Predicting Fifa 2018.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.ticker as plticker\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data \n",
    "world_cup = pd.read_csv('datasets/World Cup 2018 Dataset.csv')\n",
    "results = pd.read_csv('datasets/results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Team</th>\n",
       "      <th>Group</th>\n",
       "      <th>Previous \n",
       "appearances</th>\n",
       "      <th>Previous \n",
       "titles</th>\n",
       "      <th>Previous\n",
       " finals</th>\n",
       "      <th>Previous\n",
       " semifinals</th>\n",
       "      <th>Current \n",
       "FIFA rank</th>\n",
       "      <th>First match \n",
       "against</th>\n",
       "      <th>Match index</th>\n",
       "      <th>history with \n",
       "first opponent\n",
       " W-L</th>\n",
       "      <th>history with\n",
       " first opponent\n",
       " goals</th>\n",
       "      <th>Second match\n",
       " against</th>\n",
       "      <th>Match index.1</th>\n",
       "      <th>history with\n",
       " second opponent\n",
       " W-L</th>\n",
       "      <th>history with\n",
       " second opponent\n",
       " goals</th>\n",
       "      <th>Third match\n",
       " against</th>\n",
       "      <th>Match index.2</th>\n",
       "      <th>history with\n",
       " third opponent\n",
       " W-L</th>\n",
       "      <th>history with\n",
       " third opponent\n",
       " goals</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Russia</td>\n",
       "      <td>A</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>A</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>A</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Porugal</td>\n",
       "      <td>B</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>Iran</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Team Group  Previous \\nappearances  Previous \\ntitles  \\\n",
       "0        Russia     A                    10.0                0.0   \n",
       "1  Saudi Arabia     A                     4.0                0.0   \n",
       "2         Egypt     A                     2.0                0.0   \n",
       "3       Uruguay     A                    12.0                2.0   \n",
       "4       Porugal     B                     6.0                0.0   \n",
       "\n",
       "   Previous\\n finals  Previous\\n semifinals  Current \\nFIFA rank  \\\n",
       "0                0.0                    1.0                 65.0   \n",
       "1                0.0                    0.0                 63.0   \n",
       "2                0.0                    0.0                 31.0   \n",
       "3                2.0                    5.0                 21.0   \n",
       "4                0.0                    2.0                  3.0   \n",
       "\n",
       "  First match \\nagainst  Match index  history with \\nfirst opponent\\n W-L  \\\n",
       "0          Saudi Arabia          1.0                                 -1.0   \n",
       "1                Russia          1.0                                  1.0   \n",
       "2               Uruguay          2.0                                 -1.0   \n",
       "3                 Egypt          2.0                                  1.0   \n",
       "4                 Spain          3.0                                -12.0   \n",
       "\n",
       "   history with\\n first opponent\\n goals Second match\\n against  \\\n",
       "0                                   -2.0                  Egypt   \n",
       "1                                    2.0                Uruguay   \n",
       "2                                   -2.0                 Russia   \n",
       "3                                    2.0           Saudi Arabia   \n",
       "4                                  -31.0                Morocco   \n",
       "\n",
       "   Match index.1  history with\\n second opponent\\n W-L  \\\n",
       "0           17.0                                   NaN   \n",
       "1           18.0                                   1.0   \n",
       "2           17.0                                   NaN   \n",
       "3           18.0                                  -1.0   \n",
       "4           19.0                                  -1.0   \n",
       "\n",
       "   history with\\n second opponent\\n goals Third match\\n against  \\\n",
       "0                                     NaN               Uruguay   \n",
       "1                                     1.0                 Egypt   \n",
       "2                                     NaN          Saudi Arabia   \n",
       "3                                    -1.0                Russia   \n",
       "4                                    -2.0                  Iran   \n",
       "\n",
       "   Match index.2  history with\\n third opponent\\n W-L  \\\n",
       "0           33.0                                  0.0   \n",
       "1           34.0                                 -5.0   \n",
       "2           34.0                                  5.0   \n",
       "3           33.0                                  0.0   \n",
       "4           35.0                                  2.0   \n",
       "\n",
       "   history with\\n third opponent\\n goals  Unnamed: 19  \n",
       "0                                    0.0          NaN  \n",
       "1                                   -5.0          NaN  \n",
       "2                                    5.0          NaN  \n",
       "3                                    0.0          NaN  \n",
       "4                                    5.0          NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_cup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>away_score</th>\n",
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       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
       "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
       "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
       "\n",
       "    country  \n",
       "0  Scotland  \n",
       "1   England  \n",
       "2  Scotland  \n",
       "3   England  \n",
       "4  Scotland  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
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       "      <td>Friendly</td>\n",
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       "      <td>England</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Scotland</td>\n",
