log_reg_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Unnamed: 0</th>\n",
       "      <th>Row_ID</th>\n",
       "      <th>Game_ID</th>\n",
       "      <th>Team</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Min</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>ORebs</th>\n",
       "      <th>DRebs</th>\n",
       "      <th>Rebs</th>\n",
       "      <th>As</th>\n",
       "      <th>PF</th>\n",
       "      <th>To</th>\n",
       "      <th>Stl</th>\n",
       "      <th>Blks</th>\n",
       "      <th>Pts</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>01_04_01</td>\n",
       "      <td>ANGOLA</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTALS</td>\n",
       "      <td>ANGOLA</td>\n",
       "      <td>200</td>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>01_04_01</td>\n",
       "      <td>LITHUANIA</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTALS</td>\n",
       "      <td>LITHUANIA</td>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>47</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>01_04_02</td>\n",
       "      <td>ITALY</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTALS</td>\n",
       "      <td>ITALY</td>\n",
       "      <td>200</td>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>01_04_02</td>\n",
       "      <td>NEW ZEALAND</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTALS</td>\n",
       "      <td>NEW ZEALAND</td>\n",
       "      <td>200</td>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>01_04_03</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTALS</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Row_ID   Game_ID         Team  Player_ID  Number         Name  \\\n",
       "0           0      13  01_04_01       ANGOLA          0  TOTALS       ANGOLA   \n",
       "1           1      26  01_04_01    LITHUANIA          0  TOTALS    LITHUANIA   \n",
       "2           2      39  01_04_02        ITALY          0  TOTALS        ITALY   \n",
       "3           3      52  01_04_02  NEW ZEALAND          0  TOTALS  NEW ZEALAND   \n",
       "4           4      65  01_04_03        CHINA          0  TOTALS        CHINA   \n",
       "\n",
       "   Min  FGM  FGA ...  ORebs  DRebs  Rebs  As  PF  To  Stl  Blks  Pts  y  \n",
       "0  200   26   63 ...      4     17    21  12  37  11    5     3   73  0  \n",
       "1  200   20   50 ...     18     29    47  12  20  16    5     3   78  1  \n",
       "2  200   25   64 ...      6     25    31  14  24  10    5     0   71  1  \n",
       "3  200   23   63 ...      7     32    39  15  21  14    2     2   69  0  \n",
       "4  200   20   62 ...     13     23    36   5  23  16    3     0   58  0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country_box_score_data.to_csv(\"country_box_scores.csv\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "country_box_score_data = pd.read_csv('country_box_scores_with_results_PP.csv', header = 0, delimiter=',')\n",
    "\n",
    "country_box_score_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FGM', 'FGA', 'FG%', '2ptsM', '2ptsA', '2pts%', '3ptsM', '3ptsA',\n",
       "       '3pts%', 'FTM', 'FTA', 'FT%', 'ORebs', 'DRebs', 'Rebs', 'As', 'PF',\n",
       "       'To', 'Stl', 'Blks', 'Pts', 'y', 'ALBANIA', 'ALGERIA',\n",
       "       'AMERICAN SAMOA', 'ANDORRA', 'ANGOLA', 'ANTIGUA', 'ARGENTINA',\n",
       "       'ARMENIA', 'ARUBA', 'AUSTRALIA', 'AUSTRIA', 'AZERBAIJAN',\n",
       "       'BAHAMAS', 'BAHRAIN', 'BANGLADESH', 'BARBADOS', 'BELARUS',\n",
       "       'BELGIUM', 'BELIZE', 'BERMUDA', 'BOLIVIA',\n",
       "       'BOSNIA AND HERZEGOVINA', 'BOTSWANA', 'BRAZIL',\n",
       "       'BRITISH VIRGIN ISLANDS', 'BULGARIA', 'BURKINA FASO', 'BURUNDI',\n",
       "       'CAF', 'CAMEROON', 'CANADA', 'CAPE VERDE', 'CAYMAN ISLANDS',\n",
       "       'CENTRAL AFRICAN REPUBLIC', 'CHAD', 'CHILE', 'CHINA',\n",
       "       'CHINESE TAIPEI', 'COLOMBIA', 'COSTA RICA', \"COTE D'IVOIRE\",\n",
       "       'CROATIA', 'CUBA', 'CYPRUS', 'CZECH REPUBLIC', 'DEM.REP. OF CONGO',\n",
       "       'DENMARK', 'DOMINICA', 'DOMINICAN REPUBLIC', 'ECUADOR', 'EGYPT',\n",
       "       'EL SALVADOR', 'ENGLAND', 'ESTONIA', 'ETHIOPIA', 'FIJI', 'FINLAND',\n",
       "       'FRANCE', 'GABON', 'GEORGIA', 'GERMANY', 'GIBRALTAR',\n",
       "       'GREAT BRITAIN', 'GREECE', 'GUAM', 'GUATEMALA', 'GUINEA',\n",
       "       'GUINEA-BISSAU', 'GUYANA', 'HONDURAS', 'HONG KONG', 'HUNGARY',\n",
       "       'ICELAND', 'INDIA', 'INDONESIA', 'IRAN', 'IRAQ', 'IRELAND',\n",
       "       'ISRAEL', 'ITALY', 'JAMAICA', 'JAPAN', 'JORDAN', 'KAZAKHSTAN',\n",
       "       'KENYA', 'KINGDOM OF SAUDI ARABIA', 'KOREA', 'KOSOVO', 'KUWAIT',\n",
       "       'LATVIA', 'LEBANON', 'LESOTHO', 'LIBERIA', 'LIBYA', 'LITHUANIA',\n",
       "       'LUXEMBOURG', 'MACAU', 'MADAGASCAR', 'MALAWI', 'MALAYSIA', 'MALI',\n",
       "       'MALTA', 'MARSHALL ISLANDS', 'MAURITANIA', 'MEXICO', 'MKD',\n",
       "       'MOLDOVA', 'MONTENEGRO', 'MOROCCO', 'MOZAMBIQUE', 'MYANMAR',\n",
       "       'NETHERLANDS', 'NEW CALEDONIA', 'NEW ZEALAND', 'NICARAGUA',\n",
       "       'NIGERIA', 'NORWAY', 'PALAU', 'PALESTINE', 'PANAMA',\n",
