Predicting Football Match Outcome using Machine Learning.ipynb

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    " "
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   "source": [
    " "
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   "source": [
    "   European Football or soccer is the world’s most popular team sport. It is played by over 150 million men and women of all ages in more than 200 countries [1].It is also one of the favorite sports for betting. The current estimations, which include both the illegal markets and the legal markets, suggest the sports match-betting industry is worth anywhere between 700bn and 1tn dollars a year [2]. \n",
    "   \n",
    "The bookies have their own prediction model and they get it right about 53% of the time [3].\n",
    "Football pundit Mark Lawrenson had prediction accuracy of 52.6% [4]. \n",
    "The aim of this project is to build a system to predict the outcome using machine learning algorithms like Support Vector Machine, Naïve Bayes and logistic Regression."
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    " "
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   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import numpy as np\n",
    "import matplotlib.gridspec as gridspec\n",
    "from numpy import random\n",
    "import scipy.stats as scipy\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "with sqlite3.connect('database.sqlite') as con:\n",
    "    countries = pd.read_sql_query(\"SELECT * from Country\", con)\n",
    "    matches = pd.read_sql_query(\"SELECT * from Match\", con)\n",
    "    leagues = pd.read_sql_query(\"SELECT * from League\", con)\n",
    "    teams = pd.read_sql_query(\"SELECT * from Team\", con)\n",
    "    tempmatch = pd.read_sql_query(\"SELECT * from Match\", con)\n",
    "    matches3 = matches2 = matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
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       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>...</th>\n",
       "      <th>SJA</th>\n",
       "      <th>VCH</th>\n",
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       "      <th>VCA</th>\n",
       "      <th>GBH</th>\n",
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       "      <th>GBA</th>\n",
       "      <th>BSH</th>\n",
       "      <th>BSD</th>\n",
       "      <th>BSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>25974</th>\n",
       "      <td>25975</td>\n",
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       "      <td>10190</td>\n",
       "      <td>10191</td>\n",
       "      <td>1</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25975</th>\n",
       "      <td>25976</td>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-23 00:00:00</td>\n",
       "      <td>1992092</td>\n",
       "      <td>9824</td>\n",
       "      <td>10199</td>\n",
       "      <td>1</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25976</th>\n",
       "      <td>25977</td>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-23 00:00:00</td>\n",
       "      <td>1992093</td>\n",
       "      <td>9956</td>\n",
       "      <td>10179</td>\n",
       "      <td>2</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25977</th>\n",
       "      <td>25978</td>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-22 00:00:00</td>\n",
       "      <td>1992094</td>\n",
       "      <td>7896</td>\n",
       "      <td>10243</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25978</th>\n",
       "      <td>25979</td>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-23 00:00:00</td>\n",
       "      <td>1992095</td>\n",
       "      <td>10192</td>\n",
       "      <td>9931</td>\n",
       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 115 columns</p>\n",
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       "          id  country_id  league_id     season  stage                 date  \\\n",
       "25974  25975       24558      24558  2015/2016      9  2015-09-22 00:00:00   \n",
       "25975  25976       24558      24558  2015/2016      9  2015-09-23 00:00:00   \n",
       "25976  25977       24558      24558  2015/2016      9  2015-09-23 00:00:00   \n",
       "25977  25978       24558      24558  2015/2016      9  2015-09-22 00:00:00   \n",
       "25978  25979       24558      24558  2015/2016      9  2015-09-23 00:00:00   \n",
       "\n",
       "       match_api_id  home_team_api_id  away_team_api_id  home_team_goal  ...  \\\n",
       "25974       1992091             10190             10191               1  ...   \n",
       "25975       1992092              9824             10199               1  ...   \n",
       "25976       1992093              9956             10179               2  ...   \n",
       "25977       1992094              7896             10243               0  ...   \n",
       "25978       1992095             10192              9931               4  ...   \n",
       "\n",
       "       SJA  VCH  VCD  VCA  GBH  GBD  GBA  BSH  BSD  BSA  \n",
       "25974  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "25975  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "25976  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "25977  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "25978  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 115 columns]"
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   ],
   "source": [
    "matches.tail()\n",
    "#matches.describe()"
   ]
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  {
   "cell_type": "code",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
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       "      <th>2</th>\n",
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       "      <td>France</td>\n",
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       "      <td>Italy</td>\n",
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       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands</td>\n",
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       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland</td>\n",
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       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
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       "      <th>10</th>\n",
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      "text/plain": [
       "       id         name\n",
       "0       1      Belgium\n",
       "1    1729      England\n",
       "2    4769       France\n",
       "3    7809      Germany\n",
       "4   10257        Italy\n",
       "5   13274  Netherlands\n",
       "6   15722       Poland\n",
       "7   17642     Portugal\n",
       "8   19694     Scotland\n",
       "9   21518        Spain\n",
       "10  24558  Switzerland"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "countries"
   ]
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   "execution_count": 4,
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       id  country_id                      name\n",
       "0       1           1    Belgium Jupiler League\n",
       "1    1729        1729    England Premier League\n",
       "2    4769        4769            France Ligue 1\n",
       "3    7809        7809     Germany 1. Bundesliga\n",
       "4   10257       10257             Italy Serie A\n",
       "5   13274       13274    Netherlands Eredivisie\n",
       "6   15722       15722        Poland Ekstraklasa\n",
       "7   17642       17642  Portugal Liga ZON Sagres\n",
       "8   19694       19694   Scotland Premier League\n",
       "9   21518       21518           Spain LIGA BBVA\n",
       "10  24558       24558  Switzerland Super League"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leagues"
   ]
  },
  {
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       "      <td>Beerschot AC</td>\n",
       "      <td>BAC</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>15005.0</td>\n",
       "      <td>SV Zulte-Waregem</td>\n",
       "      <td>ZUL</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2007.0</td>\n",
       "      <td>Sporting Lokeren</td>\n",
       "      <td>LOK</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9984</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>KSV Cercle Brugge</td>\n",
       "      <td>CEB</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>49479</td>\n",
       "      <td>10190</td>\n",
       "      <td>898.0</td>\n",
       "      <td>FC St. Gallen</td>\n",
       "      <td>GAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>49837</td>\n",
       "      <td>10191</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>FC Thun</td>\n",
       "      <td>THU</td>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>50201</td>\n",
       "      <td>9777</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Servette FC</td>\n",
       "      <td>SER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>50204</td>\n",
       "      <td>7730</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>FC Lausanne-Sports</td>\n",
       "      <td>LAU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>51606</td>\n",
       "      <td>7896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lugano</td>\n",
       "      <td>LUG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  team_api_id  team_fifa_api_id      team_long_name team_short_name\n",
       "0        1         9987             673.0            KRC Genk             GEN\n",
       "1        2         9993             675.0        Beerschot AC             BAC\n",
       "2        3        10000           15005.0    SV Zulte-Waregem             ZUL\n",
       "3        4         9994            2007.0    Sporting Lokeren             LOK\n",
       "4        5         9984            1750.0   KSV Cercle Brugge             CEB\n",
       "..     ...          ...               ...                 ...             ...\n",
       "294  49479        10190             898.0       FC St. Gallen             GAL\n",
       "295  49837        10191            1715.0             FC Thun             THU\n",
       "296  50201         9777             324.0         Servette FC             SER\n",
       "297  50204         7730            1862.0  FC Lausanne-Sports             LAU\n",
       "298  51606         7896               NaN              Lugano             LUG\n",
       "\n",
       "[299 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the countries of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will to select the countries of interest for our analysis. I am selecting England, Germany and Spain as these are the most popular and most watched leagues in football. These leagues are the most competitive league and it would give better understanding of the game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>country_id</th>\n",
       "      <th>name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7809</td>\n",
       "      <td>Germany</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id     name  country_id                  name_y\n",
       "0   1729  England        1729  England Premier League\n",
       "1   7809  Germany        7809   Germany 1. Bundesliga\n",
       "2  21518    Spain       21518         Spain LIGA BBVA"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_countries = ['England','Germany','Spain']\n",
    "countries = countries[countries.name.isin(main_countries)]\n",
    "leagues = countries.merge(leagues,on='id',suffixes=('', '_y'))\n",
    "seasons = matches.season.unique()\n",
    "leagues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the leagues with corresponding matches\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>season</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>1730</td>\n",
       "      <td>1729</td>\n",
       "      <td>9825</td>\n",
       "      <td>8659</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>1731</td>\n",
       "      <td>1729</td>\n",
       "      <td>8472</td>\n",
       "      <td>8650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>1732</td>\n",
       "      <td>1729</td>\n",
       "      <td>8654</td>\n",
       "      <td>8528</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>1733</td>\n",
       "      <td>1729</td>\n",
       "      <td>10252</td>\n",
       "      <td>8456</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "1728  1729       1729             10260             10261               1   \n",
       "1729  1730       1729              9825              8659               1   \n",
       "1730  1731       1729              8472              8650               0   \n",
       "1731  1732       1729              8654              8528               2   \n",
       "1732  1733       1729             10252              8456               4   \n",
       "\n",
       "      away_team_goal     season  total_goals  \n",