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       "      <td>Friendly</td>\n",
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       "      <td>Scotland</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
       "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
       "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
       "\n",
       "    country winning_team  goal_difference  \n",
       "0  Scotland         Draw                0  \n",
       "1   England      England                2  \n",
       "2  Scotland     Scotland                1  \n",
       "3   England         Draw                0  \n",
       "4  Scotland     Scotland                3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding goal difference and establishing who is the winner \n",
    "winner = []\n",
    "for i in range (len(results['home_team'])):\n",
    "    if results ['home_score'][i] > results['away_score'][i]:\n",
    "        winner.append(results['home_team'][i])\n",
    "    elif results['home_score'][i] < results ['away_score'][i]:\n",
    "        winner.append(results['away_team'][i])\n",
    "    else:\n",
    "        winner.append('Draw')\n",
    "results['winning_team'] = winner\n",
    "\n",
    "#adding goal difference column\n",
    "results['goal_difference'] = np.absolute(results['home_score'] - results['away_score'])\n",
    "\n",
    "results.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>1950-05-28</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Accra</td>\n",
       "      <td>Gold Coast</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>1951-10-20</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492</th>\n",
       "      <td>1953-10-11</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Accra</td>\n",
       "      <td>Gold Coast</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>1954-10-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     home_team away_team  home_score  away_score tournament  \\\n",
       "2977  1949-10-08  Sierra Leone   Nigeria           0           2   Friendly   \n",
       "3050  1950-05-28         Ghana   Nigeria           1           0   Friendly   \n",
       "3219  1951-10-20       Nigeria     Ghana           5           0   Friendly   \n",
       "3492  1953-10-11         Ghana   Nigeria           1           0   Friendly   \n",
       "3654  1954-10-30       Nigeria     Ghana           3           0   Friendly   \n",
       "\n",
       "          city       country winning_team  goal_difference  \n",
       "2977  Freetown  Sierra Leone      Nigeria                2  \n",
       "3050     Accra    Gold Coast        Ghana                1  \n",
       "3219     Lagos       Nigeria      Nigeria                5  \n",
       "3492     Accra    Gold Coast        Ghana                1  \n",
       "3654     Lagos       Nigeria      Nigeria                3  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets work with a subset of the data one that includes games played by Nigeria in a Nigeria dataframe\n",
    "df = results[(results['home_team'] == 'Nigeria') | (results['away_team'] == 'Nigeria')]\n",
    "nigeria = df.iloc[:]\n",
    "nigeria.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date               542\n",
       "home_team          542\n",
       "away_team          542\n",
       "home_score         542\n",
       "away_score         542\n",
       "tournament         542\n",
       "city               542\n",
       "country            542\n",
       "winning_team       542\n",
       "goal_difference    542\n",
       "match_year         542\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a column for year and the first world cup was held in 1930\n",
    "year = []\n",
    "for row in nigeria['date']:\n",
    "    year.append(int(row[:4]))\n",
    "nigeria ['match_year']= year\n",
    "nigeria_1930 = nigeria[nigeria.match_year >= 1930]\n",
    "nigeria_1930.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108e8ccf8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e8c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#what is the common game outcome for nigeria visualisation\n",
    "wins = []\n",
    "for row in nigeria_1930['winning_team']:\n",
    "    if row != 'Nigeria' and row != 'Draw':\n",
    "        wins.append('Loss')\n",
    "    else:\n",
    "        wins.append(row)\n",
    "winsdf= pd.DataFrame(wins, columns=[ 'Nigeria_Results'])\n",
    "\n",
    "#plotting\n",
    "fig, ax = plt.subplots(1)\n",
    "fig.set_size_inches(10.7, 6.27)\n",
    "sns.set(style='darkgrid')\n",
    "sns.countplot(x='Nigeria_Results', data=winsdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wins is a good metric to analyze and predict outcomes of matches in the tournament \n",
    "#tournament and venue won't add much to our predictions\n",
    "#historical match records will be used \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date               20565\n",
       "home_team          20565\n",
       "away_team          20565\n",
       "home_score         20565\n",
       "away_score         20565\n",
       "tournament         20565\n",
       "city               20565\n",
       "country            20565\n",
       "winning_team       20565\n",
       "goal_difference    20565\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#narrowing to team patcipating in the world cup\n",
    "worldcup_teams = ['Australia', ' Iran', 'Japan', 'Korea Republic', \n",
    "            'Saudi Arabia', 'Egypt', 'Morocco', 'Nigeria', \n",
    "            'Senegal', 'Tunisia', 'Costa Rica', 'Mexico', \n",
    "            'Panama', 'Argentina', 'Brazil', 'Colombia', \n",
    "            'Peru', 'Uruguay', 'Belgium', 'Croatia', \n",
    "            'Denmark', 'England', 'France', 'Germany', \n",
    "            'Iceland', 'Poland', 'Portugal', 'Russia', \n",
    "            'Serbia', 'Spain', 'Sweden', 'Switzerland']\n",
    "df_teams_home = results[results['home_team'].isin(worldcup_teams)]\n",
    "df_teams_away = results[results['away_team'].isin(worldcup_teams)]\n",
    "df_teams = pd.concat((df_teams_home, df_teams_away))\n",
    "df_teams.drop_duplicates()\n",
    "df_teams.count()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>winning_team</th>\n",
