Football Predictive Bot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BPL SOCCER PREDICTIVE BOT**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#importing all the libraries\n",
    "import pandas as pd \n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import classification_report\n",
    "from scipy import io\n",
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import voting_classifier\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Introduction to XGBoost**\n",
    "\n",
    "XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. XGBoost is recognized for its speed and performance\n",
    "\n",
    "Documentation:https://xgboost.readthedocs.io/en/latest/ \n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/\n",
    "\n",
    "**Note that my dataset \"Premier League Stats\" is updated weekly at :**\n",
    "http://www.football-data.co.uk/englandm.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 304 entries, 0 to 303\n",
      "Data columns (total 62 columns):\n",
      "Div         304 non-null object\n",
      "Date        304 non-null object\n",
      "HomeTeam    304 non-null object\n",
      "AwayTeam    304 non-null object\n",
      "FTHG        304 non-null int64\n",
      "FTAG        304 non-null int64\n",
      "FTR         304 non-null object\n",
      "HTHG        304 non-null int64\n",
      "HTAG        304 non-null int64\n",
      "HTR         304 non-null object\n",
      "Referee     304 non-null object\n",
      "HS          304 non-null int64\n",
      "AS          304 non-null int64\n",
      "HST         304 non-null int64\n",
      "AST         304 non-null int64\n",
      "HF          304 non-null int64\n",
      "AF          304 non-null int64\n",
      "HC          304 non-null int64\n",
      "AC          304 non-null int64\n",
      "HY          304 non-null int64\n",
      "AY          304 non-null int64\n",
      "HR          304 non-null int64\n",
      "AR          304 non-null int64\n",
      "B365H       304 non-null float64\n",
      "B365D       304 non-null float64\n",
      "B365A       304 non-null float64\n",
      "BWH         304 non-null float64\n",
      "BWD         304 non-null float64\n",
      "BWA         304 non-null float64\n",
      "IWH         304 non-null float64\n",
      "IWD         304 non-null float64\n",
      "IWA         304 non-null float64\n",
      "PSH         304 non-null float64\n",
      "PSD         304 non-null float64\n",
      "PSA         304 non-null float64\n",
      "WHH         304 non-null float64\n",
      "WHD         304 non-null float64\n",
      "WHA         304 non-null float64\n",
      "VCH         304 non-null float64\n",
      "VCD         304 non-null float64\n",
      "VCA         304 non-null float64\n",
      "Bb1X2       304 non-null int64\n",
      "BbMxH       304 non-null float64\n",
      "BbAvH       304 non-null float64\n",
      "BbMxD       304 non-null float64\n",
      "BbAvD       304 non-null float64\n",
      "BbMxA       304 non-null float64\n",
      "BbAvA       304 non-null float64\n",
      "BbOU        304 non-null int64\n",
      "BbMx>2.5    304 non-null float64\n",
      "BbAv>2.5    304 non-null float64\n",
      "BbMx<2.5    304 non-null float64\n",
      "BbAv<2.5    304 non-null float64\n",
      "BbAH        304 non-null int64\n",
      "BbAHh       304 non-null float64\n",
      "BbMxAHH     304 non-null float64\n",
      "BbAvAHH     304 non-null float64\n",
      "BbMxAHA     304 non-null float64\n",
      "BbAvAHA     304 non-null float64\n",
      "PSCH        304 non-null float64\n",
      "PSCD        304 non-null float64\n",
      "PSCA        304 non-null float64\n",
      "dtypes: float64(36), int64(19), object(7)\n",
      "memory usage: 147.3+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Div</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>...</th>\n",
       "      <th>BbAv&lt;2.5</th>\n",
       "      <th>BbAH</th>\n",
       "      <th>BbAHh</th>\n",
       "      <th>BbMxAHH</th>\n",
       "      <th>BbAvAHH</th>\n",
       "      <th>BbMxAHA</th>\n",
       "      <th>BbAvAHA</th>\n",
       "      <th>PSCH</th>\n",
       "      <th>PSCD</th>\n",
       "      <th>PSCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E0</td>\n",
       "      <td>10/08/2018</td>\n",
       "      <td>Man United</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>1.79</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.55</td>\n",
       "      <td>4.07</td>\n",
       "      <td>7.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E0</td>\n",
       "      <td>11/08/2018</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Cardiff</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>1.83</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0</td>\n",
       "      <td>11/08/2018</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>1.87</td>\n",
       "      <td>22</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>3.38</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0</td>\n",
       "      <td>11/08/2018</td>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>1.84</td>\n",
       "      <td>23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.06</td>\n",
       "      <td>7.24</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0</td>\n",
       "      <td>11/08/2018</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>1.81</td>\n",
       "      <td>20</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.74</td>\n",
       "      <td>3.53</td>\n",
       "      <td>1.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Div        Date      HomeTeam        AwayTeam  FTHG  FTAG FTR  HTHG  HTAG  \\\n",
       "0  E0  10/08/2018    Man United       Leicester     2     1   H     1     0   \n",
       "1  E0  11/08/2018   Bournemouth         Cardiff     2     0   H     1     0   \n",
       "2  E0  11/08/2018        Fulham  Crystal Palace     0     2   A     0     1   \n",
       "3  E0  11/08/2018  Huddersfield         Chelsea     0     3   A     0     2   \n",
       "4  E0  11/08/2018     Newcastle       Tottenham     1     2   A     1     2   \n",
       "\n",
       "  HTR  ...  BbAv<2.5  BbAH  BbAHh  BbMxAHH  BbAvAHH  BbMxAHA  BbAvAHA  PSCH  \\\n",
       "0   H  ...      1.79    17  -0.75     1.75     1.70     2.29     2.21  1.55   \n",
       "1   H  ...      1.83    20  -0.75     2.20     2.13     1.80     1.75  1.88   \n",
       "2   A  ...      1.87    22  -0.25     2.18     2.11     1.81     1.77  2.62   \n",
       "3   A  ...      1.84    23   1.00     1.84     1.80     2.13     2.06  7.24   \n",
       "4   A  ...      1.81    20   0.25     2.20     2.12     1.80     1.76  4.74   \n",
       "\n",
       "   PSCD  PSCA  \n",
       "0  4.07  7.69  \n",
       "1  3.61  4.70  \n",
       "2  3.38  2.90  \n",
       "3  3.95  1.58  \n",
       "4  3.53  1.89  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Mlej\\futbol-prediction-bot\\csv\\Premier League Stats.csv\")\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleaning**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       HomeTeam        AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR  HS  AS  HST  \\\n",
      "0    Man United       Leicester     2     1   H     1     0   H   8  13    6   \n",
      "1   Bournemouth         Cardiff     2     0   H     1     0   H  12  10    4   \n",
      "2        Fulham  Crystal Palace     0     2   A     0     1   A  15  10    6   \n",
      "3  Huddersfield         Chelsea     0     3   A     0     2   A   6  13    1   \n",
      "4     Newcastle       Tottenham     1     2   A     1     2   A  15  15    2   \n",
      "5       Watford        Brighton     2     0   H     1     0   H  19   6    5   \n",
      "6        Wolves         Everton     2     2   D     1     1   D  11   6    4   \n",
      "7       Arsenal        Man City     0     2   A     0     1   A   9  17    3   \n",
      "8     Liverpool        West Ham     4     0   H     2     0   H  18   5    8   \n",
      "9   Southampton         Burnley     0     0   D     0     0   D  18  16    3   \n",
      "\n",
      "   AST  HF  AF  HC  AC  HY  AY  HR  AR  \n",
      "0    4  11   8   2   5   2   1   0   0  \n",
      "1    1  11   9   7   4   1   1   0   0  \n",
      "2    9   9  11   5   5   1   2   0   0  \n",
      "3    4   9   8   2   5   2   1   0   0  \n",
      "4    5  11  12   3   5   2   2   0   0  \n",
      "5    0  10  16   8   2   2   2   0   0  \n",
      "6    5   8   7   3   6   0   1   0   1  \n",
      "7    8  11  14   2   9   2   2   0   0  \n",
      "8    2  14   9   5   4   1   2   0   0  \n",
      "9    6  10   9   8   5   0   1   0   0  \n",
      "(304, 20)\n"
     ]
    }
   ],
   "source": [
    "#Getting rid of all betting data from websites sur as Bet365, Betfair, etc.\n",
    "desired_columns=['HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','HS','AS','HST','AST','HF','AF','HC','AC','HY','AY','HR','AR']\n",
    "BPLData = df[desired_columns]\n",
    "print(BPLData.head(10))\n",
    "#print(BPLData.tail())\n",
    "print(BPLData.shape)\n",
    "#df.drop(['Div','Date','Referee','B365H','B365D', 'B365A', 'BWH', 'BWD','BWA','IWH','IWD','IWA','PSH', 'PSD', 'PSA','WHH','WHD','WHA','VCH','VCD','VCA','Bb1X2','BbMxH','BbAvH','BbMxD','BbAvD','BbMxA','BbAvA','BbOU','BbMx>2.5','BbAv>2.5','BbMx<2.5','BbAv<2.5','BbAH','bAHh','BbMxAHH','BbAvAHH','BbMxAHA','BbAvAHA','PSCH','PSCD','PSCA'], axis=1, inplace=True)\n",
    "\n",
    "#Removed Division (useless), Data, Referee, and all betting data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Exploration** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 features\n",
      "304 matches \n",
      "          AwayTeam  HomeTeam\n",
      "0          Arsenal        14\n",
      "1      Bournemouth        15\n",
      "2         Brighton        15\n",
      "3          Burnley        16\n",
      "4          Cardiff        14\n",
      "5          Chelsea        15\n",
      "6   Crystal Palace        15\n",
      "7          Everton        15\n",
      "8           Fulham        16\n",
      "9     Huddersfield        15\n",
      "10       Leicester        16\n",
      "11       Liverpool        16\n",
      "12        Man City        14\n",
      "13      Man United        16\n",
      "14       Newcastle        15\n",
      "15     Southampton        15\n",
      "16       Tottenham        16\n",
      "17         Watford        16\n",
      "18        West Ham        15\n",
      "19          Wolves        15\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Inspecting our dataset to find interesting insights \n",
    "\n",
    "n_features = BPLData.shape[1] #gives the number of features .shape[1] gives the number of columns (number of features)\n",
    "n_matches = BPLData.shape[0] #gives the number of matches .shape[0] gives the number of sample in the dataset\n",
    "print(str(n_features) + ' features')\n",
    "print(str(n_matches) + ' matches ')\n",
    "\n",
    "total_home_goal_per_team = BPLData.groupby('HomeTeam').FTHG.sum().reset_index()\n",
    "#print(total_home_goal_per_team)#we can clearly see that Home teams score more goals than away teams on average\n",
    "\n",
    "total_away_goal_per_team = BPLData.groupby('AwayTeam').FTAG.sum().reset_index()\n",
    "#print(total_away_goal_per_team)\n",
    "\n",
    "total_goal_conceded_per_home_team = BPLData.groupby('HomeTeam').FTAG.sum().reset_index()\n",
    "#print(total_goal_conceded_per_home_team)\n",
    "\n",
    "total_goal_conceded_per_away_team = BPLData.groupby('AwayTeam').FTHG.sum().reset_index()\n",
    "#print(total_goal_conceded_per_away_team)\n",
    "\n",
    "#Let's find the average home and away goals scored by each team\n",
    "#First we have to find how many home and away game each team played ***Considering \n",
    "numberOfHomeGamesPerTeam = BPLData.groupby('HomeTeam').AwayTeam.count().reset_index()\n",
    "#print(numberOfHomeGamesPerTeam)\n",
    "#numberOfHomeGamesPerTeam.info()\n",
    "numberOfAwayGamesPerTeam = BPLData.groupby('AwayTeam').HomeTeam.count().reset_index()\n",
    "print(numberOfAwayGamesPerTeam)\n",
    "\n",
    "\n",
    "#geting a list of the teams\n",
    "arrayOfTeamsInOrder = BPLData.HomeTeam.sort_values().unique()\n",
    "listOfTeamsInOrder = list(arrayOfTeamsInOrder)\n",
    "#print(listOfTeamsInOrder)\n",
    "\n",
    "#We divide by the number of home/away goals by the number of home/away games to find the average of \n",
    "total_home_goal_per_team['AverageHomeGoalsPerGame'] = (total_home_goal_per_team['FTHG']/numberOfHomeGamesPerTeam['AwayTeam'])\n",
    "#print(total_home_goal_per_team)\n",
    "\n",
    "plt.figure(figsize=(16, 5))\n",
    "plt.bar(range(len(total_home_goal_per_team['AverageHomeGoalsPerGame'])),total_home_goal_per_team['AverageHomeGoalsPerGame'], Color='#0000cc')\n",
    "plt.xlabel('Barclays Premier League Team')\n",
    "plt.ylabel('Average Goals Per Game')\n",
    "plt.title('Graph of Average Home Goals Per Game')\n",
    "ax = plt.subplot(1,1,1)\n",
    "ax.set_xticks(range(len(total_home_goal_per_team['AverageHomeGoalsPerGame'])))\n",
    "ax.set_xticklabels(listOfTeamsInOrder, rotation = 50)\n",
    "plt.show()\n",
    "plt.savefig('AverageGoalsHome.png')\n",
    "#From the graph we can clearly see the higher performance of the top six teams (Manchester City, Manchester United, Arsenal, Chelsea, Tottenham, Liverpool)\n",
    "\n",
    "total_away_goal_per_team['AverageAwayGoalsPerGame'] = (total_away_goal_per_team['FTAG']/numberOfAwayGamesPerTeam['HomeTeam'])\n",
    "#print(total_away_goal_per_team)\n",
    "plt.close('all')\n",
    "plt.figure(figsize=(16, 5))\n",
    "plt.bar(range(len(total_away_goal_per_team['AverageAwayGoalsPerGame'])),total_away_goal_per_team['AverageAwayGoalsPerGame'],Color='#9933ff')\n",
    "plt.xlabel('Barclays Premier League Team')\n",
    "plt.ylabel('Average Goals Per Game')\n",
    "plt.title('Graph of Average Away Goals Per Game')\n",
    "ax = plt.subplot(1,1,1)\n",
    "ax.set_xticks(range(len(total_home_goal_per_team['AverageHomeGoalsPerGame'])))\n",
    "ax.set_xticklabels(listOfTeamsInOrder, rotation = 50)\n",
    "plt.show()\n",
    "plt.savefig('AverageGoalsAway.png')\n",
    "\n",
    "\n",
    "#percentage of Home wins this season to see if home team advantage is true \n",
    "homeTeamStats = BPLData.groupby('FTR').HomeTeam.count().reset_index()\n",
    "#print(homeTeamStats)  #We can clearly see that there's a higher amount of H (represents HomeTeam wins) than A (represents Away team), therefore it confirms our hypothesis that Home Team has a higher winning chance\n",
    "#print(len(BPLData[BPLData.FTR == 'H']))\n",
    "\n",
    "#Calculating Home Win Percentage\n",
    "homeWinRate = (len(BPLData[BPLData.FTR == 'H'])/n_matches ) * 100\n",
    "#print('Home Team Win Rate: ' + str(homeWinRate)) #Home team has a 47.4% winning rate compared to 33.2% from the Away team\n",
    "\n",
    "awayWinRate = (len(BPLData[BPLData.FTR == 'A'])/n_matches ) * 100\n",
    "#print('Away Team Win Rate: ' + str(awayWinRate))\n",
    "\n",
    "#average goals by home team (without considering any particular team)\n",
    "totalHomeGoals = BPLData.FTHG.sum()\n",
    "averageHomeTeamGoal = float(totalHomeGoals) / n_matches \n",
    "#print(\"Average number of home goals :\", averageHomeTeamGoal) #We can clearly see from this print statement that on average the home team scores more than the away team.\n",
    "\n",
    "#average goals by away team (without considering any particular team)\n",
    "totalAwayGoals = BPLData.FTAG.sum()\n",
    "averageAwayTeamGoal = float(totalAwayGoals) / n_matches \n",
    "#print(\"Average number of away goals :\", averageAwayTeamGoal)\n",
    "\n",
    "#Inspecting every game of a single team, displaying every Man City games...\n",
    "df = BPLData[(BPLData['HomeTeam'] == 'Man City') | (BPLData['AwayTeam'] == 'Man City')]\n",
    "ManCity = df.iloc[:]\n",
    "#print(ManCity.head())\n",
    "\n",
    "averageHomeGoalsConceded = averageAwayTeamGoal\n",
    "averageAwayGoalsConceded = averageHomeTeamGoal\n",
    "#print(\"Average number of goals conceded at home\",avg_home_conceded_16)\n",
    "#print(\"Average number of goals conceded away\",avg_away_conceded_16)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Engineering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       HomeTeam        AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR  HS  AS  HST  \\\n",
      "0    Man United       Leicester     2     1   H     1     0   H   8  13    6   \n",
      "1   Bournemouth         Cardiff     2     0   H     1     0   H  12  10    4   \n",
      "2        Fulham  Crystal Palace     0     2   A     0     1   A  15  10    6   \n",
      "3  Huddersfield         Chelsea     0     3   A     0     2   A   6  13    1   \n",
      "4     Newcastle       Tottenham     1     2   A     1     2   A  15  15    2   \n",
      "\n",
      "   AST  HF  AF  HC  AC  HY  AY  HR  AR  \n",
      "0    4  11   8   2   5   2   1   0   0  \n",
      "1    1  11   9   7   4   1   1   0   0  \n",
      "2    9   9  11   5   5   1   2   0   0  \n",
      "3    4   9   8   2   5   2   1   0   0  \n",
      "4    5  11  12   3   5   2   2   0   0  \n"
     ]
    }
   ],
   "source": [
    "print(BPLData.head())\n",
    "#BPLData.groupby('FTR').HomeTeam.count().reset_index() example \n",
    "\n",
    "#print(numberOfHomeGamesPerTeam)\n",
    "#print(numberOfAwayGamesPerTeam)\n",
    "\n",
    "#Finding an index for yellow cards for teams at home or away\n",
    "totalYellowCardPerHomeTeam = BPLData.groupby('HomeTeam').HY.sum().reset_index()\n",
    "#print(totalYellowCardPerHomeTeam)\n",