       "       'PAPUA NEW GUINEA', 'PARAGUAY', 'PERU', 'PHILIPPINES', 'POLAND',\n",
       "       'PORTUGAL', 'PRINCIPALITY OF MONACO', 'PUERTO RICO', 'QATAR',\n",
       "       'REPUBLIC OF BENIN', 'REPUBLIC OF CONGO', 'ROMANIA', 'RUSSIA',\n",
       "       'RWANDA', 'SAMOA', 'SAN MARINO', 'SCOTLAND', 'SENEGAL', 'SERBIA',\n",
       "       'SERBIA & MONTENEGRO', 'SINGAPORE', 'SLOVAK REPUBLIC', 'SLOVENIA',\n",
       "       'SOLOMON ISLANDS', 'SOMALIA', 'SOUTH AFRICA', 'SOUTH SUDAN',\n",
       "       'SPAIN', 'SRI LANKA', 'ST. KITTS', 'ST.VINCENT AND THE GRENADINES',\n",
       "       'SUDAN', 'SURINAM', 'SWEDEN', 'SWITZERLAND', 'SYRIA', 'TAHITI',\n",
       "       'THAILAND', 'TOGO', 'TRINIDAD AND TOBAGO', 'TUNISIA', 'TURKEY',\n",
       "       'UGANDA', 'UKRAINE', 'UNITED ARAB EMIRATES', 'URUGUAY', 'USA',\n",
       "       'UZBEKISTAN', 'VENEZUELA', 'VIETNAM', 'VIRGIN ISLANDS', 'WALES',\n",
       "       'ZAMBIA', 'ZIMBABWE'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# do one-hot encoding so that we have it\n",
    "\n",
    "cat_vars=['Team']\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(country_box_score_data[var])\n",
    "    data1=country_box_score_data.join(cat_list) # it can't join it right now, due to something. IT IS DUE TO ALL THE re-running!\n",
    "    country_box_score_data=data1\n",
    "    \n",
    "cat_vars=['Team', 'Game_ID', 'Player_ID', 'Unnamed: 0', 'Name', 'Row_ID', 'Number', \"Min\"]\n",
    "data_vars=country_box_score_data.columns.values.tolist()\n",
    "to_keep=[i for i in data_vars if i not in cat_vars]\n",
    "data_final=country_box_score_data[to_keep]\n",
    "data_final.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['y']\n",
      "['FGM', 'FGA', 'FG%', '2ptsM', '2ptsA', '2pts%', '3ptsM', '3ptsA', '3pts%', 'FTM', 'FTA', 'FT%', 'ORebs', 'DRebs', 'Rebs', 'As', 'PF', 'To', 'Stl', 'Blks', 'Pts', 'ALBANIA', 'ALGERIA', 'AMERICAN SAMOA', 'ANDORRA', 'ANGOLA', 'ANTIGUA', 'ARGENTINA', 'ARMENIA', 'ARUBA', 'AUSTRALIA', 'AUSTRIA', 'AZERBAIJAN', 'BAHAMAS', 'BAHRAIN', 'BANGLADESH', 'BARBADOS', 'BELARUS', 'BELGIUM', 'BELIZE', 'BERMUDA', 'BOLIVIA', 'BOSNIA AND HERZEGOVINA', 'BOTSWANA', 'BRAZIL', 'BRITISH VIRGIN ISLANDS', 'BULGARIA', 'BURKINA FASO', 'BURUNDI', 'CAF', 'CAMEROON', 'CANADA', 'CAPE VERDE', 'CAYMAN ISLANDS', 'CENTRAL AFRICAN REPUBLIC', 'CHAD', 'CHILE', 'CHINA', 'CHINESE TAIPEI', 'COLOMBIA', 'COSTA RICA', \"COTE D'IVOIRE\", 'CROATIA', 'CUBA', 'CYPRUS', 'CZECH REPUBLIC', 'DEM.REP. OF CONGO', 'DENMARK', 'DOMINICA', 'DOMINICAN REPUBLIC', 'ECUADOR', 'EGYPT', 'EL SALVADOR', 'ENGLAND', 'ESTONIA', 'ETHIOPIA', 'FIJI', 'FINLAND', 'FRANCE', 'GABON', 'GEORGIA', 'GERMANY', 'GIBRALTAR', 'GREAT BRITAIN', 'GREECE', 'GUAM', 'GUATEMALA', 'GUINEA', 'GUINEA-BISSAU', 'GUYANA', 'HONDURAS', 'HONG KONG', 'HUNGARY', 'ICELAND', 'INDIA', 'INDONESIA', 'IRAN', 'IRAQ', 'IRELAND', 'ISRAEL', 'ITALY', 'JAMAICA', 'JAPAN', 'JORDAN', 'KAZAKHSTAN', 'KENYA', 'KINGDOM OF SAUDI ARABIA', 'KOREA', 'KOSOVO', 'KUWAIT', 'LATVIA', 'LEBANON', 'LESOTHO', 'LIBERIA', 'LIBYA', 'LITHUANIA', 'LUXEMBOURG', 'MACAU', 'MADAGASCAR', 'MALAWI', 'MALAYSIA', 'MALI', 'MALTA', 'MARSHALL ISLANDS', 'MAURITANIA', 'MEXICO', 'MKD', 'MOLDOVA', 'MONTENEGRO', 'MOROCCO', 'MOZAMBIQUE', 'MYANMAR', 'NETHERLANDS', 'NEW CALEDONIA', 'NEW ZEALAND', 'NICARAGUA', 'NIGERIA', 'NORWAY', 'PALAU', 'PALESTINE', 'PANAMA', 'PAPUA NEW GUINEA', 'PARAGUAY', 'PERU', 'PHILIPPINES', 'POLAND', 'PORTUGAL', 'PRINCIPALITY OF MONACO', 'PUERTO RICO', 'QATAR', 'REPUBLIC OF BENIN', 'REPUBLIC OF CONGO', 'ROMANIA', 'RUSSIA', 'RWANDA', 'SAMOA', 'SAN MARINO', 'SCOTLAND', 'SENEGAL', 'SERBIA', 'SERBIA & MONTENEGRO', 'SINGAPORE', 'SLOVAK REPUBLIC', 'SLOVENIA', 'SOLOMON ISLANDS', 'SOMALIA', 'SOUTH AFRICA', 'SOUTH SUDAN', 'SPAIN', 'SRI LANKA', 'ST. KITTS', 'ST.VINCENT AND THE GRENADINES', 'SUDAN', 'SURINAM', 'SWEDEN', 'SWITZERLAND', 'SYRIA', 'TAHITI', 'THAILAND', 'TOGO', 'TRINIDAD AND TOBAGO', 'TUNISIA', 'TURKEY', 'UGANDA', 'UKRAINE', 'UNITED ARAB EMIRATES', 'URUGUAY', 'USA', 'UZBEKISTAN', 'VENEZUELA', 'VIETNAM', 'VIRGIN ISLANDS', 'WALES', 'ZAMBIA', 'ZIMBABWE']\n"
     ]
    }
   ],
   "source": [
    "data_final_vars=data_final.columns.values.tolist()\n",
    "y=['y']\n",
    "X=[i for i in data_final_vars if i not in y]\n",
    "\n",
    "print (y)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of        FGM  FGA   FG%  2ptsM  2ptsA  2pts%  3ptsM  3ptsA  3pts%  FTM  \\\n",
      "0       26   63  41.3     11     34   32.4     15     29   51.7    6   \n",
      "1       20   50  40.0     14     30   46.7      6     20   30.0   32   \n",
      "2       25   64  39.1     15     33   45.5     10     31   32.3   11   \n",
      "3       23   63  36.5     14     34   41.2      9     29   31.0   14   \n",
      "4       20   62  32.3     11     36   30.6      9     26   34.6    9   \n",
      "5       31   66  47.0     26     41   63.4      5     25   20.0   16   \n",
      "6       30   54  55.6     22     37   59.5      8     17   47.1   15   \n",
      "7       28   51  54.9     20     33   60.6      8     18   44.4   18   \n",
      "8       31   55  56.4     23     39   59.0      8     16   50.0   22   \n",