       "      <th>goal_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1877-03-03</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1879-01-18</td>\n",
       "      <td>England</td>\n",
       "      <td>Wales</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1879-04-05</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date home_team away_team  home_score  away_score tournament    city  \\\n",
       "1   1873-03-08   England  Scotland           4           2   Friendly  London   \n",
       "3   1875-03-06   England  Scotland           2           2   Friendly  London   \n",
       "6   1877-03-03   England  Scotland           1           3   Friendly  London   \n",
       "10  1879-01-18   England     Wales           2           1   Friendly  London   \n",
       "11  1879-04-05   England  Scotland           5           4   Friendly  London   \n",
       "\n",
       "    country winning_team  goal_difference  \n",
       "1   England      England                2  \n",
       "3   England         Draw                0  \n",
       "6   England     Scotland                2  \n",
       "10  England      England                1  \n",
       "11  England      England                1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_teams.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>winning_team</th>\n",
       "      <th>goal_difference</th>\n",
       "      <th>match_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1930-01-01</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>1930-01-12</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>1</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>1930-02-23</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Porto</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>2</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>1930-03-02</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Italy</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Frankfurt am Main</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>1930-03-23</td>\n",
       "      <td>France</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Colombes</td>\n",
       "      <td>France</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date home_team       away_team  home_score  away_score tournament  \\\n",
       "1230  1930-01-01     Spain  Czechoslovakia           1           0   Friendly   \n",
       "1231  1930-01-12  Portugal  Czechoslovakia           1           0   Friendly   \n",
       "1237  1930-02-23  Portugal          France           2           0   Friendly   \n",
       "1238  1930-03-02   Germany           Italy           0           2   Friendly   \n",
       "1240  1930-03-23    France     Switzerland           3           3   Friendly   \n",
       "\n",
       "                   city   country winning_team  goal_difference  match_year  \n",
       "1230          Barcelona     Spain        Spain                1        1930  \n",
       "1231             Lisbon  Portugal     Portugal                1        1930  \n",
       "1237              Porto  Portugal     Portugal                2        1930  \n",
       "1238  Frankfurt am Main   Germany        Italy                2        1930  \n",
       "1240           Colombes    France         Draw                0        1930  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create an year column to drop games before 1930\n",
    "year = []\n",
    "for row in df_teams['date']:\n",
    "    year.append(int(row[:4]))\n",
    "df_teams['match_year'] = year\n",
    "df_teams_1930 = df_teams[df_teams.match_year >= 1930]\n",
    "df_teams_1930.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>winning_team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>England</td>\n",
       "      <td>Wales</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   home_team away_team winning_team\n",
       "1    England  Scotland      England\n",
       "3    England  Scotland         Draw\n",
       "6    England  Scotland     Scotland\n",
       "10   England     Wales      England\n",
       "11   England  Scotland      England"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping columns that wll not affect matchoutcomes\n",
    "df_teams_1930 = df_teams.drop(['date', 'home_score', 'away_score', 'tournament', 'city', 'country', 'goal_difference', 'match_year'], axis=1)\n",
    "df_teams_1930.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>winning_team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>Wales</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  home_team away_team winning_team\n",
       "0   England  Scotland            2\n",
       "1   England  Scotland            1\n",
       "2   England  Scotland            0\n",
       "3   England     Wales            2\n",
       "4   England  Scotland            2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Building the model\n",
    "#the prediction label: The winning_team column will show \"2\" if the home team has won, \"1\" if it was a tie, and \"0\" if the away team has won.\n",
    "\n",
    "df_teams_1930 = df_teams_1930.reset_index(drop=True)\n",
    "df_teams_1930.loc[df_teams_1930.winning_team == df_teams_1930.home_team,'winning_team']=2\n",
    "df_teams_1930.loc[df_teams_1930.winning_team == 'Draw', 'winning_team']=1\n",
    "df_teams_1930.loc[df_teams_1930.winning_team == df_teams_1930.away_team, 'winning_team']=0\n",
    "\n",
    "df_teams_1930.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert home team and away team from categorical variables to continous inputs \n",
    "# Get dummy variables\n",
    "final = pd.get_dummies(df_teams_1930, prefix=['home_team', 'away_team'], columns=['home_team', 'away_team'])\n",
    "\n",
    "# Separate X and y sets\n",
    "X = final.drop(['winning_team'], axis=1)\n",
    "y = final[\"winning_team\"]\n",
    "y = y.astype('int')\n",
    "\n",
    "# Separate train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>away_team_Western Australia</th>\n",
       "      <th>away_team_Yemen</th>\n",
       "      <th>away_team_Yemen DPR</th>\n",
       "      <th>away_team_Yugoslavia</th>\n",
       "      <th>away_team_Zambia</th>\n",
       "      <th>away_team_Zimbabwe</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 417 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  winning_team  home_team_Afghanistan  home_team_Albania  home_team_Algeria  \\\n",