    "totalYellowCardPerAwayTeam = BPLData.groupby('AwayTeam').AY.sum().reset_index()\n",
    "#print(totalYellowCardPerAwayTeam)\n",
    "\n",
    "totalYellowCardHome = BPLData['HY'].sum()\n",
    "#print(totalYellowCardHome)\n",
    "totalYellowCardAway = BPLData['AY'].sum()\n",
    "#print(totalYellowCardAway)\n",
    "\n",
    "averageYellowCardHome = totalYellowCardHome/n_matches\n",
    "#print(averageYellowCardHome)\n",
    "averageYellowCardAway = totalYellowCardAway/n_matches\n",
    "#print(averageYellowCardAway)\n",
    "\n",
    "yellowCardIndexPerHomeTeam = (totalYellowCardPerHomeTeam.HY/numberOfHomeGamesPerTeam.AwayTeam) / averageYellowCardHome\n",
    "#print(yellowCardIndexPerHomeTeam)\n",
    "yellowCardIndexPerAwayTeam = (totalYellowCardPerAwayTeam.AY/numberOfAwayGamesPerTeam.HomeTeam) / averageYellowCardAway\n",
    "#print(yellowCardIndexPerAwayTeam)\n",
    "\n",
    "#Finding an index for red cards for teams at home or away\n",
    "\n",
    "totalRedCardPerHomeTeam = BPLData.groupby('HomeTeam').HR.sum().reset_index()\n",
    "#print(totalRedCardPerAwayTeam)\n",
    "totalRedCardPerAwayTeam = BPLData.groupby('AwayTeam').AR.sum().reset_index()\n",
    "#print(totalRedCardPerAwayTeam)\n",
    "totalRedCardHome = BPLData.HR.sum()\n",
    "# print(totalRedCardHome)\n",
    "totalRedCardAway = BPLData.AR.sum()\n",
    "# print(totalRedCardAway)\n",
    "averageRedCardHome = totalRedCardHome / n_matches\n",
    "#print(averageRedCardHome)\n",
    "averageRedCardAway = totalRedCardAway / n_matches \n",
    "#print(averageRedCardAway)\n",
    "\n",
    "redCardIndexPerHomeTeam = (totalRedCardPerHomeTeam.HR/numberOfHomeGamesPerTeam.AwayTeam) / averageRedCardHome\n",
    "#print(redCardIndexPerHomeTeam)\n",
    "redCardIndexPerAwayTeam = (totalRedCardPerAwayTeam.AR/numberOfAwayGamesPerTeam.HomeTeam) / averageRedCardAway\n",
    "#print(redCardIndexPerAwayTeam)\n",
    "\n",
    "#Finding an index for the # of shots on target for teams at home or away\n",
    "\n",
    "totalShotOnTargetPerHomeTeam = BPLData.groupby('HomeTeam').HST.sum().reset_index()\n",
    "#print(totalShotOnTargetPerHomeTeam)\n",
    "totalShotOnTargetPerAwayTeam = BPLData.groupby('AwayTeam').AST.sum().reset_index()\n",
    "#print(totalShotOnTargetPerAwayTeam)\n",
    "totalShotOnTargetHome = BPLData.HST.sum()\n",
    "#print(totalShotOnTargetHome)\n",
    "totalShotOnTargetAway = BPLData.AST.sum()\n",
    "#print(totalShotOnTargetAway)\n",
    "\n",
    "shotOnTargetIndexPerHomeTeam = (totalShotOnTargetPerHomeTeam.HST/numberOfHomeGamesPerTeam.AwayTeam) / (totalShotOnTargetHome/n_matches)\n",
    "#print(shotOnTargetIndexPerHomeTeam)\n",
    "shotOnTargetIndexPerAwayTeam = (totalShotOnTargetPerAwayTeam.AST/numberOfAwayGamesPerTeam.HomeTeam) / (totalShotOnTargetAway/n_matches)\n",
    "#print(shotOnTargetIndexPerAwayTeam)\n",
    "\n",
    "#Finding an index for the number of fouls \n",
    "\n",
    "totalFoulsPerHomeTeam = BPLData.groupby('HomeTeam').HF.sum().reset_index() \n",
    "#print(totalFoulsPerHomeTeam)\n",
    "totalFoulsPerAwayTeam = BPLData.groupby('AwayTeam').AF.sum().reset_index() \n",
    "#print(totalFoulsPerAwayTeam)\n",
    "totalHomeFouls = BPLData.HF.sum()\n",
    "#print(totalHomeFouls)\n",
    "totalAwayFouls = BPLData.AF.sum()\n",
    "#print(totalAwayFouls)\n",
    "foulIndexPerHomeTeam = (totalFoulsPerHomeTeam.HF/numberOfHomeGamesPerTeam.AwayTeam) / (totalHomeFouls/n_matches)\n",
    "# print(foulIndexPerHomeTeam)\n",
    "foulIndexPerAwayTeam = (totalFoulsPerAwayTeam.AF/numberOfAwayGamesPerTeam.HomeTeam) / (totalAwayFouls/n_matches)\n",
    "# print(foulIndexPerAwayTeam)\n",
    "\n",
    "#Finding an index for the number of shots \n",
    "totalShotsPerHomeTeam = BPLData.groupby('HomeTeam').HS.sum().reset_index()\n",
    "#print(totalShotsPerHomeTeam)\n",
    "totalShotsPerAwayTeam = BPLData.groupby('AwayTeam').AS.sum().reset_index()\n",
    "#print(totalShotsPerAwayTeam)\n",
    "totalHomeShots = BPLData.HS.sum()\n",
    "#print(totalHomeShots)\n",
    "totalAwayShots = BPLData.AS.sum()\n",
    "#print(totalAwayShots)\n",
    "shotIndexPerHomeTeam = (totalShotsPerHomeTeam.HS/numberOfHomeGamesPerTeam.AwayTeam) / (totalHomeShots/n_matches)\n",
    "# print(shotIndexPerHomeTeam)\n",
    "shotIndexPerAwayTeam = (totalShotsPerAwayTeam.AS/numberOfAwayGamesPerTeam.HomeTeam) / (totalAwayShots/n_matches)\n",
    "# print(shotIndexPerAwayTeam)\n",
    "\n",
    "#Finding an index for the number of cornerkicks\n",
    "totalNumberOfCornerKicksPerHomeTeam = BPLData.groupby('HomeTeam').HC.sum().reset_index()\n",
    "#print(totalNumberOfCornerKicksPerHomeTeam)\n",
    "totalNumberOfCornerKicksPerAwayTeam = BPLData.groupby('AwayTeam').AC.sum().reset_index()\n",
    "#print(totalNumberOfCornerKicksPerAwayTeam)\n",
    "totalCornerKicksHome = BPLData.HC.sum()\n",
    "#print(totalCornerKicksHome)\n",
    "totalCornerKicksAway = BPLData.AC.sum()\n",
    "#print(totalCornerKicksAway)\n",
    "cornerKicksIndexPerHomeTeam = (totalNumberOfCornerKicksPerHomeTeam.HC/numberOfHomeGamesPerTeam.AwayTeam) / (totalCornerKicksHome/n_matches)\n",
    "#print(cornerKicksIndexPerHomeTeam)\n",
    "cornerKicksIndexPerAwayTeam = (totalNumberOfCornerKicksPerAwayTeam.AC/numberOfAwayGamesPerTeam.HomeTeam) / (totalCornerKicksAway/n_matches)\n",
    "#print(cornerKicksIndexPerAwayTeam)\n",
    "\n",
    "#Finding an index of goal efficiency or we can say Conversion Rate (Number of Goals / Number of Shots) VERY IMPORTANT tells us how a team is efficient in front of the goal\n",
    "totalNumberOfGoalsPerHomeTeam = BPLData.groupby('HomeTeam').FTHG.sum().reset_index()\n",
    "#print(totalNumberOfGoalsPerHomeTeam)\n",
    "totalNumberOfGoalsPerAwayTeam = BPLData.groupby('AwayTeam').FTAG.sum().reset_index()\n",
    "#print(totalNumberOfGoalsPerAwayTeam)\n",
    "totalNumberOfShotsPerHomeTeam = BPLData.groupby('HomeTeam').HS.sum().reset_index()\n",
    "#print(totalNumberOfShotsPerHomeTeam)\n",
    "totalNumberOfShotsPerAwayTeam = BPLData.groupby('AwayTeam').AS.sum().reset_index()\n",
    "#print(totalNumberOfShotsPerAwayTeam)\n",
    "\n",
    "homeConversionRate = (totalNumberOfGoalsPerHomeTeam.FTHG/totalNumberOfShotsPerHomeTeam.HS) * 100\n",
    "#print(homeConversionRate)\n",
    "awayConversionRate = (totalNumberOfGoalsPerAwayTeam.FTAG/totalNumberOfShotsPerAwayTeam.AS) * 100\n",
    "#print(awayConversionRate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Graph that demonstrate home advantage**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    " \n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "from : https://link.springer.com/article/10.1007/s10994-018-5741-1\n",
    "\n",
    "\n",
    "The number of matches per number of goals scored by the home (dark grey) and away team (light grey)\n",
    "Figure 2 shows the number of matches per number of goals scored by the home (dark grey) and away (light grey) teams. Home teams appear to score more goals than away teams, with home teams having consistently higher frequencies for two or more goals and away teams having higher frequencies for no goal and one goal. Overall, home teams scored 304,918 goals over the whole data set, whereas away teams scored 228,293 goals. In Section 1 of the Supplementary Material, the trend shown in Fig. 2 is also found to be present within each country, pointing towards the existence of a home advantage.\n",
    "\n",
    "\n",
    "From this historical statistic, we can also affirm that home teams definitely have an advantage as we can think of the following reasons:\n",
    "\n",
    "- Football is a team sport, a cheering crowd helps morale\n",
    "- Familarity with pitch and weather conditions helps\n",
    "- No need to travel (less fatigue)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data preprocessing**\n",
    "\n",
    "This is a supervised learning problem. We have one target variable which is FTR (stands for full-time result). We want to predict FTR given all the features in the dataset. We also want to standarize our dataset and put it on the same scale (mean=0, variance=1) \n",
    "\n",
    "We want to combine or extract certain columns of this dataset to find the final features that we are going to work with.\n",
    "\n",
    "features:\n",
    "- last 5 games form (in the form of an integer 1.0 representing a Win, 0.5 Draw and 0.0 Loss)\n",
    "- Attacking Score (float, based on the number of chances created per game, the number of goals scored and more...)\n",
    "- Defending Score (float, based on the number of goals conceded per game, fouls committed, yellow and red cards conceded and more..)\n",
    "\n",
    "To do list:\n",
    "- find a way to turn free kicks and corner kicks into goal scoring opportunities (how to consider them)?\n",
    "- append last 5 games results to model\n",
    "- Hyperparameter tuning to increase the performance of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Team  HGS  AGS       HAS       AAS  HGC  AGC       HDS  \\\n",
      "0          Arsenal   37   26  1.455487  1.474077   12   27  0.595300   \n",
      "1      Bournemouth   28   15  1.101449  0.793734   21   35  1.041775   \n",
      "2         Brighton   17   15  0.764271  0.793734   15   27  0.850429   \n",
      "3          Burnley   19   16  0.797239  0.793734   28   31  1.481636   \n",
      "4          Cardiff   18    9  0.708075  0.510257   31   26  1.537859   \n",
      "5          Chelsea   29   21  1.216839  1.111227   10   23  0.529156   \n",
      "6   Crystal Palace   11   25  0.461560  1.322889   17   24  0.899565   \n",
      "7          Everton   23   20  0.904762  1.058312   21   21  1.041775   \n",
      "8           Fulham   19   10  0.797239  0.496084   30   40  1.587467   \n",
      "9     Huddersfield    7   11  0.275362  0.582071   24   33  1.190601   \n",
      "10       Leicester   19   21  0.797239  1.041775   19   24  1.005396   \n",
      "11       Liverpool   44   26  1.846239  1.289817    9    9  0.476240   \n",
      "12        Man City   53   26  2.084886  1.474077   12    9  0.595300   \n",
      "13      Man United   28   30  1.258799  1.488251   18   22  1.020515   \n",
      "14       Newcastle   19   12  0.747412  0.634987   20   20  0.992167   \n",
      "15     Southampton   19   15  0.797239  0.793734   22   28  1.164143   \n",
      "16       Tottenham   25   32  1.123928  1.587467   13   19  0.737038   \n",
      "17         Watford   19   23  0.854185  1.140992   19   25  1.077210   \n",
      "18        West Ham   27   14  1.062112  0.740818   23   23  1.140992   \n",
      "19          Wolves   22   16  0.923119  0.846649   19   17  1.005396   \n",
      "\n",
      "         ADS     HTYCI    ...        HTSOTI    ATSOTI      HTFI      ATFI  \\\n",
      "0   1.213842  1.162447    ...      1.023874  1.059233  1.090652  1.042835   \n",
      "1   1.468599  1.282700    ...      0.894270  1.005663  0.911857  0.749194   \n",
      "2   1.132919  1.145268    ...      0.666537  0.818167  1.178347  1.267867   \n",
      "3   1.219462  1.026160    ...      0.732697  0.671152  0.947219  0.876461   \n",
      "4   1.168885  1.202532    ...      0.712824  0.730506  0.995295  0.926202   \n",
      "5   0.965079  0.684107    ...      1.230377  1.142024  0.801004  0.896472   \n",
      "6   1.007039  0.855134    ...      0.829468  0.920437  0.921790  1.056556   \n",
      "7   0.881159  0.881857    ...      0.842428  1.056798  1.138331  1.037346   \n",
      "8   1.573499  1.239944    ...      1.064484  0.783011  1.087077  1.116588   \n",
      "9   1.384679  0.841772    ...      0.609141  0.869302  1.048934  1.069363   \n",
      "10  0.944099  1.197187    ...      1.036835  1.102607  0.953576  0.852449   \n",
      "11  0.354037  0.555837    ...      1.437744  1.358284  0.832790  0.900474   \n",
      "12  0.404614  0.521097    ...      1.658936  1.424486  0.810540  0.809569   \n",
      "13  0.865424  1.007836    ...      1.496005  1.390244  1.069367  1.152607   \n",
      "14  0.839199  0.921941    ...      0.803547  0.835212  1.048934  1.159010   \n",
      "15  1.174879  1.453727    ...      0.995362  1.073844  1.157005  0.992522   \n",
      "16  0.747412  0.732972    ...      1.155330  1.166526  0.926331  1.020537   \n",
      "17  0.983437  1.099458    ...      0.888716  0.814971  1.137480  1.092575   \n",
      "18  0.965079  0.962025    ...      1.010914  0.801121  0.882058  0.870858   \n",
      "19  0.713320  1.239944    ...      0.953888  0.971573  1.087077  1.094976   \n",
      "\n",
      "        HTSI      ATSI     HTCKI     ATCKI       HTCR       ATCR  \n",
      "0   0.959953  0.987013  1.073818  0.908023  17.050691  17.333333  \n",
      "1   0.813970  1.050182  0.876586  1.256621  15.217391   8.771930  \n",
      "2   0.753301  0.859798  0.826495  0.847489  11.409396  10.714286  \n",
      "3   0.825766  0.731212  0.783083  0.712329  10.857143  12.598425  \n",
      "4   0.871478  0.875152  0.854671  0.876712   9.137056   6.766917  \n",
      "5   1.184385  1.375677  1.051903  1.095890  11.553785   9.375000  \n",
      "6   1.146636  0.933495  1.332411  0.891324   4.526749  16.447368  \n",
      "7   0.977648  0.933495  1.008074  1.022831  10.407240  13.157895  \n",
      "8   1.099449  0.800303  0.935025  0.643836   8.154506   7.194245  \n",
      "9   0.765308  0.982626  0.788927  0.920548   4.046243   6.875000  \n",
      "10  1.108886  1.042121  1.028527  1.219178   8.085106  11.602210  \n",
      "11  1.283477  1.116970  1.238908  1.342466  16.176471  13.402062  \n",
      "12  1.437718  1.316017  1.325836  1.737769  16.307692  13.000000  \n",
      "13  1.097090  1.116970  1.152084  0.958904  12.903226  15.463918  \n",
      "14  0.968801  0.872081  1.062860  0.803653   8.675799   8.450704  \n",
      "15  0.953170  1.105455  0.899962  0.949772   9.405941   8.333333  \n",
      "16  1.041477  1.128485  0.939199  1.082192  12.135922  16.326531  \n",
      "17  0.788691  0.944242  0.813973  1.000000  12.179487  14.024390  \n",
      "18  0.946682  0.841374  1.051903  0.803653  12.616822  10.218978  \n",
      "19  0.972045  1.007192  0.935025  0.964384  10.679612   9.756098  \n",
      "\n",
      "[20 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "#Creating new DataFrame that displays characteristics per team with the following features: Home Goals Score, Away Goals Score, Home/Away Attack Strength, Home Goals Conceded, Away Goals Conceded, Home/Away Defensive Strength\n",
    "columns = ['Team','HGS','AGS','HAS','AAS','HGC','AGC','HDS','ADS','HTYCI','ATYCI','HTRCI',\n",
    "           'ATRCI','HTSOTI','ATSOTI','HTFI','ATFI','HTSI','ATSI','HTCKI','ATCKI','HTCR','ATCR']\n",
    "season19 = pd.DataFrame(columns=columns)\n",
    "season19.Team = listOfTeamsInOrder #list of the teams in order \n",
    "season19.HGS = total_home_goal_per_team.FTHG #Total home goal scored per team\n",
    "season19.AGS = total_away_goal_per_team.FTAG #Total Away goal scored per team \n",
    "season19.HAS = (season19.HGS/numberOfHomeGamesPerTeam.AwayTeam) / averageHomeTeamGoal #Home team attacking strength\n",
    "season19.AAS = (season19.AGS/numberOfAwayGamesPerTeam.HomeTeam) / averageAwayTeamGoal #Away team attacking strenth\n",
    "season19.HGC = total_goal_conceded_per_home_team.FTAG #Total home goal conceded per team \n",
    "season19.AGC = total_goal_conceded_per_away_team.FTHG #Total away goal conceded per team \n",
    "season19.HDS = (season19.HGC/numberOfHomeGamesPerTeam.AwayTeam) / averageHomeGoalsConceded #Home team defensive strength\n",
    "season19.ADS = (season19.AGC/numberOfAwayGamesPerTeam.HomeTeam) / averageAwayGoalsConceded #Away team defensive strength \n",
    "season19.HTYCI = yellowCardIndexPerHomeTeam\n",
    "season19.ATYCI = yellowCardIndexPerAwayTeam\n",
    "season19.HTRCI = redCardIndexPerHomeTeam\n",
    "season19.ATRCI = redCardIndexPerAwayTeam\n",
    "season19.HTSOTI = shotOnTargetIndexPerHomeTeam\n",
    "season19.ATSOTI = shotOnTargetIndexPerAwayTeam\n",
    "season19.HTFI = foulIndexPerHomeTeam\n",
    "season19.ATFI = foulIndexPerAwayTeam\n",
    "season19.HTSI = shotIndexPerHomeTeam\n",
    "season19.ATSI = shotIndexPerAwayTeam\n",
    "season19.HTCKI = cornerKicksIndexPerHomeTeam\n",
    "season19.ATCKI = cornerKicksIndexPerAwayTeam\n",
    "season19.HTCR = homeConversionRate\n",
    "season19.ATCR = awayConversionRate\n",
    "print(season19)\n",
    "\n",
    "season19.to_csv(r'C:\\Users\\Mlej\\futbol-prediction-bot\\statsPerEPLTeam.csv', index = None, header = True)\n",
    "#Important note: The higher a team's HAS/AAS score is, the better they are at scoring goals\n",
    "#In the other side: The lower a team's HDS/ADS is, the better they are at not conceding goals, which means the stronger they are in defense, the lower their HDS/ADS will be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:90: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:91: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:92: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:93: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:94: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:96: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:97: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:98: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:99: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:100: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:101: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           HomeTeam        AwayTeam FTR       HAS       HDS     HTYCI  \\\n",
      "0        Man United       Leicester   H  1.258799  1.020515  1.007836   \n",
      "1       Bournemouth         Cardiff   H  1.101449  1.041775  1.282700   \n",
      "2            Fulham  Crystal Palace   A  0.797239  1.587467  1.239944   \n",