      "9       26   76  34.2     23     52   44.2      3     24   12.5   18   \n",
      "10      28   54  51.9     24     40   60.0      4     14   28.6   16   \n",
      "11      18   47  38.3     14     34   41.2      4     13   30.8   14   \n",
      "12      23   66  34.8     13     29   44.8     10     37   27.0    6   \n",
      "13      25   60  41.7     24     49   49.0      1     11    9.1   18   \n",
      "14      29   59  49.2     22     41   53.7      7     18   38.9   18   \n",
      "15      19   71  26.8     10     41   24.4      9     30   30.0   12   \n",
      "16      30   56  53.6     14     26   53.8     16     30   53.3   22   \n",
      "17      25   64  39.1     13     38   34.2     12     26   46.2   28   \n",
      "18      25   53  47.2     15     34   44.1     10     19   52.6   14   \n",
      "19      28   59  47.5     20     41   48.8      8     18   44.4    8   \n",
      "20      26   50  52.0     19     35   54.3      7     15   46.7   28   \n",
      "21      30   70  42.9     26     50   52.0      4     20   20.0   12   \n",
      "22      27   67  40.3     23     46   50.0      4     21   19.0   19   \n",
      "23      27   61  44.3     19     42   45.2      8     19   42.1    9   \n",
      "24      32   58  55.2     24     35   68.6      8     23   34.8   15   \n",
      "25      33   62  53.2     22     38   57.9     11     24   45.8   13   \n",
      "26      24   68  35.3     12     33   36.4     12     35   34.3    3   \n",
      "27      23   51  45.1     21     40   52.5      2     11   18.2   23   \n",
      "28      39   68  57.4     36     51   70.6      3     17   17.6    8   \n",
      "29      30   60  50.0     18     34   52.9     12     26   46.2    7   \n",
      "...    ...  ...   ...    ...    ...    ...    ...    ...    ...  ...   \n",
      "23946   15   56  26.8     13     38   34.2      2     18   11.1    2   \n",
      "23947   63  109  57.8     58     94   61.7      5     15   33.3   20   \n",
      "23948   39   87  44.8     34     61   55.7      5     26   19.2   14   \n",
      "23949   13   67  19.4      9     36   25.0      4     31   12.9    6   \n",
      "23950   30   84  35.7     25     64   39.1      5     20   25.0   17   \n",
      "23951   24   76  31.6     20     56   35.7      4     20   20.0   12   \n",
      "23952   29   72  40.3     18     49   36.7     11     23   47.8    9   \n",
      "23953   31   59  52.5     27     46   58.7      4     13   30.8   24   \n",
      "23954   11   44  25.0     11     39   28.2      0      5    0.0   10   \n",
      "23955   66   99  66.7     53     67   79.1     13     32   40.6   23   \n",
      "23956   26   96  27.1     21     78   26.9      5     18   27.8    8   \n",
      "23957   17   67  25.4     15     56   26.8      2     11   18.2    5   \n",
      "23958   58  113  51.3     45     78   57.7     13     35   37.1   13   \n",
      "23959   13   46  28.3     11     40   27.5      2      6   33.3    6   \n",
      "23960   43  100  43.0     35     71   49.3      8     29   27.6   24   \n",
      "23961   28   60  46.7     27     55   49.1      1      5   20.0   17   \n",
      "23962   52   76  68.4     41     54   75.9     11     22   50.0   10   \n",
      "23963   13   59  22.0      8     27   29.6      5     32   15.6    2   \n",
      "23964   19   79  24.1     17     64   26.6      2     15   13.3   22   \n",
      "23965   18   72  25.0     10     35   28.6      8     37   21.6    6   \n",
      "23966   29   84  34.5     25     62   40.3      4     22   18.2   15   \n",
      "23967   26   72  36.1     23     55   41.8      3     17   17.6   15   \n",
      "23968   38   79  48.1     23     43   53.5     15     36   41.7    5   \n",
      "23969   25   71  35.2     22     57   38.6      3     14   21.4    5   \n",
      "23970   68   95  71.6     65     84   77.4      3     11   27.3   16   \n",
      "23971   12   65  18.5      8     46   17.4      4     19   21.1   11   \n",
      "23972   28   58  48.3     23     44   52.3      5     14   35.7    9   \n",
      "23973   46   85  54.1     39     62   62.9      7     23   30.4    6   \n",
      "23974   21   56  37.5     19     40   47.5      2     16   12.5   11   \n",
      "23975   39   86  45.3     33     68   48.5      6     18   33.3    9   \n",
      "\n",
      "         ...     UNITED ARAB EMIRATES  URUGUAY  USA  UZBEKISTAN  VENEZUELA  \\\n",
      "0        ...                        0        0    0           0          0   \n",
      "1        ...                        0        0    0           0          0   \n",
      "2        ...                        0        0    0           0          0   \n",
      "3        ...                        0        0    0           0          0   \n",
      "4        ...                        0        0    0           0          0   \n",
      "5        ...                        0        0    0           0          0   \n",
      "6        ...                        0        0    0           0          0   \n",
      "7        ...                        0        0    0           0          0   \n",
      "8        ...                        0        0    0           0          0   \n",