       "0            2                      0                  0                  0   \n",
       "1            1                      0                  0                  0   \n",
       "2            0                      0                  0                  0   \n",
       "3            2                      0                  0                  0   \n",
       "4            2                      0                  0                  0   \n",
       "\n",
       "   home_team_Andorra  home_team_Angola  home_team_Argentina  \\\n",
       "0                  0                 0                    0   \n",
       "1                  0                 0                    0   \n",
       "2                  0                 0                    0   \n",
       "3                  0                 0                    0   \n",
       "4                  0                 0                    0   \n",
       "\n",
       "   home_team_Armenia  home_team_Aruba  home_team_Australia  \\\n",
       "0                  0                0                    0   \n",
       "1                  0                0                    0   \n",
       "2                  0                0                    0   \n",
       "3                  0                0                    0   \n",
       "4                  0                0                    0   \n",
       "\n",
       "          ...          away_team_Venezuela  away_team_Vietnam  \\\n",
       "0         ...                            0                  0   \n",
       "1         ...                            0                  0   \n",
       "2         ...                            0                  0   \n",
       "3         ...                            0                  0   \n",
       "4         ...                            0                  0   \n",
       "\n",
       "   away_team_Vietnam Republic  away_team_Wales  away_team_Western Australia  \\\n",
       "0                           0                0                            0   \n",
       "1                           0                0                            0   \n",
       "2                           0                0                            0   \n",
       "3                           0                1                            0   \n",
       "4                           0                0                            0   \n",
       "\n",
       "   away_team_Yemen  away_team_Yemen DPR  away_team_Yugoslavia  \\\n",
       "0                0                    0                     0   \n",
       "1                0                    0                     0   \n",
       "2                0                    0                     0   \n",
       "3                0                    0                     0   \n",
       "4                0                    0                     0   \n",
       "\n",
       "   away_team_Zambia  away_team_Zimbabwe  \n",
       "0                 0                   0  \n",
       "1                 0                   0  \n",
       "2                 0                   0  \n",
       "3                 0                   0  \n",
       "4                 0                   0  \n",
       "\n",
       "[5 rows x 417 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set accuracy:  0.573\n",
      "Test set accuracy:  0.551\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "score = logreg.score(X_train, y_train)\n",
    "score2 = logreg.score(X_test, y_test)\n",
    "\n",
    "print(\"Training set accuracy: \", '%.3f'%(score))\n",
    "print(\"Test set accuracy: \", '%.3f'%(score2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding Fifa rankings\n",
    "#the team which is positioned higher on the FIFA Ranking will be considered \"favourite\" for the match\n",
    "#and therefore, will be positioned under the \"home_teams\" column\n",
    "#since there are no \"home\" or \"away\" teams in World Cup games. \n",
    "\n",
    "# Loading new datasets\n",
    "ranking = pd.read_csv('datasets/fifa_rankings.csv') \n",
    "fixtures = pd.read_csv('datasets/fixtures.csv')\n",
    "\n",
    "# List for storing the group stage games\n",
    "pred_set = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Round Number</th>\n",
       "      <th>first_position</th>\n",
       "      <th>second_position</th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Home Team</th>\n",
       "      <th>Away Team</th>\n",
       "      <th>Group</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27/06/2018 21:00</td>\n",
       "      <td>Nizhny Novgorod Stadium</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Group E</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28/06/2018 17:00</td>\n",
       "      <td>Volgograd Stadium</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Group H</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28/06/2018 17:00</td>\n",
       "      <td>Samara Stadium</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Group H</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28/06/2018 21:00</td>\n",
       "      <td>Saransk Stadium</td>\n",
       "      <td>Panama</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Group G</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28/06/2018 21:00</td>\n",
       "      <td>Kaliningrad Stadium</td>\n",
       "      <td>England</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Group G</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Round Number  first_position  second_position              Date  \\\n",
       "43            3             6.0             25.0  27/06/2018 21:00   \n",
       "44            3            60.0             10.0  28/06/2018 17:00   \n",
       "45            3            28.0             16.0  28/06/2018 17:00   \n",
       "46            3            55.0             14.0  28/06/2018 21:00   \n",
       "47            3            13.0              3.0  28/06/2018 21:00   \n",
       "\n",
       "                   Location    Home Team   Away Team    Group  Result  \n",
       "43  Nizhny Novgorod Stadium  Switzerland  Costa Rica  Group E     NaN  \n",
       "44        Volgograd Stadium        Japan      Poland  Group H     NaN  \n",
       "45           Samara Stadium      Senegal    Colombia  Group H     NaN  \n",
       "46          Saransk Stadium       Panama     Tunisia  Group G     NaN  \n",