       "1728               1  2008/2009            2  \n",
       "1729               0  2008/2009            1  \n",
       "1730               1  2008/2009            1  \n",
       "1731               1  2008/2009            3  \n",
       "1732               2  2008/2009            6  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "req_matches = matches[matches.league_id.isin(leagues['id'])]\n",
    "req_matches = req_matches[['id','league_id','home_team_api_id','away_team_api_id','home_team_goal','away_team_goal','season']]\n",
    "req_matches[\"total_goals\"] = req_matches['home_team_goal'] + req_matches['away_team_goal']\n",
    "req_matches.dropna(inplace=True)\n",
    "req_matches.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding results of the match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As these dataset did not contain the result of the match we would add the results by comparing the home team goal and away team goal for that match. If the home team has more goals than the away team then the home team won the match and if not then the away team won the match. If both the goals are equal matche ended in draw. I am giving win as 1, draw as 0 and loss as -1 and then appended it to the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>season</th>\n",
       "      <th>total_goals</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>1730</td>\n",
       "      <td>1729</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>1731</td>\n",
       "      <td>1729</td>\n",
       "      <td>8472</td>\n",
       "      <td>8650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>1732</td>\n",
       "      <td>1729</td>\n",
       "      <td>8654</td>\n",
       "      <td>8528</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>1733</td>\n",
       "      <td>1729</td>\n",
       "      <td>10252</td>\n",
       "      <td>8456</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "1728  1729       1729             10260             10261               1   \n",
       "1729  1730       1729              9825              8659               1   \n",
       "1730  1731       1729              8472              8650               0   \n",
       "1731  1732       1729              8654              8528               2   \n",
       "1732  1733       1729             10252              8456               4   \n",
       "\n",
       "      away_team_goal     season  total_goals  result  \n",
       "1728               1  2008/2009            2       0  \n",
       "1729               0  2008/2009            1       1  \n",
       "1730               1  2008/2009            1      -1  \n",
       "1731               1  2008/2009            3       1  \n",
       "1732               2  2008/2009            6       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def res(row):\n",
    "    if row['home_team_goal'] == row['away_team_goal']:\n",
    "        val = 0\n",
    "    elif row['home_team_goal'] > row['away_team_goal']:\n",
    "        val = 1\n",
    "    else:\n",
    "        val = -1\n",
    "    return val\n",
    "\n",
    "req_matches[\"result\"] = req_matches.apply(res,axis = 1)\n",
    "req_matches.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating the leagues\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The matches need to be separated and grouped by their leagues. A new dataframe for storing the separated leagues is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
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       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
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       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
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       "      <th>2</th>\n",
       "      <td>1729</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1729</td>\n",
       "      <td>8654</td>\n",
       "      <td>8528</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1729</td>\n",
       "      <td>10252</td>\n",
       "      <td>8456</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "0       1729             10260             10261               1   \n",
       "1       1729              9825              8659               1   \n",
       "2       1729              8472              8650               0   \n",
       "3       1729              8654              8528               2   \n",
       "4       1729             10252              8456               4   \n",
       "\n",
       "   away_team_goal     season  total_goals  result     name  \\\n",
       "0               1  2008/2009            2       0  England   \n",
       "1               0  2008/2009            1       1  England   \n",
       "2               1  2008/2009            1      -1  England   \n",
       "3               1  2008/2009            3       1  England   \n",
       "4               2  2008/2009            6       1  England   \n",
       "\n",
       "                   name_y  \n",
       "0  England Premier League  \n",
       "1  England Premier League  \n",
       "2  England Premier League  \n",
       "3  England Premier League  \n",
       "4  England Premier League  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
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       "      <td>2008/2009</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>7809</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>7809</td>\n",
       "      <td>10189</td>\n",
       "      <td>9904</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043</th>\n",
       "      <td>7809</td>\n",
       "      <td>8721</td>\n",
       "      <td>8722</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>7809</td>\n",
       "      <td>9810</td>\n",
       "      <td>8177</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "3040       7809              9823              9790               2   \n",
       "3041       7809              8178              9789               2   \n",
       "3042       7809             10189              9904               3   \n",
       "3043       7809              8721              8722               2   \n",
       "3044       7809              9810              8177               0   \n",
       "\n",
       "      away_team_goal     season  total_goals  result     name  \\\n",
       "3040               2  2008/2009            4       0  Germany   \n",
       "3041               3  2008/2009            5      -1  Germany   \n",
       "3042               0  2008/2009            3       1  Germany   \n",
       "3043               1  2008/2009            3       1  Germany   \n",
       "3044               2  2008/2009            2      -1  Germany   \n",
       "\n",
       "                     name_y  \n",
       "3040  Germany 1. Bundesliga  \n",
       "3041  Germany 1. Bundesliga  \n",
       "3042  Germany 1. Bundesliga  \n",
       "3043  Germany 1. Bundesliga  \n",
       "3044  Germany 1. Bundesliga  "
      ]
     },
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     "data": {
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       "      <th></th>\n",
       "      <th>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>season</th>\n",
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       "      <th>name</th>\n",
       "      <th>name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8523</th>\n",
       "      <td>21518</td>\n",
       "      <td>9906</td>\n",
       "      <td>10267</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8524</th>\n",
       "      <td>21518</td>\n",
       "      <td>9864</td>\n",
       "      <td>9783</td>\n",
       "      <td>2</td>\n",
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       "      <td>2015/2016</td>\n",
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       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8525</th>\n",
       "      <td>21518</td>\n",
       "      <td>8315</td>\n",
       "      <td>9869</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8526</th>\n",
       "      <td>21518</td>\n",
       "      <td>7878</td>\n",
       "      <td>8603</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8527</th>\n",
       "      <td>21518</td>\n",
       "      <td>8370</td>\n",
       "      <td>8558</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "8523      21518              9906             10267               2   \n",
       "8524      21518              9864              9783               2   \n",
       "8525      21518              8315              9869               3   \n",
       "8526      21518              7878              8603               1   \n",
       "8527      21518              8370              8558               3   \n",
       "\n",
       "      away_team_goal     season  total_goals  result   name           name_y  \n",
       "8523               1  2015/2016            3       1  Spain  Spain LIGA BBVA  \n",
       "8524               0  2015/2016            2       1  Spain  Spain LIGA BBVA  \n",
       "8525               0  2015/2016            3       1  Spain  Spain LIGA BBVA  \n",
       "8526               1  2015/2016            2       0  Spain  Spain LIGA BBVA  \n",
       "8527               0  2015/2016            3       1  Spain  Spain LIGA BBVA  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_matches = pd.merge(req_matches,leagues,left_on='league_id', right_on='id')\n",
    "new_matches = new_matches.drop(['id_x','id_y','country_id'],axis = 1)\n",
    "english = new_matches[new_matches.name == \"England\"]\n",
    "spanish = new_matches[new_matches.name == \"Spain\"]\n",
    "german = new_matches[new_matches.name == \"Germany\"]\n",
    "\n",
    "display(english.head())\n",
    "display(german.head())\n",
    "display(spanish.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping and dividing by seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then dividing them by their seasons and storing them into new dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>home_team_api_id</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1729</td>\n",
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       "      <td>8658</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1729</td>\n",
       "      <td>8586</td>\n",
       "      <td>8650</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>1729</td>\n",
       "      <td>10252</td>\n",
       "      <td>8654</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>1729</td>\n",
       "      <td>8655</td>\n",
       "      <td>8668</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
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       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>1729</td>\n",
       "      <td>8655</td>\n",
       "      <td>8602</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>1729</td>\n",
       "      <td>9879</td>\n",
       "      <td>10252</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>1729</td>\n",
       "      <td>9825</td>\n",
       "      <td>8472</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>1729</td>\n",
       "      <td>8668</td>\n",
       "      <td>10260</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>1729</td>\n",
       "      <td>9825</td>\n",
       "      <td>10252</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>1729</td>\n",
       "      <td>8455</td>\n",
       "      <td>8667</td>\n",
       "      <td>2</td>\n",
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       "      <td>2013/2014</td>\n",
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       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td>1729</td>\n",
       "      <td>9825</td>\n",
       "      <td>9826</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>1729</td>\n",
       "      <td>8191</td>\n",
       "      <td>8455</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>1729</td>\n",
       "      <td>9825</td>\n",
       "      <td>8654</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2661</th>\n",
       "      <td>1729</td>\n",
       "      <td>8678</td>\n",
       "      <td>10252</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "0          1729             10260             10261               1   \n",
       "1          1729              9825              8659               1   \n",
       "380        1729             10260              8658               1   \n",
       "381        1729              8586              8650               2   \n",
       "760        1729             10252              8654               3   \n",
       "761        1729              8655              8668               1   \n",
       "1140       1729              8655              8602               1   \n",
       "1141       1729              9879             10252               0   \n",
       "1520       1729              9825              8472               0   \n",