      "9        ...                        0        0    1           0          0   \n",
      "10       ...                        0        0    0           0          0   \n",
      "11       ...                        0        0    0           0          0   \n",
      "12       ...                        0        0    0           0          0   \n",
      "13       ...                        0        0    0           0          0   \n",
      "14       ...                        0        0    0           0          0   \n",
      "15       ...                        0        0    0           0          0   \n",
      "16       ...                        0        0    0           0          0   \n",
      "17       ...                        0        0    0           0          0   \n",
      "18       ...                        0        0    0           0          0   \n",
      "19       ...                        0        0    0           0          0   \n",
      "20       ...                        0        0    0           0          0   \n",
      "21       ...                        0        0    0           0          0   \n",
      "22       ...                        0        0    1           0          0   \n",
      "23       ...                        0        0    0           0          0   \n",
      "24       ...                        0        0    0           0          0   \n",
      "25       ...                        0        0    0           0          0   \n",
      "26       ...                        0        0    0           0          0   \n",
      "27       ...                        0        0    0           0          0   \n",
      "28       ...                        0        0    1           0          0   \n",
      "29       ...                        0        0    0           0          0   \n",
      "...      ...                      ...      ...  ...         ...        ...   \n",
      "23946    ...                        0        0    0           0          0   \n",
      "23947    ...                        0        0    0           0          0   \n",
      "23948    ...                        0        0    0           0          0   \n",
      "23949    ...                        0        0    0           0          0   \n",
      "23950    ...                        0        0    0           0          0   \n",
      "23951    ...                        0        0    0           0          0   \n",
      "23952    ...                        0        0    0           0          0   \n",
      "23953    ...                        0        0    0           0          0   \n",
      "23954    ...                        0        0    0           0          0   \n",
      "23955    ...                        0        0    0           0          0   \n",
      "23956    ...                        0        0    0           0          0   \n",
      "23957    ...                        0        0    0           0          0   \n",
      "23958    ...                        0        0    0           0          0   \n",
      "23959    ...                        0        0    0           0          0   \n",
      "23960    ...                        0        0    0           0          0   \n",
      "23961    ...                        0        0    0           0          0   \n",
      "23962    ...                        0        0    0           0          0   \n",
      "23963    ...                        0        0    0           0          0   \n",
      "23964    ...                        0        0    0           0          0   \n",
      "23965    ...                        0        0    0           0          0   \n",
      "23966    ...                        0        0    0           0          0   \n",
      "23967    ...                        0        0    0           0          0   \n",
      "23968    ...                        0        0    0           0          0   \n",
      "23969    ...                        0        0    0           0          0   \n",
      "23970    ...                        0        0    0           0          0   \n",
      "23971    ...                        0        0    0           0          0   \n",
      "23972    ...                        0        0    0           0          0   \n",
      "23973    ...                        0        0    0           0          0   \n",
      "23974    ...                        0        0    0           0          0   \n",
      "23975    ...                        0        0    0           0          0   \n",
      "\n",
      "       VIETNAM  VIRGIN ISLANDS  WALES  ZAMBIA  ZIMBABWE  \n",
      "0            0               0      0       0         0  \n",
      "1            0               0      0       0         0  \n",
      "2            0               0      0       0         0  \n",
      "3            0               0      0       0         0  \n",
      "4            0               0      0       0         0  \n",
      "5            0               0      0       0         0  \n",
      "6            0               0      0       0         0  \n",
      "7            0               0      0       0         0  \n",
      "8            0               0      0       0         0  \n",
      "9            0               0      0       0         0  \n",
      "10           0               0      0       0         0  \n",
      "11           0               0      0       0         0  \n",
      "12           0               0      0       0         0  \n",
      "13           0               0      0       0         0  \n",
      "14           0               0      0       0         0  \n",
      "15           0               0      0       0         0  \n",
      "16           0               0      0       0         0  \n",
      "17           0               0      0       0         0  \n",
      "18           0               0      0       0         0  \n",
      "19           0               0      0       0         0  \n",
      "20           0               0      0       0         0  \n",
      "21           0               0      0       0         0  \n",
      "22           0               0      0       0         0  \n",
      "23           0               0      0       0         0  \n",
      "24           0               0      0       0         0  \n",
      "25           0               0      0       0         0  \n",
      "26           0               0      0       0         0  \n",
      "27           0               0      0       0         0  \n",
      "28           0               0      0       0         0  \n",
      "29           0               0      0       0         0  \n",
      "...        ...             ...    ...     ...       ...  \n",
      "23946        0               0      0       0         0  \n",
      "23947        0               0      0       0         0  \n",
      "23948        0               0      0       0         0  \n",
      "23949        0               0      0       0         0  \n",
      "23950        0               0      0       0         0  \n",
      "23951        0               0      0       0         0  \n",
      "23952        0               0      0       0         0  \n",
      "23953        0               0      0       0         0  \n",
      "23954        0               0      0       0         0  \n",
      "23955        0               0      0       0         0  \n",
      "23956        0               0      0       0         0  \n",
      "23957        0               0      0       0         0  \n",
      "23958        0               0      0       0         0  \n",
      "23959        0               0      0       0         0  \n",
      "23960        0               0      0       0         0  \n",
      "23961        0               0      0       0         0  \n",
      "23962        0               0      0       0         0  \n",
      "23963        0               0      0       0         0  \n",
      "23964        0               0      0       0         0  \n",
      "23965        0               0      0       0         0  \n",
      "23966        0               0      0       0         0  \n",
      "23967        0               0      0       0         0  \n",
      "23968        0               0      0       0         0  \n",
      "23969        0               0      0       0         0  \n",
      "23970        0               0      0       0         0  \n",
      "23971        0               0      0       0         0  \n",
      "23972        0               0      0       0         0  \n",
      "23973        0               0      0       0         0  \n",
      "23974        0               0      0       0         0  \n",
      "23975        0               0      0       0         0  \n",
      "\n",
      "[23976 rows x 195 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(data_final[X].head)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must pass DataFrame with boolean values only",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-817eea77b4da>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mlogreg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mrfe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRFE\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogreg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mrfe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrfe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\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     11\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupport_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mranking_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2133\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2134\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2135\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2136\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_mi_columns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2137\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_frame\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2216\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2217\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_bool_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2218\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Must pass DataFrame with boolean values only'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2219\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2220\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Must pass DataFrame with boolean values only"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# X=data_final[sig_vars]\n",
