Random Forest to predict the pass outcome in a match.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsbombpy import sb\n",
    "import matplotlib.pyplot as plt\n",
    "from mplsoccer.pitch import Pitch\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier # random forest is an ensemble classifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credentials were not supplied. open data access only\n"
     ]
    },
    {
     "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>competition_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>competition_name</th>\n",
       "      <th>competition_gender</th>\n",
       "      <th>season_name</th>\n",
       "      <th>match_updated</th>\n",
       "      <th>match_available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2021-04-11T22:02:53.549443</td>\n",
       "      <td>2021-04-11T22:02:53.549443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2017/2018</td>\n",
       "      <td>2021-01-23T21:55:30.425330</td>\n",
       "      <td>2021-01-23T21:55:30.425330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2016/2017</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2006/2007</td>\n",
       "      <td>2021-03-31T04:18:30.437060</td>\n",
       "      <td>2021-03-31T04:18:30.437060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2004/2005</td>\n",
       "      <td>2021-04-01T06:18:57.459032</td>\n",
       "      <td>2021-04-01T06:18:57.459032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>2021-04-01T00:34:59.472485</td>\n",
       "      <td>2021-04-01T00:34:59.472485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>1999/2000</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>2020-10-20T18:35:33.568528</td>\n",
       "      <td>2020-10-20T18:35:33.568528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2021-04-09T20:39:24.201269</td>\n",
       "      <td>2021-04-09T20:39:24.201269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>International</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>male</td>\n",
       "      <td>2018</td>\n",
       "      <td>2020-10-25T14:03:50.263266</td>\n",
       "      <td>2020-10-25T14:03:50.263266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>2020-12-18T12:10:38.985394</td>\n",
       "      <td>2020-12-18T12:10:38.985394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2021-04-12T07:10:50.252564</td>\n",
       "      <td>2021-04-12T07:10:50.252564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2017/2018</td>\n",
       "      <td>2021-04-12T07:10:50.238089</td>\n",
       "      <td>2021-04-12T07:10:50.238089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2016/2017</td>\n",
       "      <td>2021-02-02T23:24:58.985975</td>\n",
       "      <td>2021-02-02T23:24:58.985975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2007/2008</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2006/2007</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2005/2006</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2004/2005</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NWSL</td>\n",
       "      <td>female</td>\n",
       "      <td>2018</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>England</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>male</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>2020-08-31T20:40:28.969635</td>\n",
       "      <td>2020-08-31T20:40:28.969635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>International</td>\n",
       "      <td>Women's World Cup</td>\n",
       "      <td>female</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    competition_id  season_id              country_name  \\\n",
       "0               16          4                    Europe   \n",
       "1               16          1                    Europe   \n",
       "2               16          2                    Europe   \n",
       "3               16         27                    Europe   \n",
       "4               16         26                    Europe   \n",
       "5               16         25                    Europe   \n",
       "6               16         24                    Europe   \n",
       "7               16         23                    Europe   \n",
       "8               16         22                    Europe   \n",
       "9               16         21                    Europe   \n",
       "10              16         41                    Europe   \n",
       "11              16         39                    Europe   \n",
       "12              16         37                    Europe   \n",
       "13              16         44                    Europe   \n",
       "14              16         76                    Europe   \n",
       "15              37         42                   England   \n",
       "16              37          4                   England   \n",
       "17              43          3             International   \n",
       "18              11         42                     Spain   \n",