       "47      Kaliningrad Stadium      England     Belgium  Group G     NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new columns with ranking position of each team\n",
    "fixtures.insert(1, 'first_position', fixtures['Home Team'].map(ranking.set_index('Team')['Position']))\n",
    "fixtures.insert(2, 'second_position', fixtures['Away Team'].map(ranking.set_index('Team')['Position']))\n",
    "\n",
    "# We only need the group stage games, so we have to slice the dataset\n",
    "fixtures = fixtures.iloc[:48, :]\n",
    "fixtures.tail()"
   ]
  },
  {
   "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>away_team</th>\n",
       "      <th>home_team</th>\n",
       "      <th>winning_team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Russia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>Iran</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>France</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      away_team home_team winning_team\n",
       "0  Saudi Arabia    Russia         None\n",
       "1         Egypt   Uruguay         None\n",
       "2       Morocco      Iran         None\n",
       "3         Spain  Portugal         None\n",
       "4     Australia    France         None"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loop to add teams to new prediction dataset based on the ranking position of each team\n",
    "for index, row in fixtures.iterrows():\n",
    "    if row['first_position'] < row['second_position']:\n",
    "        pred_set.append({'home_team': row['Home Team'], 'away_team': row['Away Team'], 'winning_team': None})\n",
    "    else:\n",
    "        pred_set.append({'home_team': row['Away Team'], 'away_team': row['Home Team'], 'winning_team': None})\n",
    "        \n",
    "pred_set = pd.DataFrame(pred_set)\n",
    "backup_pred_set = pred_set\n",
    "\n",
    "pred_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>home_team_Afghanistan</th>\n",
       "      <th>home_team_Albania</th>\n",
       "      <th>home_team_Algeria</th>\n",
       "      <th>home_team_Andorra</th>\n",
       "      <th>home_team_Angola</th>\n",
       "      <th>home_team_Argentina</th>\n",
       "      <th>home_team_Armenia</th>\n",
       "      <th>home_team_Aruba</th>\n",
       "      <th>home_team_Australia</th>\n",
       "      <th>home_team_Austria</th>\n",
       "      <th>...</th>\n",
       "      <th>away_team_Venezuela</th>\n",
       "      <th>away_team_Vietnam</th>\n",
       "      <th>away_team_Vietnam Republic</th>\n",
       "      <th>away_team_Wales</th>\n",
       "      <th>away_team_Western Australia</th>\n",
       "      <th>away_team_Yemen</th>\n",
       "      <th>away_team_Yemen DPR</th>\n",
       "      <th>away_team_Yugoslavia</th>\n",
       "      <th>away_team_Zambia</th>\n",
       "      <th>away_team_Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 416 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   home_team_Afghanistan  home_team_Albania  home_team_Algeria  \\\n",
       "0                      0                  0                  0   \n",
       "1                      0                  0                  0   \n",
       "2                      0                  0                  0   \n",
       "3                      0                  0                  0   \n",
       "4                      0                  0                  0   \n",
       "\n",
       "   home_team_Andorra  home_team_Angola  home_team_Argentina  \\\n",
       "0                  0                 0                    0   \n",
       "1                  0                 0                    0   \n",
       "2                  0                 0                    0   \n",
       "3                  0                 0                    0   \n",
       "4                  0                 0                    0   \n",
       "\n",
       "   home_team_Armenia  home_team_Aruba  home_team_Australia  home_team_Austria  \\\n",
       "0                  0                0                    0                  0   \n",
       "1                  0                0                    0                  0   \n",
       "2                  0                0                    0                  0   \n",
       "3                  0                0                    0                  0   \n",
       "4                  0                0                    0                  0   \n",
       "\n",
       "          ...          away_team_Venezuela  away_team_Vietnam  \\\n",
       "0         ...                            0                  0   \n",
       "1         ...                            0                  0   \n",
       "2         ...                            0                  0   \n",
       "3         ...                            0                  0   \n",
       "4         ...                            0                  0   \n",
       "\n",
       "   away_team_Vietnam Republic  away_team_Wales  away_team_Western Australia  \\\n",
       "0                           0                0                            0   \n",
       "1                           0                0                            0   \n",
       "2                           0                0                            0   \n",
       "3                           0                0                            0   \n",
       "4                           0                0                            0   \n",
       "\n",
       "   away_team_Yemen  away_team_Yemen DPR  away_team_Yugoslavia  \\\n",
       "0                0                    0                     0   \n",
       "1                0                    0                     0   \n",
       "2                0                    0                     0   \n",
       "3                0                    0                     0   \n",
       "4                0                    0                     0   \n",
       "\n",
       "   away_team_Zambia  away_team_Zimbabwe  \n",
       "0                 0                   0  \n",
       "1                 0                   0  \n",
       "2                 0                   0  \n",
       "3                 0                   0  \n",
       "4                 0                   0  \n",
       "\n",
       "[5 rows x 416 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get dummy variables and drop winning_team column\n",