    "# y=data_final['y']\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "rfe = RFE(logreg, 18)\n",
    "rfe = rfe.fit(data_final[X],data_final[y])\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)\n",
    "count = 0\n",
    "for boolean in rfe.support_:\n",
    "    if boolean:\n",
    "        count +=1\n",
    "print(\"we select this many features \" + str(count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARUBA\n",
      "BAHRAIN\n",
      "BOTSWANA\n",
      "CYPRUS\n",
      "GABON\n",
      "GUINEA-BISSAU\n",
      "HONDURAS\n",
      "IRELAND\n",
      "MALAWI\n",
      "NEW ZEALAND\n",
      "NORWAY\n",
      "PERU\n",
      "SINGAPORE\n",
      "SOMALIA\n",
      "SYRIA\n",
      "TOGO\n",
      "UGANDA\n",
      "ZIMBABWE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['FGM',\n",
       " 'FGA',\n",
       " 'FG%',\n",
       " '2ptsM',\n",
       " '2ptsA',\n",
       " '2pts%',\n",
       " '3ptsM',\n",
       " '3ptsA',\n",
       " '3pts%',\n",
       " 'FTM',\n",
       " 'FTA',\n",
       " 'FT%',\n",
       " 'ORebs',\n",
       " 'DRebs',\n",
       " 'Rebs',\n",
       " 'As',\n",
       " 'PF',\n",
       " 'To',\n",
       " 'Stl',\n",
       " 'Blks',\n",
       " 'Pts']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now get the features we want...\n",
    "sig_vars = []\n",
    "for support,vari in zip(rfe.support_, data_final[X]):\n",
    "    \n",
    "    if support:\n",
    "        print(vari)\n",
    "    sig_vars.append(vari)\n",
    "    \n",
    "sig_vars = data_final[data_final[X].columns[0:21]]\n",
    "sig_vars = data_final[X]\n",
    "sig_vars = sig_vars[0:21]\n",
    "\n",
    "\n",
    "sig_vars = []\n",
    "for index,vari in enumerate(data_final[X]):    \n",
    "    if index < 21:\n",
    "        sig_vars.append(vari)\n",
    "\n",
    "sig_vars\n",
    "# for support,vari in zip(rfe.support_, data_final[X]):\n",
    "#     if support:\n",
    "#         print(vari)\n",
    "#     sig_vars.append(vari)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 0.5804008758821679\n",
      "            Iterations: 54\n",
      "            Function evaluations: 71\n",
      "            Gradient evaluations: 54\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                23976\n",
      "Model:                          Logit   Df Residuals:                    23955\n",
      "Method:                           MLE   Df Model:                           20\n",
      "Date:                Sun, 11 Mar 2018   Pseudo R-squ.:                  0.1627\n",
      "Time:                        18:51:40   Log-Likelihood:                -13916.\n",
      "converged:                       True   LL-Null:                       -16619.\n",
      "                                        LLR p-value:                     0.000\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "FGM           -0.1648   1.44e+04  -1.14e-05      1.000   -2.83e+04    2.83e+04\n",
      "FGA           -0.0652   7102.235  -9.19e-06      1.000   -1.39e+04    1.39e+04\n",
      "FG%            0.0262      0.013      2.035      0.042       0.001       0.051\n",
      "2ptsM         -0.1181   1.44e+04   -8.2e-06      1.000   -2.82e+04    2.82e+04\n",
      "2ptsA          0.0049   7105.446   6.87e-07      1.000   -1.39e+04    1.39e+04\n",
      "2pts%          0.0197      0.009      2.078      0.038       0.001       0.038\n",
      "3ptsM         -0.1006   1.44e+04  -6.98e-06      1.000   -2.82e+04    2.82e+04\n",
      "3ptsA         -0.0448   7101.851  -6.31e-06      1.000   -1.39e+04    1.39e+04\n",
      "3pts%         -0.0070      0.004     -1.666      0.096      -0.015       0.001\n",
      "FTM           -0.0244      0.206     -0.118      0.906      -0.428       0.379\n",
      "FTA           -0.0680      0.008     -8.579      0.000      -0.084      -0.052\n",
      "FT%           -0.0175      0.002     -7.508      0.000      -0.022      -0.013\n",
      "ORebs          0.0222    604.445   3.67e-05      1.000   -1184.668    1184.713\n",
      "DRebs          0.0260    604.445   4.31e-05      1.000   -1184.664    1184.716\n",
      "Rebs           0.0620    604.445      0.000      1.000   -1184.628    1184.752\n",
      "As             0.0040      0.004      1.081      0.280      -0.003       0.011\n",
      "PF            -0.0414      0.003    -12.907      0.000      -0.048      -0.035\n",
      "To            -0.0546      0.003    -17.735      0.000      -0.061      -0.049\n",
      "Stl            0.0843      0.004     21.090      0.000       0.076       0.092\n",
      "Blks           0.0197      0.007      2.823      0.005       0.006       0.033\n",