       "1521       1729              8668             10260               1   \n",
       "1900       1729              9825             10252               1   \n",
       "1901       1729              8455              8667               2   \n",
       "2280       1729              9825              9826               2   \n",
       "2281       1729              8191              8455               1   \n",
       "2660       1729              9825              8654               0   \n",
       "2661       1729              8678             10252               0   \n",
       "\n",
       "      away_team_goal     season  total_goals  result     name  \\\n",
       "0                  1  2008/2009            2       0  England   \n",
       "1                  0  2008/2009            1       1  England   \n",
       "380                0  2009/2010            1       1  England   \n",
       "381                1  2009/2010            3       1  England   \n",
       "760                0  2010/2011            3       1  England   \n",
       "761                0  2010/2011            1       1  England   \n",
       "1140               2  2011/2012            3      -1  England   \n",
       "1141               0  2011/2012            0       0  England   \n",
       "1520               0  2012/2013            0       0  England   \n",
       "1521               0  2012/2013            1       1  England   \n",
       "1900               3  2013/2014            4      -1  England   \n",
       "1901               0  2013/2014            2       1  England   \n",
       "2280               1  2014/2015            3       1  England   \n",
       "2281               3  2014/2015            4      -1  England   \n",
       "2660               2  2015/2016            2      -1  England   \n",
       "2661               1  2015/2016            1      -1  England   \n",
       "\n",
       "                      name_y  \n",
       "0     England Premier League  \n",
       "1     England Premier League  \n",
       "380   England Premier League  \n",
       "381   England Premier League  \n",
       "760   England Premier League  \n",
       "761   England Premier League  \n",
       "1140  England Premier League  \n",
       "1141  England Premier League  \n",
       "1520  England Premier League  \n",
       "1521  England Premier League  \n",
       "1900  England Premier League  \n",
       "1901  England Premier League  \n",
       "2280  England Premier League  \n",
       "2281  England Premier League  \n",
       "2660  England Premier League  \n",
       "2661  England Premier League  "
      ]
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       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5867</th>\n",
       "      <td>21518</td>\n",
       "      <td>10267</td>\n",
       "      <td>8696</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6246</th>\n",
       "      <td>21518</td>\n",
       "      <td>8394</td>\n",
       "      <td>9865</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>21518</td>\n",
       "      <td>9864</td>\n",
       "      <td>10267</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626</th>\n",
       "      <td>21518</td>\n",
       "      <td>9783</td>\n",
       "      <td>8558</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6627</th>\n",
       "      <td>21518</td>\n",
       "      <td>8661</td>\n",
       "      <td>8581</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7006</th>\n",
       "      <td>21518</td>\n",
       "      <td>8603</td>\n",
       "      <td>8370</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7007</th>\n",
       "      <td>21518</td>\n",
       "      <td>9869</td>\n",
       "      <td>7878</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7386</th>\n",
       "      <td>21518</td>\n",
       "      <td>9910</td>\n",
       "      <td>9783</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7387</th>\n",
       "      <td>21518</td>\n",
       "      <td>8370</td>\n",
       "      <td>8634</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7766</th>\n",
       "      <td>21518</td>\n",
       "      <td>9865</td>\n",
       "      <td>8370</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7767</th>\n",
       "      <td>21518</td>\n",
       "      <td>10267</td>\n",
       "      <td>8560</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8146</th>\n",
       "      <td>21518</td>\n",
       "      <td>8372</td>\n",
       "      <td>7878</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8147</th>\n",
       "      <td>21518</td>\n",
       "      <td>9864</td>\n",
       "      <td>8370</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8526</th>\n",
       "      <td>21518</td>\n",
       "      <td>7878</td>\n",
       "      <td>8603</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8527</th>\n",
       "      <td>21518</td>\n",
       "      <td>8370</td>\n",
       "      <td>8558</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "5866      21518              9906              8661               2   \n",
       "5867      21518             10267              8696               2   \n",
       "6246      21518              8394              9865               2   \n",
       "6247      21518              9864             10267               0   \n",
       "6626      21518              9783              8558               3   \n",
       "6627      21518              8661              8581               2   \n",
       "7006      21518              8603              8370               0   \n",
       "7007      21518              9869              7878               2   \n",
       "7386      21518              9910              9783               1   \n",
       "7387      21518              8370              8634               0   \n",
       "7766      21518              9865              8370               0   \n",
       "7767      21518             10267              8560               1   \n",
       "8146      21518              8372              7878               1   \n",
       "8147      21518              9864              8370               4   \n",
       "8526      21518              7878              8603               1   \n",
       "8527      21518              8370              8558               3   \n",
       "\n",
       "      away_team_goal     season  total_goals  result   name           name_y  \n",
       "5866               0  2008/2009            2       1  Spain  Spain LIGA BBVA  \n",
       "5867               4  2008/2009            6      -1  Spain  Spain LIGA BBVA  \n",
       "6246               1  2009/2010            3       1  Spain  Spain LIGA BBVA  \n",
       "6247               1  2009/2010            1      -1  Spain  Spain LIGA BBVA  \n",
       "6626               0  2010/2011            3       1  Spain  Spain LIGA BBVA  \n",
       "6627               1  2010/2011            3       1  Spain  Spain LIGA BBVA  \n",
       "7006               2  2011/2012            2      -1  Spain  Spain LIGA BBVA  \n",
       "7007               0  2011/2012            2       1  Spain  Spain LIGA BBVA  \n",
       "7386               1  2012/2013            2       0  Spain  Spain LIGA BBVA  \n",
       "7387               5  2012/2013            5      -1  Spain  Spain LIGA BBVA  \n",
       "7766               1  2013/2014            1      -1  Spain  Spain LIGA BBVA  \n",
       "7767               2  2013/2014            3      -1  Spain  Spain LIGA BBVA  \n",
       "8146               1  2014/2015            2       0  Spain  Spain LIGA BBVA  \n",
       "8147               0  2014/2015            4       1  Spain  Spain LIGA BBVA  \n",
       "8526               1  2015/2016            2       0  Spain  Spain LIGA BBVA  \n",
       "8527               0  2015/2016            3       1  Spain  Spain LIGA BBVA  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>Germany 1. Bundesliga</td>\n",
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       "      <td>7809</td>\n",
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       "      <td>2009/2010</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
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       "    <tr>\n",
       "      <th>4262</th>\n",
       "      <td>7809</td>\n",
       "      <td>8721</td>\n",
       "      <td>8165</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>3</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
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       "      <th>4263</th>\n",
       "      <td>7809</td>\n",
       "      <td>9788</td>\n",
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       "      <td>7809</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
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       "    <tr>\n",
       "      <th>4569</th>\n",
       "      <td>7809</td>\n",
       "      <td>8194</td>\n",
       "      <td>8721</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
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       "    <tr>\n",
       "      <th>4874</th>\n",
       "      <td>7809</td>\n",
       "      <td>9810</td>\n",
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       "      <th>5180</th>\n",
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       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
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       "      <th>5181</th>\n",
       "      <td>7809</td>\n",
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       "    <tr>\n",
       "      <th>5486</th>\n",
       "      <td>7809</td>\n",
       "      <td>8722</td>\n",
       "      <td>9904</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5487</th>\n",
       "      <td>7809</td>\n",
       "      <td>9905</td>\n",
       "      <td>9789</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "3344       7809              9788              8295               1   \n",
       "3345       7809              8226              9790               3   \n",
       "3650       7809              8722              9905               1   \n",
       "3651       7809              8165              8177               3   \n",
       "3956       7809              9904              8722               2   \n",
       "3957       7809              8165              8721               2   \n",
       "4262       7809              8721              8165               2   \n",
       "4263       7809              9788              8178               2   \n",
       "4568       7809              8357              8697               1   \n",
       "4569       7809              8194              8721               1   \n",
       "4874       7809              9810              8165               1   \n",
       "4875       7809              8177              9788               1   \n",
       "5180       7809              8697              8722               0   \n",
       "5181       7809              9810             10269               4   \n",
       "5486       7809              8722              9904               0   \n",
       "5487       7809              9905              9789               0   \n",
       "\n",
       "      away_team_goal     season  total_goals  result     name  \\\n",
       "3344               0  2008/2009            1       1  Germany   \n",
       "3345               0  2008/2009            3       1  Germany   \n",
       "3650               0  2009/2010            1       1  Germany   \n",
       "3651               0  2009/2010            3       1  Germany   \n",
       "3956               1  2010/2011            3       1  Germany   \n",
       "3957               1  2010/2011            3       1  Germany   \n",
       "4262               1  2011/2012            3       1  Germany   \n",
       "4263               2  2011/2012            4       0  Germany   \n",
       "4568               1  2012/2013            2       0  Germany   \n",
       "4569               4  2012/2013            5      -1  Germany   \n",
       "4874               1  2013/2014            2       0  Germany   \n",
       "4875               0  2013/2014            1       1  Germany   \n",
       "5180               1  2014/2015            1      -1  Germany   \n",
       "5181               5  2014/2015            9      -1  Germany   \n",
       "5486               1  2015/2016            1      -1  Germany   \n",
       "5487               2  2015/2016            2      -1  Germany   \n",
       "\n",
       "                     name_y  \n",
       "3344  Germany 1. Bundesliga  \n",
       "3345  Germany 1. Bundesliga  \n",
       "3650  Germany 1. Bundesliga  \n",
       "3651  Germany 1. Bundesliga  \n",
       "3956  Germany 1. Bundesliga  \n",
       "3957  Germany 1. Bundesliga  \n",
       "4262  Germany 1. Bundesliga  \n",