      "3      Huddersfield         Chelsea   A  0.275362  1.190601  0.841772   \n",
      "4         Newcastle       Tottenham   A  0.747412  0.992167  0.921941   \n",
      "5           Watford        Brighton   H  0.854185  1.077210  1.099458   \n",
      "6            Wolves         Everton   D  0.923119  1.005396  1.239944   \n",
      "7           Arsenal        Man City   A  1.455487  0.595300  1.162447   \n",
      "8         Liverpool        West Ham   H  1.846239  0.476240  0.555837   \n",
      "9       Southampton         Burnley   D  0.797239  1.164143  1.453727   \n",
      "10          Cardiff       Newcastle   D  0.708075  1.537859  1.202532   \n",
      "11          Chelsea         Arsenal   H  1.216839  0.529156  0.684107   \n",
      "12          Everton     Southampton   H  0.904762  1.041775  0.881857   \n",
      "13        Leicester          Wolves   H  0.797239  1.005396  1.197187   \n",
      "14        Tottenham          Fulham   H  1.123928  0.737038  0.732972   \n",
      "15         West Ham     Bournemouth   A  1.062112  1.140992  0.962025   \n",
      "16         Brighton      Man United   H  0.764271  0.850429  1.145268   \n",
      "17          Burnley         Watford   A  0.797239  1.481636  1.026160   \n",
      "18         Man City    Huddersfield   H  2.084886  0.595300  0.521097   \n",
      "19   Crystal Palace       Liverpool   A  0.461560  0.899565  0.855134   \n",
      "20          Arsenal        West Ham   H  1.455487  0.595300  1.162447   \n",
      "21      Bournemouth         Everton   D  1.101449  1.041775  1.282700   \n",
      "22     Huddersfield         Cardiff   D  0.275362  1.190601  0.841772   \n",
      "23        Liverpool        Brighton   H  1.846239  0.476240  0.555837   \n",
      "24      Southampton       Leicester   A  0.797239  1.164143  1.453727   \n",
      "25           Wolves        Man City   D  0.923119  1.005396  1.239944   \n",
      "26           Fulham         Burnley   H  0.797239  1.587467  1.239944   \n",
      "27        Newcastle         Chelsea   A  0.747412  0.992167  0.921941   \n",
      "28          Watford  Crystal Palace   H  0.854185  1.077210  1.099458   \n",
      "29       Man United       Tottenham   A  1.258799  1.020515  1.007836   \n",
      "..              ...             ...  ..       ...       ...       ...   \n",
      "274         Chelsea       Tottenham   H  1.216839  0.529156  0.684107   \n",
      "275  Crystal Palace      Man United   A  0.461560  0.899565  0.855134   \n",
      "276       Liverpool         Watford   H  1.846239  0.476240  0.555837   \n",
      "277        Man City        West Ham   H  2.084886  0.595300  0.521097   \n",
      "278     Southampton          Fulham   H  0.797239  1.164143  1.453727   \n",
      "279     Bournemouth        Man City   A  1.101449  1.041775  1.282700   \n",
      "280        Brighton    Huddersfield   H  0.764271  0.850429  1.145268   \n",
      "281         Burnley  Crystal Palace   A  0.797239  1.481636  1.026160   \n",
      "282      Man United     Southampton   H  1.258799  1.020515  1.007836   \n",
      "283       Tottenham         Arsenal   D  1.123928  0.737038  0.732972   \n",
      "284        West Ham       Newcastle   H  1.062112  1.140992  0.962025   \n",
      "285          Wolves         Cardiff   H  0.923119  1.005396  1.239944   \n",
      "286         Everton       Liverpool   D  0.904762  1.041775  0.881857   \n",
      "287          Fulham         Chelsea   A  0.797239  1.587467  1.239944   \n",
      "288         Watford       Leicester   H  0.854185  1.077210  1.099458   \n",
      "289         Cardiff        West Ham   H  0.708075  1.537859  1.202532   \n",
      "290  Crystal Palace        Brighton   A  0.461560  0.899565  0.855134   \n",
      "291    Huddersfield     Bournemouth   A  0.275362  1.190601  0.841772   \n",
      "292       Leicester          Fulham   H  0.797239  1.005396  1.197187   \n",
      "293        Man City         Watford   H  2.084886  0.595300  0.521097   \n",
      "294       Newcastle         Everton   H  0.747412  0.992167  0.921941   \n",
      "295     Southampton       Tottenham   H  0.797239  1.164143  1.453727   \n",
      "296         Arsenal      Man United   H  1.455487  0.595300  1.162447   \n",
      "297         Chelsea          Wolves   D  1.216839  0.529156  0.684107   \n",
      "298       Liverpool         Burnley   H  1.846239  0.476240  0.555837   \n",
      "299     Bournemouth       Newcastle   D  1.101449  1.041775  1.282700   \n",
      "300         Burnley       Leicester   A  0.797239  1.481636  1.026160   \n",
      "301        West Ham    Huddersfield   H  1.062112  1.140992  0.962025   \n",
      "302         Everton         Chelsea   H  0.904762  1.041775  0.881857   \n",
      "303          Fulham       Liverpool   A  0.797239  1.587467  1.239944   \n",
      "\n",
      "        HTRCI    HTSOTI      HTFI      HTSI    ...           HTCR       AAS  \\\n",
      "0    1.447619  1.496005  1.069367  1.097090    ...      12.903226  1.041775   \n",
      "1    1.266667  0.894270  0.911857  0.813970    ...      15.217391  0.510257   \n",
      "2    1.351111  1.064484  1.087077  1.099449    ...       8.154506  1.322889   \n",
      "3    3.800000  0.609141  1.048934  0.765308    ...       4.046243  1.111227   \n",
      "4    1.266667  0.803547  1.048934  0.968801    ...       8.675799  1.587467   \n",
      "5    1.447619  0.888716  1.137480  0.788691    ...      12.179487  0.793734   \n",
      "6    0.000000  0.953888  1.087077  0.972045    ...      10.679612  1.058312   \n",
      "7    0.000000  1.023874  1.090652  0.959953    ...      17.050691  1.474077   \n",
      "8    1.351111  1.437744  0.832790  1.283477    ...      16.176471  0.740818   \n",
      "9    2.702222  0.995362  1.157005  0.953170    ...       9.405941  0.793734   \n",
      "10   0.000000  0.712824  0.995295  0.871478    ...       9.137056  0.634987   \n",
      "11   0.000000  1.230377  0.801004  1.184385    ...      11.553785  1.474077   \n",
      "12   0.000000  0.842428  1.138331  0.977648    ...      10.407240  0.793734   \n",
      "13   2.702222  1.036835  0.953576  1.108886    ...       8.085106  0.846649   \n",
      "14   0.000000  1.155330  0.926331  1.041477    ...      12.135922  0.496084   \n",
      "15   0.000000  1.010914  0.882058  0.946682    ...      12.616822  0.793734   \n",
      "16   1.447619  0.666537  1.178347  0.753301    ...      11.409396  1.488251   \n",
      "17   0.000000  0.732697  0.947219  0.825766    ...      10.857143  1.140992   \n",
      "18   0.000000  1.658936  0.810540  1.437718    ...      16.307692  0.582071   \n",
      "19   1.351111  0.829468  0.921790  1.146636    ...       4.526749  1.289817   \n",
      "20   0.000000  1.023874  1.090652  0.959953    ...      17.050691  0.740818   \n",
      "21   1.266667  0.894270  0.911857  0.813970    ...      15.217391  1.058312   \n",
      "22   3.800000  0.609141  1.048934  0.765308    ...       4.046243  0.510257   \n",
      "23   1.351111  1.437744  0.832790  1.283477    ...      16.176471  0.793734   \n",
      "24   2.702222  0.995362  1.157005  0.953170    ...       9.405941  1.041775   \n",
      "25   0.000000  0.953888  1.087077  0.972045    ...      10.679612  1.474077   \n",
      "26   1.351111  1.064484  1.087077  1.099449    ...       8.154506  0.793734   \n",
      "27   1.266667  0.803547  1.048934  0.968801    ...       8.675799  1.111227   \n",
      "28   1.447619  0.888716  1.137480  0.788691    ...      12.179487  1.322889   \n",
      "29   1.447619  1.496005  1.069367  1.097090    ...      12.903226  1.587467   \n",
      "..        ...       ...       ...       ...    ...            ...       ...   \n",
      "274  0.000000  1.230377  0.801004  1.184385    ...      11.553785  1.587467   \n",
      "275  1.351111  0.829468  0.921790  1.146636    ...       4.526749  1.488251   \n",
      "276  1.351111  1.437744  0.832790  1.283477    ...      16.176471  1.140992   \n",
      "277  0.000000  1.658936  0.810540  1.437718    ...      16.307692  0.740818   \n",
      "278  2.702222  0.995362  1.157005  0.953170    ...       9.405941  0.496084   \n",
      "279  1.266667  0.894270  0.911857  0.813970    ...      15.217391  1.474077   \n",
      "280  1.447619  0.666537  1.178347  0.753301    ...      11.409396  0.582071   \n",
      "281  0.000000  0.732697  0.947219  0.825766    ...      10.857143  1.322889   \n",
      "282  1.447619  1.496005  1.069367  1.097090    ...      12.903226  0.793734   \n",
      "283  0.000000  1.155330  0.926331  1.041477    ...      12.135922  1.474077   \n",
      "284  0.000000  1.010914  0.882058  0.946682    ...      12.616822  0.634987   \n",
      "285  0.000000  0.953888  1.087077  0.972045    ...      10.679612  0.510257   \n",
      "286  0.000000  0.842428  1.138331  0.977648    ...      10.407240  1.289817   \n",
      "287  1.351111  1.064484  1.087077  1.099449    ...       8.154506  1.111227   \n",
      "288  1.447619  0.888716  1.137480  0.788691    ...      12.179487  1.041775   \n",
      "289  0.000000  0.712824  0.995295  0.871478    ...       9.137056  0.740818   \n",
      "290  1.351111  0.829468  0.921790  1.146636    ...       4.526749  0.793734   \n",
      "291  3.800000  0.609141  1.048934  0.765308    ...       4.046243  0.793734   \n",
      "292  2.702222  1.036835  0.953576  1.108886    ...       8.085106  0.496084   \n",
      "293  0.000000  1.658936  0.810540  1.437718    ...      16.307692  1.140992   \n",
      "294  1.266667  0.803547  1.048934  0.968801    ...       8.675799  1.058312   \n",
      "295  2.702222  0.995362  1.157005  0.953170    ...       9.405941  1.587467   \n",
      "296  0.000000  1.023874  1.090652  0.959953    ...      17.050691  1.488251   \n",
      "297  0.000000  1.230377  0.801004  1.184385    ...      11.553785  0.846649   \n",
      "298  1.351111  1.437744  0.832790  1.283477    ...      16.176471  0.793734   \n",
      "299  1.266667  0.894270  0.911857  0.813970    ...      15.217391  0.634987   \n",
      "300  0.000000  0.732697  0.947219  0.825766    ...      10.857143  1.041775   \n",
      "301  0.000000  1.010914  0.882058  0.946682    ...      12.616822  0.582071   \n",
      "302  0.000000  0.842428  1.138331  0.977648    ...      10.407240  1.111227   \n",
      "303  1.351111  1.064484  1.087077  1.099449    ...       8.154506  1.289817   \n",
      "\n",
      "          ADS     ATYCI     ATRCI    ATSOTI      ATFI      ATSI     ATCKI  \\\n",
      "0    0.944099  0.796190  2.280000  1.102607  0.852449  1.042121  1.219178   \n",
      "1    1.168885  0.909932  0.868571  0.730506  0.926202  0.875152  0.876712   \n",
      "2    1.007039  1.158095  0.810667  0.920437  1.056556  0.933495  0.891324   \n",
      "3    0.965079  0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   \n",
      "4    0.747412  1.013333  0.760000  1.166526  1.020537  1.128485  1.082192   \n",
      "5    1.132919  0.965079  1.621333  0.818167  1.267867  0.859798  0.847489   \n",
      "6    0.881159  1.042286  3.242667  1.056798  1.037346  0.933495  1.022831   \n",
      "7    0.404614  0.992653  0.868571  1.424486  0.809569  1.316017  1.737769   \n",
      "8    0.965079  1.042286  0.810667  0.801121  0.870858  0.841374  0.803653   \n",
      "9    1.219462  1.302857  0.760000  0.671152  0.876461  0.731212  0.712329   \n",
      "10   0.839199  1.003683  0.810667  0.835212  1.159010  0.872081  0.803653   \n",
      "11   1.213842  0.951293  0.868571  1.059233  1.042835  0.987013  0.908023   \n",
      "12   1.174879  0.965079  0.810667  1.073844  0.992522  1.105455  0.949772   \n",
      "13   0.713320  1.196698  0.810667  0.971573  1.094976  1.007192  0.964384   \n",
      "14   1.573499  0.940952  0.760000  0.783011  1.116588  0.800303  0.643836   \n",
      "15   1.468599  0.656254  0.000000  1.005663  0.749194  1.050182  1.256621   \n",
      "16   0.865424  1.339048  1.520000  1.390244  1.152607  1.116970  0.958904   \n",
      "17   0.983437  1.230476  0.760000  0.814971  1.092575  0.944242  1.000000   \n",
      "18   1.384679  0.965079  0.810667  0.869302  1.069363  0.982626  0.920548   \n",
      "19   0.354037  0.651429  0.760000  1.358284  0.900474  1.116970  1.342466   \n",
      "20   0.965079  1.042286  0.810667  0.801121  0.870858  0.841374  0.803653   \n",
      "21   0.881159  1.042286  3.242667  1.056798  1.037346  0.933495  1.022831   \n",
      "22   1.168885  0.909932  0.868571  0.730506  0.926202  0.875152  0.876712   \n",
      "23   1.132919  0.965079  1.621333  0.818167  1.267867  0.859798  0.847489   \n",
      "24   0.944099  0.796190  2.280000  1.102607  0.852449  1.042121  1.219178   \n",
      "25   0.404614  0.992653  0.868571  1.424486  0.809569  1.316017  1.737769   \n",
      "26   1.219462  1.302857  0.760000  0.671152  0.876461  0.731212  0.712329   \n",
      "27   0.965079  0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   \n",
      "28   1.007039  1.158095  0.810667  0.920437  1.056556  0.933495  0.891324   \n",
      "29   0.747412  1.013333  0.760000  1.166526  1.020537  1.128485  1.082192   \n",
      "..        ...       ...       ...       ...       ...       ...       ...   \n",
      "274  0.747412  1.013333  0.760000  1.166526  1.020537  1.128485  1.082192   \n",
      "275  0.865424  1.339048  1.520000  1.390244  1.152607  1.116970  0.958904   \n",
      "276  0.983437  1.230476  0.760000  0.814971  1.092575  0.944242  1.000000   \n",
      "277  0.965079  1.042286  0.810667  0.801121  0.870858  0.841374  0.803653   \n",
      "278  1.573499  0.940952  0.760000  0.783011  1.116588  0.800303  0.643836   \n",
      "279  0.404614  0.992653  0.868571  1.424486  0.809569  1.316017  1.737769   \n",
      "280  1.384679  0.965079  0.810667  0.869302  1.069363  0.982626  0.920548   \n",
      "281  1.007039  1.158095  0.810667  0.920437  1.056556  0.933495  0.891324   \n",
      "282  1.174879  0.965079  0.810667  1.073844  0.992522  1.105455  0.949772   \n",
      "283  1.213842  0.951293  0.868571  1.059233  1.042835  0.987013  0.908023   \n",
      "284  0.839199  1.003683  0.810667  0.835212  1.159010  0.872081  0.803653   \n",
      "285  1.168885  0.909932  0.868571  0.730506  0.926202  0.875152  0.876712   \n",
      "286  0.354037  0.651429  0.760000  1.358284  0.900474  1.116970  1.342466   \n",
      "287  0.965079  0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   \n",
      "288  0.944099  0.796190  2.280000  1.102607  0.852449  1.042121  1.219178   \n",
      "289  0.965079  1.042286  0.810667  0.801121  0.870858  0.841374  0.803653   \n",
      "290  1.132919  0.965079  1.621333  0.818167  1.267867  0.859798  0.847489   \n",
      "291  1.468599  0.656254  0.000000  1.005663  0.749194  1.050182  1.256621   \n",
      "292  1.573499  0.940952  0.760000  0.783011  1.116588  0.800303  0.643836   \n",
      "293  0.983437  1.230476  0.760000  0.814971  1.092575  0.944242  1.000000   \n",
      "294  0.881159  1.042286  3.242667  1.056798  1.037346  0.933495  1.022831   \n",
      "295  0.747412  1.013333  0.760000  1.166526  1.020537  1.128485  1.082192   \n",
      "296  0.865424  1.339048  1.520000  1.390244  1.152607  1.116970  0.958904   \n",
      "297  0.713320  1.196698  0.810667  0.971573  1.094976  1.007192  0.964384   \n",
      "298  1.219462  1.302857  0.760000  0.671152  0.876461  0.731212  0.712329   \n",
      "299  0.839199  1.003683  0.810667  0.835212  1.159010  0.872081  0.803653   \n",
      "300  0.944099  0.796190  2.280000  1.102607  0.852449  1.042121  1.219178   \n",
      "301  1.384679  0.965079  0.810667  0.869302  1.069363  0.982626  0.920548   \n",
      "302  0.965079  0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   \n",
      "303  0.354037  0.651429  0.760000  1.358284  0.900474  1.116970  1.342466   \n",
      "\n",
      "          ATCR  \n",
      "0    11.602210  \n",
      "1     6.766917  \n",
      "2    16.447368  \n",
      "3     9.375000  \n",
      "4    16.326531  \n",
      "5    10.714286  \n",
      "6    13.157895  \n",
      "7    13.000000  \n",
      "8    10.218978  \n",
      "9    12.598425  \n",
      "10    8.450704  \n",
      "11   17.333333  \n",
      "12    8.333333  \n",
      "13    9.756098  \n",
      "14    7.194245  \n",
      "15    8.771930  \n",
      "16   15.463918  \n",
      "17   14.024390  \n",
      "18    6.875000  \n",
      "19   13.402062  \n",
      "20   10.218978  \n",
      "21   13.157895  \n",
      "22    6.766917  \n",
      "23   10.714286  \n",
      "24   11.602210  \n",
      "25   13.000000  \n",
      "26   12.598425  \n",
      "27    9.375000  \n",
      "28   16.447368  \n",
      "29   16.326531  \n",
      "..         ...  \n",
      "274  16.326531  \n",
      "275  15.463918  \n",
      "276  14.024390  \n",
      "277  10.218978  \n",
      "278   7.194245  \n",
      "279  13.000000  \n",
      "280   6.875000  \n",
      "281  16.447368  \n",
      "282   8.333333  \n",
      "283  17.333333  \n",
      "284   8.450704  \n",
      "285   6.766917  \n",
      "286  13.402062  \n",
      "287   9.375000  \n",
      "288  11.602210  \n",
      "289  10.218978  \n",
      "290  10.714286  \n",
      "291   8.771930  \n",
      "292   7.194245  \n",
      "293  14.024390  \n",
      "294  13.157895  \n",
      "295  16.326531  \n",
      "296  15.463918  \n",
      "297   9.756098  \n",
      "298  12.598425  \n",
      "299   8.450704  \n",
      "300  11.602210  \n",
      "301   6.875000  \n",
      "302   9.375000  \n",
      "303  13.402062  \n",
      "\n",
      "[304 rows x 21 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:103: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:104: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:105: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#dataframe with engineered features \n",
    "\n",
    "bot = BPLData[['HomeTeam', 'AwayTeam', 'FTR']]\n",
    "# bot = pd.DataFrame ({'HomeTeam': 'Man City', 'AwayTeam': 'Chelsea'},  index=[0])\n",
    "HAS = []\n",
    "HDS = []\n",
    "AAS = []\n",
    "ADS = []\n",
    "HTYCI = []\n",
    "ATYCI = []\n",
    "HTRCI = []\n",
    "ATRCI = []\n",
    "HTSOTI = []\n",
    "ATSOTI = []\n",
    "HTFI = []\n",
    "ATFI = []\n",
    "HTSI = []\n",
    "ATSI = []\n",
    "HTCKI = []\n",
    "ATCKI = []\n",
    "HTCR = []\n",
    "ATCR = []\n",
    "# df = bot.iloc[0,1]\n",
    "\n",
    "# print(df)\n",
    "# v = season19[season19.Team == df]['HAS'].reset_index()\n",
    "# print(type(v))\n",
    "# # for index,row in bot.iterrows(): #Iterate over DataFrame rows as (index, Series) pairs. \n",
    "# HAS = (season19[season19.Team == bot['HomeTeam']]['HAS'].values[0])\n",
    "# HDS.append(season19[season19.Team == row['HomeTeam']]['HDS'].values[0])\n",
    "# AAS.append(season19[season19.Team == row['AwayTeam']]['AAS'].values[0])\n",
    "# ADS.append(season19[season19.Team == row['AwayTeam']]['ADS'].values[0])\n",
    "# HTYCI.append(season19[season19.Team== row['HomeTeam']]['HTYCI'].values[0])\n",
    "# ATYCI.append(season19[season19.Team== row['AwayTeam']]['ATYCI'].values[0])\n",
    "# HTRCI.append(season19[season19.Team== row['HomeTeam']]['HTRCI'].values[0])\n",
    "# ATRCI.append(season19[season19.Team== row['AwayTeam']]['ATRCI'].values[0])\n",
    "# HTSOTI.append(season19[season19.Team== row['HomeTeam']]['HTSOTI'].values[0])\n",
    "# ATSOTI.append(season19[season19.Team== row['AwayTeam']]['ATSOTI'].values[0])\n",
    "# HTFI.append(season19[season19.Team== row['HomeTeam']]['HTFI'].values[0])\n",
    "# ATFI.append(season19[season19.Team== row['AwayTeam']]['ATFI'].values[0])\n",
    "# HTSI.append(season19[season19.Team== row['HomeTeam']]['HTSI'].values[0])\n",
    "# ATSI.append(season19[season19.Team== row['AwayTeam']]['ATSI'].values[0])\n",
    "# HTCKI.append(season19[season19.Team==row['HomeTeam']]['HTCKI'].values[0])\n",
    "# ATCKI.append(season19[season19.Team==row['AwayTeam']]['ATCKI'].values[0])\n",
    "# HTCR.append(season19[season19.Team==row['HomeTeam']]['HTCR'].values[0])\n",
    "# ATCR.append(season19[season19.Team==row['AwayTeam']]['ATCR'].values[0])\n",
    "# print(value)\n",
    "\n",
    "\n",
    "\n",
    "for index,row in bot.iterrows(): #Iterate over DataFrame rows as (index, Series) pairs. \n",
    "    HAS.append(season19[season19.Team == row['HomeTeam']]['HAS'].values[0])     #error here,Like the error says, row is a tuple, so you can't do row[\"pool_number\"]. You need to use the index: row[0].\n",
    "    HDS.append(season19[season19.Team == row['HomeTeam']]['HDS'].values[0])\n",
    "    AAS.append(season19[season19.Team == row['AwayTeam']]['AAS'].values[0])\n",
    "    ADS.append(season19[season19.Team == row['AwayTeam']]['ADS'].values[0])\n",
    "    HTYCI.append(season19[season19.Team==row['HomeTeam']]['HTYCI'].values[0])\n",
    "    ATYCI.append(season19[season19.Team==row['AwayTeam']]['ATYCI'].values[0])\n",
    "    HTRCI.append(season19[season19.Team==row['HomeTeam']]['HTRCI'].values[0])\n",
    "    ATRCI.append(season19[season19.Team==row['AwayTeam']]['ATRCI'].values[0])\n",
    "    HTSOTI.append(season19[season19.Team==row['HomeTeam']]['HTSOTI'].values[0])\n",
    "    ATSOTI.append(season19[season19.Team==row['AwayTeam']]['ATSOTI'].values[0])\n",
    "    HTFI.append(season19[season19.Team==row['HomeTeam']]['HTFI'].values[0])\n",
    "    ATFI.append(season19[season19.Team==row['AwayTeam']]['ATFI'].values[0])\n",
    "    HTSI.append(season19[season19.Team==row['HomeTeam']]['HTSI'].values[0])\n",
    "    ATSI.append(season19[season19.Team==row['AwayTeam']]['ATSI'].values[0])\n",
    "    HTCKI.append(season19[season19.Team==row['HomeTeam']]['HTCKI'].values[0])\n",
    "    ATCKI.append(season19[season19.Team==row['AwayTeam']]['ATCKI'].values[0])\n",
    "    HTCR.append(season19[season19.Team==row['HomeTeam']]['HTCR'].values[0])\n",
    "    ATCR.append(season19[season19.Team==row['AwayTeam']]['ATCR'].values[0])\n",
    "\n",
    "# data['HAS']=HAS\n",
    "# data['HDS']=HDS\n",
    "# data['HTYCI']=HTYCI\n",
    "# data['HTRCI']=HTRCI\n",
    "# data['HTSOTI']=HTSOTI\n",
    "# data['HTFI']=HTFI\n",
    "# data['HTSI']=HTSI\n",
    "# data['HTCKI']=HTCKI\n",
    "# data['HTCR']=HTCR\n",
    "# data['AAS']=AAS\n",
    "# data['ADS']=ADS\n",
    "# data['ATYCI']=ATYCI\n",
    "# data['ATRCI']=ATRCI\n",
    "# data['ATSOTI']=ATSOTI\n",
    "# data['ATFI']=ATFI\n",
    "# data['ATSI']=ATSI\n",
    "# data['ATCKI']=ATCKI\n",
    "# data['ATCR']=ATCR\n",
    "\n",
    "bot['HAS'] =HAS\n",
    "bot['HDS']=HDS\n",
    "bot['HTYCI']=HTYCI\n",
    "bot['HTRCI']=HTRCI\n",
    "bot['HTSOTI']=HTSOTI\n",
    "bot['HTFI']=HTFI\n",
    "bot['HTSI']=HTSI\n",
    "bot['HTCKI']=HTCKI\n",
    "bot['HTCR']=HTCR\n",
    "bot['AAS']=AAS\n",
    "bot['ADS']=ADS\n",
    "bot['ATYCI']=ATYCI\n",
    "bot['ATRCI']=ATRCI\n",
    "bot['ATSOTI']=ATSOTI\n",
    "bot['ATFI']=ATFI\n",
    "bot['ATSI']=ATSI\n",
    "bot['ATCKI']=ATCKI\n",
    "bot['ATCR']=ATCR\n",
    "\n",
    "print(bot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'predict'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-200-82c3473a0f64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mprediction\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-200-82c3473a0f64>\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[0mscaler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[0mscaledData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredictData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscaledData\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     67\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mprediction\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'predict'"
     ]
    }
   ],
   "source": [
    "bot = pd.DataFrame ({'HomeTeam': 'Man City', 'AwayTeam': 'Chelsea'},  index=[0])\n",
    "# data = pd.DataFrame(columns=['HomeTeam', 'AwayTeam','HAS', 'HDS', 'HTYCI', 'HTRCI', 'HTSOTI', 'HTFI', 'HTSI',\n",
    "#     'HTCKI', 'HTCR', 'AAS', 'ADS', 'ATYCI', 'ATRCI', 'ATSOTI', 'ATFI',\n",
    "#      'ATSI', 'ATCKI', 'ATCR'], index=[0])\n",
    "# data = bot\n",
    "\n",
    "# HomeTeam = bot.iloc[0,0]\n",
    "# AwayTeam = bot.iloc[0,1]\n",
    "# #print(AwayTeam)\n",
    "# #print(HomeTeam)\n",
    "# data['HAS'] = season19[season19.Team == HomeTeam]['HAS'].reset_index(drop=True)\n",
    "# data['HDS'] = season19[season19.Team == HomeTeam]['HDS'].reset_index(drop=True)\n",
    "# data['AAS'] = season19[season19.Team == AwayTeam]['AAS'].reset_index(drop=True)\n",
    "# data['ADS'] = season19[season19.Team == AwayTeam]['ADS'].reset_index(drop=True)\n",
    "# data['HTYCI'] =season19[season19.Team==HomeTeam]['HTYCI'].reset_index(drop=True)\n",
    "# data['ATYCI'] =season19[season19.Team==AwayTeam]['ATYCI'].reset_index(drop=True)\n",
    "# data['HTRCI'] =season19[season19.Team==HomeTeam]['HTRCI'].reset_index(drop=True)\n",
    "# data['ATRCI'] =season19[season19.Team==AwayTeam]['ATRCI'].reset_index(drop=True)\n",
    "# data['HTSOTI'] =season19[season19.Team==HomeTeam]['HTSOTI'].reset_index(drop=True)\n",
    "# data['ATSOTI'] =season19[season19.Team==AwayTeam]['ATSOTI'].reset_index(drop=True)\n",
    "# data['HTFI'] =season19[season19.Team==HomeTeam]['HTFI'].reset_index(drop=True)\n",
    "# data['ATFI'] =season19[season19.Team==AwayTeam]['ATFI'].reset_index(drop=True)\n",
    "# data['HTSI'] =season19[season19.Team==HomeTeam]['HTSI'].reset_index(drop=True)\n",
    "# data['ATSI'] =season19[season19.Team==AwayTeam]['ATSI'].reset_index(drop=True)\n",
    "# data['HTCKI'] =season19[season19.Team==HomeTeam]['HTCKI'].reset_index(drop=True)\n",
    "# data['ATCKI'] =season19[season19.Team==AwayTeam]['ATCKI'].reset_index(drop=True)\n",
    "# data['HTCR'] =season19[season19.Team==HomeTeam]['HTCR'].reset_index(drop=True)\n",
    "# data['ATCR'] =season19[season19.Team==AwayTeam]['ATCR'].reset_index(drop=True)\n",
    "# print(data)\n",
    "def preprocess(dataframe):\n",
    "    #Still contains the teams \n",
    "    data = pd.DataFrame(columns=['HomeTeam', 'AwayTeam'], index=[0])\n",
    "    data = dataframe\n",
    "    HomeTeam = data.iloc[0,0]\n",
    "    AwayTeam = data.iloc[0,1]\n",
    "\n",
    "    data['HAS'] = season19[season19.Team == HomeTeam]['HAS'].reset_index(drop=True)\n",
    "    data['HDS'] = season19[season19.Team == HomeTeam]['HDS'].reset_index(drop=True)\n",
    "    data['AAS'] = season19[season19.Team == AwayTeam]['AAS'].reset_index(drop=True)\n",
    "    data['ADS'] = season19[season19.Team == AwayTeam]['ADS'].reset_index(drop=True)\n",
    "    data['HTYCI'] = season19[season19.Team==HomeTeam]['HTYCI'].reset_index(drop=True)\n",
    "    data['ATYCI'] =season19[season19.Team==AwayTeam]['ATYCI'].reset_index(drop=True)\n",
    "    data['HTRCI'] =season19[season19.Team==HomeTeam]['HTRCI'].reset_index(drop=True)\n",
    "    data['ATRCI'] =season19[season19.Team==AwayTeam]['ATRCI'].reset_index(drop=True)\n",
    "    data['HTSOTI'] =season19[season19.Team==HomeTeam]['HTSOTI'].reset_index(drop=True)\n",
    "    data['ATSOTI'] =season19[season19.Team==AwayTeam]['ATSOTI'].reset_index(drop=True)\n",
    "    data['HTFI'] =season19[season19.Team==HomeTeam]['HTFI'].reset_index(drop=True)\n",
    "    data['ATFI'] =season19[season19.Team==AwayTeam]['ATFI'].reset_index(drop=True)\n",
    "    data['HTSI'] =season19[season19.Team==HomeTeam]['HTSI'].reset_index(drop=True)\n",
    "    data['ATSI'] =season19[season19.Team==AwayTeam]['ATSI'].reset_index(drop=True)\n",
    "    data['HTCKI'] =season19[season19.Team==HomeTeam]['HTCKI'].reset_index(drop=True)\n",
    "    data['ATCKI'] =season19[season19.Team==AwayTeam]['ATCKI'].reset_index(drop=True)\n",
    "    data['HTCR'] =season19[season19.Team==HomeTeam]['HTCR'].reset_index(drop=True)\n",
    "    data['ATCR'] =season19[season19.Team==AwayTeam]['ATCR'].reset_index(drop=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "hey= preprocess(bot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1 entries, 0 to 0\n",
      "Data columns (total 2 columns):\n",
      "HomeTeam    1 non-null object\n",
      "AwayTeam    1 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 24.0+ bytes\n",
      "None\n",
      "  HomeTeam AwayTeam  HAS  HDS HTYCI HTRCI HTSOTI HTFI HTSI HTCKI HTCR  AAS  \\\n",
      "1      NaN      NaN  NaN  NaN   NaN   NaN    NaN  NaN  NaN   NaN  NaN  NaN   \n",
      "\n",
      "   ADS ATYCI ATRCI ATSOTI ATFI ATSI ATCKI ATCR  \n",
      "1  NaN   NaN   NaN    NaN  NaN  NaN   NaN  NaN  \n"
     ]
    }
   ],
   "source": [
    "newDF = pd.DataFrame({'HomeTeam' : 'Man United','AwayTeam':'Leicester'}, index=[0])\n",
    "print(newDF.info())\n",
    "\n",
    "df = pd.DataFrame(index=[0], columns = ['HomeTeam', 'AwayTeam','HAS', 'HDS', 'HTYCI', 'HTRCI', 'HTSOTI', 'HTFI', 'HTSI',\n",
    "        'HTCKI', 'HTCR', 'AAS', 'ADS', 'ATYCI', 'ATRCI', 'ATSOTI', 'ATFI',\n",
    "         'ATSI', 'ATCKI', 'ATCR'])\n",
    "print(df)\n",
    "# df['HomeTeam'] = newDF.HomeTeam\n",
    "# HAS = [3]\n",
    "# df['HAS'] = HAS[0]\n",
    "\n",
    "# df = newDF\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       HomeTeam        AwayTeam FTR  HST  AST  HF  AF  HC  AC  HR  AR  \\\n",
      "0    Man United       Leicester   H    6    4  11   8   2   5   0   0   \n",
      "1   Bournemouth         Cardiff   H    4    1  11   9   7   4   0   0   \n",
      "2        Fulham  Crystal Palace   A    6    9   9  11   5   5   0   0   \n",
      "3  Huddersfield         Chelsea   A    1    4   9   8   2   5   0   0   \n",
      "4     Newcastle       Tottenham   A    2    5  11  12   3   5   0   0   \n",
      "5       Watford        Brighton   H    5    0  10  16   8   2   0   0   \n",
      "6        Wolves         Everton   D    4    5   8   7   3   6   0   1   \n",
      "7       Arsenal        Man City   A    3    8  11  14   2   9   0   0   \n",
      "8     Liverpool        West Ham   H    8    2  14   9   5   4   0   0   \n",
      "9   Southampton         Burnley   D    3    6  10   9   8   5   0   0   \n",
      "\n",
      "        HAS       HDS       AAS       ADS  \n",
      "0  1.258799  1.020515  1.041775  0.944099  \n",
      "1  1.101449  1.041775  0.510257  1.168885  \n",
      "2  0.797239  1.587467  1.322889  1.007039  \n",
      "3  0.275362  1.190601  1.111227  0.965079  \n",
      "4  0.747412  0.992167  1.587467  0.747412  \n",
      "5  0.854185  1.077210  0.793734  1.132919  \n",
      "6  0.923119  1.005396  1.058312  0.881159  \n",
      "7  1.455487  0.595300  1.474077  0.404614  \n",
      "8  1.846239  0.476240  0.740818  0.965079  \n",
      "9  0.797239  1.164143  0.793734  1.219462  \n"
     ]
    }
   ],
   "source": [
    "#final dataframe \n",
    "predictiveBot = BPLData[['HomeTeam','AwayTeam','FTR','HST','AST','HF','AF','HC','AC','HR','AR']]\n",
    "\n",
    "\n",
    "HAS = []\n",
    "HDS = []\n",
    "AAS = []\n",
    "ADS = []\n",
    "\n",
    "for index, row in predictiveBot.iterrows(): #Iterate over DataFrame rows as (index, Series) pairs. \n",
    "    HAS.append(season19[season19.Team == row['HomeTeam']]['HAS'].values[0])\n",
    "    HDS.append(season19[season19.Team == row['HomeTeam']]['HDS'].values[0])\n",
    "    AAS.append(season19[season19.Team == row['AwayTeam']]['AAS'].values[0])\n",
    "    ADS.append(season19[season19.Team == row['AwayTeam']]['ADS'].values[0])\n",
    "    \n",
    "predictiveBot['HAS'] = HAS\n",
    "predictiveBot['HDS'] = HDS\n",
    "predictiveBot['AAS'] = AAS\n",
    "predictiveBot['ADS'] = ADS\n",
    "\n",
    "print(predictiveBot.head(10))\n",
    "# FTR : Full Time Result\n",
    "# HST : Home Shots on Target (goal opportunity)\n",
    "# AST : Away Shots on Target (goal opportunity)\n",
    "# HF : Home Faults\n",
    "# AF : Away Faults\n",
    "# HC : Home corners (goal opportunity)\n",
    "# AC : Away corners (goal opportunity)\n",
    "# HR : Home Red Card (decided to ignore Yellow cards, because it only has an impact on the game if it results to a red card)\n",
    "# AR : Away Red Card\n",
    "# HAS : Home Attacking Strength\n",
    "# HDS : Home Defensive Strength\n",
    "# AAS : Away Attacking Strength\n",
    "# ADS : Away Defensive Strength\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>HTYCI</th>\n",
       "      <th>HTRCI</th>\n",
       "      <th>HTSOTI</th>\n",
       "      <th>HTFI</th>\n",
       "      <th>HTSI</th>\n",
       "      <th>...</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "      <th>ATYCI</th>\n",
       "      <th>ATRCI</th>\n",
       "      <th>ATSOTI</th>\n",
       "      <th>ATFI</th>\n",
       "      <th>ATSI</th>\n",
       "      <th>ATCKI</th>\n",
       "      <th>ATCR</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>H</td>\n",
       "      <td>1.258799</td>\n",
       "      <td>1.020515</td>\n",
       "      <td>1.007836</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>1.496005</td>\n",
       "      <td>1.069367</td>\n",
       "      <td>1.097090</td>\n",
       "      <td>...</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.944099</td>\n",
       "      <td>0.796190</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>1.102607</td>\n",
       "      <td>0.852449</td>\n",
       "      <td>1.042121</td>\n",
       "      <td>1.219178</td>\n",
       "      <td>11.602210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Cardiff</td>\n",
       "      <td>H</td>\n",
       "      <td>1.101449</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>1.282700</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.894270</td>\n",
       "      <td>0.911857</td>\n",
       "      <td>0.813970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510257</td>\n",
       "      <td>1.168885</td>\n",
       "      <td>0.909932</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>0.730506</td>\n",
       "      <td>0.926202</td>\n",
       "      <td>0.875152</td>\n",
       "      <td>0.876712</td>\n",
       "      <td>6.766917</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.064484</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>1.099449</td>\n",
       "      <td>...</td>\n",
       "      <td>1.322889</td>\n",
       "      <td>1.007039</td>\n",
       "      <td>1.158095</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.920437</td>\n",
       "      <td>1.056556</td>\n",
       "      <td>0.933495</td>\n",
       "      <td>0.891324</td>\n",
       "      <td>16.447368</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>A</td>\n",
       "      <td>0.275362</td>\n",
       "      <td>1.190601</td>\n",
       "      <td>0.841772</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.609141</td>\n",
       "      <td>1.048934</td>\n",
       "      <td>0.765308</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111227</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.142024</td>\n",
       "      <td>0.896472</td>\n",
       "      <td>1.375677</td>\n",
       "      <td>1.095890</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>A</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>0.992167</td>\n",
       "      <td>0.921941</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.803547</td>\n",
       "      <td>1.048934</td>\n",
       "      <td>0.968801</td>\n",
       "      <td>...</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>1.013333</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.166526</td>\n",
       "      <td>1.020537</td>\n",
       "      <td>1.128485</td>\n",
       "      <td>1.082192</td>\n",
       "      <td>16.326531</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>H</td>\n",
       "      <td>0.854185</td>\n",
       "      <td>1.077210</td>\n",
       "      <td>1.099458</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>0.888716</td>\n",
       "      <td>1.137480</td>\n",
       "      <td>0.788691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.132919</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.621333</td>\n",
       "      <td>0.818167</td>\n",
       "      <td>1.267867</td>\n",
       "      <td>0.859798</td>\n",
       "      <td>0.847489</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Everton</td>\n",
       "      <td>D</td>\n",
       "      <td>0.923119</td>\n",
       "      <td>1.005396</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.953888</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>0.972045</td>\n",
       "      <td>...</td>\n",
       "      <td>1.058312</td>\n",
       "      <td>0.881159</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>3.242667</td>\n",
       "      <td>1.056798</td>\n",
       "      <td>1.037346</td>\n",
       "      <td>0.933495</td>\n",
       "      <td>1.022831</td>\n",
       "      <td>13.157895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>A</td>\n",
       "      <td>1.455487</td>\n",
       "      <td>0.595300</td>\n",
       "      <td>1.162447</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.023874</td>\n",
       "      <td>1.090652</td>\n",
       "      <td>0.959953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.474077</td>\n",
       "      <td>0.404614</td>\n",
       "      <td>0.992653</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>1.424486</td>\n",
       "      <td>0.809569</td>\n",
       "      <td>1.316017</td>\n",
       "      <td>1.737769</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>1.846239</td>\n",
       "      <td>0.476240</td>\n",
       "      <td>0.555837</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.437744</td>\n",
       "      <td>0.832790</td>\n",
       "      <td>1.283477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740818</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.801121</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.841374</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>10.218978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>D</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.164143</td>\n",
       "      <td>1.453727</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.995362</td>\n",
       "      <td>1.157005</td>\n",
       "      <td>0.953170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.219462</td>\n",
       "      <td>1.302857</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.671152</td>\n",
       "      <td>0.876461</td>\n",
       "      <td>0.731212</td>\n",
       "      <td>0.712329</td>\n",
       "      <td>12.598425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cardiff</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>D</td>\n",
       "      <td>0.708075</td>\n",
       "      <td>1.537859</td>\n",
       "      <td>1.202532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.712824</td>\n",
       "      <td>0.995295</td>\n",
       "      <td>0.871478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634987</td>\n",
       "      <td>0.839199</td>\n",
       "      <td>1.003683</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.835212</td>\n",
       "      <td>1.159010</td>\n",
       "      <td>0.872081</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>H</td>\n",
       "      <td>1.216839</td>\n",
       "      <td>0.529156</td>\n",
       "      <td>0.684107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.230377</td>\n",
       "      <td>0.801004</td>\n",
       "      <td>1.184385</td>\n",
       "      <td>...</td>\n",
       "      <td>1.474077</td>\n",
       "      <td>1.213842</td>\n",
       "      <td>0.951293</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>1.059233</td>\n",
       "      <td>1.042835</td>\n",
       "      <td>0.987013</td>\n",
       "      <td>0.908023</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>H</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.881857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.842428</td>\n",
       "      <td>1.138331</td>\n",
       "      <td>0.977648</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.174879</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>1.073844</td>\n",
       "      <td>0.992522</td>\n",
       "      <td>1.105455</td>\n",
       "      <td>0.949772</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>H</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.005396</td>\n",
       "      <td>1.197187</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>1.036835</td>\n",
       "      <td>0.953576</td>\n",
       "      <td>1.108886</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846649</td>\n",
       "      <td>0.713320</td>\n",
       "      <td>1.196698</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.971573</td>\n",
       "      <td>1.094976</td>\n",
       "      <td>1.007192</td>\n",
       "      <td>0.964384</td>\n",
       "      <td>9.756098</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>H</td>\n",
       "      <td>1.123928</td>\n",
       "      <td>0.737038</td>\n",
       "      <td>0.732972</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.155330</td>\n",
       "      <td>0.926331</td>\n",
       "      <td>1.041477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.496084</td>\n",
       "      <td>1.573499</td>\n",
       "      <td>0.940952</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.783011</td>\n",
       "      <td>1.116588</td>\n",
       "      <td>0.800303</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>7.194245</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>A</td>\n",
       "      <td>1.062112</td>\n",
       "      <td>1.140992</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.010914</td>\n",
       "      <td>0.882058</td>\n",
       "      <td>0.946682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.468599</td>\n",
       "      <td>0.656254</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005663</td>\n",
       "      <td>0.749194</td>\n",
       "      <td>1.050182</td>\n",
       "      <td>1.256621</td>\n",
       "      <td>8.771930</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Man United</td>\n",
       "      <td>H</td>\n",
       "      <td>0.764271</td>\n",
       "      <td>0.850429</td>\n",
       "      <td>1.145268</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>0.666537</td>\n",
       "      <td>1.178347</td>\n",
       "      <td>0.753301</td>\n",
       "      <td>...</td>\n",
       "      <td>1.488251</td>\n",
       "      <td>0.865424</td>\n",
       "      <td>1.339048</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>1.390244</td>\n",
       "      <td>1.152607</td>\n",
       "      <td>1.116970</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>15.463918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Watford</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.481636</td>\n",
       "      <td>1.026160</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.732697</td>\n",
       "      <td>0.947219</td>\n",
       "      <td>0.825766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.140992</td>\n",
       "      <td>0.983437</td>\n",
       "      <td>1.230476</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.814971</td>\n",
       "      <td>1.092575</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.024390</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>H</td>\n",
       "      <td>2.084886</td>\n",
       "      <td>0.595300</td>\n",
       "      <td>0.521097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.658936</td>\n",
       "      <td>0.810540</td>\n",
       "      <td>1.437718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.582071</td>\n",
       "      <td>1.384679</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.869302</td>\n",
       "      <td>1.069363</td>\n",
       "      <td>0.982626</td>\n",
       "      <td>0.920548</td>\n",
       "      <td>6.875000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>A</td>\n",
       "      <td>0.461560</td>\n",
       "      <td>0.899565</td>\n",
       "      <td>0.855134</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>0.829468</td>\n",
       "      <td>0.921790</td>\n",
       "      <td>1.146636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.289817</td>\n",
       "      <td>0.354037</td>\n",
       "      <td>0.651429</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.358284</td>\n",
       "      <td>0.900474</td>\n",
       "      <td>1.116970</td>\n",
       "      <td>1.342466</td>\n",
       "      <td>13.402062</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>1.455487</td>\n",
       "      <td>0.595300</td>\n",
       "      <td>1.162447</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.023874</td>\n",
       "      <td>1.090652</td>\n",
       "      <td>0.959953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740818</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.801121</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.841374</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>10.218978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Everton</td>\n",
       "      <td>D</td>\n",
       "      <td>1.101449</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>1.282700</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.894270</td>\n",
       "      <td>0.911857</td>\n",
       "      <td>0.813970</td>\n",
       "      <td>...</td>\n",
       "      <td>1.058312</td>\n",
       "      <td>0.881159</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>3.242667</td>\n",
       "      <td>1.056798</td>\n",
       "      <td>1.037346</td>\n",
       "      <td>0.933495</td>\n",
       "      <td>1.022831</td>\n",
       "      <td>13.157895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>Cardiff</td>\n",
       "      <td>D</td>\n",
       "      <td>0.275362</td>\n",
       "      <td>1.190601</td>\n",
       "      <td>0.841772</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.609141</td>\n",
       "      <td>1.048934</td>\n",
       "      <td>0.765308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510257</td>\n",
       "      <td>1.168885</td>\n",
       "      <td>0.909932</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>0.730506</td>\n",
       "      <td>0.926202</td>\n",
       "      <td>0.875152</td>\n",
       "      <td>0.876712</td>\n",
       "      <td>6.766917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>H</td>\n",
       "      <td>1.846239</td>\n",
       "      <td>0.476240</td>\n",
       "      <td>0.555837</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.437744</td>\n",
       "      <td>0.832790</td>\n",
       "      <td>1.283477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.132919</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.621333</td>\n",
       "      <td>0.818167</td>\n",
       "      <td>1.267867</td>\n",
       "      <td>0.859798</td>\n",
       "      <td>0.847489</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.164143</td>\n",
       "      <td>1.453727</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.995362</td>\n",
       "      <td>1.157005</td>\n",
       "      <td>0.953170</td>\n",
       "      <td>...</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.944099</td>\n",
       "      <td>0.796190</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>1.102607</td>\n",
       "      <td>0.852449</td>\n",
       "      <td>1.042121</td>\n",
       "      <td>1.219178</td>\n",
       "      <td>11.602210</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Man City</td>\n",
       "      <td>D</td>\n",
       "      <td>0.923119</td>\n",
       "      <td>1.005396</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.953888</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>0.972045</td>\n",
       "      <td>...</td>\n",
       "      <td>1.474077</td>\n",
       "      <td>0.404614</td>\n",
       "      <td>0.992653</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>1.424486</td>\n",
       "      <td>0.809569</td>\n",
       "      <td>1.316017</td>\n",
       "      <td>1.737769</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>H</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.064484</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>1.099449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.219462</td>\n",
       "      <td>1.302857</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.671152</td>\n",
       "      <td>0.876461</td>\n",
       "      <td>0.731212</td>\n",
       "      <td>0.712329</td>\n",
       "      <td>12.598425</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>A</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>0.992167</td>\n",
       "      <td>0.921941</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.803547</td>\n",
       "      <td>1.048934</td>\n",
       "      <td>0.968801</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111227</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.142024</td>\n",
       "      <td>0.896472</td>\n",
       "      <td>1.375677</td>\n",
       "      <td>1.095890</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>H</td>\n",
       "      <td>0.854185</td>\n",
       "      <td>1.077210</td>\n",
       "      <td>1.099458</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>0.888716</td>\n",
       "      <td>1.137480</td>\n",
       "      <td>0.788691</td>\n",
       "      <td>...</td>\n",
       "      <td>1.322889</td>\n",
       "      <td>1.007039</td>\n",
       "      <td>1.158095</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.920437</td>\n",
       "      <td>1.056556</td>\n",
       "      <td>0.933495</td>\n",
       "      <td>0.891324</td>\n",
       "      <td>16.447368</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>A</td>\n",
       "      <td>1.258799</td>\n",
       "      <td>1.020515</td>\n",
       "      <td>1.007836</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>1.496005</td>\n",
       "      <td>1.069367</td>\n",
       "      <td>1.097090</td>\n",
       "      <td>...</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>1.013333</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.166526</td>\n",
       "      <td>1.020537</td>\n",
       "      <td>1.128485</td>\n",
       "      <td>1.082192</td>\n",
       "      <td>16.326531</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>H</td>\n",
       "      <td>1.216839</td>\n",
       "      <td>0.529156</td>\n",
       "      <td>0.684107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.230377</td>\n",
       "      <td>0.801004</td>\n",
       "      <td>1.184385</td>\n",
       "      <td>...</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>1.013333</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.166526</td>\n",
       "      <td>1.020537</td>\n",
       "      <td>1.128485</td>\n",
       "      <td>1.082192</td>\n",
       "      <td>16.326531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Man United</td>\n",
       "      <td>A</td>\n",
       "      <td>0.461560</td>\n",
       "      <td>0.899565</td>\n",
       "      <td>0.855134</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>0.829468</td>\n",
       "      <td>0.921790</td>\n",
       "      <td>1.146636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.488251</td>\n",
       "      <td>0.865424</td>\n",
       "      <td>1.339048</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>1.390244</td>\n",
       "      <td>1.152607</td>\n",
       "      <td>1.116970</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>15.463918</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Watford</td>\n",
       "      <td>H</td>\n",
       "      <td>1.846239</td>\n",
       "      <td>0.476240</td>\n",
       "      <td>0.555837</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.437744</td>\n",
       "      <td>0.832790</td>\n",
       "      <td>1.283477</td>\n",
       "      <td>...</td>\n",
       "      <td>1.140992</td>\n",
       "      <td>0.983437</td>\n",
       "      <td>1.230476</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.814971</td>\n",
       "      <td>1.092575</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.024390</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Man City</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>2.084886</td>\n",
       "      <td>0.595300</td>\n",
       "      <td>0.521097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.658936</td>\n",
       "      <td>0.810540</td>\n",
       "      <td>1.437718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740818</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.801121</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.841374</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>10.218978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>H</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.164143</td>\n",
       "      <td>1.453727</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.995362</td>\n",
       "      <td>1.157005</td>\n",
       "      <td>0.953170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.496084</td>\n",
       "      <td>1.573499</td>\n",
       "      <td>0.940952</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.783011</td>\n",
       "      <td>1.116588</td>\n",
       "      <td>0.800303</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>7.194245</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Man City</td>\n",
       "      <td>A</td>\n",
       "      <td>1.101449</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>1.282700</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.894270</td>\n",
       "      <td>0.911857</td>\n",
       "      <td>0.813970</td>\n",
       "      <td>...</td>\n",
       "      <td>1.474077</td>\n",
       "      <td>0.404614</td>\n",
       "      <td>0.992653</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>1.424486</td>\n",
       "      <td>0.809569</td>\n",
       "      <td>1.316017</td>\n",
       "      <td>1.737769</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>H</td>\n",
       "      <td>0.764271</td>\n",
       "      <td>0.850429</td>\n",
       "      <td>1.145268</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>0.666537</td>\n",
       "      <td>1.178347</td>\n",
       "      <td>0.753301</td>\n",
       "      <td>...</td>\n",
       "      <td>0.582071</td>\n",
       "      <td>1.384679</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.869302</td>\n",
       "      <td>1.069363</td>\n",
       "      <td>0.982626</td>\n",
       "      <td>0.920548</td>\n",
       "      <td>6.875000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.481636</td>\n",
       "      <td>1.026160</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.732697</td>\n",
       "      <td>0.947219</td>\n",
       "      <td>0.825766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.322889</td>\n",
       "      <td>1.007039</td>\n",
       "      <td>1.158095</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.920437</td>\n",
       "      <td>1.056556</td>\n",
       "      <td>0.933495</td>\n",
       "      <td>0.891324</td>\n",
       "      <td>16.447368</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>H</td>\n",
       "      <td>1.258799</td>\n",
       "      <td>1.020515</td>\n",
       "      <td>1.007836</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>1.496005</td>\n",
       "      <td>1.069367</td>\n",
       "      <td>1.097090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.174879</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>1.073844</td>\n",
       "      <td>0.992522</td>\n",
       "      <td>1.105455</td>\n",
       "      <td>0.949772</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>D</td>\n",
       "      <td>1.123928</td>\n",
       "      <td>0.737038</td>\n",
       "      <td>0.732972</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.155330</td>\n",
       "      <td>0.926331</td>\n",
       "      <td>1.041477</td>\n",
       "      <td>...</td>\n",
       "      <td>1.474077</td>\n",
       "      <td>1.213842</td>\n",
       "      <td>0.951293</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>1.059233</td>\n",
       "      <td>1.042835</td>\n",
       "      <td>0.987013</td>\n",
       "      <td>0.908023</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>H</td>\n",
       "      <td>1.062112</td>\n",
       "      <td>1.140992</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.010914</td>\n",
       "      <td>0.882058</td>\n",
       "      <td>0.946682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634987</td>\n",
       "      <td>0.839199</td>\n",
       "      <td>1.003683</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.835212</td>\n",
       "      <td>1.159010</td>\n",
       "      <td>0.872081</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Cardiff</td>\n",
       "      <td>H</td>\n",
       "      <td>0.923119</td>\n",
       "      <td>1.005396</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.953888</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>0.972045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510257</td>\n",
       "      <td>1.168885</td>\n",
       "      <td>0.909932</td>\n",
       "      <td>0.868571</td>\n",
       "      <td>0.730506</td>\n",
       "      <td>0.926202</td>\n",
       "      <td>0.875152</td>\n",
       "      <td>0.876712</td>\n",
       "      <td>6.766917</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>D</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.881857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.842428</td>\n",
       "      <td>1.138331</td>\n",
       "      <td>0.977648</td>\n",
       "      <td>...</td>\n",
       "      <td>1.289817</td>\n",
       "      <td>0.354037</td>\n",
       "      <td>0.651429</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.358284</td>\n",
       "      <td>0.900474</td>\n",
       "      <td>1.116970</td>\n",
       "      <td>1.342466</td>\n",
       "      <td>13.402062</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.064484</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>1.099449</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111227</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.142024</td>\n",
       "      <td>0.896472</td>\n",
       "      <td>1.375677</td>\n",
       "      <td>1.095890</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>H</td>\n",
       "      <td>0.854185</td>\n",
       "      <td>1.077210</td>\n",
       "      <td>1.099458</td>\n",
       "      <td>1.447619</td>\n",
       "      <td>0.888716</td>\n",
       "      <td>1.137480</td>\n",
       "      <td>0.788691</td>\n",
       "      <td>...</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.944099</td>\n",
       "      <td>0.796190</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>1.102607</td>\n",
       "      <td>0.852449</td>\n",
       "      <td>1.042121</td>\n",
       "      <td>1.219178</td>\n",
       "      <td>11.602210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Cardiff</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>0.708075</td>\n",
       "      <td>1.537859</td>\n",
       "      <td>1.202532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.712824</td>\n",
       "      <td>0.995295</td>\n",
       "      <td>0.871478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740818</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.801121</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.841374</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>10.218978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>A</td>\n",
       "      <td>0.461560</td>\n",
       "      <td>0.899565</td>\n",
       "      <td>0.855134</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>0.829468</td>\n",
       "      <td>0.921790</td>\n",
       "      <td>1.146636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.132919</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>1.621333</td>\n",
       "      <td>0.818167</td>\n",
       "      <td>1.267867</td>\n",
       "      <td>0.859798</td>\n",
       "      <td>0.847489</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>A</td>\n",
       "      <td>0.275362</td>\n",
       "      <td>1.190601</td>\n",
       "      <td>0.841772</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.609141</td>\n",
       "      <td>1.048934</td>\n",
       "      <td>0.765308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.468599</td>\n",
       "      <td>0.656254</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005663</td>\n",
       "      <td>0.749194</td>\n",
       "      <td>1.050182</td>\n",
       "      <td>1.256621</td>\n",
       "      <td>8.771930</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>H</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.005396</td>\n",
       "      <td>1.197187</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>1.036835</td>\n",
       "      <td>0.953576</td>\n",
       "      <td>1.108886</td>\n",
       "      <td>...</td>\n",
       "      <td>0.496084</td>\n",
       "      <td>1.573499</td>\n",
       "      <td>0.940952</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.783011</td>\n",
       "      <td>1.116588</td>\n",
       "      <td>0.800303</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>7.194245</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Watford</td>\n",
       "      <td>H</td>\n",
       "      <td>2.084886</td>\n",
       "      <td>0.595300</td>\n",
       "      <td>0.521097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.658936</td>\n",
       "      <td>0.810540</td>\n",
       "      <td>1.437718</td>\n",
       "      <td>...</td>\n",
       "      <td>1.140992</td>\n",
       "      <td>0.983437</td>\n",
       "      <td>1.230476</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.814971</td>\n",
       "      <td>1.092575</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.024390</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Everton</td>\n",
       "      <td>H</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>0.992167</td>\n",
       "      <td>0.921941</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.803547</td>\n",
       "      <td>1.048934</td>\n",
       "      <td>0.968801</td>\n",
       "      <td>...</td>\n",
       "      <td>1.058312</td>\n",
       "      <td>0.881159</td>\n",
       "      <td>1.042286</td>\n",
       "      <td>3.242667</td>\n",
       "      <td>1.056798</td>\n",
       "      <td>1.037346</td>\n",
       "      <td>0.933495</td>\n",
       "      <td>1.022831</td>\n",
       "      <td>13.157895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>H</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.164143</td>\n",
       "      <td>1.453727</td>\n",
       "      <td>2.702222</td>\n",
       "      <td>0.995362</td>\n",
       "      <td>1.157005</td>\n",
       "      <td>0.953170</td>\n",
       "      <td>...</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>0.747412</td>\n",
       "      <td>1.013333</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.166526</td>\n",
       "      <td>1.020537</td>\n",
       "      <td>1.128485</td>\n",
       "      <td>1.082192</td>\n",
       "      <td>16.326531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man United</td>\n",
       "      <td>H</td>\n",
       "      <td>1.455487</td>\n",
       "      <td>0.595300</td>\n",
       "      <td>1.162447</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.023874</td>\n",
       "      <td>1.090652</td>\n",
       "      <td>0.959953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.488251</td>\n",
       "      <td>0.865424</td>\n",
       "      <td>1.339048</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>1.390244</td>\n",
       "      <td>1.152607</td>\n",
       "      <td>1.116970</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>15.463918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>D</td>\n",
       "      <td>1.216839</td>\n",
       "      <td>0.529156</td>\n",
       "      <td>0.684107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.230377</td>\n",
       "      <td>0.801004</td>\n",
       "      <td>1.184385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846649</td>\n",
       "      <td>0.713320</td>\n",
       "      <td>1.196698</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.971573</td>\n",
       "      <td>1.094976</td>\n",
       "      <td>1.007192</td>\n",
       "      <td>0.964384</td>\n",
       "      <td>9.756098</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>H</td>\n",
       "      <td>1.846239</td>\n",
       "      <td>0.476240</td>\n",
       "      <td>0.555837</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.437744</td>\n",
       "      <td>0.832790</td>\n",
       "      <td>1.283477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793734</td>\n",
       "      <td>1.219462</td>\n",
       "      <td>1.302857</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.671152</td>\n",
       "      <td>0.876461</td>\n",
       "      <td>0.731212</td>\n",
       "      <td>0.712329</td>\n",
       "      <td>12.598425</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>D</td>\n",
       "      <td>1.101449</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>1.282700</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.894270</td>\n",
       "      <td>0.911857</td>\n",
       "      <td>0.813970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634987</td>\n",
       "      <td>0.839199</td>\n",
       "      <td>1.003683</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.835212</td>\n",
       "      <td>1.159010</td>\n",
       "      <td>0.872081</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.481636</td>\n",
       "      <td>1.026160</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.732697</td>\n",
       "      <td>0.947219</td>\n",
       "      <td>0.825766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.944099</td>\n",
       "      <td>0.796190</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>1.102607</td>\n",
       "      <td>0.852449</td>\n",
       "      <td>1.042121</td>\n",
       "      <td>1.219178</td>\n",
       "      <td>11.602210</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>H</td>\n",
       "      <td>1.062112</td>\n",
       "      <td>1.140992</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.010914</td>\n",
       "      <td>0.882058</td>\n",
       "      <td>0.946682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.582071</td>\n",
       "      <td>1.384679</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.810667</td>\n",
       "      <td>0.869302</td>\n",
       "      <td>1.069363</td>\n",
       "      <td>0.982626</td>\n",
       "      <td>0.920548</td>\n",
       "      <td>6.875000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>H</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>1.041775</td>\n",
       "      <td>0.881857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.842428</td>\n",
       "      <td>1.138331</td>\n",
       "      <td>0.977648</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111227</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.142024</td>\n",
       "      <td>0.896472</td>\n",
       "      <td>1.375677</td>\n",
       "      <td>1.095890</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>A</td>\n",
       "      <td>0.797239</td>\n",
       "      <td>1.587467</td>\n",
       "      <td>1.239944</td>\n",
       "      <td>1.351111</td>\n",
       "      <td>1.064484</td>\n",
       "      <td>1.087077</td>\n",
       "      <td>1.099449</td>\n",
       "      <td>...</td>\n",
       "      <td>1.289817</td>\n",
       "      <td>0.354037</td>\n",
       "      <td>0.651429</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.358284</td>\n",
       "      <td>0.900474</td>\n",
       "      <td>1.116970</td>\n",
       "      <td>1.342466</td>\n",
       "      <td>13.402062</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           HomeTeam        AwayTeam FTR       HAS       HDS     HTYCI  \\\n",
       "0        Man United       Leicester   H  1.258799  1.020515  1.007836   \n",
       "1       Bournemouth         Cardiff   H  1.101449  1.041775  1.282700   \n",
       "2            Fulham  Crystal Palace   A  0.797239  1.587467  1.239944   \n",
       "3      Huddersfield         Chelsea   A  0.275362  1.190601  0.841772   \n",
       "4         Newcastle       Tottenham   A  0.747412  0.992167  0.921941   \n",
       "5           Watford        Brighton   H  0.854185  1.077210  1.099458   \n",
       "6            Wolves         Everton   D  0.923119  1.005396  1.239944   \n",
       "7           Arsenal        Man City   A  1.455487  0.595300  1.162447   \n",
       "8         Liverpool        West Ham   H  1.846239  0.476240  0.555837   \n",
       "9       Southampton         Burnley   D  0.797239  1.164143  1.453727   \n",
       "10          Cardiff       Newcastle   D  0.708075  1.537859  1.202532   \n",
       "11          Chelsea         Arsenal   H  1.216839  0.529156  0.684107   \n",
       "12          Everton     Southampton   H  0.904762  1.041775  0.881857   \n",
       "13        Leicester          Wolves   H  0.797239  1.005396  1.197187   \n",
       "14        Tottenham          Fulham   H  1.123928  0.737038  0.732972   \n",
       "15         West Ham     Bournemouth   A  1.062112  1.140992  0.962025   \n",
       "16         Brighton      Man United   H  0.764271  0.850429  1.145268   \n",
       "17          Burnley         Watford   A  0.797239  1.481636  1.026160   \n",
       "18         Man City    Huddersfield   H  2.084886  0.595300  0.521097   \n",
       "19   Crystal Palace       Liverpool   A  0.461560  0.899565  0.855134   \n",
       "20          Arsenal        West Ham   H  1.455487  0.595300  1.162447   \n",
       "21      Bournemouth         Everton   D  1.101449  1.041775  1.282700   \n",
       "22     Huddersfield         Cardiff   D  0.275362  1.190601  0.841772   \n",
       "23        Liverpool        Brighton   H  1.846239  0.476240  0.555837   \n",
       "24      Southampton       Leicester   A  0.797239  1.164143  1.453727   \n",
       "25           Wolves        Man City   D  0.923119  1.005396  1.239944   \n",
       "26           Fulham         Burnley   H  0.797239  1.587467  1.239944   \n",
       "27        Newcastle         Chelsea   A  0.747412  0.992167  0.921941   \n",
       "28          Watford  Crystal Palace   H  0.854185  1.077210  1.099458   \n",
       "29       Man United       Tottenham   A  1.258799  1.020515  1.007836   \n",
       "..              ...             ...  ..       ...       ...       ...   \n",
       "274         Chelsea       Tottenham   H  1.216839  0.529156  0.684107   \n",
       "275  Crystal Palace      Man United   A  0.461560  0.899565  0.855134   \n",
       "276       Liverpool         Watford   H  1.846239  0.476240  0.555837   \n",
       "277        Man City        West Ham   H  2.084886  0.595300  0.521097   \n",
       "278     Southampton          Fulham   H  0.797239  1.164143  1.453727   \n",
       "279     Bournemouth        Man City   A  1.101449  1.041775  1.282700   \n",
       "280        Brighton    Huddersfield   H  0.764271  0.850429  1.145268   \n",
       "281         Burnley  Crystal Palace   A  0.797239  1.481636  1.026160   \n",
       "282      Man United     Southampton   H  1.258799  1.020515  1.007836   \n",
       "283       Tottenham         Arsenal   D  1.123928  0.737038  0.732972   \n",
       "284        West Ham       Newcastle   H  1.062112  1.140992  0.962025   \n",
       "285          Wolves         Cardiff   H  0.923119  1.005396  1.239944   \n",
       "286         Everton       Liverpool   D  0.904762  1.041775  0.881857   \n",
       "287          Fulham         Chelsea   A  0.797239  1.587467  1.239944   \n",
       "288         Watford       Leicester   H  0.854185  1.077210  1.099458   \n",
       "289         Cardiff        West Ham   H  0.708075  1.537859  1.202532   \n",
       "290  Crystal Palace        Brighton   A  0.461560  0.899565  0.855134   \n",
       "291    Huddersfield     Bournemouth   A  0.275362  1.190601  0.841772   \n",
       "292       Leicester          Fulham   H  0.797239  1.005396  1.197187   \n",
       "293        Man City         Watford   H  2.084886  0.595300  0.521097   \n",
       "294       Newcastle         Everton   H  0.747412  0.992167  0.921941   \n",
       "295     Southampton       Tottenham   H  0.797239  1.164143  1.453727   \n",
       "296         Arsenal      Man United   H  1.455487  0.595300  1.162447   \n",
       "297         Chelsea          Wolves   D  1.216839  0.529156  0.684107   \n",
       "298       Liverpool         Burnley   H  1.846239  0.476240  0.555837   \n",
       "299     Bournemouth       Newcastle   D  1.101449  1.041775  1.282700   \n",
       "300         Burnley       Leicester   A  0.797239  1.481636  1.026160   \n",
       "301        West Ham    Huddersfield   H  1.062112  1.140992  0.962025   \n",
       "302         Everton         Chelsea   H  0.904762  1.041775  0.881857   \n",
       "303          Fulham       Liverpool   A  0.797239  1.587467  1.239944   \n",
       "\n",
       "        HTRCI    HTSOTI      HTFI      HTSI   ...         AAS       ADS  \\\n",
       "0    1.447619  1.496005  1.069367  1.097090   ...    1.041775  0.944099   \n",
       "1    1.266667  0.894270  0.911857  0.813970   ...    0.510257  1.168885   \n",
       "2    1.351111  1.064484  1.087077  1.099449   ...    1.322889  1.007039   \n",
       "3    3.800000  0.609141  1.048934  0.765308   ...    1.111227  0.965079   \n",
       "4    1.266667  0.803547  1.048934  0.968801   ...    1.587467  0.747412   \n",
       "5    1.447619  0.888716  1.137480  0.788691   ...    0.793734  1.132919   \n",
       "6    0.000000  0.953888  1.087077  0.972045   ...    1.058312  0.881159   \n",
       "7    0.000000  1.023874  1.090652  0.959953   ...    1.474077  0.404614   \n",
       "8    1.351111  1.437744  0.832790  1.283477   ...    0.740818  0.965079   \n",
       "9    2.702222  0.995362  1.157005  0.953170   ...    0.793734  1.219462   \n",
       "10   0.000000  0.712824  0.995295  0.871478   ...    0.634987  0.839199   \n",
       "11   0.000000  1.230377  0.801004  1.184385   ...    1.474077  1.213842   \n",
       "12   0.000000  0.842428  1.138331  0.977648   ...    0.793734  1.174879   \n",
       "13   2.702222  1.036835  0.953576  1.108886   ...    0.846649  0.713320   \n",
       "14   0.000000  1.155330  0.926331  1.041477   ...    0.496084  1.573499   \n",
       "15   0.000000  1.010914  0.882058  0.946682   ...    0.793734  1.468599   \n",
       "16   1.447619  0.666537  1.178347  0.753301   ...    1.488251  0.865424   \n",
       "17   0.000000  0.732697  0.947219  0.825766   ...    1.140992  0.983437   \n",
       "18   0.000000  1.658936  0.810540  1.437718   ...    0.582071  1.384679   \n",
       "19   1.351111  0.829468  0.921790  1.146636   ...    1.289817  0.354037   \n",
       "20   0.000000  1.023874  1.090652  0.959953   ...    0.740818  0.965079   \n",
       "21   1.266667  0.894270  0.911857  0.813970   ...    1.058312  0.881159   \n",
       "22   3.800000  0.609141  1.048934  0.765308   ...    0.510257  1.168885   \n",
       "23   1.351111  1.437744  0.832790  1.283477   ...    0.793734  1.132919   \n",
       "24   2.702222  0.995362  1.157005  0.953170   ...    1.041775  0.944099   \n",
       "25   0.000000  0.953888  1.087077  0.972045   ...    1.474077  0.404614   \n",
       "26   1.351111  1.064484  1.087077  1.099449   ...    0.793734  1.219462   \n",
       "27   1.266667  0.803547  1.048934  0.968801   ...    1.111227  0.965079   \n",
       "28   1.447619  0.888716  1.137480  0.788691   ...    1.322889  1.007039   \n",
       "29   1.447619  1.496005  1.069367  1.097090   ...    1.587467  0.747412   \n",
       "..        ...       ...       ...       ...   ...         ...       ...   \n",
       "274  0.000000  1.230377  0.801004  1.184385   ...    1.587467  0.747412   \n",
       "275  1.351111  0.829468  0.921790  1.146636   ...    1.488251  0.865424   \n",
       "276  1.351111  1.437744  0.832790  1.283477   ...    1.140992  0.983437   \n",
       "277  0.000000  1.658936  0.810540  1.437718   ...    0.740818  0.965079   \n",
       "278  2.702222  0.995362  1.157005  0.953170   ...    0.496084  1.573499   \n",
       "279  1.266667  0.894270  0.911857  0.813970   ...    1.474077  0.404614   \n",
       "280  1.447619  0.666537  1.178347  0.753301   ...    0.582071  1.384679   \n",
       "281  0.000000  0.732697  0.947219  0.825766   ...    1.322889  1.007039   \n",
       "282  1.447619  1.496005  1.069367  1.097090   ...    0.793734  1.174879   \n",
       "283  0.000000  1.155330  0.926331  1.041477   ...    1.474077  1.213842   \n",
       "284  0.000000  1.010914  0.882058  0.946682   ...    0.634987  0.839199   \n",
       "285  0.000000  0.953888  1.087077  0.972045   ...    0.510257  1.168885   \n",
       "286  0.000000  0.842428  1.138331  0.977648   ...    1.289817  0.354037   \n",
       "287  1.351111  1.064484  1.087077  1.099449   ...    1.111227  0.965079   \n",
       "288  1.447619  0.888716  1.137480  0.788691   ...    1.041775  0.944099   \n",
       "289  0.000000  0.712824  0.995295  0.871478   ...    0.740818  0.965079   \n",
       "290  1.351111  0.829468  0.921790  1.146636   ...    0.793734  1.132919   \n",
       "291  3.800000  0.609141  1.048934  0.765308   ...    0.793734  1.468599   \n",
       "292  2.702222  1.036835  0.953576  1.108886   ...    0.496084  1.573499   \n",
       "293  0.000000  1.658936  0.810540  1.437718   ...    1.140992  0.983437   \n",
       "294  1.266667  0.803547  1.048934  0.968801   ...    1.058312  0.881159   \n",
       "295  2.702222  0.995362  1.157005  0.953170   ...    1.587467  0.747412   \n",
       "296  0.000000  1.023874  1.090652  0.959953   ...    1.488251  0.865424   \n",
       "297  0.000000  1.230377  0.801004  1.184385   ...    0.846649  0.713320   \n",
       "298  1.351111  1.437744  0.832790  1.283477   ...    0.793734  1.219462   \n",
       "299  1.266667  0.894270  0.911857  0.813970   ...    0.634987  0.839199   \n",
       "300  0.000000  0.732697  0.947219  0.825766   ...    1.041775  0.944099   \n",
       "301  0.000000  1.010914  0.882058  0.946682   ...    0.582071  1.384679   \n",
       "302  0.000000  0.842428  1.138331  0.977648   ...    1.111227  0.965079   \n",
       "303  1.351111  1.064484  1.087077  1.099449   ...    1.289817  0.354037   \n",
       "\n",
       "        ATYCI     ATRCI    ATSOTI      ATFI      ATSI     ATCKI       ATCR  \\\n",
       "0    0.796190  2.280000  1.102607  0.852449  1.042121  1.219178  11.602210   \n",
       "1    0.909932  0.868571  0.730506  0.926202  0.875152  0.876712   6.766917   \n",
       "2    1.158095  0.810667  0.920437  1.056556  0.933495  0.891324  16.447368   \n",
       "3    0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   9.375000   \n",
       "4    1.013333  0.760000  1.166526  1.020537  1.128485  1.082192  16.326531   \n",
       "5    0.965079  1.621333  0.818167  1.267867  0.859798  0.847489  10.714286   \n",
       "6    1.042286  3.242667  1.056798  1.037346  0.933495  1.022831  13.157895   \n",
       "7    0.992653  0.868571  1.424486  0.809569  1.316017  1.737769  13.000000   \n",
       "8    1.042286  0.810667  0.801121  0.870858  0.841374  0.803653  10.218978   \n",
       "9    1.302857  0.760000  0.671152  0.876461  0.731212  0.712329  12.598425   \n",
       "10   1.003683  0.810667  0.835212  1.159010  0.872081  0.803653   8.450704   \n",
       "11   0.951293  0.868571  1.059233  1.042835  0.987013  0.908023  17.333333   \n",
       "12   0.965079  0.810667  1.073844  0.992522  1.105455  0.949772   8.333333   \n",
       "13   1.196698  0.810667  0.971573  1.094976  1.007192  0.964384   9.756098   \n",
       "14   0.940952  0.760000  0.783011  1.116588  0.800303  0.643836   7.194245   \n",
       "15   0.656254  0.000000  1.005663  0.749194  1.050182  1.256621   8.771930   \n",
       "16   1.339048  1.520000  1.390244  1.152607  1.116970  0.958904  15.463918   \n",
       "17   1.230476  0.760000  0.814971  1.092575  0.944242  1.000000  14.024390   \n",
       "18   0.965079  0.810667  0.869302  1.069363  0.982626  0.920548   6.875000   \n",
       "19   0.651429  0.760000  1.358284  0.900474  1.116970  1.342466  13.402062   \n",
       "20   1.042286  0.810667  0.801121  0.870858  0.841374  0.803653  10.218978   \n",
       "21   1.042286  3.242667  1.056798  1.037346  0.933495  1.022831  13.157895   \n",
       "22   0.909932  0.868571  0.730506  0.926202  0.875152  0.876712   6.766917   \n",
       "23   0.965079  1.621333  0.818167  1.267867  0.859798  0.847489  10.714286   \n",
       "24   0.796190  2.280000  1.102607  0.852449  1.042121  1.219178  11.602210   \n",
       "25   0.992653  0.868571  1.424486  0.809569  1.316017  1.737769  13.000000   \n",
       "26   1.302857  0.760000  0.671152  0.876461  0.731212  0.712329  12.598425   \n",
       "27   0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   9.375000   \n",
       "28   1.158095  0.810667  0.920437  1.056556  0.933495  0.891324  16.447368   \n",
       "29   1.013333  0.760000  1.166526  1.020537  1.128485  1.082192  16.326531   \n",
       "..        ...       ...       ...       ...       ...       ...        ...   \n",
       "274  1.013333  0.760000  1.166526  1.020537  1.128485  1.082192  16.326531   \n",
       "275  1.339048  1.520000  1.390244  1.152607  1.116970  0.958904  15.463918   \n",
       "276  1.230476  0.760000  0.814971  1.092575  0.944242  1.000000  14.024390   \n",
       "277  1.042286  0.810667  0.801121  0.870858  0.841374  0.803653  10.218978   \n",
       "278  0.940952  0.760000  0.783011  1.116588  0.800303  0.643836   7.194245   \n",
       "279  0.992653  0.868571  1.424486  0.809569  1.316017  1.737769  13.000000   \n",
       "280  0.965079  0.810667  0.869302  1.069363  0.982626  0.920548   6.875000   \n",
       "281  1.158095  0.810667  0.920437  1.056556  0.933495  0.891324  16.447368   \n",
       "282  0.965079  0.810667  1.073844  0.992522  1.105455  0.949772   8.333333   \n",
       "283  0.951293  0.868571  1.059233  1.042835  0.987013  0.908023  17.333333   \n",
       "284  1.003683  0.810667  0.835212  1.159010  0.872081  0.803653   8.450704   \n",
       "285  0.909932  0.868571  0.730506  0.926202  0.875152  0.876712   6.766917   \n",
       "286  0.651429  0.760000  1.358284  0.900474  1.116970  1.342466  13.402062   \n",
       "287  0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   9.375000   \n",
       "288  0.796190  2.280000  1.102607  0.852449  1.042121  1.219178  11.602210   \n",
       "289  1.042286  0.810667  0.801121  0.870858  0.841374  0.803653  10.218978   \n",
       "290  0.965079  1.621333  0.818167  1.267867  0.859798  0.847489  10.714286   \n",
       "291  0.656254  0.000000  1.005663  0.749194  1.050182  1.256621   8.771930   \n",
       "292  0.940952  0.760000  0.783011  1.116588  0.800303  0.643836   7.194245   \n",
       "293  1.230476  0.760000  0.814971  1.092575  0.944242  1.000000  14.024390   \n",
       "294  1.042286  3.242667  1.056798  1.037346  0.933495  1.022831  13.157895   \n",
       "295  1.013333  0.760000  1.166526  1.020537  1.128485  1.082192  16.326531   \n",
       "296  1.339048  1.520000  1.390244  1.152607  1.116970  0.958904  15.463918   \n",
       "297  1.196698  0.810667  0.971573  1.094976  1.007192  0.964384   9.756098   \n",
       "298  1.302857  0.760000  0.671152  0.876461  0.731212  0.712329  12.598425   \n",
       "299  1.003683  0.810667  0.835212  1.159010  0.872081  0.803653   8.450704   \n",
       "300  0.796190  2.280000  1.102607  0.852449  1.042121  1.219178  11.602210   \n",
       "301  0.965079  0.810667  0.869302  1.069363  0.982626  0.920548   6.875000   \n",
       "302  0.849270  0.000000  1.142024  0.896472  1.375677  1.095890   9.375000   \n",
       "303  0.651429  0.760000  1.358284  0.900474  1.116970  1.342466  13.402062   \n",
       "\n",
       "     Result  \n",
       "0         1  \n",
       "1         1  \n",
       "2        -1  \n",
       "3        -1  \n",
       "4        -1  \n",
       "5         1  \n",
       "6         0  \n",
       "7        -1  \n",
       "8         1  \n",
       "9         0  \n",
       "10        0  \n",
       "11        1  \n",
       "12        1  \n",
       "13        1  \n",
       "14        1  \n",
       "15       -1  \n",
       "16        1  \n",
       "17       -1  \n",
       "18        1  \n",
       "19       -1  \n",
       "20        1  \n",
       "21        0  \n",
       "22        0  \n",
       "23        1  \n",
       "24       -1  \n",
       "25        0  \n",
       "26        1  \n",
       "27       -1  \n",
       "28        1  \n",
       "29       -1  \n",
       "..      ...  \n",
       "274       1  \n",
       "275      -1  \n",
       "276       1  \n",
       "277       1  \n",
       "278       1  \n",
       "279      -1  \n",
       "280       1  \n",
       "281      -1  \n",
       "282       1  \n",
       "283       0  \n",
       "284       1  \n",
       "285       1  \n",
       "286       0  \n",
       "287      -1  \n",
       "288       1  \n",
       "289       1  \n",
       "290      -1  \n",
       "291      -1  \n",
       "292       1  \n",
       "293       1  \n",
       "294       1  \n",
       "295       1  \n",
       "296       1  \n",
       "297       0  \n",
       "298       1  \n",
       "299       0  \n",
       "300      -1  \n",
       "301       1  \n",
       "302       1  \n",
       "303      -1  \n",
       "\n",
       "[304 rows x 22 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transforming H,D,A to integers \n",
    "def stringToInteger(str):\n",
    "    if str.FTR == 'H':\n",
    "        return 1\n",
    "    elif str.FTR == 'A':\n",
    "        return -1\n",
    "    else:\n",
    "        return 0\n",
    "   \n",
    "bot['Result'] = bot.apply(lambda row : stringToInteger(row), axis = 1)\n",
    "bot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          HAS       HDS     HTYCI     HTRCI    HTSOTI      HTFI      HTSI  \\\n",
      "0    1.258799  1.020515  1.007836  1.447619  1.496005  1.069367  1.097090   \n",
      "1    1.101449  1.041775  1.282700  1.266667  0.894270  0.911857  0.813970   \n",
      "2    0.797239  1.587467  1.239944  1.351111  1.064484  1.087077  1.099449   \n",
      "3    0.275362  1.190601  0.841772  3.800000  0.609141  1.048934  0.765308   \n",
      "4    0.747412  0.992167  0.921941  1.266667  0.803547  1.048934  0.968801   \n",
      "5    0.854185  1.077210  1.099458  1.447619  0.888716  1.137480  0.788691   \n",
      "6    0.923119  1.005396  1.239944  0.000000  0.953888  1.087077  0.972045   \n",
      "7    1.455487  0.595300  1.162447  0.000000  1.023874  1.090652  0.959953   \n",
      "8    1.846239  0.476240  0.555837  1.351111  1.437744  0.832790  1.283477   \n",
      "9    0.797239  1.164143  1.453727  2.702222  0.995362  1.157005  0.953170   \n",
      "10   0.708075  1.537859  1.202532  0.000000  0.712824  0.995295  0.871478   \n",
      "11   1.216839  0.529156  0.684107  0.000000  1.230377  0.801004  1.184385   \n",
      "12   0.904762  1.041775  0.881857  0.000000  0.842428  1.138331  0.977648   \n",
      "13   0.797239  1.005396  1.197187  2.702222  1.036835  0.953576  1.108886   \n",
      "14   1.123928  0.737038  0.732972  0.000000  1.155330  0.926331  1.041477   \n",
      "15   1.062112  1.140992  0.962025  0.000000  1.010914  0.882058  0.946682   \n",
      "16   0.764271  0.850429  1.145268  1.447619  0.666537  1.178347  0.753301   \n",
      "17   0.797239  1.481636  1.026160  0.000000  0.732697  0.947219  0.825766   \n",
      "18   2.084886  0.595300  0.521097  0.000000  1.658936  0.810540  1.437718   \n",
      "19   0.461560  0.899565  0.855134  1.351111  0.829468  0.921790  1.146636   \n",
      "20   1.455487  0.595300  1.162447  0.000000  1.023874  1.090652  0.959953   \n",
      "21   1.101449  1.041775  1.282700  1.266667  0.894270  0.911857  0.813970   \n",
      "22   0.275362  1.190601  0.841772  3.800000  0.609141  1.048934  0.765308   \n",
      "23   1.846239  0.476240  0.555837  1.351111  1.437744  0.832790  1.283477   \n",
      "24   0.797239  1.164143  1.453727  2.702222  0.995362  1.157005  0.953170   \n",
      "25   0.923119  1.005396  1.239944  0.000000  0.953888  1.087077  0.972045   \n",
      "26   0.797239  1.587467  1.239944  1.351111  1.064484  1.087077  1.099449   \n",
      "27   0.747412  0.992167  0.921941  1.266667  0.803547  1.048934  0.968801   \n",
      "28   0.854185  1.077210  1.099458  1.447619  0.888716  1.137480  0.788691   \n",
      "29   1.258799  1.020515  1.007836  1.447619  1.496005  1.069367  1.097090   \n",
      "..        ...       ...       ...       ...       ...       ...       ...   \n",
      "274  1.216839  0.529156  0.684107  0.000000  1.230377  0.801004  1.184385   \n",
      "275  0.461560  0.899565  0.855134  1.351111  0.829468  0.921790  1.146636   \n",
      "276  1.846239  0.476240  0.555837  1.351111  1.437744  0.832790  1.283477   \n",
      "277  2.084886  0.595300  0.521097  0.000000  1.658936  0.810540  1.437718   \n",
      "278  0.797239  1.164143  1.453727  2.702222  0.995362  1.157005  0.953170   \n",
      "279  1.101449  1.041775  1.282700  1.266667  0.894270  0.911857  0.813970   \n",
      "280  0.764271  0.850429  1.145268  1.447619  0.666537  1.178347  0.753301   \n",
      "281  0.797239  1.481636  1.026160  0.000000  0.732697  0.947219  0.825766   \n",
      "282  1.258799  1.020515  1.007836  1.447619  1.496005  1.069367  1.097090   \n",
      "283  1.123928  0.737038  0.732972  0.000000  1.155330  0.926331  1.041477   \n",
      "284  1.062112  1.140992  0.962025  0.000000  1.010914  0.882058  0.946682   \n",
      "285  0.923119  1.005396  1.239944  0.000000  0.953888  1.087077  0.972045   \n",
      "286  0.904762  1.041775  0.881857  0.000000  0.842428  1.138331  0.977648   \n",
      "287  0.797239  1.587467  1.239944  1.351111  1.064484  1.087077  1.099449   \n",
      "288  0.854185  1.077210  1.099458  1.447619  0.888716  1.137480  0.788691   \n",
      "289  0.708075  1.537859  1.202532  0.000000  0.712824  0.995295  0.871478   \n",
      "290  0.461560  0.899565  0.855134  1.351111  0.829468  0.921790  1.146636   \n",
      "291  0.275362  1.190601  0.841772  3.800000  0.609141  1.048934  0.765308   \n",
      "292  0.797239  1.005396  1.197187  2.702222  1.036835  0.953576  1.108886   \n",
      "293  2.084886  0.595300  0.521097  0.000000  1.658936  0.810540  1.437718   \n",
      "294  0.747412  0.992167  0.921941  1.266667  0.803547  1.048934  0.968801   \n",
      "295  0.797239  1.164143  1.453727  2.702222  0.995362  1.157005  0.953170   \n",
      "296  1.455487  0.595300  1.162447  0.000000  1.023874  1.090652  0.959953   \n",
      "297  1.216839  0.529156  0.684107  0.000000  1.230377  0.801004  1.184385   \n",
      "298  1.846239  0.476240  0.555837  1.351111  1.437744  0.832790  1.283477   \n",
      "299  1.101449  1.041775  1.282700  1.266667  0.894270  0.911857  0.813970   \n",
      "300  0.797239  1.481636  1.026160  0.000000  0.732697  0.947219  0.825766   \n",
      "301  1.062112  1.140992  0.962025  0.000000  1.010914  0.882058  0.946682   \n",
      "302  0.904762  1.041775  0.881857  0.000000  0.842428  1.138331  0.977648   \n",
      "303  0.797239  1.587467  1.239944  1.351111  1.064484  1.087077  1.099449   \n",
      "\n",
      "        HTCKI       HTCR       AAS       ADS     ATYCI     ATRCI    ATSOTI  \\\n",
      "0    1.152084  12.903226  1.041775  0.944099  0.796190  2.280000  1.102607   \n",
      "1    0.876586  15.217391  0.510257  1.168885  0.909932  0.868571  0.730506   \n",
      "2    0.935025   8.154506  1.322889  1.007039  1.158095  0.810667  0.920437   \n",
      "3    0.788927   4.046243  1.111227  0.965079  0.849270  0.000000  1.142024   \n",
      "4    1.062860   8.675799  1.587467  0.747412  1.013333  0.760000  1.166526   \n",
      "5    0.813973  12.179487  0.793734  1.132919  0.965079  1.621333  0.818167   \n",
      "6    0.935025  10.679612  1.058312  0.881159  1.042286  3.242667  1.056798   \n",
      "7    1.073818  17.050691  1.474077  0.404614  0.992653  0.868571  1.424486   \n",
      "8    1.238908  16.176471  0.740818  0.965079  1.042286  0.810667  0.801121   \n",
      "9    0.899962   9.405941  0.793734  1.219462  1.302857  0.760000  0.671152   \n",
      "10   0.854671   9.137056  0.634987  0.839199  1.003683  0.810667  0.835212   \n",
      "11   1.051903  11.553785  1.474077  1.213842  0.951293  0.868571  1.059233   \n",
      "12   1.008074  10.407240  0.793734  1.174879  0.965079  0.810667  1.073844   \n",
      "13   1.028527   8.085106  0.846649  0.713320  1.196698  0.810667  0.971573   \n",
      "14   0.939199  12.135922  0.496084  1.573499  0.940952  0.760000  0.783011   \n",
      "15   1.051903  12.616822  0.793734  1.468599  0.656254  0.000000  1.005663   \n",
      "16   0.826495  11.409396  1.488251  0.865424  1.339048  1.520000  1.390244   \n",
      "17   0.783083  10.857143  1.140992  0.983437  1.230476  0.760000  0.814971   \n",
      "18   1.325836  16.307692  0.582071  1.384679  0.965079  0.810667  0.869302   \n",
      "19   1.332411   4.526749  1.289817  0.354037  0.651429  0.760000  1.358284   \n",
      "20   1.073818  17.050691  0.740818  0.965079  1.042286  0.810667  0.801121   \n",
      "21   0.876586  15.217391  1.058312  0.881159  1.042286  3.242667  1.056798   \n",
      "22   0.788927   4.046243  0.510257  1.168885  0.909932  0.868571  0.730506   \n",
      "23   1.238908  16.176471  0.793734  1.132919  0.965079  1.621333  0.818167   \n",
      "24   0.899962   9.405941  1.041775  0.944099  0.796190  2.280000  1.102607   \n",
      "25   0.935025  10.679612  1.474077  0.404614  0.992653  0.868571  1.424486   \n",
      "26   0.935025   8.154506  0.793734  1.219462  1.302857  0.760000  0.671152   \n",
      "27   1.062860   8.675799  1.111227  0.965079  0.849270  0.000000  1.142024   \n",
      "28   0.813973  12.179487  1.322889  1.007039  1.158095  0.810667  0.920437   \n",
      "29   1.152084  12.903226  1.587467  0.747412  1.013333  0.760000  1.166526   \n",
      "..        ...        ...       ...       ...       ...       ...       ...   \n",
      "274  1.051903  11.553785  1.587467  0.747412  1.013333  0.760000  1.166526   \n",
      "275  1.332411   4.526749  1.488251  0.865424  1.339048  1.520000  1.390244   \n",
      "276  1.238908  16.176471  1.140992  0.983437  1.230476  0.760000  0.814971   \n",
      "277  1.325836  16.307692  0.740818  0.965079  1.042286  0.810667  0.801121   \n",
      "278  0.899962   9.405941  0.496084  1.573499  0.940952  0.760000  0.783011   \n",
      "279  0.876586  15.217391  1.474077  0.404614  0.992653  0.868571  1.424486   \n",
      "280  0.826495  11.409396  0.582071  1.384679  0.965079  0.810667  0.869302   \n",
      "281  0.783083  10.857143  1.322889  1.007039  1.158095  0.810667  0.920437   \n",
      "282  1.152084  12.903226  0.793734  1.174879  0.965079  0.810667  1.073844   \n",
      "283  0.939199  12.135922  1.474077  1.213842  0.951293  0.868571  1.059233   \n",
      "284  1.051903  12.616822  0.634987  0.839199  1.003683  0.810667  0.835212   \n",
      "285  0.935025  10.679612  0.510257  1.168885  0.909932  0.868571  0.730506   \n",
      "286  1.008074  10.407240  1.289817  0.354037  0.651429  0.760000  1.358284   \n",
      "287  0.935025   8.154506  1.111227  0.965079  0.849270  0.000000  1.142024   \n",
      "288  0.813973  12.179487  1.041775  0.944099  0.796190  2.280000  1.102607   \n",
      "289  0.854671   9.137056  0.740818  0.965079  1.042286  0.810667  0.801121   \n",
      "290  1.332411   4.526749  0.793734  1.132919  0.965079  1.621333  0.818167   \n",
      "291  0.788927   4.046243  0.793734  1.468599  0.656254  0.000000  1.005663   \n",
      "292  1.028527   8.085106  0.496084  1.573499  0.940952  0.760000  0.783011   \n",
      "293  1.325836  16.307692  1.140992  0.983437  1.230476  0.760000  0.814971   \n",
      "294  1.062860   8.675799  1.058312  0.881159  1.042286  3.242667  1.056798   \n",
      "295  0.899962   9.405941  1.587467  0.747412  1.013333  0.760000  1.166526   \n",
      "296  1.073818  17.050691  1.488251  0.865424  1.339048  1.520000  1.390244   \n",
      "297  1.051903  11.553785  0.846649  0.713320  1.196698  0.810667  0.971573   \n",
      "298  1.238908  16.176471  0.793734  1.219462  1.302857  0.760000  0.671152   \n",
      "299  0.876586  15.217391  0.634987  0.839199  1.003683  0.810667  0.835212   \n",
      "300  0.783083  10.857143  1.041775  0.944099  0.796190  2.280000  1.102607   \n",
      "301  1.051903  12.616822  0.582071  1.384679  0.965079  0.810667  0.869302   \n",
      "302  1.008074  10.407240  1.111227  0.965079  0.849270  0.000000  1.142024   \n",
      "303  0.935025   8.154506  1.289817  0.354037  0.651429  0.760000  1.358284   \n",
      "\n",
      "         ATFI      ATSI     ATCKI       ATCR  \n",
      "0    0.852449  1.042121  1.219178  11.602210  \n",
      "1    0.926202  0.875152  0.876712   6.766917  \n",
      "2    1.056556  0.933495  0.891324  16.447368  \n",
      "3    0.896472  1.375677  1.095890   9.375000  \n",
      "4    1.020537  1.128485  1.082192  16.326531  \n",
      "5    1.267867  0.859798  0.847489  10.714286  \n",
      "6    1.037346  0.933495  1.022831  13.157895  \n",
      "7    0.809569  1.316017  1.737769  13.000000  \n",
      "8    0.870858  0.841374  0.803653  10.218978  \n",
      "9    0.876461  0.731212  0.712329  12.598425  \n",
      "10   1.159010  0.872081  0.803653   8.450704  \n",
      "11   1.042835  0.987013  0.908023  17.333333  \n",
      "12   0.992522  1.105455  0.949772   8.333333  \n",
      "13   1.094976  1.007192  0.964384   9.756098  \n",
      "14   1.116588  0.800303  0.643836   7.194245  \n",
      "15   0.749194  1.050182  1.256621   8.771930  \n",
      "16   1.152607  1.116970  0.958904  15.463918  \n",
      "17   1.092575  0.944242  1.000000  14.024390  \n",
      "18   1.069363  0.982626  0.920548   6.875000  \n",
      "19   0.900474  1.116970  1.342466  13.402062  \n",
      "20   0.870858  0.841374  0.803653  10.218978  \n",
      "21   1.037346  0.933495  1.022831  13.157895  \n",
      "22   0.926202  0.875152  0.876712   6.766917  \n",
      "23   1.267867  0.859798  0.847489  10.714286  \n",
      "24   0.852449  1.042121  1.219178  11.602210  \n",
      "25   0.809569  1.316017  1.737769  13.000000  \n",
      "26   0.876461  0.731212  0.712329  12.598425  \n",
      "27   0.896472  1.375677  1.095890   9.375000  \n",
      "28   1.056556  0.933495  0.891324  16.447368  \n",
      "29   1.020537  1.128485  1.082192  16.326531  \n",
      "..        ...       ...       ...        ...  \n",
      "274  1.020537  1.128485  1.082192  16.326531  \n",
      "275  1.152607  1.116970  0.958904  15.463918  \n",
      "276  1.092575  0.944242  1.000000  14.024390  \n",
      "277  0.870858  0.841374  0.803653  10.218978  \n",
      "278  1.116588  0.800303  0.643836   7.194245  \n",
      "279  0.809569  1.316017  1.737769  13.000000  \n",
      "280  1.069363  0.982626  0.920548   6.875000  \n",
      "281  1.056556  0.933495  0.891324  16.447368  \n",
      "282  0.992522  1.105455  0.949772   8.333333  \n",
      "283  1.042835  0.987013  0.908023  17.333333  \n",
      "284  1.159010  0.872081  0.803653   8.450704  \n",
      "285  0.926202  0.875152  0.876712   6.766917  \n",
      "286  0.900474  1.116970  1.342466  13.402062  \n",
      "287  0.896472  1.375677  1.095890   9.375000  \n",
      "288  0.852449  1.042121  1.219178  11.602210  \n",
      "289  0.870858  0.841374  0.803653  10.218978  \n",
      "290  1.267867  0.859798  0.847489  10.714286  \n",
      "291  0.749194  1.050182  1.256621   8.771930  \n",
      "292  1.116588  0.800303  0.643836   7.194245  \n",
      "293  1.092575  0.944242  1.000000  14.024390  \n",
      "294  1.037346  0.933495  1.022831  13.157895  \n",
      "295  1.020537  1.128485  1.082192  16.326531  \n",
      "296  1.152607  1.116970  0.958904  15.463918  \n",
      "297  1.094976  1.007192  0.964384   9.756098  \n",
      "298  0.876461  0.731212  0.712329  12.598425  \n",
      "299  1.159010  0.872081  0.803653   8.450704  \n",
      "300  0.852449  1.042121  1.219178  11.602210  \n",
      "301  1.069363  0.982626  0.920548   6.875000  \n",
      "302  0.896472  1.375677  1.095890   9.375000  \n",
      "303  0.900474  1.116970  1.342466  13.402062  \n",
      "\n",
      "[304 rows x 18 columns]\n",
      "0      1\n",
      "1      1\n",
      "2     -1\n",
      "3     -1\n",
      "4     -1\n",
      "5      1\n",
      "6      0\n",
      "7     -1\n",
      "8      1\n",
      "9      0\n",
      "10     0\n",
      "11     1\n",
      "12     1\n",
      "13     1\n",
      "14     1\n",
      "15    -1\n",
      "16     1\n",
      "17    -1\n",
      "18     1\n",
      "19    -1\n",
      "20     1\n",
      "21     0\n",
      "22     0\n",
      "23     1\n",
      "24    -1\n",
      "25     0\n",
      "26     1\n",
      "27    -1\n",
      "28     1\n",
      "29    -1\n",
      "      ..\n",
      "274    1\n",
      "275   -1\n",
      "276    1\n",
      "277    1\n",
      "278    1\n",
      "279   -1\n",
      "280    1\n",
      "281   -1\n",
      "282    1\n",
      "283    0\n",
      "284    1\n",
      "285    1\n",
      "286    0\n",
      "287   -1\n",
      "288    1\n",
      "289    1\n",
      "290   -1\n",
      "291   -1\n",
      "292    1\n",
      "293    1\n",
      "294    1\n",
      "295    1\n",
      "296    1\n",
      "297    0\n",
      "298    1\n",
      "299    0\n",
      "300   -1\n",
      "301    1\n",
      "302    1\n",
      "303   -1\n",
      "Name: Result, Length: 304, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Separate into feature set and target variable\n",
    "#Target is 'Result' (1 == H which means home team wins, 0 == D means Draw,-1 == A means Away team wins)\n",
    "\n",
    "Y = bot['Result']\n",
    "x = bot[['HAS', 'HDS', 'HTYCI', 'HTRCI', 'HTSOTI', 'HTFI', 'HTSI',\n",
    "        'HTCKI', 'HTCR', 'AAS', 'ADS', 'ATYCI', 'ATRCI', 'ATSOTI', 'ATFI', 'ATSI', 'ATCKI', 'ATCR']]\n",
    "\n",
    "print(x)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "[[ 0.61468569  0.06659381  0.03186509 ...  0.26692319  0.92540318\n",
      "   0.02673912]\n",
      " [ 0.24095679  0.13560922  1.14959452 ... -0.791168   -0.52053929\n",
      "  -1.46371166]\n",
      " [-0.48158575  1.90700468  0.9757255  ... -0.42144419 -0.45884575\n",
      "   1.52023099]\n",
      " ...\n",
      " [ 0.14752457  0.45768112 -0.15442314 ... -0.11009781 -0.33545865\n",
      "  -1.4303958 ]\n",
      " [-0.22620433  0.13560922 -0.48042756 ...  2.38067317  0.40486389\n",
      "  -0.65978536]\n",
      " [-0.48158575  1.90700468  0.9757255  ...  0.74123993  1.44594248\n",
      "   0.58153299]]\n"
     ]
    }
   ],
   "source": [
    "#Standardising the data.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "print(type(x))\n",
    "scaledData = scaler.fit_transform(x)\n",
    "\n",
    "\n",
    "print(scaledData)\n",
    "#print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "\n",
    "#Encoding my target values \n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(bot.FTR)\n",
    "encoded_Y = encoder.transform(bot.FTR)\n",
    "\n",
    "y_one_hot_encoded = np_utils.to_categorical(encoded_Y)\n",
    "print(y_one_hot_encoded)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7049180327868853\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, Y, test_size=0.20, train_size = 0.80)\n",
    "\n",
    "# print(X_train)\n",
    "# print(y_train)\n",
    "# print(X_test.shape)\n",
    "# print(y_test.shape)\n",
    "\n",
    "\n",
    "LogisticClassifier = LogisticRegression(solver = 'newton-cg', multi_class= 'multinomial', dual = False, C = 0.75, max_iter = 3) \n",
    "y_pred = LogisticClassifier.fit(X_train,y_train).predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "import pickle\n",
    "pickle.dump(LogisticClassifier, open(\"LogisticRegression.pkl\",\"wb\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression accuracy: 0.60 (+/- 0.09)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.60      0.66      0.63       100\n",
      "           0       0.23      0.05      0.08        58\n",
      "           1       0.63      0.78      0.70       146\n",
      "\n",
      "   micro avg       0.60      0.60      0.60       304\n",
      "   macro avg       0.49      0.50      0.47       304\n",
      "weighted avg       0.54      0.60      0.56       304\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy: 0.57 (+/- 0.17)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC accuracy: 0.61 (+/- 0.12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost accuracy: 0.58 (+/- 0.12)\n",
      "SVC accuracy: 0.60 (+/- 0.04)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.75      0.46      0.57       100\n",
      "           0       0.00      0.00      0.00        58\n",
      "           1       0.56      0.94      0.70       146\n",
      "\n",
      "   micro avg       0.60      0.60      0.60       304\n",
      "   macro avg       0.44      0.47      0.43       304\n",
      "weighted avg       0.52      0.60      0.53       304\n",
      "\n",
      "[[ 66   4  30]\n",
      " [ 18   3  37]\n",
      " [ 26   6 114]]\n",
      "[[69  9 22]\n",
      " [21 10 27]\n",
      " [36 20 90]]\n",
      "[[ 72   2  26]\n",
      " [ 19   2  37]\n",
      " [ 32   2 112]]\n",
      "[[ 65  10  25]\n",
      " [ 13  10  35]\n",
      " [ 29  15 102]]\n",
      "[[ 46   0  54]\n",
      " [  6   0  52]\n",
      " [  9   0 137]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "#Trying out different models\n",
    "\n",
    "#Trying Logistic Regression \n",
    "LogisticClassifier = LogisticRegression(solver = 'newton-cg', multi_class= 'multinomial', dual = False, C = 0.75, max_iter = 3) \n",
    "scores = cross_val_score(LogisticClassifier, scaledData, