_db.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UFC Fights History \n",
    "\n",
    "Dataset that contains fight history of each fight card with **fight odds**. As I have proven that including fight odds makes it the most important variable, the importance of having odds for fights exceeds the need for having each and every UFC fights.\n",
    "\n",
    "As odds are only available from www.betmma.tips from **UFC 159**, the dataset only contains fights from **UFC 159** to **UFC 211**.\n",
    "\n",
    "Source(s):\n",
    "* http://www.fightmetric.com/statistics/events/completed\n",
    "* http://www.betmma.tips/mma_betting_favorites_vs_underdogs.php\n",
    "\n",
    "Web scraping code(s):\n",
    "* https://github.com/jasonchanhku/web_scraping/blob/master/MMA%20Project/MMA%20events%20database.R\n",
    "* https://github.com/jasonchanhku/web_scraping/blob/master/MMA%20Project/favourite_vs_underdogs.R\n",
    "\n",
    "### Dataset Preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RecordID</th>\n",
       "      <th>Events</th>\n",
       "      <th>Fighter1</th>\n",
       "      <th>Fighter2</th>\n",
       "      <th>Winner</th>\n",
       "      <th>fighter1_odds</th>\n",
       "      <th>fighter2_odds</th>\n",
       "      <th>F1 or F2</th>\n",
       "      <th>Label</th>\n",
       "      <th>Combine</th>\n",
       "      <th>Favourite</th>\n",
       "      <th>Underdog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Jon Jones</td>\n",
       "      <td>Chael Sonnen</td>\n",
       "      <td>Jon Jones</td>\n",
       "      <td>1.13</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Favourite</td>\n",
       "      <td>Favourite 1</td>\n",
       "      <td>Jon Jones</td>\n",
       "      <td>Chael Sonnen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Michael Bisping</td>\n",
       "      <td>Alan Belcher</td>\n",
       "      <td>Michael Bisping</td>\n",
       "      <td>1.57</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1</td>\n",
       "      <td>Favourite</td>\n",
       "      <td>Favourite 1</td>\n",
       "      <td>Michael Bisping</td>\n",
       "      <td>Alan Belcher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Roy Nelson</td>\n",
       "      <td>Cheick Kongo</td>\n",
       "      <td>Roy Nelson</td>\n",
       "      <td>1.43</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1</td>\n",
       "      <td>Favourite</td>\n",
       "      <td>Favourite 1</td>\n",
       "      <td>Roy Nelson</td>\n",
       "      <td>Cheick Kongo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Phil Davis</td>\n",
       "      <td>Vinny Magalhaes</td>\n",
       "      <td>Phil Davis</td>\n",
       "      <td>1.36</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1</td>\n",
       "      <td>Favourite</td>\n",
       "      <td>Favourite 1</td>\n",
       "      <td>Phil Davis</td>\n",
       "      <td>Vinny Magalhaes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Pat Healy</td>\n",
       "      <td>Jim Miller</td>\n",
       "      <td>Pat Healy</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>Underdog</td>\n",
       "      <td>Underdog 1</td>\n",
       "      <td>Jim Miller</td>\n",
       "      <td>Pat Healy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RecordID                      Events         Fighter1         Fighter2  \\\n",
       "0         1  UFC 159 - Jones vs. Sonnen        Jon Jones     Chael Sonnen   \n",
       "1         2  UFC 159 - Jones vs. Sonnen  Michael Bisping     Alan Belcher   \n",
       "2         3  UFC 159 - Jones vs. Sonnen       Roy Nelson     Cheick Kongo   \n",
       "3         4  UFC 159 - Jones vs. Sonnen       Phil Davis  Vinny Magalhaes   \n",
       "4         5  UFC 159 - Jones vs. Sonnen        Pat Healy       Jim Miller   \n",
       "\n",
       "            Winner  fighter1_odds  fighter2_odds  F1 or F2      Label  \\\n",
       "0        Jon Jones           1.13           9.00         1  Favourite   \n",
       "1  Michael Bisping           1.57           4.50         1  Favourite   \n",
       "2       Roy Nelson           1.43           3.20         1  Favourite   \n",
       "3       Phil Davis           1.36           3.55         1  Favourite   \n",
       "4        Pat Healy           3.40           1.40         1   Underdog   \n",
       "\n",
       "       Combine        Favourite         Underdog  \n",
       "0  Favourite 1        Jon Jones     Chael Sonnen  \n",
       "1  Favourite 1  Michael Bisping     Alan Belcher  \n",
       "2  Favourite 1       Roy Nelson     Cheick Kongo  \n",
       "3  Favourite 1       Phil Davis  Vinny Magalhaes  \n",
       "4   Underdog 1       Jim Miller        Pat Healy  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fights_db = pd.read_csv('https://raw.githubusercontent.com/jasonchanhku/UFC-MMA-Predictor/master/Datasets/UFC_Fights.csv')\n",
    "fights_db.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleansing and Blending\n",
    "\n",
    "The two datasets above were cleansed and blended together using the following process.\n",
    "\n",
    "### Feature Mapping\n",
    "\n",
    "Note that for each feature `x`. It is the difference between the Favourite vs Underdog. Hence if the feature is positive, this implies the favourite fighter has an advantage over the underdog for that feature.\n",
    "\n",
    "\n",
    "\n",
    "$Feature\\quad { X }_{ i }=\\quad { X }_{ favourite }\\quad -\\quad { X }_{ underdog }$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finalized Dataset\n",
    "\n",
    "The following are the response variable and 10 features used in the dataset. Note that each feature has a suffix of **delta** due to the fact that it undergone the feature mapping stated above.\n",
    "\n",
    "* Label - This is the response variable. Either Favourite or Underdog will win\n",
    "* REACH - Fighter's reach. (Probabaly the least important feature)\n",
    "* SLPM - Significant Strikes Landed per Minute\n",
    "* STRA. - Significant Striking Accuracy\n",
    "* SAPM - Significant Strikes Absorbed per Minute\n",
    "* STRD - Significant Strike Defence (the % of opponents strikes that did not land)\n",
    "* TD - Average Takedowns Landed per 15 minutes\n",
    "* TDA - Takedown Accuracy\n",
    "* TDD - Takedown Defense (the % of opponents TD attempts that did not land)\n",
    "* SUBA - Average Submissions Attempted per 15 minutes\n",
    "* Odds - Fighter's decimal odds spread for that specific matchup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Events</th>\n",
       "      <th>Favourite</th>\n",
       "      <th>Underdog</th>\n",
       "      <th>Label</th>\n",
       "      <th>REACH_delta</th>\n",
       "      <th>SLPM_delta</th>\n",
       "      <th>SAPM_delta</th>\n",
       "      <th>STRA_delta</th>\n",
       "      <th>STRD_delta</th>\n",
       "      <th>TD_delta</th>\n",
       "      <th>TDA_delta</th>\n",
       "      <th>TDD_delta</th>\n",
       "      <th>SUBA_delta</th>\n",
       "      <th>Odds_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Jon Jones</td>\n",
       "      <td>Chael Sonnen</td>\n",
       "      <td>Favourite</td>\n",
       "      <td>10</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UFC 159 - Jones vs. Sonnen</td>\n",
       "      <td>Leonard Garcia</td>\n",
       "      <td>Cody McKenzie</td>\n",
       "      <td>Underdog</td>\n",
       "      <td>-3</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.29</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UFC Fight Night 34 - Saffiedine vs. Lim</td>\n",
       "      <td>Mairbek Taisumov</td>\n",
       "      <td>Tae Hyun Bang</td>\n",
       "      <td>Favourite</td>\n",
       "      <td>2</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UFC Fight Night 91 - McDonald vs. Lineker</td>\n",
       "      <td>Cody Pfister</td>\n",
       "      <td>Scott Holtzman</td>\n",
       "      <td>Underdog</td>\n",