       "4263  Germany 1. Bundesliga  \n",
       "4568  Germany 1. Bundesliga  \n",
       "4569  Germany 1. Bundesliga  \n",
       "4874  Germany 1. Bundesliga  \n",
       "4875  Germany 1. Bundesliga  \n",
       "5180  Germany 1. Bundesliga  \n",
       "5181  Germany 1. Bundesliga  \n",
       "5486  Germany 1. Bundesliga  \n",
       "5487  Germany 1. Bundesliga  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e = english.groupby('season')\n",
    "s = spanish.groupby('season')\n",
    "g = german.groupby('season')\n",
    "seasons\n",
    "display(e.head(2))\n",
    "display(s.tail(2))\n",
    "display(g.tail(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting total goals scored each season\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the total number of goals scored by all team in each season. We plot this on pyplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2008/2009', '2009/2010', '2010/2011', '2011/2012', '2012/2013',\n",
       "       '2013/2014', '2014/2015', '2015/2016'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.title(\"Total goals in each season\")\n",
    "plt.xticks(range(len(seasons)),seasons)\n",
    "plt.style.use('ggplot')\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Total Goals Scored\")\n",
    "num_seasons = range(len(seasons))\n",
    "\n",
    "plt.plot(num_seasons,e.total_goals.sum().values,label = \"English Premier League\", marker = 'o')\n",
    "plt.plot(num_seasons,g.total_goals.sum().values,label = \"German Bundesliga\", marker = 'o')\n",
    "plt.plot(num_seasons,s.total_goals.sum().values,label = \"Spanish La Liga\", marker = 'o')\n",
    "plt.legend()\n",
    "seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  We can see that the Spanish teams overall score more than the teams in Premier League and in Bundesliga. \n",
    "  But the total goals in Bundesliga is low due to the fact that only 18 teams compete in the league. So each team in Bundesliga plays only 34 matches in a season whereas each team in premier league plays 38 matches. So the total games played in Premier League and La Liga is 380 whereas total games played in Bundesliga is 306."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We calculate average goals scored in each game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24fd0f6c970>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting average goals scored each season\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.xticks(range(len(seasons)),seasons)\n",
    "plt.style.use('ggplot')\n",
    "plt.xlabel(\"Season\")\n",
    "plt.title(\"Average goals per game each season\")\n",
    "plt.ylabel(\"Average goals per game\")\n",
    "\n",
    "plt.plot(num_seasons,e.total_goals.mean().values,label = \"English Premier League\", marker = 'o')\n",
    "plt.plot(num_seasons,g.total_goals.mean().values,label = \"German Bundesliga\", marker = 'o')\n",
    "plt.plot(num_seasons,s.total_goals.mean().values,label = \"Spanish La Liga\", marker = 'o')\n",
    "plt.legend(loc = 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Bundesliga teams score way more on average in each game that the teams in other leagues. In the 2013/2014 season, Bundesliga teams scored more than 3 goals each game on an average (967 goals in 306 matches - 3.16 per match). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_goals_english = english.total_goals.mean()\n",
    "average_goals_spanish = spanish.total_goals.mean()\n",
    "average_goals_german = german.total_goals.mean()\n",
    "#print(\"{:.2}\".format(average_goals_english))\n",
    "#print(\"{:.2}\".format(average_goals_spanish))\n",
    "#print(\"{:.2}\".format(average_goals_german))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Average goals in the English Premier League is 2.7\n",
    "\n",
    " Average goals in the Spanish La Liga is 2.8\n",
    "\n",
    " Average goals in the Germany BundesLiga is 2.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Goal Scoring Team at Home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which team scores the most goal when playing on home turf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>season</th>\n",
       "      <th>total_goals</th>\n",
       "      <th>result</th>\n",
       "      <th>country_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>home_team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>MUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>8654</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>MUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>8667</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>MUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>10194</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>MUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>8472</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>MUN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "0       1729             10260             10261               1   \n",
       "1       1729             10260              8654               2   \n",
       "2       1729             10260              8667               4   \n",
       "3       1729             10260             10194               5   \n",
       "4       1729             10260              8472               1   \n",
       "\n",
       "   away_team_goal     season  total_goals  result country_name  \\\n",
       "0               1  2008/2009            2       0      England   \n",
       "1               0  2008/2009            2       1      England   \n",
       "2               3  2008/2009            7       1      England   \n",
       "3               0  2008/2009            5       1      England   \n",
       "4               0  2008/2009            1       1      England   \n",
       "\n",
       "              league_name home_team_long_name team_short_name  \n",
       "0  England Premier League   Manchester United             MUN  \n",
       "1  England Premier League   Manchester United             MUN  \n",
       "2  England Premier League   Manchester United             MUN  \n",
       "3  England Premier League   Manchester United             MUN  \n",
       "4  England Premier League   Manchester United             MUN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging Home team and matches\n",
    "\n",
    "matches_h_teams = pd.merge(new_matches,teams,left_on='home_team_api_id', right_on='team_api_id')\n",
    "matches_h_teams = matches_h_teams.drop(['id','team_api_id','team_fifa_api_id'],axis = 1)\n",
    "matches_h_teams = matches_h_teams.rename(columns={'team_long_name':'home_team_long_name','name_y':'league_name','name':'country_name'})\n",
    "matches_h_teams.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Team name')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_goal_scorers_home = matches_h_teams.groupby('home_team_long_name').total_goals.sum().sort_values(ascending = False)\n",
    "top_goal_scorers_home.head(15)\n",
    "%matplotlib inline \n",
    "fig = plt.figure(figsize = (10,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "rects = ax.bar(range(15), top_goal_scorers_home.head(15).values,align = \"center\")\n",
    "ax.set_xticks(range(15))\n",
    "ax.set_xticklabels(top_goal_scorers_home.head(15).index,rotation = \"vertical\")\n",
    "ax.set_title(\"Top goal scorers at home\")\n",
    "ax.set_ylabel(\"Number of goals\")\n",
    "ax.set_xlabel(\"Team name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Goal Scoring Team at away\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which team scores the most in away matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>season</th>\n",
       "      <th>total_goals</th>\n",
       "      <th>result</th>\n",
       "      <th>country_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>away_team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1729</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>NEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>9879</td>\n",
       "      <td>10261</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>NEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1729</td>\n",
       "      <td>8455</td>\n",
       "      <td>10261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>NEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1729</td>\n",
       "      <td>8549</td>\n",
       "      <td>10261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>NEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1729</td>\n",
       "      <td>8462</td>\n",
       "      <td>10261</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>England</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>NEW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "0       1729             10260             10261               1   \n",
       "1       1729              9879             10261               2   \n",
       "2       1729              8455             10261               0   \n",
       "3       1729              8549             10261               0   \n",
       "4       1729              8462             10261               0   \n",
       "\n",
       "   away_team_goal     season  total_goals  result country_name  \\\n",
       "0               1  2008/2009            2       0      England   \n",
       "1               1  2008/2009            3       1      England   \n",
       "2               0  2008/2009            0       0      England   \n",
       "3               0  2008/2009            0       0      England   \n",
       "4               3  2008/2009            3      -1      England   \n",
       "\n",
       "              league_name away_team_long_name team_short_name  \n",
       "0  England Premier League    Newcastle United             NEW  \n",
       "1  England Premier League    Newcastle United             NEW  \n",
       "2  England Premier League    Newcastle United             NEW  \n",
       "3  England Premier League    Newcastle United             NEW  \n",
       "4  England Premier League    Newcastle United             NEW  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging Away team and matches\n",
    "\n",
    "matches_a_teams = pd.merge(new_matches,teams,left_on='away_team_api_id', right_on='team_api_id')\n",
    "matches_a_teams = matches_a_teams.drop(['id','team_api_id','team_fifa_api_id'],axis = 1)\n",
    "matches_a_teams = matches_a_teams.rename(columns={'team_long_name':'away_team_long_name','name_y':'league_name','name':'country_name'})\n",
    "matches_a_teams.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Team name')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_goal_scorers_away = matches_a_teams.groupby('away_team_long_name').total_goals.sum().sort_values(ascending = False)\n",
    "top_goal_scorers_away.head(15)\n",
    "%matplotlib inline \n",
    "fig = plt.figure(figsize = (10,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "rects = ax.bar(range(15), top_goal_scorers_away.head(15).values,align = \"center\")\n",
    "ax.set_xticks(range(15))\n",
    "ax.set_xticklabels(top_goal_scorers_away.head(15).index,rotation = \"vertical\")\n",
    "ax.set_title(\"Top goal scorers at away\")\n",
    "ax.set_ylabel(\"Number of goals\")\n",
    "ax.set_xlabel(\"Team name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>B365H</th>\n",
       "      <th>B365D</th>\n",
       "      <th>B365A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>489042</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1.29</td>\n",
       "      <td>5.5</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>1730</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>489043</td>\n",
       "      <td>9825</td>\n",
       "      <td>8659</td>\n",
       "      <td>1.20</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>1731</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>489044</td>\n",
       "      <td>8472</td>\n",
       "      <td>8650</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>1732</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>489045</td>\n",
       "      <td>8654</td>\n",
       "      <td>8528</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>1733</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>489046</td>\n",
       "      <td>10252</td>\n",
       "      <td>8456</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  country_id  league_id     season  stage                 date  \\\n",
       "1728  1729        1729       1729  2008/2009      1  2008-08-17 00:00:00   \n",
       "1729  1730        1729       1729  2008/2009      1  2008-08-16 00:00:00   \n",
       "1730  1731        1729       1729  2008/2009      1  2008-08-16 00:00:00   \n",
       "1731  1732        1729       1729  2008/2009      1  2008-08-16 00:00:00   \n",
       "1732  1733        1729       1729  2008/2009      1  2008-08-17 00:00:00   \n",
       "\n",