    "pred_set = pd.get_dummies(pred_set, prefix=['home_team', 'away_team'], columns=['home_team', 'away_team'])\n",
    "\n",
    "# Add missing columns compared to the model's training dataset\n",
    "missing_cols = set(final.columns) - set(pred_set.columns)\n",
    "for c in missing_cols:\n",
    "    pred_set[c] = 0\n",
    "pred_set = pred_set[final.columns]\n",
    "\n",
    "# Remove winning team column\n",
    "pred_set = pred_set.drop(['winning_team'], axis=1)\n",
    "\n",
    "pred_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Russia and Saudi Arabia\n",
      "Winner: Russia\n",
      "Probability of Russia winning:  0.664\n",
      "Probability of Draw:  0.220\n",
      "Probability of Saudi Arabia winning:  0.115\n",
      "\n",
      "Uruguay and Egypt\n",
      "Winner: Uruguay\n",
      "Probability of Uruguay winning:  0.590\n",
      "Probability of Draw:  0.341\n",
      "Probability of Egypt winning:  0.069\n",
      "\n",
      "Iran and Morocco\n",
      "Draw\n",
      "Probability of Iran winning:  0.236\n",
      "Probability of Draw:  0.429\n",
      "Probability of Morocco winning:  0.335\n",
      "\n",
      "Portugal and Spain\n",
      "Draw\n",
      "Probability of Portugal winning:  0.297\n",
      "Probability of Draw:  0.351\n",
      "Probability of Spain winning:  0.351\n",
      "\n",
      "France and Australia\n",
      "Winner: France\n",
      "Probability of France winning:  0.628\n",
      "Probability of Draw:  0.222\n",
      "Probability of Australia winning:  0.151\n",
      "\n",
      "Argentina and Iceland\n",
      "Winner: Argentina\n",
      "Probability of Argentina winning:  0.822\n",
      "Probability of Draw:  0.144\n",
      "Probability of Iceland winning:  0.034\n",
      "\n",
      "Peru and Denmark\n",
      "Winner: Peru\n",
      "Probability of Peru winning:  0.440\n",
      "Probability of Draw:  0.166\n",
      "Probability of Denmark winning:  0.394\n",
      "\n",
      "Croatia and Nigeria\n",
      "Winner: Croatia\n",
      "Probability of Croatia winning:  0.579\n",
      "Probability of Draw:  0.280\n",
      "Probability of Nigeria winning:  0.141\n",
      "\n",
      "Costa Rica and Serbia\n",
      "Winner: Costa Rica\n",
      "Probability of Costa Rica winning:  0.338\n",
      "Probability of Draw:  0.324\n",
      "Probability of Serbia winning:  0.337\n",
      "\n",
      "Germany and Mexico\n",
      "Winner: Germany\n",
      "Probability of Germany winning:  0.589\n",
      "Probability of Draw:  0.258\n",
      "Probability of Mexico winning:  0.153\n",
      "\n",
      "Brazil and Switzerland\n",
      "Winner: Brazil\n",
      "Probability of Brazil winning:  0.756\n",
      "Probability of Draw:  0.157\n",
      "Probability of Switzerland winning:  0.087\n",
      "\n",
      "Sweden and Korea Republic\n",
      "Winner: Sweden\n",
      "Probability of Sweden winning:  0.523\n",
      "Probability of Draw:  0.312\n",
      "Probability of Korea Republic winning:  0.166\n",
      "\n",
      "Belgium and Panama\n",
      "Winner: Belgium\n",
      "Probability of Belgium winning:  0.771\n",
      "Probability of Draw:  0.145\n",
      "Probability of Panama winning:  0.084\n",
      "\n",
      "England and Tunisia\n",
      "Winner: England\n",
      "Probability of England winning:  0.665\n",
      "Probability of Draw:  0.277\n",
      "Probability of Tunisia winning:  0.058\n",
      "\n",
      "Colombia and Japan\n",
      "Winner: Colombia\n",
      "Probability of Colombia winning:  0.513\n",
      "Probability of Draw:  0.204\n",
      "Probability of Japan winning:  0.284\n",
      "\n",
      "Poland and Senegal\n",
      "Winner: Poland\n",
      "Probability of Poland winning:  0.586\n",
      "Probability of Draw:  0.248\n",
      "Probability of Senegal winning:  0.166\n",
      "\n",
      "Egypt and Russia\n",
      "Winner: Russia\n",
      "Probability of Egypt winning:  0.216\n",
      "Probability of Draw:  0.289\n",
      "Probability of Russia winning:  0.495\n",
      "\n",
      "Portugal and Morocco\n",
      "Winner: Portugal\n",
      "Probability of Portugal winning:  0.512\n",
      "Probability of Draw:  0.364\n",
      "Probability of Morocco winning:  0.124\n",
      "\n",
      "Uruguay and Saudi Arabia\n",
      "Winner: Uruguay\n",
      "Probability of Uruguay winning:  0.670\n",
      "Probability of Draw:  0.252\n",
      "Probability of Saudi Arabia winning:  0.078\n",
      "\n",
      "Spain and Iran\n",
      "Winner: Spain\n",
      "Probability of Spain winning:  0.708\n",
      "Probability of Draw:  0.242\n",
      "Probability of Iran winning:  0.050\n",
      "\n",
      "Denmark and Australia\n",
      "Winner: Denmark\n",
      "Probability of Denmark winning:  0.560\n",
      "Probability of Draw:  0.225\n",
      "Probability of Australia winning:  0.215\n",
      "\n",
      "France and Peru\n",
      "Winner: France\n",
      "Probability of France winning:  0.633\n",
      "Probability of Draw:  0.222\n",
      "Probability of Peru winning:  0.145\n",
      "\n",
      "Argentina and Croatia\n",
      "Winner: Argentina\n",
      "Probability of Argentina winning:  0.596\n",
      "Probability of Draw:  0.259\n",
      "Probability of Croatia winning:  0.145\n",
      "\n",
      "Brazil and Costa Rica\n",
      "Winner: Brazil\n",
      "Probability of Brazil winning:  0.792\n",
      "Probability of Draw:  0.160\n",
      "Probability of Costa Rica winning:  0.048\n",
      "\n",
      "Iceland and Nigeria\n",
      "Winner: Nigeria\n",
      "Probability of Iceland winning:  0.280\n",
      "Probability of Draw:  0.285\n",
      "Probability of Nigeria winning:  0.435\n",
      "\n",
      "Switzerland and Serbia\n",
      "Winner: Switzerland\n",
      "Probability of Switzerland winning:  0.438\n",
      "Probability of Draw:  0.231\n",
      "Probability of Serbia winning:  0.331\n",
      "\n",
      "Belgium and Tunisia\n",
      "Winner: Belgium\n",
      "Probability of Belgium winning:  0.622\n",
      "Probability of Draw:  0.248\n",
      "Probability of Tunisia winning:  0.130\n",
      "\n",
      "Mexico and Korea Republic\n",
      "Winner: Mexico\n",
      "Probability of Mexico winning:  0.510\n",
      "Probability of Draw:  0.313\n",
      "Probability of Korea Republic winning:  0.177\n",