       "      <td>4</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UFC Fight Night 91 - McDonald vs. Lineker</td>\n",
       "      <td>Matthew Lopez</td>\n",
       "      <td>Rani Yahya</td>\n",
       "      <td>Underdog</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.37</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Events         Favourite  \\\n",
       "0                 UFC 159 - Jones vs. Sonnen         Jon Jones   \n",
       "1                 UFC 159 - Jones vs. Sonnen    Leonard Garcia   \n",
       "2    UFC Fight Night 34 - Saffiedine vs. Lim  Mairbek Taisumov   \n",
       "3  UFC Fight Night 91 - McDonald vs. Lineker      Cody Pfister   \n",
       "4  UFC Fight Night 91 - McDonald vs. Lineker     Matthew Lopez   \n",
       "\n",
       "         Underdog      Label  REACH_delta  SLPM_delta  SAPM_delta  STRA_delta  \\\n",
       "0    Chael Sonnen  Favourite           10        1.17        0.90        0.12   \n",
       "1   Cody McKenzie   Underdog           -3        1.03        2.29       -0.10   \n",
       "2   Tae Hyun Bang  Favourite            2        0.54        0.08        0.05   \n",
       "3  Scott Holtzman   Underdog            4       -3.15       -0.85       -0.24   \n",
       "4      Rani Yahya   Underdog            2        0.02        0.86        0.13   \n",
       "\n",
       "   STRD_delta  TD_delta  TDA_delta  TDD_delta  SUBA_delta  Odds_delta  \n",
       "0        0.03     -1.56      -0.07       0.28         0.2       -7.87  \n",
       "1       -0.15     -2.20       0.01       0.28        -2.0        1.40  \n",
       "2       -0.05      1.75       0.44       0.28        -0.5       -2.89  \n",
       "3       -0.06      0.55      -0.27      -0.58        -0.4        6.89  \n",
       "4       -0.06     -0.08       0.51       0.37        -0.5        0.81  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/jasonchanhku/UFC-MMA-Predictor/master/Datasets/Cleansed_Data.csv')\n",
    "df = df.drop('Sum_delta', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries Used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as matplot\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.metrics import roc_auc_score, classification_report, make_scorer, accuracy_score\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "#Progress bar\n",
    "def log_progress(sequence, every=None, size=None, name='Items'):\n",
    "    from ipywidgets import IntProgress, HTML, VBox\n",
    "    from IPython.display import display\n",
    "\n",
    "    is_iterator = False\n",
    "    if size is None:\n",
    "        try:\n",
    "            size = len(sequence)\n",
    "        except TypeError:\n",
    "            is_iterator = True\n",
    "    if size is not None:\n",
    "        if every is None:\n",
    "            if size <= 200:\n",
    "                every = 1\n",
    "            else:\n",
    "                every = int(size / 200)     # every 0.5%\n",
    "    else:\n",
    "        assert every is not None, 'sequence is iterator, set every'\n",
    "\n",
    "    if is_iterator:\n",
    "        progress = IntProgress(min=0, max=1, value=1)\n",
    "        progress.bar_style = 'info'\n",
    "    else:\n",
    "        progress = IntProgress(min=0, max=size, value=0)\n",
    "    label = HTML()\n",
    "    box = VBox(children=[label, progress])\n",
    "    display(box)\n",
    "\n",
    "    index = 0\n",
    "    try:\n",
    "        for index, record in enumerate(sequence, 1):\n",
    "            if index == 1 or index % every == 0:\n",
    "                if is_iterator:\n",
    "                    label.value = '{name}: {index} / ?'.format(\n",
    "                        name=name,\n",
    "                        index=index\n",
    "                    )\n",
    "                else:\n",
    "                    progress.value = index\n",
    "                    label.value = u'{name}: {index} / {size}'.format(\n",
    "                        name=name,\n",
    "                        index=index,\n",
    "                        size=size\n",
    "                    )\n",
    "            yield record\n",
    "    except:\n",
    "        progress.bar_style = 'danger'\n",
    "        raise\n",
    "    else:\n",
    "        progress.bar_style = 'success'\n",
    "        progress.value = index\n",
    "        label.value = \"{name}: {index}\".format(\n",
    "            name=name,\n",
    "            index=str(index or '?')\n",
    "        )\n",
    "\n",
    "# Creating Dummies\n",
    "def create_dummies(df,column_name):\n",
    "    \"\"\"Create Dummy Columns (One Hot Encoding) from a single Column\n",
    "\n",
    "    Usage\n",
    "    ------\n",
    "\n",
    "    train = create_dummies(train,\"Age\")\n",
    "    \"\"\"\n",
    "    dummies = pd.get_dummies(df[column_name],prefix=column_name)\n",
    "    df = pd.concat([df,dummies],axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)\n",
    "\n",
    "## Statistical Overview\n",
    "\n",
    "From the **finalized dataset**, we know that:\n",
    "\n",
    "* 1,315 rows which implies the number of historical fights in the dataset\n",
    "* rougly 62% of Favourite fighters win over Underdogs\n",
    "* On average, Favourites that win have all features advantage compared to the underdog. They get hit less and are more accurate with their striking, making Favourite winners more efficient over Underdog winners\n",
    "* Meanwhile Underdog winner historically end up taking more hits and less efficient on average but somehow end up winning. Could this be **luck** from landing a sudden KO or submission?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1315, 15)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Events          object\n",
       "Favourite       object\n",
       "Underdog        object\n",
       "Label           object\n",
       "REACH_delta      int64\n",
       "SLPM_delta     float64\n",
       "SAPM_delta     float64\n",
       "STRA_delta     float64\n",
       "STRD_delta     float64\n",
       "TD_delta       float64\n",
       "TDA_delta      float64\n",
       "TDD_delta      float64\n",
       "SUBA_delta     float64\n",
       "Odds_delta     float64\n",
       "Sum_delta      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data types of \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Favourite    825\n",
       "Underdog     490\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What percentage of Favourite fighters win?\n",
    "df['Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Favourite    0.627376\n",
       "Underdog     0.372624\n",
       "Name: Label, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df['Label'].value_counts()/len(df)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1179cd278>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VwCjwn8BIRIw1t1n7Il/jKcuQnxweAS6NiMeBjwJfAb6vWbsbOA/4bHP5vcC7Om57fXP9\nB2n/T/PHwL91eb7PAzfR/h3y7wYejIi9wI3Al/rxglRLZk7SPrb9YEQ8Qfunvg8DB+e4yy8Bt0TE\nI8Drm8cYp33I7rGI+GvgrcA/znieLwCfoX1Y7wHgv5ql3wQuabbrTwHvbH419k209/D/AvgBwL+/\nmYWn6Es6aUXEzcAHMvNoRNwH/HlmfmzQc51sPEYu6WR2GNgXEUeAfwX+aLDjnJzcI5ek4jxGLknF\nGXJJKs6QS1JxhlySijPkklTc/wIf6oWnBbzJPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189ac7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a.plot(kind='bar', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REACH_delta</th>\n",
       "      <th>SLPM_delta</th>\n",
       "      <th>SAPM_delta</th>\n",
       "      <th>STRA_delta</th>\n",
       "      <th>STRD_delta</th>\n",
       "      <th>TD_delta</th>\n",
       "      <th>TDA_delta</th>\n",
       "      <th>TDD_delta</th>\n",
       "      <th>SUBA_delta</th>\n",
       "      <th>Odds_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.219011</td>\n",
       "      <td>0.264205</td>\n",
       "      <td>-0.302084</td>\n",
       "      <td>0.013338</td>\n",
       "      <td>0.017886</td>\n",
       "      <td>0.280897</td>\n",