       "      match_api_id  home_team_api_id  away_team_api_id  B365H  B365D  B365A  \n",
       "1728        489042             10260             10261   1.29    5.5  11.00  \n",
       "1729        489043              9825              8659   1.20    6.5  15.00  \n",
       "1730        489044              8472              8650   5.50    3.6   1.67  \n",
       "1731        489045              8654              8528   1.91    3.4   4.20  \n",
       "1732        489046             10252              8456   1.91    3.4   4.33  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select fields\n",
    "matches = matches3[matches3.league_id.isin(leagues.id)]\n",
    "matches = matches[['id', 'country_id' ,'league_id', 'season', 'stage', 'date','match_api_id', 'home_team_api_id', 'away_team_api_id','B365H', 'B365D' ,'B365A']]\n",
    "matches.dropna(inplace=True)\n",
    "matches.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   #                      Probability of winning from Betting odds\n",
    "![\"Betting Odds\"](files/odds.png \"Betting odds probability\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating entropy to determine predictability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entropy is calculated to measure the disorder using bet365 odds. The higher the entropy value the more unpredictable are the resuts of the matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import entropy\n",
    "\n",
    "def match_entropy(row):\n",
    "    odds = [row['B365H'],row['B365D'],row['B365A']]\n",
    "    #change odds to probability\n",
    "    probs = [1/o for o in odds]\n",
    "    #normalize to sum to 1\n",
    "    norm = sum(probs)\n",
    "    probs = [p/norm for p in probs]\n",
    "    return entropy(probs)\n",
    "\n",
    "#compute match entropy\n",
    "matches['entropy'] = matches.apply(match_entropy,axis=1)"
   ]
  },
  {
   "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>England</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Spain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008/2009</th>\n",
       "      <td>0.984556</td>\n",
       "      <td>1.004276</td>\n",
       "      <td>1.010859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009/2010</th>\n",
       "      <td>0.956496</td>\n",
       "      <td>0.997925</td>\n",
       "      <td>0.977810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010/2011</th>\n",
       "      <td>0.983258</td>\n",
       "      <td>1.016264</td>\n",
       "      <td>0.975032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011/2012</th>\n",
       "      <td>0.969593</td>\n",
       "      <td>1.001298</td>\n",
       "      <td>0.955339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012/2013</th>\n",
       "      <td>0.981472</td>\n",
       "      <td>0.993993</td>\n",
       "      <td>0.970580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013/2014</th>\n",
       "      <td>0.960473</td>\n",
       "      <td>0.977500</td>\n",
       "      <td>0.944218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014/2015</th>\n",
       "      <td>0.980301</td>\n",
       "      <td>0.985835</td>\n",
       "      <td>0.933637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015/2016</th>\n",
       "      <td>0.997819</td>\n",
       "      <td>0.974657</td>\n",
       "      <td>0.946813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            England   Germany     Spain\n",
       "season                                 \n",
       "2008/2009  0.984556  1.004276  1.010859\n",
       "2009/2010  0.956496  0.997925  0.977810\n",
       "2010/2011  0.983258  1.016264  0.975032\n",
       "2011/2012  0.969593  1.001298  0.955339\n",
       "2012/2013  0.981472  0.993993  0.970580\n",
       "2013/2014  0.960473  0.977500  0.944218\n",
       "2014/2015  0.980301  0.985835  0.933637\n",
       "2015/2016  0.997819  0.974657  0.946813"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute mean entropy for every league in every season\n",
    "entropy_means = matches.groupby(['season','league_id']).entropy.mean()\n",
    "entropy_means = entropy_means.reset_index().pivot(index='season', columns='league_id', values='entropy')\n",
    "entropy_means.columns = [leagues[leagues.id==x].name.values[0] for x in entropy_means.columns]\n",
    "entropy_means.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting entropy of the leagues for visual representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(7.3, 0.952, 'more predictable')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph\n",
    "ax = entropy_means.plot(figsize=(12,8),marker='o')\n",
    "\n",
    "#set title\n",
    "plt.title('Leagues Predictability', fontsize=16)\n",
    "\n",
    "#set ticks roatation\n",
    "plt.xticks(rotation=50)\n",
    "\n",
    "#keep colors for next graph\n",
    "colors = [x.get_color() for x in ax.get_lines()]\n",
    "colors_mapping = dict(zip(leagues.id,colors))\n",
    "\n",
    "#remove x label\n",
    "ax.set_xlabel('')\n",
    "\n",
    "#locate legend \n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "#add arrows\n",
    "ax.annotate('', xytext=(7.2, 1),xy=(7.2, 1.039),\n",
    "            arrowprops=dict(facecolor='black',arrowstyle=\"->, head_length=.7, head_width=.3\",linewidth=1), annotation_clip=False)\n",
    "\n",
    "ax.annotate('', xytext=(7.2, 0.96),xy=(7.2, 0.921),\n",
    "            arrowprops=dict(facecolor='black',arrowstyle=\"->, head_length=.7, head_width=.3\",linewidth=1), annotation_clip=False)\n",
    "\n",
    "ax.annotate('less predictable', xy=(7.3, 1.028), annotation_clip=False,fontsize=14,rotation='vertical')\n",
    "ax.annotate('more predictable', xy=(7.3, 0.952), annotation_clip=False,fontsize=14,rotation='vertical')\n",
    "\n",
    "#plt.savefig('figures/leagues_pred.png', bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since 2008 Bayern Munich and Barcelona have won Bundesliga and La Liga 6 times each. \n",
    "\n",
    "The top three teams in La Liga are same since 2012. The competition for title race is always between Barcelona, Real Madrid and Athletico Madrid, where Real Madrid won twice and Athletico won once since 2008.\n",
    "\n",
    "From 2012-13 season Bayern Munich has won Bundesliga 5 times in a row. Borussia Dortmund also performs well and have won the league twice in 09-10 and 10-11 seasons.\n",
    "\n",
    "Since 2008-09 in Premier League Manchester United have won the league thrice(08-09,10-11,12-13), Manchester City has won twice(11-12,13-14), Chelsea has won thrice(09-10,14-15,16-17) and Leceister City once(15-16). Leceister City was promoted to premier league in the previous season and it was placed 14th at the end of the season. Still it won the league the very next season, so we can see that it is very difficult to predict Premier League.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The English Premier League is more unpredicatable than the rest of the leagues. So we will try to predict the matches for these leagues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>name_y</th>\n",
       "      <th>country_id</th>\n",
       "      <th>name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id     name   name_y  country_id                  name_y\n",
       "0  1729  England  England        1729  England Premier League"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering English Premier League\n",
    "\n",
    "main_countries = ['England']\n",
    "countries = countries[countries.name.isin(main_countries)]\n",
    "leagues = countries.merge(leagues,on='id',suffixes=('', '_y'))\n",
    "seasons = matches.season.unique()\n",
    "leagues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>season</th>\n",
       "      <th>total_goals</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4763</th>\n",
       "      <td>4764</td>\n",
       "      <td>1729</td>\n",
       "      <td>8466</td>\n",
       "      <td>8197</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4764</th>\n",
       "      <td>4765</td>\n",
       "      <td>1729</td>\n",
       "      <td>10003</td>\n",
       "      <td>10194</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4765</th>\n",
       "      <td>4766</td>\n",
       "      <td>1729</td>\n",
       "      <td>8586</td>\n",
       "      <td>8650</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4766</th>\n",
       "      <td>4767</td>\n",
       "      <td>1729</td>\n",
       "      <td>9817</td>\n",
       "      <td>9825</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4767</th>\n",
       "      <td>4768</td>\n",
       "      <td>1729</td>\n",
       "      <td>8659</td>\n",
       "      <td>8472</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  league_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "4763  4764       1729              8466              8197               2   \n",
       "4764  4765       1729             10003             10194               0   \n",
       "4765  4766       1729              8586              8650               0   \n",
       "4766  4767       1729              9817              9825               0   \n",
       "4767  4768       1729              8659              8472               1   \n",
       "\n",
       "      away_team_goal     season  total_goals  result  \n",
       "4763               2  2015/2016            4       0  \n",
       "4764               1  2015/2016            1      -1  \n",
       "4765               0  2015/2016            0       0  \n",
       "4766               3  2015/2016            3      -1  \n",
       "4767               0  2015/2016            1       1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge the leagues with corresponding matches\n",
    "\n",
    "req_matches = matches2[matches2.league_id.isin(leagues['id'])]\n",
    "req_matches = req_matches[['id','league_id','home_team_api_id','away_team_api_id','home_team_goal','away_team_goal','season']]\n",
    "req_matches[\"total_goals\"] = req_matches['home_team_goal'] + req_matches['away_team_goal']\n",
    "req_matches[\"result\"] = req_matches.apply(res,axis = 1)\n",
    "req_matches.dropna(inplace=True)\n",
    "req_matches.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_matches = pd.merge(req_matches,leagues,left_on='league_id', right_on='id')\n",
    "new_matches = new_matches.drop(['id_x','id_y','country_id'],axis = 1)\n",
    "english = new_matches[new_matches.name == \"England\"]\n",
    "\n",
    "# sum_goals = new_group_matches.home_team_goal.sum()\n",
    "e = english.groupby('season')\n",
    "seasons\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Home advantages always played important part in match outcome. But by what factor does home advantage affects the match."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting total number of goals scored in Home matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24fd4468e80>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting home scored each season\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.xticks(range(len(seasons)),seasons)\n",
    "plt.style.use('ggplot')\n",
    "plt.title('Home goal each season')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Home-goals')\n",
    "plt.plot(num_seasons,e.home_team_goal.sum().values,label = \"English Premier League\", marker = 'o')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total number of goals scored in Away matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24fd21981c0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting away scored each season\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.xticks(range(len(seasons)),seasons)\n",
    "plt.style.use('ggplot')\n",
    "plt.title('Away goal each season')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Away-goals')\n",
    "plt.plot(num_seasons,e.away_team_goal.sum().values,label = \"English Premier League\", marker = 'o')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home goals to Away goals ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24fd5725880>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting home/away scored each season\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.xticks(range(len(seasons)),seasons)\n",
    "plt.style.use('ggplot')\n",
    "plt.title('Home-away goal ratio each season')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Home-goals to Away-goals ratio')\n",
    "plt.plot(num_seasons,e.home_team_goal.sum().values / e.away_team_goal.sum().values,label = \"English Premier League\", marker = 'o')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home wins vs Home Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.57272727, 2.12087912, 1.98888889, 1.47413793, 1.56603774,\n",
       "       1.45528455, 1.49565217, 1.35344828])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Subsetting homewins vs homeloss from each of the leagues - ignoring draws.\n",