       "19              11          4                     Spain   \n",
       "20              11          1                     Spain   \n",
       "21              11          2                     Spain   \n",
       "22              11         27                     Spain   \n",
       "23              11         26                     Spain   \n",
       "24              11         25                     Spain   \n",
       "25              11         24                     Spain   \n",
       "26              11         23                     Spain   \n",
       "27              11         22                     Spain   \n",
       "28              11         21                     Spain   \n",
       "29              11         41                     Spain   \n",
       "30              11         40                     Spain   \n",
       "31              11         39                     Spain   \n",
       "32              11         38                     Spain   \n",
       "33              11         37                     Spain   \n",
       "34              49          3  United States of America   \n",
       "35               2         44                   England   \n",
       "36              72         30             International   \n",
       "\n",
       "           competition_name competition_gender season_name  \\\n",
       "0          Champions League               male   2018/2019   \n",
       "1          Champions League               male   2017/2018   \n",
       "2          Champions League               male   2016/2017   \n",
       "3          Champions League               male   2015/2016   \n",
       "4          Champions League               male   2014/2015   \n",
       "5          Champions League               male   2013/2014   \n",
       "6          Champions League               male   2012/2013   \n",
       "7          Champions League               male   2011/2012   \n",
       "8          Champions League               male   2010/2011   \n",
       "9          Champions League               male   2009/2010   \n",
       "10         Champions League               male   2008/2009   \n",
       "11         Champions League               male   2006/2007   \n",
       "12         Champions League               male   2004/2005   \n",
       "13         Champions League               male   2003/2004   \n",
       "14         Champions League               male   1999/2000   \n",
       "15  FA Women's Super League             female   2019/2020   \n",
       "16  FA Women's Super League             female   2018/2019   \n",
       "17           FIFA World Cup               male        2018   \n",
       "18                  La Liga               male   2019/2020   \n",
       "19                  La Liga               male   2018/2019   \n",
       "20                  La Liga               male   2017/2018   \n",
       "21                  La Liga               male   2016/2017   \n",
       "22                  La Liga               male   2015/2016   \n",
       "23                  La Liga               male   2014/2015   \n",
       "24                  La Liga               male   2013/2014   \n",
       "25                  La Liga               male   2012/2013   \n",
       "26                  La Liga               male   2011/2012   \n",
       "27                  La Liga               male   2010/2011   \n",
       "28                  La Liga               male   2009/2010   \n",
       "29                  La Liga               male   2008/2009   \n",
       "30                  La Liga               male   2007/2008   \n",
       "31                  La Liga               male   2006/2007   \n",
       "32                  La Liga               male   2005/2006   \n",
       "33                  La Liga               male   2004/2005   \n",
       "34                     NWSL             female        2018   \n",
       "35           Premier League               male   2003/2004   \n",
       "36        Women's World Cup             female        2019   \n",
       "\n",
       "                 match_updated             match_available  \n",
       "0   2021-04-11T22:02:53.549443  2021-04-11T22:02:53.549443  \n",
       "1   2021-01-23T21:55:30.425330  2021-01-23T21:55:30.425330  \n",
       "2   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "3   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "4   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "5   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "6   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "7   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "8             2020-07-29T05:00            2020-07-29T05:00  \n",
       "9             2020-07-29T05:00            2020-07-29T05:00  \n",
       "10  2020-08-30T10:18:39.435424  2020-08-30T10:18:39.435424  \n",
       "11  2021-03-31T04:18:30.437060  2021-03-31T04:18:30.437060  \n",
       "12  2021-04-01T06:18:57.459032  2021-04-01T06:18:57.459032  \n",