       "      <td>0.052259</td>\n",
       "      <td>0.055749</td>\n",
       "      <td>0.103194</td>\n",
       "      <td>-0.859810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.321775</td>\n",
       "      <td>1.475753</td>\n",
       "      <td>1.571591</td>\n",
       "      <td>0.111590</td>\n",
       "      <td>0.104947</td>\n",
       "      <td>1.886774</td>\n",
       "      <td>0.286079</td>\n",
       "      <td>0.299480</td>\n",
       "      <td>1.173894</td>\n",
       "      <td>2.313673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-6.020000</td>\n",
       "      <td>-10.560000</td>\n",
       "      <td>-0.490000</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>-10.750000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-12.100000</td>\n",
       "      <td>-12.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-0.620000</td>\n",
       "      <td>-1.110000</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>-0.900000</td>\n",
       "      <td>-0.120000</td>\n",
       "      <td>-0.120000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-1.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.175000</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.365000</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.480000</td>\n",
       "      <td>7.910000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>9.780000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>7.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       REACH_delta   SLPM_delta   SAPM_delta   STRA_delta   STRD_delta  \\\n",
       "count  1315.000000  1315.000000  1315.000000  1315.000000  1315.000000   \n",
       "mean      0.219011     0.264205    -0.302084     0.013338     0.017886   \n",
       "std       3.321775     1.475753     1.571591     0.111590     0.104947   \n",
       "min     -10.000000    -6.020000   -10.560000    -0.490000    -0.350000   \n",
       "25%      -2.000000    -0.620000    -1.110000    -0.060000    -0.050000   \n",
       "50%       0.000000     0.300000    -0.200000     0.010000     0.020000   \n",
       "75%       2.000000     1.175000     0.605000     0.080000     0.090000   \n",
       "max      12.000000     7.480000     7.910000     0.570000     0.420000   \n",
       "\n",
       "          TD_delta    TDA_delta    TDD_delta   SUBA_delta   Odds_delta  \n",
       "count  1315.000000  1315.000000  1315.000000  1315.000000  1315.000000  \n",
       "mean      0.280897     0.052259     0.055749     0.103194    -0.859810  \n",
       "std       1.886774     0.286079     0.299480     1.173894     2.313673  \n",
       "min     -10.750000    -1.000000    -1.000000   -12.100000   -12.950000  \n",
       "25%      -0.900000    -0.120000    -0.120000    -0.500000    -1.940000  \n",
       "50%       0.240000     0.040000     0.040000     0.000000    -0.780000  \n",
       "75%       1.365000     0.225000     0.230000     0.600000     0.575000  \n",
       "max       9.780000     1.000000     1.000000    11.800000     7.380000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical overview of dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x118c51cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1189632e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1187189b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118d70e10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118d5a940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118d5a978>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118b72cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118c8f048>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1189206a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118a0f898>]], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wEpiXmWdR3GK/HFgQEfMHmeIyio9rHpyZf5GZpwOvBX4OnB8RLxm76CVJkiQ1y8yngHsp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118aacba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Does mean of each feature distinguish the Favourite / Underdog to win ?\n",
    "# Does a specific feature advantage give the underdog winners an edge ?\n",
    "df.groupby('Label').mean().plot(kind = 'bar', subplots=True, layout=(5,2), legend=False, figsize=(25,20), fontsize=20, rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrix and Heatmap\n",
    "From the correlation matrix, we know that:\n",
    "* Predictors are not too correlated with each other. Low possibility of multicollinearity. Not too much of a worry if regression is applied\n",
    "* Positive correlation to strikes landed, striking defense to make a favourite more favourable to win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REACH_delta</th>\n",
       "      <th>SLPM_delta</th>\n",
       "      <th>SAPM_delta</th>\n",
       "      <th>STRA_delta</th>\n",
       "      <th>STRD_delta</th>\n",
       "      <th>TD_delta</th>\n",
       "      <th>TDA_delta</th>\n",
       "      <th>TDD_delta</th>\n",
       "      <th>SUBA_delta</th>\n",
       "      <th>Odds_delta</th>\n",
       "      <th>Label_Favourite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REACH_delta</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037076</td>\n",
       "      <td>-0.077292</td>\n",
       "      <td>-0.042400</td>\n",
       "      <td>-0.090796</td>\n",
       "      <td>-0.078721</td>\n",
       "      <td>0.027693</td>\n",
       "      <td>-0.072749</td>\n",
       "      <td>0.060381</td>\n",
       "      <td>-0.030485</td>\n",
       "      <td>0.070252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLPM_delta</th>\n",
       "      <td>0.037076</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089550</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>0.268306</td>\n",
       "      <td>-0.167199</td>\n",
       "      <td>0.041915</td>\n",
       "      <td>0.195507</td>\n",
       "      <td>-0.167117</td>\n",
       "      <td>-0.154969</td>\n",
       "      <td>0.205441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAPM_delta</th>\n",
       "      <td>-0.077292</td>\n",
       "      <td>0.089550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.298355</td>\n",
       "      <td>-0.412943</td>\n",
       "      <td>-0.280762</td>\n",
       "      <td>-0.230730</td>\n",
       "      <td>-0.064397</td>\n",
       "      <td>-0.040124</td>\n",
       "      <td>0.148382</td>\n",
       "      <td>-0.226802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRA_delta</th>\n",
       "      <td>-0.042400</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>-0.298355</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114704</td>\n",
       "      <td>0.202333</td>\n",
       "      <td>0.246022</td>\n",
       "      <td>0.143988</td>\n",
       "      <td>0.038443</td>\n",
       "      <td>-0.103734</td>\n",
       "      <td>0.168435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRD_delta</th>\n",
       "      <td>-0.090796</td>\n",
       "      <td>0.268306</td>\n",
       "      <td>-0.412943</td>\n",
       "      <td>0.114704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043988</td>\n",
       "      <td>0.128065</td>\n",
       "      <td>0.188816</td>\n",
       "      <td>-0.153664</td>\n",
       "      <td>-0.111798</td>\n",
       "      <td>0.220450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TD_delta</th>\n",
       "      <td>-0.078721</td>\n",
       "      <td>-0.167199</td>\n",
       "      <td>-0.280762</td>\n",
       "      <td>0.202333</td>\n",
       "      <td>0.043988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427436</td>\n",
       "      <td>0.003178</td>\n",
       "      <td>0.192474</td>\n",
       "      <td>-0.072800</td>\n",
       "      <td>0.067313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TDA_delta</th>\n",
       "      <td>0.027693</td>\n",
       "      <td>0.041915</td>\n",
       "      <td>-0.230730</td>\n",
       "      <td>0.246022</td>\n",
       "      <td>0.128065</td>\n",
       "      <td>0.427436</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.221375</td>\n",
       "      <td>0.098257</td>\n",
       "      <td>-0.106092</td>\n",
       "      <td>0.091885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TDD_delta</th>\n",
       "      <td>-0.072749</td>\n",
       "      <td>0.195507</td>\n",
       "      <td>-0.064397</td>\n",
       "      <td>0.143988</td>\n",
       "      <td>0.188816</td>\n",
       "      <td>0.003178</td>\n",
       "      <td>0.221375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.157317</td>\n",
       "      <td>-0.101961</td>\n",
       "      <td>0.123448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBA_delta</th>\n",
       "      <td>0.060381</td>\n",
       "      <td>-0.167117</td>\n",
       "      <td>-0.040124</td>\n",
       "      <td>0.038443</td>\n",
       "      <td>-0.153664</td>\n",
       "      <td>0.192474</td>\n",
       "      <td>0.098257</td>\n",
       "      <td>-0.157317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047820</td>\n",
       "      <td>0.085332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odds_delta</th>\n",
       "      <td>-0.030485</td>\n",
       "      <td>-0.154969</td>\n",
       "      <td>0.148382</td>\n",
       "      <td>-0.103734</td>\n",
       "      <td>-0.111798</td>\n",
       "      <td>-0.072800</td>\n",
       "      <td>-0.106092</td>\n",
       "      <td>-0.101961</td>\n",
       "      <td>-0.047820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.306930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Label_Favourite</th>\n",
       "      <td>0.070252</td>\n",
       "      <td>0.205441</td>\n",
       "      <td>-0.226802</td>\n",
       "      <td>0.168435</td>\n",
       "      <td>0.220450</td>\n",
       "      <td>0.067313</td>\n",
       "      <td>0.091885</td>\n",
       "      <td>0.123448</td>\n",
       "      <td>0.085332</td>\n",
       "      <td>-0.306930</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 REACH_delta  SLPM_delta  SAPM_delta  STRA_delta  STRD_delta  \\\n",
       "REACH_delta         1.000000    0.037076   -0.077292   -0.042400   -0.090796   \n",
       "SLPM_delta          0.037076    1.000000    0.089550    0.361854    0.268306   \n",
       "SAPM_delta         -0.077292    0.089550    1.000000   -0.298355   -0.412943   \n",
       "STRA_delta         -0.042400    0.361854   -0.298355    1.000000    0.114704   \n",
       "STRD_delta         -0.090796    0.268306   -0.412943    0.114704    1.000000   \n",
       "TD_delta           -0.078721   -0.167199   -0.280762    0.202333    0.043988   \n",
       "TDA_delta           0.027693    0.041915   -0.230730    0.246022    0.128065   \n",
       "TDD_delta          -0.072749    0.195507   -0.064397    0.143988    0.188816   \n",
       "SUBA_delta          0.060381   -0.167117   -0.040124    0.038443   -0.153664   \n",
       "Odds_delta         -0.030485   -0.154969    0.148382   -0.103734   -0.111798   \n",
       "Label_Favourite     0.070252    0.205441   -0.226802    0.168435    0.220450   \n",
       "\n",
       "                 TD_delta  TDA_delta  TDD_delta  SUBA_delta  Odds_delta  \\\n",
       "REACH_delta     -0.078721   0.027693  -0.072749    0.060381   -0.030485   \n",
       "SLPM_delta      -0.167199   0.041915   0.195507   -0.167117   -0.154969   \n",
       "SAPM_delta      -0.280762  -0.230730  -0.064397   -0.040124    0.148382   \n",
       "STRA_delta       0.202333   0.246022   0.143988    0.038443   -0.103734   \n",
       "STRD_delta       0.043988   0.128065   0.188816   -0.153664   -0.111798   \n",
       "TD_delta         1.000000   0.427436   0.003178    0.192474   -0.072800   \n",
       "TDA_delta        0.427436   1.000000   0.221375    0.098257   -0.106092   \n",
       "TDD_delta        0.003178   0.221375   1.000000   -0.157317   -0.101961   \n",
       "SUBA_delta       0.192474   0.098257  -0.157317    1.000000   -0.047820   \n",
       "Odds_delta      -0.072800  -0.106092  -0.101961   -0.047820    1.000000   \n",
       "Label_Favourite  0.067313   0.091885   0.123448    0.085332   -0.306930   \n",
       "\n",
       "                 Label_Favourite  \n",
       "REACH_delta             0.070252  \n",
       "SLPM_delta              0.205441  \n",
       "SAPM_delta             -0.226802  \n",
       "STRA_delta              0.168435  \n",
       "STRD_delta              0.220450  \n",
       "TD_delta                0.067313  \n",
       "TDA_delta               0.091885  \n",
       "TDD_delta               0.123448  \n",
       "SUBA_delta              0.085332  \n",
       "Odds_delta             -0.306930  \n",
       "Label_Favourite         1.000000  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_dummies(df,column_name):\n",
    "    \"\"\"Create Dummy Columns (One Hot Encoding) from a single Column\n",
    "\n",
    "    Usage\n",
    "    ------\n",
    "\n",
    "    train = create_dummies(train,\"Age\")\n",
    "    \"\"\"\n",
    "    dummies = pd.get_dummies(df[column_name],prefix=column_name)\n",
    "    df = pd.concat([df,dummies],axis=1)\n",
    "    return df\n",
    "\n",
    "# Correlation Matrix\n",
    "df_corr = create_dummies(df, 'Label').drop('Label_Underdog', axis = 1)\n",
    "corr = df_corr.corr()\n",
    "corr = (corr)\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1196dccc0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c5O2ttQ2doSuSrE7yq0meUlVHVdVRSV6S5MTNnGNj5yU5NMkzMxp6uDWXJTls/P6Q8Z/f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199df9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Sample T-test (Measuring STRD_delta)\n",
    "\n",
    "A one-sample t-test checks whether a sample mean differs from the population mean. Since STRD_delta has the highest correlation with the dependent variable 'Label_Favourite', let's test to see whether the average STRD_delta of Favourite and Underdog winners differs significantly.\n",
    "\n",
    "Hypothesis Testing: Is there significant difference in the **means of STRD_delta** between favourite winners and underdog winners?\n",
    "\n",
    "<br/>\n",
    "\n",
    "<center>\n",
    "$Null\\quad Hypothesis\\quad { H }_{ 0 }:\\quad There\\quad is\\quad no\\quad difference\\quad in\\quad STRD\\_ delta\\quad between\\quad Favourite\\quad and\\quad Underdog\\quad \\quad \\quad$\n",
    "\n",
    "$Alternate\\quad Hypothesis\\quad { H }_{ 1 }:\\quad There\\quad is\\quad a\\quad difference\\quad in\\quad STRD\\_ delta\\quad between\\quad Favourite\\quad and\\quad Underdog\\quad \\quad \\quad$\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STRD_delta mean of favourite winners is: 0.03570909090909092\n",
      "STRD_delta mean of undersog winners is: -0.012122448979591837\n"
     ]
    }
   ],
   "source": [
    "# Compating both means\n",
    "\n",
    "print('STRD_delta mean of favourite winners is: ' +  '{}' .format(df['STRD_delta'][df['Label'] == 'Favourite'].mean()))\n",
    "print('STRD_delta mean of undersog winners is: ' + '{}'.format(df['STRD_delta'][df['Label'] == 'Underdog'].mean()))\n",
    "\n",
    "# However, is the marginal difference of 0.047 significant? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conducting the T-test **(95% confidence interval)**\n",
    "\n",
    "**Reject the Null Hypotheses because:**\n",
    "\n",
    "* T test scores lies outside the quantiles, 4.96 > 1.96\n",
    "* P - value lower than 5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=4.9875255620895089, pvalue=7.4599876274065799e-07)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# T-test\n",
    "stats.ttest_1samp(a=  df[df['Label']=='Favourite']['STRD_delta'], # Sample of Favourite winners\n",
    "                  popmean = df['STRD_delta'].mean())  # Fighter population mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The t-distribution left quartile range is: -1.9628436163\n",
      "The t-distribution right quartile range is: 1.9628436163\n"
     ]
    }