    "e_hw = np.true_divide(english[english.result == 1].groupby('season').result.sum().values,english[english.result == -1].groupby('season').result.sum().values * -1)\n",
    "\n",
    "\n",
    "#Plotting number of home wins vs home losses each season\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.xticks(range(len(seasons)),seasons)\n",
    "plt.style.use('ggplot')\n",
    "plt.xlim = (-20,20)\n",
    "plt.ylim = (0,120)\n",
    "plt.title(\"Number of home wins each vs home loss each season\")\n",
    "plt.xlabel(\"Season\")\n",
    "plt.ylabel(\"Home Wins vs loss\")\n",
    "plt.plot(num_seasons,e_hw,label = \"English Premier League\", marker = 'o')\n",
    "display(e_hw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that the home advantage in premier league is decreasing every season. Initially home matches were very good factor to determine the winner of the match. Now the matches have become very competitive and we cannot predict the match outcome just on the home advantage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We downloaded the data of English Premier League from 2000 from http://football-data.co.uk/data.php"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains information of all the game statistics like fulltime goals, halftime goals, corners, free kicks, fouls, yellow cards, red cards, total shots, shots on target etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from the CSV into a dataframe\n",
    "\n",
    "loc = \"Datasets\\\\\"\n",
    "\n",
    "raw_data_1 = pd.read_csv(loc + '2000-01.csv')\n",
    "raw_data_2 = pd.read_csv(loc + '2001-02.csv')\n",
    "raw_data_3 = pd.read_csv(loc + '2002-03.csv')\n",
    "raw_data_4 = pd.read_csv(loc + '2003-04.csv')\n",
    "raw_data_5 = pd.read_csv(loc + '2004-05.csv')\n",
    "raw_data_6 = pd.read_csv(loc + '2005-06.csv')\n",
    "raw_data_7 = pd.read_csv(loc + '2006-07.csv')\n",
    "raw_data_8 = pd.read_csv(loc + '2007-08.csv')\n",
    "raw_data_9 = pd.read_csv(loc + '2008-09.csv')\n",
    "raw_data_10 = pd.read_csv(loc + '2009-10.csv')\n",
    "raw_data_11 = pd.read_csv(loc + '2010-11.csv')\n",
    "raw_data_12 = pd.read_csv(loc + '2011-12.csv')\n",
    "raw_data_13 = pd.read_csv(loc + '2012-13.csv')\n",
    "raw_data_14 = pd.read_csv(loc + '2013-14.csv')\n",
    "raw_data_15 = pd.read_csv(loc + '2014-15.csv')\n",
    "raw_data_16 = pd.read_csv(loc + '2015-16.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the required features and putting it in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the features\n",
    "\n",
    "raw_data_1 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_2 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_3 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_4 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_5 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_6 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_7 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_8 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_9 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_10 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_11 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_12 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_13 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_14 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_15 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n",
    "raw_data_16 =raw_data_1[['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concating the data on one dataframe\n",
    "\n",
    "playing_stat = pd.concat([raw_data_1,raw_data_2,raw_data_3,raw_data_4,raw_data_5,raw_data_6,raw_data_7,raw_data_8,raw_data_9,raw_data_10,raw_data_11,raw_data_12,raw_data_13,raw_data_14,raw_data_15,raw_data_16],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6077</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6079</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Man United</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HomeTeam     AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR  HS  AS  HST  \\\n",
       "6075       Man City      Chelsea     1     2   A     1     1   D   3   9    1   \n",
       "6076  Middlesbrough     West Ham     2     1   H     2     1   H  19  11    7   \n",
       "6077      Newcastle  Aston Villa     3     0   H     2     0   H   9   5    5   \n",
       "6078    Southampton      Arsenal     3     2   H     0     1   A  11   8    7   \n",
       "6079      Tottenham   Man United     3     1   H     1     1   D   9  15    3   \n",
       "\n",
       "      AST  HF  AF  HC  AC  HY  AY  HR  AR  \n",
       "6075    3  22  18   8   7   4   2   0   0  \n",
       "6076    5  13  15   5   6   0   0   0   0  \n",
       "6077    1  10  14   5   1   0   5   1   1  \n",
       "6078    5  17  10   7   5   1   2   0   0  \n",
       "6079    6  13  15   3   6   0   2   0   0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playing_stat.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    FTHG - Full Time Home Goal\n",
    "    FTAG - Half Time Away Goal\n",
    "    FTR - Full Time Result\n",
    "    HTHG - Half Time Home Goal\n",
    "    HTAG - Half Time Away Goal\n",
    "    HTR - Half Time Result\n",
    "    HS - Home Shots\n",
    "    AS - Away Shots\n",
    "    HST - Home Shots on Target\n",
    "    AST - Away Shots on Target\n",
    "    HF - Home Team Foul\n",
    "    AF - Away Team Foul\n",
    "    HC - Home Team Corner\n",
    "    AC - Away Team Corner\n",
    "    HY - Home Team Yellow Card\n",
    "    Ay - Away Team Yellow Card\n",
    "    HR - Home Team Red Card\n",
    "    Ar - Away Team Red Card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating csv file from the merged dataframe\n",
    "\n",
    "playing_stat.to_csv(loc + \"Merged_dataset.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created a csv file from the data for future analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Datasets\\\\merged_dataset.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5447368421052632\n"
     ]
    }
   ],
   "source": [
    "table = pd.DataFrame(columns=('Team','HGS','AGS','HAS','AAS','HGC','AGC','HDS','ADS'))\n",
    "\n",
    "avg_home_scored = df.FTHG.sum() / 6080.0\n",
    "avg_away_scored = df.FTAG.sum() / 6080.0\n",
    "avg_home_conceded = avg_away_scored\n",
    "avg_away_conceded = avg_home_scored\n",
    "print(avg_away_conceded)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_home = df.groupby('HomeTeam')\n",
    "res_away = df.groupby('AwayTeam')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate attacking strength of each team at home and away from goals scored at home and away. We will use this as a feature in our prediction model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Team</th>\n",
       "      <th>HGS</th>\n",
       "      <th>AGS</th>\n",
       "      <th>HAS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>HGC</th>\n",
       "      <th>AGC</th>\n",
       "      <th>HDS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Arsenal]</td>\n",
       "      <td>720</td>\n",
       "      <td>288</td>\n",
       "      <td>1.533220</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>208</td>\n",
       "      <td>400</td>\n",
       "      <td>0.641975</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Aston Villa]</td>\n",
       "      <td>432</td>\n",
       "      <td>304</td>\n",
       "      <td>0.919932</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>320</td>\n",
       "      <td>368</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>0.783646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Bradford]</td>\n",
       "      <td>320</td>\n",
       "      <td>160</td>\n",
       "      <td>0.681431</td>\n",
       "      <td>0.493827</td>\n",
       "      <td>464</td>\n",
       "      <td>656</td>\n",
       "      <td>1.432099</td>\n",
       "      <td>1.396934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Charlton]</td>\n",
       "      <td>496</td>\n",
       "      <td>304</td>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>304</td>\n",
       "      <td>608</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>1.294719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Chelsea]</td>\n",
       "      <td>704</td>\n",
       "      <td>384</td>\n",
       "      <td>1.499148</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>320</td>\n",
       "      <td>400</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[Coventry]</td>\n",
       "      <td>224</td>\n",
       "      <td>352</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>368</td>\n",
       "      <td>640</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>1.362862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[Derby]</td>\n",
       "      <td>368</td>\n",
       "      <td>224</td>\n",
       "      <td>0.783646</td>\n",
       "      <td>0.691358</td>\n",
       "      <td>384</td>\n",
       "      <td>560</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>1.192504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[Everton]</td>\n",
       "      <td>464</td>\n",
       "      <td>256</td>\n",
       "      <td>0.988075</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>432</td>\n",
       "      <td>512</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.090290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[Ipswich]</td>\n",
       "      <td>496</td>\n",
       "      <td>416</td>\n",
       "      <td>1.056218</td>\n",
       "      <td>1.283951</td>\n",
       "      <td>240</td>\n",
       "      <td>432</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.919932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[Leeds]</td>\n",
       "      <td>576</td>\n",
       "      <td>448</td>\n",
       "      <td>1.226576</td>\n",
       "      <td>1.382716</td>\n",
       "      <td>336</td>\n",
       "      <td>352</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>0.749574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[Leicester]</td>\n",
       "      <td>448</td>\n",
       "      <td>176</td>\n",
       "      <td>0.954003</td>\n",
       "      <td>0.543210</td>\n",
       "      <td>368</td>\n",
       "      <td>448</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>0.954003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[Liverpool]</td>\n",
       "      <td>640</td>\n",
       "      <td>496</td>\n",
       "      <td>1.362862</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>224</td>\n",
       "      <td>400</td>\n",
       "      <td>0.691358</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[Man City]</td>\n",
       "      <td>320</td>\n",
       "      <td>336</td>\n",
       "      <td>0.681431</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>496</td>\n",
       "      <td>544</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>1.158433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[Man United]</td>\n",
       "      <td>784</td>\n",
       "      <td>480</td>\n",
       "      <td>1.669506</td>\n",
       "      <td>1.481481</td>\n",
       "      <td>192</td>\n",
       "      <td>304</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.647359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[Middlesbrough]</td>\n",
       "      <td>288</td>\n",
       "      <td>416</td>\n",
       "      <td>0.613288</td>\n",
       "      <td>1.283951</td>\n",
       "      <td>368</td>\n",
       "      <td>336</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>0.715503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[Newcastle]</td>\n",
       "      <td>416</td>\n",
       "      <td>288</td>\n",
       "      <td>0.885860</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>272</td>\n",
       "      <td>528</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>1.124361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[Southampton]</td>\n",
       "      <td>432</td>\n",
       "      <td>208</td>\n",
       "      <td>0.919932</td>\n",
       "      <td>0.641975</td>\n",
       "      <td>352</td>\n",
       "      <td>416</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>0.885860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[Sunderland]</td>\n",
       "      <td>384</td>\n",
       "      <td>352</td>\n",
       "      <td>0.817717</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>256</td>\n",
       "      <td>400</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[Tottenham]</td>\n",
       "      <td>496</td>\n",
       "      <td>256</td>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>256</td>\n",
       "      <td>608</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>1.294719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[West Ham]</td>\n",
       "      <td>384</td>\n",
       "      <td>336</td>\n",
       "      <td>0.817717</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>320</td>\n",
       "      <td>480</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>1.022147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Team  HGS  AGS       HAS       AAS  HGC  AGC       HDS  \\\n",
       "0         [Arsenal]  720  288  1.533220  0.888889  208  400  0.641975   \n",
       "1     [Aston Villa]  432  304  0.919932  0.938272  320  368  0.987654   \n",
       "2        [Bradford]  320  160  0.681431  0.493827  464  656  1.432099   \n",
       "3        [Charlton]  496  304  1.056218  0.938272  304  608  0.938272   \n",
       "4         [Chelsea]  704  384  1.499148  1.185185  320  400  0.987654   \n",
       "5        [Coventry]  224  352  0.477002  1.086420  368  640  1.135802   \n",
       "6           [Derby]  368  224  0.783646  0.691358  384  560  1.185185   \n",
       "7         [Everton]  464  256  0.988075  0.790123  432  512  1.333333   \n",
       "8         [Ipswich]  496  416  1.056218  1.283951  240  432  0.740741   \n",
       "9           [Leeds]  576  448  1.226576  1.382716  336  352  1.037037   \n",
       "10      [Leicester]  448  176  0.954003  0.543210  368  448  1.135802   \n",
       "11      [Liverpool]  640  496  1.362862  1.530864  224  400  0.691358   \n",
       "12       [Man City]  320  336  0.681431  1.037037  496  544  1.530864   \n",
       "13     [Man United]  784  480  1.669506  1.481481  192  304  0.592593   \n",
       "14  [Middlesbrough]  288  416  0.613288  1.283951  368  336  1.135802   \n",
       "15      [Newcastle]  416  288  0.885860  0.888889  272  528  0.839506   \n",
       "16    [Southampton]  432  208  0.919932  0.641975  352  416  1.086420   \n",
       "17     [Sunderland]  384  352  0.817717  1.086420  256  400  0.790123   \n",
       "18      [Tottenham]  496  256  1.056218  0.790123  256  608  0.790123   \n",
       "19       [West Ham]  384  336  0.817717  1.037037  320  480  0.987654   \n",
       "\n",
       "         ADS  \n",
       "0   0.851789  \n",
       "1   0.783646  \n",
       "2   1.396934  \n",
       "3   1.294719  \n",
       "4   0.851789  \n",
       "5   1.362862  \n",
       "6   1.192504  \n",
       "7   1.090290  \n",
       "8   0.919932  \n",
       "9   0.749574  \n",
       "10  0.954003  \n",
       "11  0.851789  \n",
       "12  1.158433  \n",
       "13  0.647359  \n",
       "14  0.715503  \n",
       "15  1.124361  \n",
       "16  0.885860  \n",
       "17  0.851789  \n",
       "18  1.294719  \n",
       "19  1.022147  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.Team = res_home.HomeTeam.unique().tolist()\n",
    "table.HGS = res_home.FTHG.sum().values\n",
    "table.HGC = res_home.FTAG.sum().values\n",
    "table.AGS = res_away.FTAG.sum().values\n",
    "table.AGC = res_away.FTHG.sum().values\n",
    "#19 Home matches for each team each season and 16 seasons therefore 304 home matches and 304 away matches\n",
    "table.HAS = (table.HGS / 304.0) / avg_home_scored\n",
    "table.AAS = (table.AGS / 304.0) / avg_away_scored\n",
    "table.HDS = (table.HGC / 304.0) / avg_home_conceded\n",
    "table.ADS = (table.AGC / 304.0) / avg_away_conceded\n",
    "\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HGS - Home Goals Scored\n",
    "\n",
    "HGC - Home Goals Conceded\n",
    "\n",
    "AGS - Away Goals Scored\n",
    "\n",
    "AGS - Away Goals Conceded\n",
    "\n",
    "HAS - Home Attacking Strength\n",
    "\n",
    "HDS - Home Defensive Strength\n",
    "\n",
    "AAS - Away Attacking Strength\n",
    "\n",
    "ADS - Away Defensive Strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Charlton</td>\n",
       "      <td>Man City</td>\n",
       "      <td>H</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.158433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.499148</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.022147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coventry</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>1.283951</td>\n",
       "      <td>0.715503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Derby</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.783646</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>0.641975</td>\n",
       "      <td>0.885860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Leeds</td>\n",
       "      <td>Everton</td>\n",
       "      <td>H</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.226576</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>1.090290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.681431</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.613288</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.022147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6077</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>H</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.885860</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.783646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>H</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.919932</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6079</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Man United</td>\n",
       "      <td>H</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>1.481481</td>\n",
       "      <td>0.647359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6080 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           HomeTeam       AwayTeam FTR  HST  AST  HC  AC       HAS       HDS  \\\n",
       "0          Charlton       Man City   H   14    4   6   6  1.056218  0.938272   \n",
       "1           Chelsea       West Ham   H   10    5   7   7  1.499148  0.987654   \n",
       "2          Coventry  Middlesbrough   A    3    9   8   4  0.477002  1.135802   \n",
       "3             Derby    Southampton   D    4    6   5   8  0.783646  1.185185   \n",
       "4             Leeds        Everton   H    8    6   6   4  1.226576  1.037037   \n",
       "...             ...            ...  ..  ...  ...  ..  ..       ...       ...   \n",
       "6075       Man City        Chelsea   A    1    3   8   7  0.681431  1.530864   \n",
       "6076  Middlesbrough       West Ham   H    7    5   5   6  0.613288  1.135802   \n",
       "6077      Newcastle    Aston Villa   H    5    1   5   1  0.885860  0.839506   \n",
       "6078    Southampton        Arsenal   H    7    5   7   5  0.919932  1.086420   \n",
       "6079      Tottenham     Man United   H    3    6   3   6  1.056218  0.790123   \n",
       "\n",
       "           AAS       ADS  \n",
       "0     1.037037  1.158433  \n",
       "1     1.037037  1.022147  \n",
       "2     1.283951  0.715503  \n",
       "3     0.641975  0.885860  \n",
       "4     0.790123  1.090290  \n",
       "...        ...       ...  \n",
       "6075  1.185185  0.851789  \n",
       "6076  1.037037  1.022147  \n",
       "6077  0.938272  0.783646  \n",
       "6078  0.888889  0.851789  \n",
       "6079  1.481481  0.647359  \n",
       "\n",
       "[6080 rows x 11 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract necessary features from the data file\n",
    "feature_table = df.iloc[:,:23]\n",
    "\n",
    "#Full Time Result(FTR), Home Shots on Target(HST), Away Shots on Target(AST), Home Corners(HC), Away Corners(AC)\n",
    "feature_table = feature_table[['HomeTeam','AwayTeam','FTR','HST','AST','HC','AC']]\n",
    "\n",
    "#Home Attacking Strength(HAS), Home Defensive Strength(HDS), Away Attacking Strength(AAS), Away Defensive Strength(ADS)\n",
    "f_HAS = []\n",
    "f_HDS = []\n",
    "f_AAS = []\n",
    "f_ADS = []\n",
    "for index,row in feature_table.iterrows():\n",
    "    f_HAS.append(table[table['Team'] == row['HomeTeam']]['HAS'].values[0])\n",
    "    f_HDS.append(table[table['Team'] == row['HomeTeam']]['HDS'].values[0])\n",
    "    f_AAS.append(table[table['Team'] == row['AwayTeam']]['AAS'].values[0])\n",
    "    f_ADS.append(table[table['Team'] == row['AwayTeam']]['ADS'].values[0])\n",
    "\n",
    "feature_table['HAS'] = f_HAS\n",
    "feature_table['HDS'] = f_HDS\n",
    "feature_table['AAS'] = f_AAS\n",
    "feature_table['ADS'] = f_ADS\n",
    "feature_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the features to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_table.to_csv(loc + \"Feature_table.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to transform FTR into numeric data type\n",
    "def transformResult(row):\n",
    "    if(row.FTR == 'H'):\n",
    "        return 1\n",
    "    elif(row.FTR == 'A'):\n",
    "        return -1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.681431</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>0.851789</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.613288</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.022147</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6077</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>H</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.885860</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.783646</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>H</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.919932</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.851789</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6079</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Man United</td>\n",
       "      <td>H</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>1.481481</td>\n",
       "      <td>0.647359</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HomeTeam     AwayTeam FTR  HST  AST  HC  AC       HAS       HDS  \\\n",
       "6075       Man City      Chelsea   A    1    3   8   7  0.681431  1.530864   \n",
       "6076  Middlesbrough     West Ham   H    7    5   5   6  0.613288  1.135802   \n",
       "6077      Newcastle  Aston Villa   H    5    1   5   1  0.885860  0.839506   \n",
       "6078    Southampton      Arsenal   H    7    5   7   5  0.919932  1.086420   \n",
       "6079      Tottenham   Man United   H    3    6   3   6  1.056218  0.790123   \n",
       "\n",
       "           AAS       ADS  Result  \n",
       "6075  1.185185  0.851789      -1  \n",
       "6076  1.037037  1.022147       1  \n",
       "6077  0.938272  0.783646       1  \n",
       "6078  0.888889  0.851789       1  \n",
       "6079  1.481481  0.647359       1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_table[\"Result\"] = feature_table.apply(lambda row: transformResult(row),axis=1)\n",
    "feature_table.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating two sets of training sets one with only has attacking and defensive strength and other set has attacking and defensive strength but also shots and corners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>0.681431</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>0.613288</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.022147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6077</th>\n",
       "      <td>0.885860</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.783646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>0.919932</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.851789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6079</th>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>1.481481</td>\n",
       "      <td>0.647359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HAS       HDS       AAS       ADS\n",
       "6075  0.681431  1.530864  1.185185  0.851789\n",
       "6076  0.613288  1.135802  1.037037  1.022147\n",
       "6077  0.885860  0.839506  0.938272  0.783646\n",
       "6078  0.919932  1.086420  0.888889  0.851789\n",
       "6079  1.056218  0.790123  1.481481  0.647359"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>0.681431</td>\n",
       "      <td>1.530864</td>\n",
       "      <td>1.185185</td>\n",
       "      <td>0.851789</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>0.613288</td>\n",
       "      <td>1.135802</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.022147</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6077</th>\n",
       "      <td>0.885860</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.783646</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>0.919932</td>\n",
       "      <td>1.086420</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.851789</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6079</th>\n",
       "      <td>1.056218</td>\n",
       "      <td>0.790123</td>\n",
       "      <td>1.481481</td>\n",
       "      <td>0.647359</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HAS       HDS       AAS       ADS  HST  AST  HC  AC\n",
       "6075  0.681431  1.530864  1.185185  0.851789    1    3   8   7\n",
       "6076  0.613288  1.135802  1.037037  1.022147    7    5   5   6\n",
       "6077  0.885860  0.839506  0.938272  0.783646    5    1   5   1\n",
       "6078  0.919932  1.086420  0.888889  0.851789    7    5   7   5\n",