       "13  2021-04-01T00:34:59.472485  2021-04-01T00:34:59.472485  \n",
       "14            2020-07-29T05:00            2020-07-29T05:00  \n",
       "15  2020-10-20T18:35:33.568528  2020-10-20T18:35:33.568528  \n",
       "16  2021-04-09T20:39:24.201269  2021-04-09T20:39:24.201269  \n",
       "17  2020-10-25T14:03:50.263266  2020-10-25T14:03:50.263266  \n",
       "18  2020-12-18T12:10:38.985394  2020-12-18T12:10:38.985394  \n",
       "19  2021-04-12T07:10:50.252564  2021-04-12T07:10:50.252564  \n",
       "20  2021-04-12T07:10:50.238089  2021-04-12T07:10:50.238089  \n",
       "21  2021-02-02T23:24:58.985975  2021-02-02T23:24:58.985975  \n",
       "22            2020-07-29T05:00            2020-07-29T05:00  \n",
       "23            2020-07-29T05:00            2020-07-29T05:00  \n",
       "24            2020-07-29T05:00            2020-07-29T05:00  \n",
       "25            2020-07-29T05:00            2020-07-29T05:00  \n",
       "26            2020-07-29T05:00            2020-07-29T05:00  \n",
       "27            2020-07-29T05:00            2020-07-29T05:00  \n",
       "28            2020-07-29T05:00            2020-07-29T05:00  \n",
       "29            2020-07-29T05:00            2020-07-29T05:00  \n",
       "30            2020-07-29T05:00            2020-07-29T05:00  \n",
       "31            2020-07-29T05:00            2020-07-29T05:00  \n",
       "32            2020-07-29T05:00            2020-07-29T05:00  \n",
       "33            2020-07-29T05:00            2020-07-29T05:00  \n",
       "34            2020-07-29T05:00            2020-07-29T05:00  \n",
       "35  2020-08-31T20:40:28.969635  2020-08-31T20:40:28.969635  \n",
       "36            2020-07-29T05:00            2020-07-29T05:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sb.competitions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credentials were not supplied. open data access only\n"
     ]
    }
   ],
   "source": [
    "mat = sb.matches(competition_id=43, season_id=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'match_date', 'kick_off', 'competition', 'season',\n",
       "       'home_team', 'away_team', 'home_score', 'away_score', 'match_status',\n",
       "       'match_status_360', 'last_updated', 'last_updated_360', 'match_week',\n",
       "       'competition_stage', 'stadium', 'referee', 'data_version',\n",
       "       'shot_fidelity_version', 'xy_fidelity_version'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>match_date</th>\n",
       "      <th>kick_off</th>\n",
       "      <th>competition</th>\n",
       "      <th>season</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>match_status</th>\n",
       "      <th>match_status_360</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>last_updated_360</th>\n",
       "      <th>match_week</th>\n",
       "      <th>competition_stage</th>\n",
       "      <th>stadium</th>\n",
       "      <th>referee</th>\n",
       "      <th>data_version</th>\n",
       "      <th>shot_fidelity_version</th>\n",
       "      <th>xy_fidelity_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7580</td>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>21:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>France</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>Round of 16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8658</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>17:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>France</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>Final</td>\n",
       "      <td>Stadion Luzhniki</td>\n",
       "      <td>N. Pitana</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7552</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>14:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>Otkrytiye Arena</td>\n",
       "      <td>J. Marrufo</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7525</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>17:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>Stadion Luzhniki</td>\n",
       "      <td>N. Pitana</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7554</td>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>14:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>England</td>\n",
       "      <td>Panama</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>Stadion Nizhny Novgorod</td>\n",
       "      <td>Ghead Grisha</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  match_date      kick_off                     competition season  \\\n",
       "21      7580  2018-06-30  21:00:00.000  International - FIFA World Cup   2018   \n",
       "24      8658  2018-07-15  17:00:00.000  International - FIFA World Cup   2018   \n",
       "31      7552  2018-06-23  14:00:00.000  International - FIFA World Cup   2018   \n",
       "44      7525  2018-06-14  17:00:00.000  International - FIFA World Cup   2018   \n",
       "50      7554  2018-06-24  14:00:00.000  International - FIFA World Cup   2018   \n",
       "\n",
       "   home_team     away_team  home_score  away_score match_status  \\\n",
       "21    France     Argentina           4           3    available   \n",
       "24    France       Croatia           4           2    available   \n",
       "31   Belgium       Tunisia           5           2    available   \n",
       "44    Russia  Saudi Arabia           5           0    available   \n",
       "50   England        Panama           6           1    available   \n",
       "\n",
       "   match_status_360      last_updated last_updated_360  match_week  \\\n",
       "21      unscheduled  2020-07-29T05:00             None           4   \n",
       "24      unscheduled  2020-07-29T05:00             None           7   \n",
       "31      unscheduled  2020-07-29T05:00             None           2   \n",
       "44      unscheduled  2020-07-29T05:00             None           1   \n",
       "50      unscheduled  2020-07-29T05:00             None           2   \n",
       "\n",
       "   competition_stage                  stadium       referee data_version  \\\n",
       "21       Round of 16                      NaN           NaN        1.0.2   \n",
       "24             Final         Stadion Luzhniki     N. Pitana        1.0.2   \n",
       "31       Group Stage          Otkrytiye Arena    J. Marrufo        1.0.2   \n",
       "44       Group Stage         Stadion Luzhniki     N. Pitana        1.0.2   \n",
       "50       Group Stage  Stadion Nizhny Novgorod  Ghead Grisha        1.0.2   \n",
       "\n",
       "   shot_fidelity_version xy_fidelity_version  \n",
       "21                  None                None  \n",
       "24                  None                None  \n",
       "31                  None                None  \n",
       "44                  None                None  \n",
       "50                  None                None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E = mat[(mat['away_score']>3) | (mat['home_score']>3)]\n",
    "E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credentials were not supplied. open data access only\n"
     ]
    },
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>00:44:03.913</td>\n",
       "      <td>Shield</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>00:49:10.660</td>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>00:49:10.660</td>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>00:48:07.000</td>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3483</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>00:48:07.000</td>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3484 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ball_receipt_outcome ball_recovery_recovery_failure block_save_block  \\\n",
       "0                     NaN                            NaN              NaN   \n",
       "1                     NaN                            NaN              NaN   \n",
       "2                     NaN                            NaN              NaN   \n",
       "3                     NaN                            NaN              NaN   \n",
       "4                     NaN                            NaN              NaN   \n",
       "...                   ...                            ...              ...   \n",
       "3479                  NaN                            NaN              NaN   \n",
       "3480                  NaN                            NaN              NaN   \n",
       "3481                  NaN                            NaN              NaN   \n",
       "3482                  NaN                            NaN              NaN   \n",
       "3483                  NaN                            NaN              NaN   \n",
       "\n",
       "     carry_end_location counterpress dribble_outcome duel_outcome duel_type  \\\n",
       "0                   NaN          NaN             NaN          NaN       NaN   \n",
       "1                   NaN          NaN             NaN          NaN       NaN   \n",
       "2                   NaN          NaN             NaN          NaN       NaN   \n",
       "3                   NaN          NaN             NaN          NaN       NaN   \n",
       "4                   NaN          NaN             NaN          NaN       NaN   \n",
       "...                 ...          ...             ...          ...       ...   \n",
       "3479                NaN          NaN             NaN          NaN       NaN   \n",
       "3480                NaN          NaN             NaN          NaN       NaN   \n",
       "3481                NaN          NaN             NaN          NaN       NaN   \n",
       "3482                NaN          NaN             NaN          NaN       NaN   \n",
       "3483                NaN          NaN             NaN          NaN       NaN   \n",
       "\n",
       "      duration foul_committed_advantage  ... shot_statsbomb_xg shot_technique  \\\n",
       "0        0.000                      NaN  ...               NaN            NaN   \n",
       "1        2.413                      NaN  ...               NaN            NaN   \n",
       "2        9.853                      NaN  ...               NaN            NaN   \n",
       "3        9.093                      NaN  ...               NaN            NaN   \n",
       "4          NaN                      NaN  ...               NaN            NaN   \n",
       "...        ...                      ...  ...               ...            ...   \n",
       "3479       NaN                      NaN  ...               NaN            NaN   \n",
       "3480       NaN                      NaN  ...               NaN            NaN   \n",
       "3481       NaN                      NaN  ...               NaN            NaN   \n",
       "3482       NaN                      NaN  ...               NaN            NaN   \n",
       "3483       NaN                      NaN  ...               NaN            NaN   \n",
       "\n",
       "     shot_type substitution_outcome substitution_replacement  \\\n",
       "0          NaN                  NaN                      NaN   \n",
       "1          NaN                  NaN                      NaN   \n",
       "2          NaN                  NaN                      NaN   \n",
       "3          NaN                  NaN                      NaN   \n",
       "4          NaN                  NaN                      NaN   \n",
       "...        ...                  ...                      ...   \n",
       "3479       NaN                  NaN                      NaN   \n",
       "3480       NaN                  NaN                      NaN   \n",
       "3481       NaN                  NaN                      NaN   \n",
       "3482       NaN                  NaN                      NaN   \n",
       "3483       NaN                  NaN                      NaN   \n",
       "\n",
       "                                                tactics     team  \\\n",
       "0     {'formation': 343, 'lineup': [{'player': {'id'...  Belgium   \n",
       "1     {'formation': 433, 'lineup': [{'player': {'id'...  Tunisia   \n",
       "2                                                   NaN  Tunisia   \n",
       "3                                                   NaN  Belgium   \n",
       "4                                                   NaN  Tunisia   \n",
       "...                                                 ...      ...   \n",
       "3479                                                NaN  Tunisia   \n",
       "3480                                                NaN  Belgium   \n",
       "3481                                                NaN  Tunisia   \n",
       "3482                                                NaN  Belgium   \n",
       "3483                                                NaN  Tunisia   \n",
       "\n",
       "         timestamp         type under_pressure  \n",
       "0     00:00:00.000  Starting XI            NaN  \n",
       "1     00:00:00.000  Starting XI            NaN  \n",
       "2     00:00:00.000   Half Start            NaN  \n",
       "3     00:00:00.000   Half Start            NaN  \n",
       "4     00:00:00.000   Half Start            NaN  \n",
       "...            ...          ...            ...  \n",
       "3479  00:44:03.913       Shield            NaN  \n",
       "3480  00:49:10.660     Half End            NaN  \n",
       "3481  00:49:10.660     Half End            NaN  \n",
       "3482  00:48:07.000     Half End            NaN  \n",
       "3483  00:48:07.000     Half End            NaN  \n",
       "\n",
       "[3484 rows x 76 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = sb.events(match_id = 7552)\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ball_receipt_outcome', 'ball_recovery_recovery_failure',\n",
       "       'block_save_block', 'carry_end_location', 'counterpress',\n",
       "       'dribble_outcome', 'duel_outcome', 'duel_type', 'duration',\n",
       "       'foul_committed_advantage', 'foul_committed_card',\n",
       "       'foul_committed_offensive', 'foul_committed_penalty',\n",
       "       'foul_committed_type', 'foul_won_advantage', 'foul_won_defensive',\n",
       "       'foul_won_penalty', 'goalkeeper_body_part', 'goalkeeper_end_location',\n",
       "       'goalkeeper_outcome', 'goalkeeper_position', 'goalkeeper_technique',\n",
       "       'goalkeeper_type', 'id', 'index', 'injury_stoppage_in_chain',\n",
       "       'interception_outcome', 'location', 'match_id', 'minute',\n",
       "       'pass_aerial_won', 'pass_angle', 'pass_assisted_shot_id',\n",
       "       'pass_backheel', 'pass_body_part', 'pass_cross', 'pass_cut_back',\n",
       "       'pass_deflected', 'pass_end_location', 'pass_goal_assist',\n",
       "       'pass_height', 'pass_length', 'pass_outcome', 'pass_recipient',\n",
       "       'pass_shot_assist', 'pass_switch', 'pass_technique',\n",
       "       'pass_through_ball', 'pass_type', 'period', 'play_pattern', 'player',\n",
       "       'position', 'possession', 'possession_team', 'related_events', 'second',\n",