   ],
   "source": [
    "# Critical point \n",
    "degree_freedom = len(df[df['Label']=='Favourite'])\n",
    "\n",
    "LQ = stats.t.ppf(0.025,degree_freedom)  # Left Quartile\n",
    "\n",
    "RQ = stats.t.ppf(0.975,degree_freedom)  # Right Quartile\n",
    "\n",
    "print ('The t-distribution left quartile range is: ' + str(LQ))\n",
    "print ('The t-distribution right quartile range is: ' + str(RQ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution Plots\n",
    "\n",
    "* In most of the predictors, distribution is relatively normal centered around 0\n",
    "* This implies most matches made in UFC are based on evenly macthed skillsets\n",
    "* Note that for the Underdog winners, it seems that the mean for the predictors tend to be lower than Favourites\n",
    "* This implies Underdog winners are more skilled in that particular area for the matchup but somehow has been labelled as Underdog by [**wisdom of the crowd**](http://www.betmma.tips/mma_betting_statistics.php) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols = df.drop(['Events', 'Favourite', 'Underdog', 'Label'], axis =1).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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M8IiIiIgOhwESUfR1epgRRZ3+O2eAFLByuQQhDEBIBhgh8B7ssAKJiIiI6HAYIBFFU3OA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il2F7Is87/+V2J41VBrlW0l2nYSC10m2nOeOXPm8eijjzst57pMmzaDr371zxEI+va1EuuzfjKb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h3pQKJAIkSBIfjzhLrLU9Sa8pexyzlrcEaJ9TPNzj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mrH5sm2OhTLMbqxYVlnsLVrVwPgGpLYp7pmEhW016xZbdaQu4UQTwMfAvcJIX4A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\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FQpELPNiyplrYss9KF0rzBc8FpGJjvusoGFQCRraZP3ktECjuX39d18O4ofVNwAzwimEY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\nSSuQWB4cmWEVSIq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12009ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 10 plots with a 2 by 5 dimension subplots\n",
    "fig, ax = plt.subplots(2,5, figsize=(20,20))\n",
    "\n",
    "# loop to plot in subplots\n",
    "for i, col in enumerate(cols):\n",
    "    x = i // 5\n",
    "    y = i % 5\n",
    "    sns.violinplot(x=\"Label\", y=col , data=df, order=[\"Favourite\", \"Underdog\"], ax=ax[x,y])\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "\n",
    "Feature selection is the process of selecting a subset of relevant predictors for use in model construction. Feature selection is used for:\n",
    "\n",
    "* simplification of models to make them easier to interpret\n",
    "* shorter training times (applicable to very huge datasets)\n",
    "* to avoid the curse of dimensionality \n",
    "* enhanced generalization by reducing overfitting (reduction of variance)\n",
    "\n",
    "From RFECV and Feature Importance as validation, we know:\n",
    "* The 4 most important features are **SAPM_delta, SLPM_delta, STRD_delta, TD_delta, Odds_delta**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination with Cross Validation (RFECV)\n",
    "\n",
    "The features will be selected based on Recursive Feature Elimination with Cross Validation [**(RFECV)**](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html). Recursive Feature Elimination (RFE) works by training the model, evaluating it, then removing the least significant features, and repeating.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a function to select features\n",
    "# Note that feature names are stored in cols\n",
    "\n",
    "def select_features(df):\n",
    "    all_X = df.drop(['Events', 'Favourite', 'Underdog', 'Label'], axis=1)\n",
    "    all_y = df['Label']\n",
    "    \n",
    "    clf = RandomForestClassifier(random_state=1)\n",
    "    selector = RFECV(clf)\n",
    "    selector.fit(all_X, all_y)\n",
    "    best_columns = list(all_X.columns[selector.support_])\n",
    "    print('Best Columns \\n' + '-'*12 + '\\n' + '{}'.format(best_columns))\n",
    "    \n",
    "    return best_columns\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Columns \n",
      "------------\n",
      "['SLPM_delta', 'SAPM_delta', 'STRD_delta', 'TD_delta', 'Odds_delta']\n"
     ]
    }
   ],
   "source": [
    "best_cols = select_features(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance\n",
    "\n",
    "* As expected **Reach_delta** is of least importance since reach does not really determine a clear winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q1qzZdRd2K7H9GoErnulg92djez/cDP1wRU/M6IcZ/XBFT8zohxn9\ncEVPci7bMBEREaH169ercePG2rFjh0JDQ41p7dq1U7t27SRJixcv1uHDh3MUJAAAAAD882UbJho2\nbKiff/5ZPXr0kNPp1LBhw7Rs2TIlJyerZcuW+VEjAAAAADeUbZjw9PTU0KFDTe+FhIS4zMeIBAAA\nAHBr4aF1AAAAACwhTAAAAACwhDABAAAAwBLCBAAAAABLCBMAAAAALCFMAAAAALCEMAEAAADAEsIE\nAAAAAEsIEwAAAAAsIUwAAAAAsIQwAQAAAMASwgQAAAAASwgTAAAAACwhTAAAAACwhDABAAAAwBLC\nBAAAAABLCBMAAAAALCFMAAAAALCEMAEAAADAEsIEAAAAAEsIEwAAAAAsIUwAAAAAsIQwAQAAAMAS\nwgQAAAAASwgTAAAAACwhTAAAAACwhDABAAAAwBLCBAAAAABLCBMAAAAALCFMAAAAALCEMAEAAADA\nEsIEAAAAAEsIEwAAAAAsIUwAAAAAsIQwAQAAAMASwgQAAAAASwgTAAAAACzxtrsASLU2+Ekb4u0u\nAwAAAMgVRiYAAAAAWMLIRBZWnThvdwm2CQ7wsrsEAAAA/EMwMgFDcICXPqlbxO4yAAAA8A/ByIQb\n+KZp0Xxb1kO3++XbsgAAAHBzY2QCAAAAgCWECQAAAACWECYAAAAAWEKYAAAAAGAJYQIAAACAJYQJ\nAAAAAJYQJgAAAABYQpgAAAAAYAlhAgAAAIAlhAkAAAAAlhAmAAAAAFhCmAAAAABgCWECAAAAgCWE\nCQAAAACWECYAAAAAWEKYAAAAAGAJYQIAAACAJd7ZzZCenq7o6Gg5HA75+PgoKipK5cuXN6YvX75c\ns2bNkpeXl0JDQzVkyBB5epJRAAAAgJtdtnv969at08WLFzVx4kT1799fo0aNMqalpKToiy++0Nix\nYxUTE6OkpCRt2LAhTwsGAAAA4B6yDRNbt25VZGSkJKlatWras2ePMc3Hx0cxMTHy9fWVJKWmpsrH\nxyePSgUAAADgTrI9zSkpKUkBAQHGa09PT6Wmpsrb21uenp4qXry4JGn27NlKTk5WnTp1crRgh8Nh\nseR8EFjO7gryjFv3/TL/lDrzC/1wRU/M6IcZ/XBFT8zohxn9cEVPMoSFhV1zerZhwt/fX0lJScZr\np9Mpb+///Vh6ero+/fRTHT16VNHR0fLw8Lghhdnp6InzdpeQZ9y575kcDsc/os78Qj9c0RMz+mFG\nP1zREzP6YUY/XNGTnMv2NKeIiAjFxsZKknbs2KHQ0FDT9Pfee08XL17Uhx9+aJzuBAAAAODml+3I\nRMOGDfXzzz+rR48ecjqdGjZsmJYtW6bk5GRVqVJFCxcu1N13361+/fpJktq1a6dGjRrleeEAAAAA\n7JVtmPD09NTQoUNN74WEhBj//fPPP9/wogAAAAC4Px4IAQAAAMASwgQAAAAASwgTAAAAACwhTAAA\nAACwhDABAAAAwBLCBAAAAABLCBMAAAAALCFMAAAAALCEMAEAAADAEsIEAAAAAEsIEwAAAAAsIUwA\nAAAAsIQwAQAAAMASb7sLwD/DqhPn829hgeV0NJ+W99DtfvmyHAAAgJsRIxMAAAAALCFMAAAAALCE\nMAEAAADAEsIEAAAAAEsIEwAAAAAsIUwAAAAAsIQwAQAAAMASnjMBWHCzPndD4tkbAAAg5xiZAAAA\nAGAJYQIAAACAJYQJAAAAAJYQJgAAAABYQpgAAAAAYAlhAgAAAIAlhAkAAAAAlhAmAAAAAFjCQ+sA\nXLd8fYiflK8P8uMhfgAAXB0jEwAAAAAsIUwAAAAAsIQwAQAAAMASwgQAAAAAS7gAGwDyQL5elM4F\n6QAAmzAyAQAAAMASwgQAAAAASwgTAAAAACwhTAAAAACwhAuwAQB57ma9IF2ydlE6T40HcLNgZAIA\nAACAJYxMAAAA292so1dWR2pu1n5IjF7dbBiZAAAAAGAJYQIAAACAJYQJAAAAAJYQJgAAAABYQpgA\nAAAAYAlhAgAAAIAl3BoWAAAAbo0HPbovRiYAAAAAWEKYAAAAAGAJYQIAAACAJYQJAAAAAJYQJgAA\nAABYQpgAAAAAYAlhAgAAAIAlhAkAAAAAlhAmAAAAAFiS7ROw09PTFR0dLYfDIR8fH0VFRal8+fLG\n9PXr1ysmJkZeXl564okn1KJFizwtGAAAAIB7yHZkYt26dbp48aImTpyo/v37a9SoUca01NRUjRw5\nUp9++qnGjRunb7/9VqdOncrTggEAAAC4h2xHJrZu3arIyEhJUrVq1bRnzx5j2qFDhxQUFKTbbrtN\nkhQREaEtW7bo4YcfzqNy88dDt/vZXYLboSdm9MOMfriiJ2b0w4x+uKInZvTDjH64r2xHJpKSkhQQ\nEPC/H/D0VGpqapbT/P39lZiYmAdlAgAAAHA32YYJf39/JSUlGa+dTqe8vb2NaefPnzemJSUlKTAw\nMA/KBAAAAOBusg0TERERio2NlSTt2LFDoaGhxrQKFSro2LFjOnPmjC5duqStW7eqWrVqeVctAAAA\nALfhkZCQ4LzWDJl3c9q/f7+cTqeGDRumvXv3Kjk5WS1btjTu5uR0OtW8eXO1bds2v2oHAAAAYKNs\nwwQAAAAAZIWH1gEAAACwhDABAAAAwBLCBAAAAABLCBMAAAAALMn2Cdi48Xbv3q2DBw8qPT1dUsaz\nOy5duqR9+/YpKirK5urcx8WLF+Xj42N3GbZITU3VyZMnTevIxYsXFRcXp0cffdTm6tzHH3/8odKl\nS9tdRr7766+/dPToUaWlpUkyb0O6d+9uc3X2oCc5c6tuV9mmAnmHMJHPvvzyS02YMEHFixfX6dOn\nVbJkSZ0+fVppaWlq1KiR3eXlu7/++kuTJk3SwYMHXXYCjhw5ojVr1thcYf5bs2aN3n33XZ07d85l\nWsmSJW+5f/gOHTqk0aNHZxnAz5w5o59++snmCvPXN998oxEjRigtLU0eHh5yOjNuyOfh4aGqVave\nkjvO9MSM7aoZ21RX6enpWrNmjct2NTNgffbZZzZXaK+sDvZGRETYXJX74jSnfDZ//ny9/PLL+u67\n71S6dGl98cUXWrZsmWrXrq0yZcrYXV6+e+utt7Rp0yZVq1ZNO3fuVEREhEqUKKF9+/bpmWeesbs8\nW4wZM0YPPvigZs+ercDAQMXExGjEiBEqU6aMevfubXd5+S5zJ6Br165KSEhQ586d1aRJEyUnJ+u1\n116zu7x8N3XqVHXr1k3r169XsWLFtHDhQs2cOVOVK1dWgwYN7C7PFvTEjO2qGdtUVx9++KHeeOMN\n/fTTT5o8ebJ+/fVXLVq0SDNmzFBISIjd5dliy5Ytat++ve677z7VrVtXdevWVb169dSwYUP179/f\n7vLcGmEin505c0aRkZGSpMqVK2v79u0KDAzUM888o1WrVtlcXf7bunWrXnvtNfXr109hYWGqX7++\n3nvvPfXt21cbNmywuzxb/Pbbb3r66acVHBys8PBwnTp1SvXq1dOQIUM0ffp0u8vLd3v37tVLL72k\nli1bKjw8XCEhIXruuec0aNAgzZ8/3+7y8t3Jkyf1+OOPy8fHR3fccYe2b9+uihUratCgQVqwYIHd\n5dmCnpixXTVjm+pq1apVevPNNzVhwgQFBQVpyJAhWrhwoZo0aaKUlBS7y7PFxx9/rKCgII0cOVKF\nChXS+++/r3//+98qXLiwXn/9dbvLc2uEiXxWqlQpxcfHS5IqVKigvXv3SpL8/PyUkJBgZ2m2cDqd\nKlWqlCRzPx5++GHt3r3bztJsExgYaGzMQ0JCtG/fPklScHCwfvvtNztLs4W3t7cCAgIkZfQgLi5O\nklSnTh0dOHDAztJsUaxYMWNbERISYvSjZMmSOnnypJ2l2YaemLFdNWOb6iopKUl33nmnJCk0NFS7\ndu2Sl5eXunTpoh9//NHm6uxx6NAhDRgwQJGRkQoPD5ePj4/atGmjl156SV999ZXd5bk1wkQ+a9Gi\nhaKiorRx40Y98MADWrBggaZMmaKPPvpIYWFhdpeX78LDw7VkyRJJGSM1mee/ZwauW1H9+vUVHR2t\nAwcOqEaNGlq6dKl27dqluXPnqmTJknaXl+8iIiI0bdo0paSkqEqVKlq7dq3S09O1c+fOW/JC0iZN\nmuiNN97Qtm3bFBkZqUWLFmnlypX68ssvFRQUZHd5tqAnZmxXzdimugoKCjJCZsWKFbVr1y5JGUE0\nKSnJztJs4+vrKw8PD0nmA1d33XWXjh49amdpbs/r5ZdffsPuIm4ld999t0qUKKEiRYooIiJCRYoU\n0dKlS+Xr66uXX35ZRYoUsbvEfBUcHKwRI0bIy8tLjz/+uL788kstWLBACxYs0COPPKL69evbXWK+\nu/fee7V37155e3urcePG2r17t0aOHKn9+/dr6NChCg4OtrvEfBUeHq5JkybJ6XTqiSee0KxZszR2\n7FitXLlSnTp1Uo0aNewuMV/VrFlTSUlJ8vf3V926dXXu3DlNmDBB586dU1RU1C15dyt6YsZ21Yxt\nqitfX1+99dZbCgoKUq1atfTBBx/ozz//1OzZs1WhQgU1bdrU7hLz3e7du7Vp0yZFREQoLS1N3377\nrRo1aqRVq1YpLi5OHTp0sLtEt+WRkJDgtLuIW8mSJUvUuHFjlyOqycnJWrhwodq1a2dTZfY5f/68\nkpOTVbx4cZ08eVJr165V4cKF1bhxY+Mowa3kxIkTKlWqlDw9/zdwmJSUpIIFC2r//v0KDw+3sTr7\npKSkyNfXV8nJyfr1119VuHBhVatWze6y8t3mzZtVvXp1eXubb8Z38eJFxcbGqmHDhvYUZiN64ort\n6v+wTc3atm3bVLBgQYWHh+vHH3/UggULVLhwYfXu3VvFixe3u7x899dff+mNN97Q/fffr9atW6t/\n//7aunWrPD09NWTIELVo0cLuEt0WYSIfnD59WsnJyZKk1q1ba+LEiSpcuLBpnri4OA0bNkzr16+3\no0TbPPPMM/rwww+Nc+Iz/f3333r++ec1depUmyqzz3333aelS5eqaNGipvePHTumjh073nLrSIsW\nLTRlyhSX78zJkyfVqVMnLV++3KbK8ld6erqcTqfq1aunxYsXq1ixYqbpe/fuVZ8+fW6p9YOeZI3t\nqhnbVFcxMTHq1KmTfH19Te8nJiYqJiZGAwcOtKky9+F0OnXw4EEFBgbKx8fnljtzJDd4zkQ+2Lp1\nq4YOHWocDbrynueZ90Rv1qxZvtdmh40bN2rnzp2SMm7FFhMTo0KFCpnmOXbsmH7//Xc7yrPFt99+\nq4kTJ0rKWB+efvppl6OHiYmJCg0NtaO8fLdy5UrjrjO///673n//fZfRvBMnTrgcib5ZzZs3T9HR\n0cYzFK62rahTp04+V2YfemLGdtWMbaqrgwcP6tSpU5IywkRoaKhL4Dx48KDmzZt3S4aJK0Onh4eH\nQkND9dtvv6lt27Zat26dzRW6r1vjX2KbPfjgg1qwYIHS09PVsmVLTZo0yXSExMPDQ4UKFXI58nqz\nCgkJ0bRp0yRlbOR37typAgUKmObx8/O7pW7F1rx5c/n4+MjpdOqtt97S008/LX9/f2O6h4eH/Pz8\ndO+999pYZf6599579dNPPxlB29PTU15eXsZ0Dw8PhYWF6fnnn7erxHzVqlUrhYSEyOl0ql+/fnrv\nvfdcthd+fn6qVKmSTRXmP3pixnbVjG2qq7///lsDBgwwXr/88ssu8/j5+alTp075WZatFi9erIUL\nF0rK+N4MHjzY5SDVqVOnVKJECTvK+8fgNCfY6s0339SgQYNcjo7cyq52/vetavz48Xr66addhuNv\nVb///rtuv/32W+6892uhJ2ZsV83Yprp68sknNWXKlFv+1J3k5GRNnz5dTqdTMTExeuqpp0wjepkH\nex988MFb8sHCOUWYyAe9evXK8T9yX375ZR5XY7/c3J6wXLlyeViJ+xg3blyO5+3Tp08eVuIefvnl\nlxzPW6tWrTysxD3k5mjy8OHD87AS90FPzNiumrFNRW4tXrxYTZo0uSVvOX69iOn5oHbt2naX4FZa\ntWplnOec+f9Z8fDwMO6PfrPbunWr3SW4lcuH4q/lVllHLj/FCxnoiRnbVTO2qa6aNWuW4wObixYt\nyuNq3EPmKU6Zli1bdtV5n3jiibwu5x+LkQkbpaamysvL65Ybms/NBYAMKwJA9tiuIjuLFy/O8by3\nyg1hnnzyyRzN5+Hhofnz5+dxNf9chAkbfP3115o5c6ZOnDihr7/+2jhvsW/fvqb7YN9Ktm3bpqNH\nj+rBBx/UiRMnVL58+Vt6qNHhcGjOnDk6duyY3nzzTa1du1bly5dXZGSk3aXZ4sKFC1qzZo2OHj2q\ndu3ayeFwKCQk5Ja9KG7Dhg2aNWuWjh07pi+++EILFixQ6dKl1bJlS7tLsw09ccV29X/YpmYtPT1d\nv//+u0qXLi2n0+ly0T6QE7fmnquNpk+frq+++ko9evQwLgarU6eOFi5cqC+++MLm6vLf6dOn1bVr\nVz377LN69913lZCQoLFjx+pf//qXjh07Znd5tvjxxx/Vo0cPpaena9euXbp06ZISEhI0ePDgaw7B\n3qyOHTumNm3aaNy4cZo8ebISExM1b948tW/fXnv27LG7vHy3dOlSvfHGG7r77rt1+vRppaenq3jx\n4vrkk080Y8YMu8uzBT0xY7tqxjbVVWpqqkaPHq0GDRqoTZs2+uOPPzRs2DC9+uqrxnOxbkWnTp3S\nxIkTNXz4cJ0+fVorV67U/v377S7L7REm8tm8efM0dOhQPf7448YoxEMPPaThw4fru+++s7m6/PfR\nRx+pTJkyWrFihQoWLChJeuONN1SpUiWNGDHC5ursMXbsWL3wwgt67bXXjPPCe/furcGDBxv3Tb+V\njBgxQg888IDmzZtnHFV9++239dBDD+njjz+2ubr8N3XqVL388svq2bOnsX60bdtWw4YN0+zZs22u\nzh705P/bu/OoKKv/D+DvgcGFxd3YElKEQDHRX4pEjrikKR0gl29mpESouKIHd3FBw4XCBReUoASU\nvpq5gsGIgSyhkbibgAmoBAiI5LAzzO8PcvJxkMW+PHfs+bzO8Ry48/zxPtdnLvfzLPdy0bjKRWOq\nqv379+PChQsIDAxUjqsfffQRMjMzsWPHDsbp2Lh16xYmT56MtLQ0SKVSVFZW4tKlS/jss88E8Z7R\nP0HFBM8ePnwIExMTlXZ9fX38+eefDBKxlZaWhpkzZ3KW/dTV1cX8+fNx9epVhsnYycnJafSlfVtb\nW8FsOPWsa9euYcqUKZx3izQ0NPDpp58iMzOTYTI2Hjx4ACsrK5V2CwsLPHr0iEEi9qhPuGhc5aIx\nVd5x4hUAACAASURBVJVUKsXy5csxePBg5dhqY2MDHx8fJCQksA3HyI4dOzBjxgwEBQUpH/dasWIF\npk+fjj179jBOp96omODZgAEDVFZJqK+vR3h4OPr3788oFTsaGhqoqqpSaS8uLlZeURMaIyMjXL9+\nXaU9KSkJRkZGDBKxpa2trdy19Vm///479PT0GCRiq2/fvkhJSVFpP336tGA2aHse9QkXjatcNKaq\nevz4MWfz3Kc6duyI6upqBonYy8zMxJgxY1TaJ0yYgJycHP4DvUJoaVieeXt7Y9GiRUhOTkZ1dTW2\nbNmCe/fuoaamBjt37mQdj3fjxo3DV199hRUrVkAkEqG8vBwXL16Ev78/Ro8ezToeE56envD19cWt\nW7cgl8tx+vRp5OXl4dy5c9iwYQPreLz78MMPsXnzZsyfPx8KhQLZ2dlIS0vDvn37MHHiRNbxeOfl\n5YXFixcjLS0NtbW1CA0Nxf3795GRkSHIx74A6pPn0bjKRWOqqiFDhiA8PByrV69WtslkMuzdu1dQ\nu4I/q2vXrsjOzsbrr7/Oab9y5Qp69uzJKNWrgVZzYqC6uhoxMTHIzc1FXV0dTE1NMX78eGhra7OO\nxrva2lrs2bMHR48eRW1tLYCGq2ouLi7w8vIS7K7HmZmZOHToEHJyciCXy2FqaoqPP/4Y1tbWrKMx\ncfjwYRw8eBAPHz4E0DDoT5s2Da6uroJcAa24uBg//PADsrOzlefH5MmTYWBgwDoaM9Qnf6NxVRWN\nqVwPHz7EsmXL8Mcff+DJkycwMTFBYWEhjIyMEBAQIMjlg48fP47g4GDMmDEDe/fuxaJFi1BQUIAj\nR45g3rx5mDJlCuuIaouKCaIWqqqqkJeXB7lcjtdff12QhRVpXmVlJeRyOXR1dVlHIUTt0bhKmvPL\nL78gNzdXWWDZ2toK8gLNU0lJSYiIiFApOt977z3W0dQaFRM8cHZ2bvHGdELYFCU9Pb3Fxw4ePLgN\nk6iPjRs3tvjYNWvWtGES9RAdHd3iYx0dHdswiXqYM2dOi48NCgpqwyTqg/qEi8ZVLhpTCeEPvTPB\nA3d3d+XP+fn5OHz4MD788ENYWVlBLBbj9u3b+OGHHzB16lSGKfnz7CTgaZGlUCjQrl07iMViVFRU\nQENDAzo6OoiLi2MVk1d1dXXKn59u0GZpaak8RzIyMnDjxg1MmDCBYUr+PD/5Ky4uhpaWFgwNDSEW\ni5GXl4eamhr07dtXEMXEwIEDlT+XlZXhxIkTGDFihPL8yMzMxE8//YTJkyczTMkv6hMuGle5aExV\nZWtr2+ILm0JZCjUkJKTFx3p4eLRhklcb3ZngmZubG1xdXVVWDEhISMD+/fvx3XffMUrGH7lcrvw5\nKioKp0+fxsqVK2FmZgagYZOyTZs24d1338Unn3zCKiYzPj4+MDU1xcyZMzntYWFhuHz5suDWAA8L\nC8P169fh4+ODLl26AGh4UXDTpk3Q19eHl5cX44T8WrBgAUaMGKEyST516hROnjyJ0NBQRsnYoT6h\ncbUpNKY2+OWXX5Q/Z2RkIDIyEu7u7pwLm99++y3+85//COYcefacUCgUuH79Orp37w5zc3OIxWLc\nuXMHDx8+hJ2dnSAXc2gp4T4Yx0h2dnajSxUaGxsLZr1rTU1N5b+goCAsX75c+QcPAHr16oUlS5bg\n22+/ZZiSncTERIwbN06l3cHBoVWPMvxbhIeHY968ecpCAmhYM3/27NmCeCzweVevXsWQIUNU2gcM\nGICsrCwGidijPqFxtSk0pjYYOnSo8t/p06exfv16TJkyBdbW1rC0tISLiwvWrFmD77//nnVU3nz9\n9dfKf/369YOLiwtOnTqFnTt3IiAgAMePH8fkyZMFuQx5a1AxwTMbGxts27YNhYWFyrZ79+7B398f\nw4YNY5iMnaKiIpW2nJwcQa6HDgAmJiaN7kVy+PBhQa6Zr62tjYyMDJX29PR0ToEhFG+++SYOHDjA\n2UdAJpNh3759GDBgAMNk7FCfqKJx9W80pqoqKipqdJ+JDh06CHIDXaDhTua0adMgFv/9BoCGhgYm\nT56M+Ph4hsnUH70zwbM1a9Zg+fLlcHZ2ho6ODhQKBSoqKvD2229j1apVrOPxbvLkyVi3bh2mTp2K\nvn37QqFQ4NatW/j+++8xe/Zs1vGY8Pb2hre3N86dOwczMzMoFApkZmaipqYGgYGBrOPx7rPPPoOf\nnx/S0tJgbm6uPEfi4+Oxdu1a1vF4t2rVKixevBjjx4+HsbExFAoFHjx4AAMDA8E8rvE86hMuGle5\naExVZW9vjy+++AKLFy/mjKvbtm1rdOM2IejRowcuXLgAU1NTTntcXByMjY0ZpXo10DsTjNy9exfZ\n2dkAADMzM7zxxhvKz2pra5GamgqJRMIoHb+OHz+OkydPcvpjypQpGD9+PONk7Dx+/BhxcXHKPunT\npw/Gjh3LudUqk8kEs0RqamoqTp06xTlHJk+ejEGDBjFOxkZtbS0uXryo3JXVzMwMQ4YMUV5Rk8vl\nuHv3LszNzRmm5Bf1CReNq1w0pnKVl5dj8+bNOHfuHBSKhmmgpqYmxo8fj2XLlqFdu3aME/IvPj4e\nq1evxsCBA5XjxK1bt5CRkYGAgAAMHTqUcUL1RcWEGiopKYGjo6NgVlNojkwmw4YNG+Dv7886itqg\nc4SrrKwM8+fPR0REBOsoaoHOD1XUJ1w0rnIJ9fyQyWS4d+8eAMDU1BQ6OjrKz6qqqnDy5El89NFH\nrOLx7u7du4iKiuIU4c7OzujVqxfjZOqNHnNSU0+vFJCGZf0SExNZx1A7dI78ra6uTjAv2rYUnR+q\nqE/+RuOqKiGeH7q6uujXr1+jn5WXl2P79u2CKib69OmDhQsXvvDz0tJSTJkyRRDLK7cGvYCtplq6\nFjQRLjpHSFPo/FBFfUKaQucHaU59fT2ePHnCOobaoWKCEEIIIYSQFqCiUxUVE4QQQgghhJCXQsUE\nIYQQQggh5KVQMUEIIYQQQgh5KVRMqCFNTU2YmJiwjqFWhLjKRnOoT7ioP/4mEomgpaXFOoZaePqy\nJPWJKvrONLxQ+xT1ByEvh5aG5ZlcLsfp06cxbNgwGBgYICQkBGfPnoWVlRWWLFkCXV1ddOnSBUeO\nHGEdVW3o6Ohg7ty5rGO0qWf/oDVHQ0MD3bp1w5kzZ9owkXqpqKhARUUFtLW1oa2trfK5np4evvji\nCwbJ+FVbW4srV64gOzsbFRUV0NHRgZmZGWxsbKCh8fe1oW7duiEpKYlhUrbq6+vx888/Izo6GsnJ\nyUhKShJ8nzxPCONqU37//XdER0cjNjYW0dHRghtTX6S+vh6pqamwt7dHu3btYGtryzqS2qGiUxVt\nWseznTt34scff8TOnTtRXFyMpUuXwsPDA6mpqTA2Nsb69etZR2xz69ata/Gxvr6+bZhEfQwbNqzF\nxwplU6WSkhIcOHAACQkJKCoqUrbr6+tjzJgx+PTTT9GlSxeGCfkVHR2NXbt2obS0FNra2tDR0UFF\nRQXKy8vRo0cPLFy4EOPGjWMdk6k7d+4gOjoaMTExKC0txWuvvQYXFxe4u7uzjsaLqqoqHDt2DAkJ\nCSoF5+jRo+Hs7CzouzNlZWWIjY1FVFQUMjMz0b59e4wdOxarV69mHY25p8VVTEwMHj16JJi/M61V\nV1eH27dvw9ramnUUtUJ3JngWGxuLLVu24M0330RkZCSGDh0Kd3d3SCQSzJ49m3U8XsTExEBDQwPW\n1tYwMTGhKh8NV5IfPXoEa2trjBw5EpaWloJefu7+/fvw9PREhw4d4OLigj59+kBHRwfl5eXIyspC\nbGwsYmNjERISAgMDA9Zx21xMTAz8/PwwY8YMuLi4QF9fX/lZfn4+Tp06hS+++AK6urqwt7dnmJR/\njx8/RkxMDKKjo5GVlQUtLS3U1tZixYoVcHJy4tyx+Td79OgR5s6di+LiYjg4OGDEiBHQ1dVFeXk5\n7ty5g6CgIBw/fhx79+5F586dWcfljVwuR3JyMqKjo/Hzzz+jtrYWIpEIHh4emDp1KnR1dVlHZObx\n48fKOzOZmZkQi8UYNWoUJk2axDoab5ydnVv8t/bEiRMQi8VUSDSCigmelZeXQ19fX3krcebMmQAA\nsVg4/xU7duxAQkICkpOTIZPJMGLECDg4OMDS0pJ1NGbOnDmDGzdu4Pz58zh58iQOHz4MiUQCBwcH\nDB48WDAToqcCAwNhbm4Of39/tGvXjvPZyJEj8dlnn2HFihUICQmBj48Po5T8iYyMxJw5c/Dpp5+q\nfGZoaIjZs2dDR0cHBw8eFEwxER8fr5wgtm/fHnZ2dpg+fTreeecdjBkzBm+99ZagvjeBgYHo2LEj\njh492ugdu7KyMixZsgTffvstFi1axCAhvzIyMpSPMZWVlcHKygozZ86Eg4MDpk6dilGjRgmykKir\nq0NKSgqioqKQmpqKuro6WFtbQyQSITg4+IW7Yf9bPX/XUqFQICAgAB4eHoK68/1PCWcGqyYsLS1x\n4MABdOnSBU+ePIFEIkFhYSH27NmDAQMGsI7HCzs7O9jZ2QEAbty4gcTERKxbtw5VVVXKwmLQoEGC\nuzJvbW0Na2trzJs3D7m5uTh//jyCgoLw4MED2Nvbw8HBAba2tmjfvj3rqG3uypUr2Llzp0oh8ZSW\nlhZmzJiBNWvW8JyMjZycHEgkkiaPGT58OMLDw3lKxN6KFSvQq1cvrF27FmPGjBHUBZnGXLhwAZs3\nb37hBKhz586YNWsWtmzZIohiYvr06ejVqxdmz54NiUSCHj16sI7EXEBAAKRSKSoqKjBo0CAsWrQI\nDg4O6NGjB+zs7NChQwfWEXnn7Oys0rZjxw6MGTMGxsbGDBK9moQ9+jKwbNkyrFmzBgUFBZg/fz70\n9fWxbds2FBYWYvPmzazj8e7pBHru3LnIzc1FYmIigoKCcP/+fdjb2wtmsvg8U1NTTJ8+HdOnT0dJ\nSQnOnDmDdevWQaFQICEhgXW8NieTydCzZ88mjzE0NOS8S/FvVl1d3exVVF1dXfz55588JWJvzpw5\nkEqlWL9+PQ4cOACJRIIRI0agf//+rKMxUVZW1uzkx8TEBA8fPuQpEVsuLi746aefsHv3biQlJUEi\nkUAikaB79+6sozFz5MgR9OrVCwsWLIC9vT26du3KOhL5l6BigmdmZmaIjIzktM2fP/+FV2CFpHPn\nzujWrRu6d++OrKwspKens47E1IMHD5CYmIikpCRcvXoVZmZmzV6d/reor6+HpqZmk8doaGi0ahWs\nV53Q7tQ1x83NDW5ubvj9998RGxuLs2fPIjw8HN27d4dCoUBOTg769OnDOiZv6uvrm707IxaLUVtb\ny1MitlauXImlS5ciNTUVZ8+exc6dO+Hv7w8rKysAfy8ZLCQhISGQSqXYs2cP/Pz8YGVlpXy/hpB/\ngooJHkRHR7f4WEdHxzZMon5ycnKUE+YbN27AwsICEokEn3/+OczNzVnH45VCocC1a9eQlJSExMRE\nPHjwAIMHD8bIkSOxbt06Qbxo/KyCggJUVla+8PPS0lIe07AXHh6Ojh07vvDzpvrq38zMzAxz587F\n3Llzcf36dcTGxuLcuXNYtWoVTE1N4eTkhE8++YR1zDYnEolQX1/fZIEtpOIbaCiehg8fjuHDh6Oq\nqgqJiYmQSqXQ1NSEp6cnbG1t4ezsjJEjR7KOyosBAwZgwIABWLx4MdLS0hAbG4uwsDDs3bsXQMO7\ne1OnTqVHwkir0dKwPPjggw84vxcXF0NLSwuGhoYQi8XIy8tDTU0N+vbti4iICEYp+XPp0iUkJSUh\nKSkJhYWFGDx4MCQSCYYPH85ZpUZIfH19lSuNvPPOO5BIJHjnnXcE+YIgANja2jZ7JV6hUEAkEgli\nCUNPT88W35kICgpq4zTqr76+HmlpaZBKpTh//jzi4uJYR2pz9J1puSdPnuDcuXOIjY3FlStXkJqa\nyjoSM7W1tUhOToZUKkVycjLkcjns7OwQEBDAOhov0tLSVNq8vb2xatUqlUfihgwZwlesVw4VEzwL\nCwvD9evX4ePjo3xRTiaTYdOmTdDX14eXlxfjhG3P1tYWWlpaGDRoEOzs7JqcMDs5OfGYjB1bW1uI\nxWJYWFhALBY3OSkIDg7mMRkb+fn5LT7W0NCwDZOQV1FlZSXkcjl0dXVRW1sriL0VoqKi0L179xYt\n0DB48GAeEr0aiouL6Ur8XyoqKhAfH4+4uDhs376ddRxetHRTPirCm0bFBM9Gjx6NkJAQ9O7dm9Oe\nm5sLNzc3xMfHM0rGn8ZWT2iMSCTCiRMn2jiNevj6669bfOzT5YSJ8JSXl0NTU7PRVVeKi4uxc+dO\nbNy4kUEyNv744w+cP38eADBq1Cjo6+tj69atOHHiBBQKBYYNGwZfX19B7KswbNgwnDlzBt26dWMd\nRW3QJn6Na2ocKSoqQmBgoKDGEfLP0TsTPNPW1kZGRoZKMZGeni6YNY1PnjzJOoLaoQJBFU0E/lZY\nWIgNGzbg0qVLABqWV/b19UWnTp0gl8sRGRmJb775RlDLo6akpGDFihUwNDREhw4dEBwcDEdHR1y4\ncAG+vr6or69HaGgogoODsXTpUtZx2xxt/slFm/iponHkxaqrq1Xu6t27dw/6+vqCWI79n6I7Ezw7\nduwYtm/fjrFjx8Lc3BwKhQK3bt1CfHw81q5di7Fjx7KOyIu8vDwkJydDS0sLw4YNg5GREetITMnl\nchw4cAAJCQnQ0tLCiBEj8MknnwhyUAdUJwK9e/fmTATOnz8PAwMDwUwElixZgrt372LWrFnQ0tJC\nWFgYzMzM4OnpiaVLlyIrKwtOTk6YM2eOYC5KuLq6Yty4ccqN/BISErBixQps3bpVuTpNeno6fHx8\ncObMGZZReWFra4uYmBha7vMv69evR25uLrZv397kJn79+/cXxL4bAI0jL3Ly5Ens2bMHO3bs4Gza\nt2DBAvz222/w9vbG+PHjGSZUf8KcqTA0ceJEGBoa4tSpU8pHeMzMzLBr1y4MGjSIcTp+JCcnY/ny\n5crlcLdv3461a9fivffeY5yMnT179uD48eMYN24cNDU1ERERgQcPHmD16tWsozFBu/lyXblyBZs2\nbcLQoUMBNGx+6erqiqysLCgUCoSGhgpu59rc3FyMGjVK+buDgwM0NTXxxhtvKNtMTEwEtepXSyc8\nQnj2mzbxU0XjiKr4+Hhs3boV7u7uMDEx4Xy2YcMGfP/999i4cSP09PTw7rvvMkqp/qiYYODZHaCF\nKDg4GJMmTcLChQshFouxd+9eBAYGCrqYkEql2Lhxo3KwGj16NLy8vLB8+XJB3p2giQCXTCbj7Jlg\nbGyMuro6GBsbw8/PT5DnSE1NjcpSuVpaWpy+EIlEgnr8x8/PD3p6eqxjqAXaxE8VjSOqIiIisGDB\nAnz88ccqn3Xt2hWzZs0CABw4cICKiSYI78xhoDUvMglhx+fs7Gxs2bJFOXC5ubkhLCwMpaWlgr1F\nX1JSAktLS+XvNjY2qKurQ0lJiSCXy6WJAJdCoYCGhganTVNTEx4eHoKcAAANhcLzq54JeWM/kUiE\nQYMG0QvYf6FN/FTROKIqOzu72c1gx44di++++46nRK8mYZ49PKurq1P+XF1djfj4eFhaWsLKygpi\nsRgZGRm4ceMGJkyYwDAlf2pqajirSGhra6Njx46oqKgQbDFRX1/PGeQ1NDTQvn17zrkjJDQRaBlt\nbW3WEZhRKBTw9vbmnCdVVVVYvXq18hFKIX1/hHQHpiVoE7+WE/I40qFDB8hksiaPqampoZewm0HF\nBA98fX2VP/v4+MDDw0Nl9Z6wsDBcvnyZ72iEqCWaCKiSSqWcP/pyuRxxcXEqBbhQ9mbx8PBQaWts\nUymhPFLq6OhIE55nKBQKlQ1jGztGaHezaBzhevvtt3Hs2DGsXLnyhcf88MMPsLa25jHVq4eKCZ4l\nJibi4MGDKu0ODg4IDQ1lkIiNgoICVFZWctoKCwtVjmvuUZd/k/DwcM4z4LW1tYiMjESnTp04x82e\nPZvvaLyjiQCXgYGBym32bt264dixY5w2kUgkmEkALafMtXbtWtYR1MqaNWtavImfUNA4osrd3R3u\n7u5QKBRwdXXlvISdnZ2NQ4cOQSqVYt++fQxTqj9aGpZnrq6usLOzw7x585Rt9fX1+Oqrr3D79m18\n8803DNPxw9bWVmUS+PQW/dP2pxNFIaw6AgCenp4tnhgHBQW1cRr2aDdf0pzo6OhG28ViMfT09GBh\nYUE7GwsYbeJHWuratWvYuHEj7t+/D21tbejo6EAmk6GyshJ9+vTBsmXLYGNjwzqmWqNigmeXL1+G\nt7c3unTpAjMzMygUCmRkZKC2thaBgYGwsLBgHbHN5efnt/hYQ0PDNkxC1BVNBLjmzJkDf39/Wqnn\nGS+6c6VQKPDkyRPU1NTg/fffh4+Pj2BfLhUyW1tb/PjjjzSGPIPGkaZdvXoVWVlZkMlk6NKlC/r1\n6yeIOdn/Ao2wPLOxscHu3btx8eJFXLt2DUZGRrCzs8PYsWMF8wVvSYFw9+5dREVFYeHChTwkejXk\n5OTg6NGjWLJkCesobY5eJuVKT08X/Mvmz4uKimry88zMTKxbtw4hISHw9PTkKRVRJ0J5DLKlaBxp\n2sCBAzFw4ECVdpqPNI+KCZ5UV1cjNDQUp06dwuPHj5WTpa5du0JHR0e5+oiQlZWVITY2FtHR0bh9\n+zZ0dXXpy/uM/Px8wRQTAE0EyD9jYWGB+fPn46uvvqJiQqBoEz/ysmg+0jpUTPCguroanp6eKCoq\ngqurK2xsbKCnp4fi4mLcvHkTkZGR+PXXX7Fv3z7BFRVyuRwpKSmIjo5GSkoK6urqYG5ujpUrV+L9\n999nHY8wRBMBrri4OOjo6DR7nKOjIw9pXg29e/dGcXEx6xiEEdrETxWNIy9G85GXR8UEDyIiIlBd\nXY3//ve/0NXVVbabmpri//7v/zBx4kR4enoiIiICn3/+OcOk/MnMzERUVBSkUilKS0thamqKadOm\nISIiAr6+vpxdOokw0USAKywsrNm7NSKRSJCTgBcpLi5G586dWccgDNAmfo2jcUQVzUf+OSomeBAb\nG4uFCxdyColn6erqYu7cudixY4cgiolp06YhJycH/fv3x8cffwyJRILevXsDaCi8CKGJgKqIiAjq\nj1b4888/ERQUBHt7e9ZRCAP03lXjaBzhovnI/wYVEzwoKCiAubl5k8eYmZmhoKCAp0RsFRQU4LXX\nXoOFhQVef/11vPbaa6wjMbdu3bpmjxHS4xo0EeCi90dUzZw5s9F+USgUkMlkuHfvHiwsLDjLcBPh\noE38VNE4oormI/8bVEzwoHPnzsjPz4eBgcELj8nLyxPM1YKYmBikpKQgNjYW69evh0KhwNtvvw2J\nRAJAmAOepqZms8fo6+tjwoQJPKRhjyYCXFRcqRo6dGij7U/3mejbty8GDhwoyPGE0CZ+jaFxRBXN\nR/43aJ8JHmzduhW5ubnYvXs3NDQ0VD6vr6/HggULYGFhAS8vLwYJ2amoqMBPP/2Es2fPIi0tDXK5\nHIMHD8akSZPg4OAguPXhb968ib59+yon0klJSbh48SK6du0KJycn9OzZk3FCwkJ6ejreeustZGRk\nwNzcXLlQA50fDd+ZF/WJs7MzbVxHyF9oHGkazUdeHhUTPCgtLYWbmxsMDAzg5uYGKysrdOrUCSUl\nJfjtt98QEhKCiooKhIaGCvplwdLSUpw7dw6xsbG4fv06OnXqBKlUyjoWL0pLS+Hl5YXMzEwcPnwY\npqamOHToEHbt2gVLS0vo6uoiKysLwcHBMDU1ZR2X8Kyx8+PgwYPYvXs35/zYv38/3njjDdZxeUF9\nQkjr0Hem5UpLSxEXFwepVCq4+cjLoGKCJ0VFRfD390dycjLnVqNIJMLo0aOxePFidO/enWFC/jV1\nRdHR0RFSqRTTp09nnJIfmzdvxp07d7Bx40YYGRlBJpNh/PjxsLGxwa5duwAAe/fuRXZ2Nr788kvG\naQnf6PxQRX1CSOvQd+bl5Ofn4+zZs4KZj7wMumfDk549e+LLL79EaWkpbt++jbKyMnTu3BlWVlbo\n0qUL63i8asnVkSNHjmD//v2so/ImJSUFfn5+MDIyAgCkpqaipqYGEydOVB4zcuRIHDt2jFVEwhCd\nH6qoTwhpHfrOqDp8+DBcXFw47+iVl5dz9uLQ09PDb7/9xiLeK0P1AX7Sprp27Qo7Ozu8//77sLOz\nE1whAQD79u2DlpYWjh07BlNTU8hkMuzfvx9DhgzBgQMHsHv3bjg7O2PPnj2so/Lm8ePHnBf009LS\noKmpiSFDhijbOnfujJqaGhbxCGN0fqiiPiGkdeg7o2r79u0oLy/ntH3wwQfIy8tT/l5dXY34+Hi+\no71SqJggvEtJScHChQubvTpy+fJlVhF5p6+vj3v37gFoeCH/559/xsCBAzl7k1y+fBmGhoasIhKG\n6PxQRX1CSOvQd0ZVYytc0apXrUfFBOEdXR1RNWHCBGzbtg3x8fEICAhAUVERJk2apPz85s2bCAoK\nwpgxYximJKzQ+aGK+oSQ1qHvDGkr9M4E4d3TqyP6+vp0deQvbm5uKC8vx6ZNmyASiTBr1izlgL5t\n2zYcPnwYw4cPx4wZMxgnJSzQ+aGK+oSQ1qHvDGkrtJoT4V1oaCji4uIwa9Ys/Prrrzh69Cj8/PyU\ng9rNmzexfPlyODs7Y+bMmYzTsnfnzh3I5XK8+eabrKMQNUTnhyrqE0JaR6jfGVtbW/z444+cTYMd\nHBxw6NAhGBsbAwBKSkrg6OiICxcusIqp9ujOBOEdXR1pnb59+7KOQNQYnR+qqE8IaR0hf2fCw8PR\nsWNH5e+1tbWIjIxEp06dAACVlZWsor0y6M4EUStCvTpCCCGEEH55enpCJBK16NigoKA2TvPqojsT\nRK0I+eoIIYQQQvizb98+AA17S2hqaqJDhw4qxxQVFSEwMJDvaK8UKiYIIYQQQojgPHz4EL6+s9in\nqAAAAVhJREFUvrh06RIAwM7ODr6+vujUqRPkcjkiIyPxzTffQCym6XJT6DEnQgghhBAiOEuWLMHd\nu3cxa9YsaGlpISwsDGZmZvD09MTSpUuRlZUFJycnzJkzR5CbDLcUlVqEEEIIIURwrly5gk2bNmHo\n0KEAAEtLS7i6uiIrKwsKhQKhoaHo168f45Tqj4oJQgghhBAiODKZDH369FH+bmxsjLq6OhgbG8PP\nz48eb2oh2gGbEEIIIYQIjkKhgIYGdyqsqakJDw8PKiRagYoJQgghhBBC/qKtrc06wiuFyi5CCCGE\nECJIUqmUUzzI5XLExcWha9eunOOcnJz4jvbKoNWcCCGEEEKI4Dg7O7foOJFIhBMnTrRxmlcXFROE\nEEIIIYSQl0LvTBBCCCGEEEJeChUThBBCCCGEkJdCxQQhhBBCCCHkpVAxQQghhBBCCHkpVEwQQggh\nhBBCXsr/AzcC7HcDigxDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f43b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = (12,6)\n",
    "\n",
    "# Create train and test splits\n",
    "target_name = 'Label'\n",
    "X = df.drop(['Events', 'Favourite', 'Underdog', 'Label'], axis=1)\n",
    "\n",
    "\n",
    "y=df[target_name]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.15, random_state=1, stratify=y)\n",
    "\n",
    "dtree = RandomForestClassifier(\n",
    "    #max_depth=3,\n",
    "    random_state = 1,\n",
    "    class_weight=\"balanced\",\n",
    "    min_weight_fraction_leaf=0.01\n",
    "    )\n",
    "dtree = dtree.fit(X_train,y_train)\n",
    "\n",
    "## plot the importances ##\n",
    "importances = dtree.feature_importances_\n",
    "feat_names = df.drop(['Events', 'Favourite', 'Underdog', 'Label'], axis=1).columns\n",
    "\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Feature importances by DecisionTreeClassifier\")\n",
    "plt.bar(range(len(indices)), importances[indices], color='lightblue',  align=\"center\")\n",
    "plt.step(range(len(indices)), np.cumsum(importances[indices]), where='mid', label='Cumulative')\n",
    "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical',fontsize=14)\n",
    "plt.xlim([-1, len(indices)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection and Hyperparameter Tuning\n",
    "\n",
    "* Model selection and hyperparameter tuning were accomplish using GridSearchCV\n",
    "* There is no need to apply **train_test_split** in this case due to the Cross Validation embedded in GridSearchCV\n",
    "* Among the models considered are: \n",
    "    * Logistic Regression\n",
    "    * Random Forest Classifier\n",
    "    * Neural Network (MLP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def select_model(df, features):\n",
    "    \n",
    "    all_X = df[features]\n",
    "    all_y = df[\"Label\"]\n",
    "    #create a list of dics which contains models and hyperparameters\n",
    "    models = [\n",
    "        \n",
    "        {\n",
    "            \"name\": \"Logistic Regression\",\n",
    "            \"estimator\": LogisticRegression(),\n",
    "            \"hyperparameters\":\n",
    "            {\n",
    "             \"solver\": [\"newton-cg\", \"lbfgs\", \"liblinear\"]   \n",
    "            }\n",
    "            \n",
    "        },\n",
    "\n",
    "        {\n",
    "            \"name\": \"RandomForestClassifier\",\n",
    "            \"estimator\": RandomForestClassifier(random_state=1),\n",
    "            \"hyperparameters\":\n",
    "                {\n",
    "                    \"n_estimators\": [4, 6, 9],\n",
    "                    \"criterion\": [\"entropy\", \"gini\"],\n",
    "                    \"max_depth\": [2, 5, 10],\n",
    "                    \"max_features\": [\"log2\", \"sqrt\"],\n",
    "                    \"min_samples_leaf\": [1, 5, 8],\n",
    "                    \"min_samples_split\": [2, 3, 5]\n",
    "\n",
    "                }\n",
    "        },\n",
    "                {\n",
    "            \"name\": \"Multi Layer Perceptron (MLP)\",\n",
    "            \"estimator\": MLPClassifier(random_state=1),\n",
    "            \"hyperparameters\":\n",
    "                {\n",
    "                    \"hidden_layer_sizes\": [(5,5), (10,10)],\n",
    "                    \"activation\": [\"relu\", \"tanh\", \"logistic\"],\n",
    "                    \"solver\": ['sgd', 'adam'],\n",
    "                    \"learning_rate\": [\"constant\", \"adaptive\"]\n",
    "\n",
    "                }\n",
    "        }   \n",
    "        \n",
    "    ]\n",
    "    \n",
    "    for model in log_progress(models):\n",
    "        print(model[\"name\"])\n",
    "        print(\"-\"*len(model[\"name\"]))\n",
    "        \n",
    "        grid = GridSearchCV(model[\"estimator\"],\n",
    "                            param_grid=model[\"hyperparameters\"],\n",
    "                            cv=10, scoring = 'accuracy')\n",
    "        grid.fit(all_X,all_y)\n",
    "        model[\"best_params\"] = grid.best_params_\n",
    "        model[\"best_score\"] = grid.best_score_\n",
    "        model[\"best_model\"] = grid.best_estimator_\n",
    "        model[\"scoring\"] = grid.scorer_\n",
    "        \n",
    "        print(\"Best Paramerters:\\n\" + \"{}\".format(model[\"best_params\"]))\n",
    "        print(\"Best Score:\\n\" + \"{}\".format(model[\"best_score\"]))\n",
    "        print(\"Best Model:\\n\" + \"{}\\n\".format(model[\"best_model\"]))\n",
    "        print(\"Scoring method:\\n\" + \"{}\\n\".format(model[\"scoring\"]))\n",
    "        \n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "-------------------\n",
      "Best Paramerters:\n",
      "{'solver': 'liblinear'}\n",
      "Best Score:\n",
      "0.6828897338403042\n",
      "Best Model:\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "\n",
      "Scoring method:\n",
      "make_scorer(accuracy_score)\n",
      "\n",
      "RandomForestClassifier\n",
      "----------------------\n",
      "Best Paramerters:\n",
      "{'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 6}\n",
      "Best Score:\n",
      "0.6935361216730038\n",
      "Best Model:\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
      "            max_depth=5, max_features='log2', max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=5,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=6, n_jobs=1, oob_score=False, random_state=1,\n",
      "            verbose=0, warm_start=False)\n",
      "\n",
      "Scoring method:\n",
      "make_scorer(accuracy_score)\n",
      "\n",
      "Multi Layer Perceptron (MLP)\n",
      "----------------------------\n",
      "Best Paramerters:\n",
      "{'activation': 'tanh', 'hidden_layer_sizes': (5, 5), 'learning_rate': 'constant', 'solver': 'adam'}\n",
      "Best Score:\n",
      "0.7041825095057034\n",
      "Best Model:\n",
      "MLPClassifier(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(5, 5), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
      "       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "       warm_start=False)\n",
      "\n",
      "Scoring method:\n",
      "make_scorer(accuracy_score)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = select_model(df, best_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Model Verdict \n",
    "\n",
    "* With the **Neural Network (MLP)** giving the highest score, this model will be chosen to be deployed on the web app\n",
    "* Note that not too many hidden layers was chosen due to efficiency and also potential overfitting\n",
    "* I did not pick the model based on **AUC, precision, or recall** because this is a **gambling problem** where a false positive or false negative is still a loss cause, unlike other problems such as predicting employee turnover. Hence **accuracy is all that matters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "* The final model selected, Neural Network (MLP) to predict winners from Favourite and Underdog has an **accuracy of 70.4%**\n",
    "* This project successfully satisfied the two crucial objectives which was to achieve an accuracy of more than 50% and 63%\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improvements\n",
    "\n",
    "* Twitter sentiment scraping on UFC predictor key opinion leaders (KOLs)\n",
    "* Include categorical variable named 'Fight Camp' as some fight camp rankings data available\n",
    "* Better structured odds data and start warehousing data\n",
    "* Improve data quality and granularity with premium data source from [**fightmetric**](http://www.fightmetric.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Citation\n",
    "\n",
    "* Shall you use this workflow in any of your work, I would appreciate it if you could site my full name (Jason Chan Jin An) and the link to this Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaboration and Sponsorship\n",
    "\n",
    "* Please do not hesitate to contact me for any sort of collaboration of discussion about this notebook\n",
    "* I am also open to sponsorship / investment opportunities to monetize this project"
   ]
  }
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