       "6079  1.056218  0.790123  1.481481  0.647359    3    6   3   6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       1\n",
      "2      -1\n",
      "3       0\n",
      "4       1\n",
      "       ..\n",
      "6075   -1\n",
      "6076    1\n",
      "6077    1\n",
      "6078    1\n",
      "6079    1\n",
      "Name: Result, Length: 6080, dtype: int64\n",
      "(6080, 4)\n"
     ]
    }
   ],
   "source": [
    "#Two sets of training sets - adding features.\n",
    "X_train = feature_table[['HAS','HDS','AAS','ADS']]\n",
    "X_train_2 = feature_table[['HAS','HDS','AAS','ADS','HST','AST','HC','AC']]\n",
    "y_train = feature_table['Result']\n",
    "display(X_train.tail())\n",
    "display(X_train_2.tail())\n",
    "print(y_train)\n",
    "print(X_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = feature_table[['HAS','HDS','AAS','ADS']]\n",
    "X_2 = feature_table[['HAS','HDS','AAS','ADS','HST','AST','HC','AC']]\n",
    "y= feature_table['Result']\n",
    "\n",
    "x_train_1,x_test_1,y_train_1,y_test_1=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "x_train_2,x_test_2,y_train_2,y_test_2=train_test_split(X_2,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3322    1\n",
       "296     1\n",
       "346     1\n",
       "3007    1\n",
       "544     1\n",
       "       ..\n",
       "4639    1\n",
       "2446    0\n",
       "3352    0\n",
       "4795   -1\n",
       "3469    1\n",
       "Name: Result, Length: 1216, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGB ALgorithim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:27:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "xgb_model=xgb.XGBClassifier(random_state=0)\n",
    "xgb_model.fit(x_train_1,y_train_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:27:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=42, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model_2=xgb.XGBClassifier(random_state=42)\n",
    "xgb_model_2.fit(x_train_2,y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[309,   0,   0],\n",
       "       [  0, 326,   0],\n",
       "       [  0,   0, 581]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp=xgb_model_2.predict(x_test_2)\n",
    "yp\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test_2,yp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model_2.score(x_test_2,y_test_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.\n",
    "\n",
    "The advantages of support vector machines are:\n",
    "\n",
    "Effective in high dimensional spaces.\n",
    "Still effective in cases where number of dimensions is greater than the number of samples.\n",
    "Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.\n",
    "Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.\n",
    "The disadvantages of support vector machines include:\n",
    "\n",
    "If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial.\n",
    "SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).  Like all regression analyses, the logistic regression is a predictive analysis.  Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression major assumptions:\n",
    "\n",
    "The dependent variable should be dichotomous in nature (e.g., presence vs. absent).\n",
    "There should be no outliers in the data, which can be assessed by converting the continuous predictors to standardized scores, and removing values below -3.29 or greater than 3.29.\n",
    "There should be no high correlations (multicollinearity) among the predictors.  This can be assessed by a correlation matrix among the predictors. Tabachnick and Fidell (2013) suggest that as long correlation coefficients among independent variables are less than 0.90 the assumption is met.\n",
    "At the center of the logistic regression analysis is the task estimating the log odds of an event.  Mathematically, logistic regression estimates a multiple linear regression function defined as:\n",
    "\n",
    "logit(p) \n",
    "\n",
    "for i = 1…n ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![\"Naive Bayes\"](files/MNB.png \"Multinomial Naive Bayes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These three methods will be implemented in the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "from sklearn.svm import SVC\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-86e0efbe7756>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mnbb_scores_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1e-1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mclf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMultinomialNB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mclf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "nbb_scores = []\n",
    "nbb_scores_2 = []\n",
    "\n",
    "for i in range(1e-1000,1000,50):\n",
    "    clf1 = MultinomialNB(alpha=i)\n",
    "    clf1.fit(X_train,y_train)\n",
    "    clf1.fit(X_train_2,y_train)\n",
    "    scores = cross_val_score(clf1, X_train, y_train, cv=10)\n",
    "    scores_2 = cross_val_score(clf1, X_train_2, y_train, cv=10)\n",
    "    print (\"NB alpha \",i,\" \", scores.mean(), \" : \", scores_2.mean())\n",
    "    \n",
    "    nbb_scores.append(scores.mean())  \n",
    "    nbb_scores_2.append(scores_2.mean())\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5485197368421053"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model_1=SVC(kernel = 'linear', C=1.5, probability=True,gamma=\"auto\")\n",
    "svc_model_1.fit(x_train_1,y_train_1)\n",
    "y_preds=svc_model_1.predict(x_test_1)\n",
    "y_preds\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test_1,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6060855263157895"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model_2=SVC(kernel = 'linear', C=1.5, probability=True,gamma=\"auto\")\n",
    "svc_model_2.fit(x_train_2,y_train_2)\n",
    "y_preds_2=svc_model_2.predict(x_test_2)\n",
    "y_preds_2\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test_2,y_preds_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes  :  0.48421052631578937  :  0.5643092105263159\n",
      "SVM  :  0.5398026315789475  :  0.6118421052631579\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log regres  :  0.5467105263157894  :  0.5909539473684211\n",
      "[01:34:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[01:34:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:34:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:34:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:34:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:34:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:34:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:35:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "xgb classifier  :  1.0  :  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "m_nb=MultinomialNB(alpha=10)\n",
    "clf = [m_nb, SVC(kernel = 'linear', C=1.5, probability=True), LogisticRegression(),xgb.XGBClassifier(random_state=0)]\n",
    "\n",
    "labels = ['Naive Bayes', 'SVM', 'Log regres','xgb classifier']\n",
    "\n",
    "mean_scores = []\n",
    "mean_scores_2 = []\n",
    "cms = []\n",
    "\n",
    "for i in range(0,4):\n",
    "\n",
    "    clf[i].fit(X_train,y_train)\n",
    "    clf[i].fit(X_train_2,y_train)\n",
    "\n",
    "    scores = cross_val_score(clf[i], X_train, y_train, cv=10)\n",
    "    scores_2 = cross_val_score(clf[i], X_train_2, y_train, cv=10)\n",
    "    print (labels[i],\" : \", scores.mean(), \" : \", scores_2.mean())\n",
    "    \n",
    "    mean_scores.append(scores.mean())  \n",
    "    mean_scores_2.append(scores_2.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize = (12,10))\n",
    "\n",
    "ax = fig.add_subplot(2,2,1)\n",
    "\n",
    "ax.bar(range(0,4),mean_scores,align='center');\n",
    "ax.set_xticks(range(4));\n",
    "ax.set_xticks(range(4));\n",
    "ax.set_ylim(0,1);\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.set_title(\"Training Set 1 - shots,corners not included\")\n",
    "ax.set_xticklabels(labels);\n",
    "\n",
    "ax2 = fig.add_subplot(2,2,2);\n",
    "ax2.bar(range(0,4),mean_scores_2,align='center');\n",
    "ax2.set_xticks(range(4))\n",
    "ax2.set_ylabel(\"Accuracy\")\n",
    "ax2.set_ylim(0,1);\n",
    "ax2.set_title(\"Training Set 2 - shots,corners included\")\n",
    "ax2.set_xticklabels(labels);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We normalize the features to check if it improves result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize\n",
    "X_train = normalize(X_train)\n",
    "X_train_2 = normalize(X_train_2)\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "\n",
    "display(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(X_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbb_scores = []\n",
    "nbb_scores_2 = []\n",
    "\n",
    "for i in range(-100,1000,50):\n",
    "    clf1 = MultinomialNB(alpha=i)\n",
    "    clf1.fit(X_train,y_train)\n",
    "    clf1.fit(X_train_2,y_train)\n",
    "    scores = cross_val_score(clf1, X_train, y_train, cv=10)\n",
    "    scores_2 = cross_val_score(clf1, X_train_2, y_train, cv=10)\n",
    "    print (\"NB alpha \",i,\" \", scores.mean(), \" : \", scores_2.mean())\n",
    "    \n",
    "    nbb_scores.append(scores.mean())  \n",
    "    nbb_scores_2.append(scores_2.mean())\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#With Normalization\n",
    "clf = [MultinomialNB(alpha=10), SVC(kernel = 'linear', C=1.5, probability=True), LogisticRegression(),xgb.XGBClassifier(random_state=0)]\n",
    "\n",
    "labels = [ 'Naive Bayes', 'SVM', 'Log regres',\"xgb classifer\"]\n",
    "\n",
    "mean_scores = []\n",
    "mean_scores_2 = []\n",
    "cms = []\n",
    "\n",
    "for i in range(0,4):\n",
    "\n",
    "    clf[i].fit(X_train,y_train)\n",
    "    clf[i].fit(X_train_2,y_train)\n",
    "\n",
    "    scores = cross_val_score(clf[i], X_train, y_train, cv=10)\n",
    "    scores_2 = cross_val_score(clf[i], X_train_2, y_train, cv=10)\n",
    "    print (labels[i],\" : \", scores.mean(), \" : \", scores_2.mean())\n",
    "    \n",
    "    mean_scores.append(scores.mean())  \n",
    "    mean_scores_2.append(scores_2.mean())\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize = (12,10))\n",
    "\n",
    "ax = fig.add_subplot(2,2,1)\n",
    "\n",
    "ax.bar(range(0,4),mean_scores,align='center');\n",
    "ax.set_xticks(range(4));\n",
    "ax.set_xticks(range(4));\n",
    "ax.set_ylim(0,1);\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.set_title(\"Training Set 1(Normalized)-shots,corners not included\")\n",
    "ax.set_xticklabels(labels);\n",
    "\n",
    "ax2 = fig.add_subplot(2,2,2);\n",
    "ax2.bar(range(0,4),mean_scores_2,align='center');\n",
    "ax2.set_xticks(range(4))\n",
    "ax2.set_ylabel(\"Accuracy\")\n",
    "ax2.set_ylim(0,1);\n",
    "ax2.set_title(\"Training Set 2(Normalized) - shots,corners included\")\n",
    "ax2.set_xticklabels(labels);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can see the performance of Naive Bayes and SVM reduces after normalization whereas the performance of Logistics Regression increases after normalization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![\"Prediction\"](files/Prediction.png \"Prediction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that when we include corners and shots in the feature we get a better output than without them. Support Vector Machine gives the best accuracy score without normalization.But after normalization there was an improvement in the Logistic Regression output. The betting accuracy of bookies was around 53% while the model created using SVM gave an accuracy of 61.29%. The accuracy can further increase if we could add more features like team form or player form. We could also add the twitter analysis for prediction. We could implement different model like xgboost with the same features which could improve the accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1] http://www.english-online.at/sports/soccer/european-football.htm\n",
    "\n",
    "[2] http://www.bbc.com/sport/football/24354124\n",
    "\n",
    "[3] https://www.kaggle.com/hugomathien/soccer\n",
    "\n",
    "[4] https://www.pinnacle.com/en/betting-articles/Soccer/Mark-Lawrenson-vs-Pinnacle-Sports/VGJ296E4BSYNURUB\n",
    "\n",
    "[5] http://scikit-learn.org/stable/modules/svm.html\n",
    "\n",
    "[6] http://www.statisticssolutions.com/what-is-logistic-regression/\n",
    "\n",
    "[7] http://scikit-learn.org/stable/modules/naive_bayes.html\n",
    "\n",
    "[8] https://github.com/llSourcell/Predicting_Winning_Teams/blob/master/Prediction.ipynb\n",
    "\n",
    "[9] https://www.kaggle.com/yonilev/the-most-predictable-league\n",
    "\n",
    "[10] https://arxiv.org/ftp/arxiv/papers/1411/1411.1243.pdf\n",
    "\n",
    "[11] https://github.com/llSourcell/Predicting_Winning_Teams/blob/master/Prediction.ipynb"
   ]
  }
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