       "       'shot_aerial_won', 'shot_body_part', 'shot_end_location',\n",
       "       'shot_first_time', 'shot_freeze_frame', 'shot_key_pass_id',\n",
       "       'shot_one_on_one', 'shot_open_goal', 'shot_outcome',\n",
       "       'shot_statsbomb_xg', 'shot_technique', 'shot_type',\n",
       "       'substitution_outcome', 'substitution_replacement', 'tactics', 'team',\n",
       "       'timestamp', 'type', 'under_pressure'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "      <th>pass_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Starting XI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starting XI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Half Start</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Half Start</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Half Start</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</th>\n",
       "      <td>Shield</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480</th>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481</th>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3483</th>\n",
       "      <td>Half End</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3484 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             type  pass_angle pass_height  pass_length pass_outcome\n",
       "0     Starting XI         NaN         NaN          NaN          NaN\n",
       "1     Starting XI         NaN         NaN          NaN          NaN\n",
       "2      Half Start         NaN         NaN          NaN          NaN\n",
       "3      Half Start         NaN         NaN          NaN          NaN\n",
       "4      Half Start         NaN         NaN          NaN          NaN\n",
       "...           ...         ...         ...          ...          ...\n",
       "3479       Shield         NaN         NaN          NaN          NaN\n",
       "3480     Half End         NaN         NaN          NaN          NaN\n",
       "3481     Half End         NaN         NaN          NaN          NaN\n",
       "3482     Half End         NaN         NaN          NaN          NaN\n",
       "3483     Half End         NaN         NaN          NaN          NaN\n",
       "\n",
       "[3484 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass = events[['type', 'pass_angle', 'pass_height', 'pass_length', 'pass_outcome']]\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>type</th>\n",
       "      <th>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "      <th>pass_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pass</td>\n",
       "      <td>3.141593</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pass</td>\n",
       "      <td>1.723446</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>13.152946</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-2.651635</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pass</td>\n",
       "      <td>1.873681</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>16.763054</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pass</td>\n",
       "      <td>0.352990</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>20.248457</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>Pass</td>\n",
       "      <td>0.764125</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>33.241540</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-0.510488</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>28.653097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-2.098871</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>13.892444</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-1.570796</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>Pass</td>\n",
       "      <td>0.484478</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>21.470911</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     type  pass_angle  pass_height  pass_length pass_outcome\n",
       "6    Pass    3.141593  Ground Pass    10.000000          NaN\n",
       "7    Pass    1.723446  Ground Pass    13.152946          NaN\n",
       "8    Pass   -2.651635  Ground Pass    17.000000          NaN\n",
       "9    Pass    1.873681  Ground Pass    16.763054          NaN\n",
       "10   Pass    0.352990  Ground Pass    20.248457          NaN\n",
       "..    ...         ...          ...          ...          ...\n",
       "943  Pass    0.764125  Ground Pass    33.241540          NaN\n",
       "944  Pass   -0.510488  Ground Pass    28.653097          NaN\n",
       "945  Pass   -2.098871  Ground Pass    13.892444          NaN\n",
       "946  Pass   -1.570796  Ground Pass     5.000000      Unknown\n",
       "947  Pass    0.484478  Ground Pass    21.470911          NaN\n",
       "\n",
       "[942 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass = E_pass[E_pass['type'] == 'Pass']\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 21,
     "metadata": {},
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   "source": [
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   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Incomplete', 'Unknown', 'Pass Offside', 'Out',\n",
       "       'Injury Clearance'], dtype=object)"
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 23,
   "metadata": {},
   "outputs": [
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      "<ipython-input-23-fc7b1a033553>:1: SettingWithCopyWarning: \n",
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      "\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",
      "  E_pass['pass_height'] = E_pass['pass_height'].replace(['Ground Pass', 'High Pass', 'Low Pass'], [3, 1, 2])\n"
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       "     type  pass_angle  pass_height  pass_length pass_outcome\n",
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       "\n",
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    "E_pass['pass_height'] = E_pass['pass_height'].replace(['Ground Pass', 'High Pass', 'Low Pass'], [3, 1, 2])\n",
    "E_pass"
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   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      "<ipython-input-24-3e78a7b7e735>:1: SettingWithCopyWarning: \n",
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      "\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",
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       "     type  pass_angle  pass_height  pass_length pass_outcome\n",
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    "E_pass"
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   "execution_count": 25,
   "metadata": {},
   "outputs": [
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      "<ipython-input-25-a6dbed5f38a2>:1: SettingWithCopyWarning: \n",
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      "\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",
      "  E_pass['pass_outcome'] = E_pass['pass_outcome'].replace(['Unknown', 'Incomplete', 'Out', 'Pass Offside', 'Injury Clearance'], 0)\n"
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       "     type  pass_angle  pass_height  pass_length  pass_outcome\n",
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    "E_pass['pass_outcome'] = E_pass['pass_outcome'].replace(['Unknown', 'Incomplete', 'Out', 'Pass Offside', 'Injury Clearance'], 0)\n",
    "E_pass"
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       "      <td>0.352990</td>\n",
       "      <td>3</td>\n",
       "      <td>20.248457</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>0.764125</td>\n",
       "      <td>3</td>\n",
       "      <td>33.241540</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>-0.510488</td>\n",
       "      <td>3</td>\n",
       "      <td>28.653097</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>-2.098871</td>\n",
       "      <td>3</td>\n",
       "      <td>13.892444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>-1.570796</td>\n",
       "      <td>3</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>0.484478</td>\n",
       "      <td>3</td>\n",
       "      <td>21.470911</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pass_angle  pass_height  pass_length  pass_outcome\n",
       "6      3.141593            3    10.000000             1\n",
       "7      1.723446            3    13.152946             1\n",
       "8     -2.651635            3    17.000000             1\n",
       "9      1.873681            3    16.763054             1\n",
       "10     0.352990            3    20.248457             1\n",
       "..          ...          ...          ...           ...\n",
       "943    0.764125            3    33.241540             1\n",
       "944   -0.510488            3    28.653097             1\n",
       "945   -2.098871            3    13.892444             1\n",
       "946   -1.570796            3     5.000000             0\n",
       "947    0.484478            3    21.470911             1\n",
       "\n",
       "[942 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass = E_pass[['pass_angle', 'pass_height', 'pass_length', 'pass_outcome']]\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here *pass_outcome* is the dependent variable and the other columns represent the independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((942, 3), (942,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = E_pass[['pass_angle', 'pass_height', 'pass_length']]\n",
    "y = E_pass['pass_outcome']\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.4, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0.674741</td>\n",
       "      <td>3</td>\n",
       "      <td>6.403124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>0.912908</td>\n",
       "      <td>3</td>\n",
       "      <td>27.802877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>-2.819842</td>\n",
       "      <td>3</td>\n",
       "      <td>9.486833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>1.471128</td>\n",
       "      <td>3</td>\n",
       "      <td>20.099750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>-1.249046</td>\n",
       "      <td>3</td>\n",
       "      <td>18.973665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>2.289626</td>\n",
       "      <td>3</td>\n",
       "      <td>10.630146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-1.971037</td>\n",
       "      <td>3</td>\n",
       "      <td>28.231188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>-2.843094</td>\n",
       "      <td>3</td>\n",
       "      <td>13.601471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>-0.244979</td>\n",
       "      <td>3</td>\n",
       "      <td>4.123106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>-1.570796</td>\n",
       "      <td>3</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>376 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pass_angle  pass_height  pass_length\n",
       "863    0.674741            3     6.403124\n",
       "883    0.912908            3    27.802877\n",
       "306   -2.819842            3     9.486833\n",
       "641    1.471128            3    20.099750\n",
       "632   -1.249046            3    18.973665\n",
       "..          ...          ...          ...\n",
       "841    2.289626            3    10.630146\n",
       "198   -1.971037            3    28.231188\n",
       "635   -2.843094            3    13.601471\n",
       "565   -0.244979            3     4.123106\n",
       "690   -1.570796            3    19.000000\n",
       "\n",
       "[376 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we apply Random Forest and build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we use the training data to fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now generate the *Error Matrix* also known as the *Confusion Matrix* which will tell us how accurate our model is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Predicted', ylabel='Original'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_matrix = pd.crosstab(y_test, y_predicted, rownames=['Original'], colnames=['Predicted'])\n",
    "sns.heatmap(error_matrix, annot=True, cmap = 'plasma', linewidths = 3, linecolor = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.784452296819788"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predicted)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11f20bc6b50>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GXXbCdby93/6dSb6Y5OUj02Z8jQaq46wkfz+yv3993GUnXMd7Rmr4epKnkqzup01kfyS5Ksm+JF+fZfow742qWpYP4CV0N2p8Dlg/yzyrgG8CPwwcAdwBnNpP+23gkn74EuC35lnHIa2nr+mvgR/qx98P/OoC98VYNQAPAC9a6M+wkDqANcCr+uEXAH818prMe18c7LUemecc4NN016OfAdwy7rITruO1wHH98JsP1HGw12igOs4Crp/PspOsY9r8bwE+O8D++FfAq4CvzzJ9kPfGsj0Crqp7qmquu+QOduvzucDWfngrcN48SznU9ZwNfLOqvjXP7U2ihkkvP/Z6qmpvVd3WD38HuAc4cZ7bGzXObe7nAn9UnS8DxyZZM+ayE6ujqr5YVX/bj36Z7nr4SVvIz7So+2OatwEfn+e2ZlVVNwN/c5BZBnlvLNsAHtOJwO6R8T08/ct+QlXthS4UgOPnuY1DXc9Gnv0Ge1f/b89V8/z3f9waCvjzJF9Nd8v3oS4/qToASHIy8ErglpHm+e6Lg73Wc80zzrKTrGPUZrojrwNme42GquM1Se5I8ukkpx3ispOsgyRHAxuAPx1pntT+mMsg740ldR3woUryF8APzDDp16rq2nFWMUPbIV+Xd7A6DnE9RwA/CVw60vxh4AN9XR8Afgf4+YFqOLOqHkpyPHBjkm/0RwZjm+C+OIbuF+3iqvp23zzWvphtlTO0TX+tZ5tnIu+TQ6ijmzF5A10Av26kecGv0SHUcRvdqbB/6M+3/xmwbsxlJ1nHAW8BvlBVo0eqk9ofcxnkvfGcDuCq+rEFruJgtz4/nGRNVe3t/9XYN586koy9HrrzfbdV1cMj6/7H4SR/AFw/VA1V9VD/vC/JJ+j+vbqZRd4XSQ6nC9+PVdU1I+sea1/MYpzb3Geb54gxlp1kHST5l8BHgDdX1aMH2g/yGk28jpE/fFTVp5JckeRF4/4Mk6pjxLP+O5zg/phvnQt6b6z0UxAHu/X5OmBTP7wJGOeIeiaHsp5nnd/qg+qAnwJm/JR2oTUk+SdJXnBgGPjxkW0t2r5IEuBK4J6q+tC0aQvZF+Pc5n4d8HP9J95nAH/fnyqZ5C3yc64ryQ8C1wDvqKq/Gmk/2Gs0RB0/0L8eJDmdLi8eHWfZSdbRb/+FwOsZec9MeH/MZZj3xkI/PVyqD7pf0D3A48DDwGf69n8KfGpkvnPoPmn/Jt2piwPt3w9sB+7rn1fPs44Z1zNDHUfTvblfOG35PwbuAu7sX9g1Q9RA9ynuHf1jZ6t9QffvdvU/7+3945xJ7IuZXmvgl4Bf6odD94UA3+y3s/5gyy7gvTlXHR8B/nbk598x12s0UB3v6rdzB92Hga9tsT/68XcCV09bbmL7g+7AZy/wBF1ubF6M94a3IktSIyv9FIQkNWMAS1IjBrAkNWIAS1IjBrAkNWIAS2NIcnF/K6w0MV6GJo0hyQN0134uha9G1zLhEbCWhCQnJ/lGkq19Zzt/kuToJL+e5Cvp+oHdMnJn1i8nubuf9+q+7fV5ut/Yrx24S2qGbSXJf+nXeVeSt/btZyW5fmS+30/yziS/THezyE1JbuqnbUhyW7qOarb3bauT/Flf05f724kP9GO8Ncmfp+u/9qeT/Ha/7Rv6W69J8iNJPp+uY5nPTLvzT8vRQu5g8eFjUg/gZLo74M7sx68CfpWRu+7o7oR7Sz/8EHBkP3xs//zJkeWPAQ6bZVv/FriRri/XE4AH6fohPouR/m+B3wfe2Q8/QN/vLDBF1wPWKf34gTv6fg/4jX74jcDt/fD7gb8EDgdeDjxG18cDwCfouuU8HPgiMNW3v5XuS2mbvzY+hnt4BKylZHdVfaEf/ijdbclvSHJLkrvoQu1Al4h3Ah9L8rPAk33bF4AP9Uesx1bVk8zsdcDHq+qp6jr4+Tzw6kOo8wzg5qq6H6Ce7p3rdXR/JKiqzwLf3/dhAPDpqnqC7jbWVcANfftddH98Xgy8lK5Hr9uB/8Qw/QBrCTGAtZRM/0CigCuA86vqZcAfAEf1036C7t78HwG+muSwqvog8AvA84EvJ/kXs2xnpi4EoQvy0d+Jo2aZLzPUOtt6D8z3OEBVfQ94oqoOtH+PrlfCADur6hX942VV9eOzbF/LhAGspeQHk7ymH34b3b/tAI+k6x/4fIAkzwPWVtVNwHuBY4Fjkvyzqrqrqn4L2AHMFsA3A29NsirJFN3X0dwKfAs4NcmR/ZHr2SPLfIfuK5IAvgS8PskpfT2rR9b79r7tLOCRGunScQ73AlMHfv4kh+fpDtC1TD2n+wPWsnMPsCnJ/6TrMe3DwHF0/6Y/QNf1H3T/wn+0D8kA/62q/i7JB9J1Yv4UcDfP/CaJUZ8AXkPXi1YB762qvwZIso3u9MZ9wNdGltkCfDrJ3qp6Q7pvX7im/2OwD/jXdOd6/1eSO+nO825iTFX13STnA7/b/1yHAZfT9fKlZcrL0LQkpPv6oeur6qWta5EWi6cgJKkRj4C1bCV5Gf1VCSMer6ofbVGPNJ0BLEmNeApCkhoxgCWpEQNYkhoxgCWpEQNYkhr5/7OfRTykx5ENAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot((y_test - y_predicted), bins = 50, color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}