      "\n",
      "Germany and Sweden\n",
      "Winner: Germany\n",
      "Probability of Germany winning:  0.585\n",
      "Probability of Draw:  0.223\n",
      "Probability of Sweden winning:  0.191\n",
      "\n",
      "England and Panama\n",
      "Winner: England\n",
      "Probability of England winning:  0.792\n",
      "Probability of Draw:  0.170\n",
      "Probability of Panama winning:  0.037\n",
      "\n",
      "Senegal and Japan\n",
      "Winner: Japan\n",
      "Probability of Senegal winning:  0.354\n",
      "Probability of Draw:  0.290\n",
      "Probability of Japan winning:  0.356\n",
      "\n",
      "Poland and Colombia\n",
      "Draw\n",
      "Probability of Poland winning:  0.367\n",
      "Probability of Draw:  0.400\n",
      "Probability of Colombia winning:  0.233\n",
      "\n",
      "Uruguay and Russia\n",
      "Winner: Uruguay\n",
      "Probability of Uruguay winning:  0.388\n",
      "Probability of Draw:  0.385\n",
      "Probability of Russia winning:  0.227\n",
      "\n",
      "Egypt and Saudi Arabia\n",
      "Winner: Egypt\n",
      "Probability of Egypt winning:  0.548\n",
      "Probability of Draw:  0.205\n",
      "Probability of Saudi Arabia winning:  0.246\n",
      "\n",
      "Portugal and Iran\n",
      "Winner: Portugal\n",
      "Probability of Portugal winning:  0.552\n",
      "Probability of Draw:  0.357\n",
      "Probability of Iran winning:  0.091\n",
      "\n",
      "Spain and Morocco\n",
      "Winner: Spain\n",
      "Probability of Spain winning:  0.682\n",
      "Probability of Draw:  0.248\n",
      "Probability of Morocco winning:  0.070\n",
      "\n",
      "France and Denmark\n",
      "Winner: France\n",
      "Probability of France winning:  0.623\n",
      "Probability of Draw:  0.160\n",
      "Probability of Denmark winning:  0.218\n",
      "\n",
      "Peru and Australia\n",
      "Winner: Peru\n",
      "Probability of Peru winning:  0.461\n",
      "Probability of Draw:  0.237\n",
      "Probability of Australia winning:  0.302\n",
      "\n",
      "Argentina and Nigeria\n",
      "Winner: Argentina\n",
      "Probability of Argentina winning:  0.717\n",
      "Probability of Draw:  0.220\n",
      "Probability of Nigeria winning:  0.063\n",
      "\n",
      "Croatia and Iceland\n",
      "Winner: Croatia\n",
      "Probability of Croatia winning:  0.739\n",
      "Probability of Draw:  0.184\n",
      "Probability of Iceland winning:  0.077\n",
      "\n",
      "Mexico and Sweden\n",
      "Winner: Mexico\n",
      "Probability of Mexico winning:  0.455\n",
      "Probability of Draw:  0.263\n",
      "Probability of Sweden winning:  0.282\n",
      "\n",
      "Germany and Korea Republic\n",
      "Winner: Germany\n",
      "Probability of Germany winning:  0.623\n",
      "Probability of Draw:  0.264\n",
      "Probability of Korea Republic winning:  0.113\n",
      "\n",
      "Brazil and Serbia\n",
      "Winner: Brazil\n",
      "Probability of Brazil winning:  0.719\n",
      "Probability of Draw:  0.174\n",
      "Probability of Serbia winning:  0.108\n",
      "\n",
      "Switzerland and Costa Rica\n",
      "Winner: Switzerland\n",
      "Probability of Switzerland winning:  0.604\n",
      "Probability of Draw:  0.222\n",
      "Probability of Costa Rica winning:  0.174\n",
      "\n",
      "Poland and Japan\n",
      "Winner: Poland\n",
      "Probability of Poland winning:  0.532\n",
      "Probability of Draw:  0.255\n",
      "Probability of Japan winning:  0.212\n",
      "\n",
      "Colombia and Senegal\n",
      "Winner: Colombia\n",
      "Probability of Colombia winning:  0.573\n",
      "Probability of Draw:  0.200\n",
      "Probability of Senegal winning:  0.227\n",
      "\n",
      "Tunisia and Panama\n",
      "Winner: Tunisia\n",
      "Probability of Tunisia winning:  0.638\n",
      "Probability of Draw:  0.254\n",
      "Probability of Panama winning:  0.108\n",
      "\n",
      "Belgium and England\n",
      "Winner: England\n",
      "Probability of Belgium winning:  0.261\n",
      "Probability of Draw:  0.242\n",
      "Probability of England winning:  0.497\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#group matches \n",
    "predictions = logreg.predict(pred_set)\n",
    "for i in range(fixtures.shape[0]):\n",
    "    print(backup_pred_set.iloc[i, 1] + \" and \" + backup_pred_set.iloc[i, 0])\n",
    "    if predictions[i] == 2:\n",
    "        print(\"Winner: \" + backup_pred_set.iloc[i, 1])\n",
    "    elif predictions[i] == 1:\n",
    "        print(\"Draw\")\n",
    "    elif predictions[i] == 0:\n",
    "        print(\"Winner: \" + backup_pred_set.iloc[i, 0])\n",
    "    print('Probability of ' + backup_pred_set.iloc[i, 1] + ' winning: ', '%.3f'%(logreg.predict_proba(pred_set)[i][2]))\n",
    "    print('Probability of Draw: ', '%.3f'%(logreg.predict_proba(pred_set)[i][1]))\n",
    "    print('Probability of ' + backup_pred_set.iloc[i, 0] + ' winning: ', '%.3f'%(logreg.predict_proba(pred_set)[i][0]))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of tuples before \n",
    "group_16 = [('Uruguay', 'Portugal'),\n",
    "            ('France', 'Croatia'),\n",
    "            ('Brazil', 'Mexico'),\n",
    "            ('England', 'Colombia'),\n",
    "            ('Spain', 'Russia'),\n",
    "            ('Argentina', 'Peru'),\n",
    "            ('Germany', 'Switzerland'),\n",
    "            ('Poland', 'Belgium')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_and_predict(matches, ranking, final, logreg):\n",
    "\n",
    "    # Initialization of auxiliary list for data cleaning\n",
    "    positions = []\n",
    "\n",
    "    # Loop to retrieve each team's position according to FIFA ranking\n",
    "    for match in matches:\n",
    "        positions.append(ranking.loc[ranking['Team'] == match[0],'Position'].iloc[0])\n",
    "        positions.append(ranking.loc[ranking['Team'] == match[1],'Position'].iloc[0])\n",
    "    \n",
    "    # Creating the DataFrame for prediction\n",
    "    pred_set = []\n",
    "\n",
    "    # Initializing iterators for while loop\n",
    "    i = 0\n",
    "    j = 0\n",
    "\n",
    "    # 'i' will be the iterator for the 'positions' list, and 'j' for the list of matches (list of tuples)\n",
    "    while i < len(positions):\n",
    "        dict1 = {}\n",
    "\n",
    "        # If position of first team is better, he will be the 'home' team, and vice-versa\n",
    "        if positions[i] < positions[i + 1]:\n",
    "            dict1.update({'home_team': matches[j][0], 'away_team': matches[j][1]})\n",
    "        else:\n",
    "            dict1.update({'home_team': matches[j][1], 'away_team': matches[j][0]})\n",
    "\n",
    "        # Append updated dictionary to the list, that will later be converted into a DataFrame\n",
    "        pred_set.append(dict1)\n",
    "        i += 2\n",
    "        j += 1\n",
    "\n",
    "    # Convert list into DataFrame\n",
    "    pred_set = pd.DataFrame(pred_set)\n",
    "    backup_pred_set = pred_set\n",
    "\n",
    "    # Get dummy variables and drop winning_team column\n",
    "    pred_set = pd.get_dummies(pred_set, prefix=['home_team', 'away_team'], columns=['home_team', 'away_team'])\n",
    "\n",
    "    # Add missing columns compared to the model's training dataset\n",
    "    missing_cols2 = set(final.columns) - set(pred_set.columns)\n",
    "    for c in missing_cols2:\n",
    "        pred_set[c] = 0\n",
    "    pred_set = pred_set[final.columns]\n",
    "\n",
    "    # Remove winning team column\n",
    "    pred_set = pred_set.drop(['winning_team'], axis=1)\n",
    "\n",
    "    # Predict!\n",
    "    predictions = logreg.predict(pred_set)\n",
    "    for i in range(len(pred_set)):\n",
    "        print(backup_pred_set.iloc[i, 1] + \" and \" + backup_pred_set.iloc[i, 0])\n",
    "        if predictions[i] == 2:\n",
    "            print(\"Winner: \" + backup_pred_set.iloc[i, 1])\n",
    "        elif predictions[i] == 1:\n",
    "            print(\"Draw\")\n",
    "        elif predictions[i] == 0:\n",
    "            print(\"Winner: \" + backup_pred_set.iloc[i, 0])\n",
    "        print('Probability of ' + backup_pred_set.iloc[i, 1] + ' winning: ' , '%.3f'%(logreg.predict_proba(pred_set)[i][2]))\n",
    "        print('Probability of Draw: ', '%.3f'%(logreg.predict_proba(pred_set)[i][1])) \n",
    "        print('Probability of ' + backup_pred_set.iloc[i, 0] + ' winning: ', '%.3f'%(logreg.predict_proba(pred_set)[i][0]))\n",
    "        print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portugal and Uruguay\n",
      "Winner: Portugal\n",
      "Probability of Portugal winning:  0.428\n",
      "Probability of Draw:  0.285\n",
      "Probability of Uruguay winning:  0.287\n",
      "\n",
      "France and Croatia\n",
      "Winner: France\n",
      "Probability of France winning:  0.481\n",
      "Probability of Draw:  0.252\n",
      "Probability of Croatia winning:  0.267\n",
      "\n",
      "Brazil and Mexico\n",
      "Winner: Brazil\n",
      "Probability of Brazil winning:  0.695\n",
      "Probability of Draw:  0.209\n",
      "Probability of Mexico winning:  0.096\n",
      "\n",
      "England and Colombia\n",
      "Winner: England\n",
      "Probability of England winning:  0.516\n",
      "Probability of Draw:  0.368\n",
      "Probability of Colombia winning:  0.116\n",
      "\n",
      "Spain and Russia\n",
      "Winner: Spain\n",
      "Probability of Spain winning:  0.529\n",
      "Probability of Draw:  0.280\n",
      "Probability of Russia winning:  0.191\n",
      "\n",
      "Argentina and Peru\n",
      "Winner: Argentina\n",
      "Probability of Argentina winning:  0.713\n",
      "Probability of Draw:  0.212\n",
      "Probability of Peru winning:  0.075\n",
      "\n",
      "Germany and Switzerland\n",
      "Winner: Germany\n",
      "Probability of Germany winning:  0.672\n",
      "Probability of Draw:  0.192\n",
      "Probability of Switzerland winning:  0.137\n",
      "\n",
      "Belgium and Poland\n",
      "Winner: Belgium\n",
      "Probability of Belgium winning:  0.513\n",
      "Probability of Draw:  0.202\n",
      "Probability of Poland winning:  0.285\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clean_and_predict(group_16, ranking, final, logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of matches\n",
    "quarters = [('Portugal', 'France'),\n",
    "            ('Spain', 'Argentina'),\n",
    "            ('Brazil', 'England'),\n",
    "            ('Germany', 'Belgium')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portugal and France\n",
      "Winner: Portugal\n",
      "Probability of Portugal winning:  0.437\n",
      "Probability of Draw:  0.256\n",
      "Probability of France winning:  0.307\n",
      "\n",
      "Argentina and Spain\n",
      "Winner: Argentina\n",
      "Probability of Argentina winning:  0.518\n",
      "Probability of Draw:  0.262\n",
      "Probability of Spain winning:  0.220\n",
      "\n",
      "Brazil and England\n",
      "Winner: Brazil\n",
      "Probability of Brazil winning:  0.525\n",
      "Probability of Draw:  0.216\n",
      "Probability of England winning:  0.260\n",
      "\n",
      "Germany and Belgium\n",
      "Winner: Germany\n",
      "Probability of Germany winning:  0.563\n",
      "Probability of Draw:  0.269\n",
      "Probability of Belgium winning:  0.167\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clean_and_predict(quarters, ranking, final, logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of matches\n",
    "semi = [('Portugal', 'Brazil'),\n",
    "        ('Argentina', 'Germany')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brazil and Portugal\n",
      "Winner: Brazil\n",
      "Probability of Brazil winning:  0.705\n",
      "Probability of Draw:  0.152\n",
      "Probability of Portugal winning:  0.143\n",
      "\n",
      "Germany and Argentina\n",
      "Winner: Germany\n",
      "Probability of Germany winning:  0.441\n",
      "Probability of Draw:  0.264\n",
      "Probability of Argentina winning:  0.295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clean_and_predict(semi, ranking, final, logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finals\n",
    "finals = [('Brazil', 'Germany')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Germany and Brazil\n",
      "Winner: Brazil\n",
      "Probability of Germany winning:  0.359\n",
      "Probability of Draw:  0.220\n",
      "Probability of Brazil winning:  0.421\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clean_and_predict(finals, ranking, final, logreg)"
   ]
  },
  {
   "cell_type": "code",
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