      "Pts            0.1505      0.205      0.733      0.464      -0.252       0.553\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# trying to do it on all variables, with strings causes TypeError: cannot perform reduce with flexible type\n",
    "# y=data_final['y']\n",
    "# X=[i for i in data_final if i not in y]\n",
    "\n",
    "X=data_final[sig_vars]\n",
    "y=data_final['y']\n",
    "\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)\n",
    "\n",
    "\n",
    "\n",
    "# model = LogisticRegression(fit_intercept=False)\n",
    "# mdl = model.fit(X, y)\n",
    "\n",
    "# X = sm.add_constant(X)\n",
    "\n",
    "logit_model=sm.Logit(y,X) #we do want to predict taht entire column of datas!\n",
    "# logit_model.add_constant()\n",
    "result=logit_model.fit_regularized()\n",
    "# result=logit_model.fit()\n",
    "\n",
    "\n",
    "\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.77\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation average accuracy: 0.773\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=0)\n",
    "modelCV = LogisticRegression()\n",
    "scoring = 'accuracy'\n",
    "results = model_selection.cross_val_score(modelCV, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "print(\"10-fold cross validation average accuracy: %.3f\" % (results.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.77      0.77      3545\n",
      "          1       0.78      0.77      0.77      3648\n",
      "\n",
      "avg / total       0.77      0.77      0.77      7193\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BOTSWANA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAURITANIA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOMALIA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIMBABWE</th>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARUBA</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOGO</th>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERU</th>\n",
       "      <td>0.203125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HONDURAS</th>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAPUA NEW GUINEA</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KINGDOM OF SAUDI ARABIA</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GABON</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAMBIA</th>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUATEMALA</th>\n",
       "      <td>0.274194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECUADOR</th>\n",
       "      <td>0.282051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAHRAIN</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAYMAN ISLANDS</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SINGAPORE</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW ZEALAND</th>\n",
       "      <td>0.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRELAND</th>\n",
       "      <td>0.294606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRINIDAD AND TOBAGO</th>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UGANDA</th>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SYRIA</th>\n",
       "      <td>0.317073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYPRUS</th>\n",
       "      <td>0.321168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KENYA</th>\n",
       "      <td>0.321429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORWAY</th>\n",
       "      <td>0.323171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRAQ</th>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHIOPIA</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIBYA</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LESOTHO</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIBERIA</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANGOLA</th>\n",
       "      <td>0.569343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREECE</th>\n",
       "      <td>0.570842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENEGAL</th>\n",
       "      <td>0.577778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUSSIA</th>\n",
       "      <td>0.579399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUERTO RICO</th>\n",
       "      <td>0.579618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARMENIA</th>\n",
       "      <td>0.579710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALAYSIA</th>\n",
       "      <td>0.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUSTRALIA</th>\n",
       "      <td>0.586735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTENEGRO</th>\n",
       "      <td>0.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZERBAIJAN</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRI LANKA</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TURKEY</th>\n",
       "      <td>0.604255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAZIL</th>\n",
       "      <td>0.608541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERBIA &amp; MONTENEGRO</th>\n",
       "      <td>0.609524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANCE</th>\n",
       "      <td>0.622642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUAM</th>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NIGERIA</th>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LITHUANIA</th>\n",
       "      <td>0.627907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERBIA</th>\n",
       "      <td>0.648276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARGENTINA</th>\n",
       "      <td>0.653012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEROON</th>\n",
       "      <td>0.653061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SURINAM</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACAU</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAHITI</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMOA</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPAIN</th>\n",
       "      <td>0.674033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW CALEDONIA</th>\n",
       "      <td>0.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PALAU</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALAWI</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUINEA-BISSAU</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                y\n",
       "Team                             \n",
       "BOTSWANA                 0.000000\n",
       "MAURITANIA               0.000000\n",
       "SOMALIA                  0.000000\n",
       "ZIMBABWE                 0.142857\n",
       "ARUBA                    0.166667\n",
       "TOGO                     0.181818\n",
       "PERU                     0.203125\n",
       "HONDURAS                 0.233333\n",
       "PAPUA NEW GUINEA         0.250000\n",
       "KINGDOM OF SAUDI ARABIA  0.250000\n",
       "GABON                    0.250000\n",
       "ZAMBIA                   0.272727\n",
       "GUATEMALA                0.274194\n",
       "ECUADOR                  0.282051\n",
       "BAHRAIN                  0.285714\n",
       "CAYMAN ISLANDS           0.285714\n",
       "SINGAPORE                0.285714\n",
       "NEW ZEALAND              0.291667\n",
       "IRELAND                  0.294606\n",
       "TRINIDAD AND TOBAGO      0.307692\n",
       "UGANDA                   0.307692\n",
       "SYRIA                    0.317073\n",
       "CYPRUS                   0.321168\n",
       "KENYA                    0.321429\n",
       "NORWAY                   0.323171\n",
       "IRAQ                     0.323529\n",
       "ETHIOPIA                 0.333333\n",
       "LIBYA                    0.333333\n",
       "LESOTHO                  0.333333\n",
       "LIBERIA                  0.333333\n",
       "...                           ...\n",
       "ANGOLA                   0.569343\n",
       "GREECE                   0.570842\n",
       "SENEGAL                  0.577778\n",
       "RUSSIA                   0.579399\n",
       "PUERTO RICO              0.579618\n",
       "ARMENIA                  0.579710\n",
       "MALAYSIA                 0.581395\n",
       "AUSTRALIA                0.586735\n",
       "MONTENEGRO               0.590909\n",
       "AZERBAIJAN               0.600000\n",
       "SRI LANKA                0.600000\n",
       "TURKEY                   0.604255\n",
       "BRAZIL                   0.608541\n",
       "SERBIA & MONTENEGRO      0.609524\n",
       "FRANCE                   0.622642\n",
       "GUAM                     0.625000\n",
       "NIGERIA                  0.625000\n",
       "LITHUANIA                0.627907\n",
       "SERBIA                   0.648276\n",
       "ARGENTINA                0.653012\n",
       "CAMEROON                 0.653061\n",
       "SURINAM                  0.666667\n",
       "MACAU                    0.666667\n",
       "TAHITI                   0.666667\n",
       "SAMOA                    0.666667\n",
       "SPAIN                    0.674033\n",
       "NEW CALEDONIA            0.722222\n",
       "PALAU                    0.750000\n",
       "MALAWI                   1.000000\n",
       "GUINEA-BISSAU            1.000000\n",
       "\n",
       "[174 rows x 1 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_box_score_data = pd.read_csv('country_box_scores_with_results_PP.csv', header = 0, delimiter=',')\n",
    "\n",
    "country_box_score_data.head()\n",
    "\n",
    "country_box_score_data.loc[(country_box_score_data['Team'] == \"CYPRUS\")  & (country_box_score_data['y'] == 1) ]\n",
    "# i want to find who won the most games, then the next most and so forth\n",
    "country_box_score_data.loc[(country_box_score_data['Team'] == \"ARGENTINA\")  & (country_box_score_data['y'] == 0)]\n",
    "\n",
    "# just do a group by for the python dictionary\n",
    "# NO NEED to do this in pandas..\n",
    "# group by, but do the avg?\n",
    "\n",
    "winning_countries = country_box_score_data.groupby(\"Team\").mean()\n",
    "winning_countries.sort_values('y')\n",
    "winning_countries\n",
    "winning_countries[[\"y\"]].sort_values('y')\n",
    "# winning_countries.columns\n",
    "# type(winning_countries)\n",
    "# sort \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next steps: try running Recursive Feature Elimination on ONLY the non-country features (FGM, FGA and so forth...)"
   ]
  }
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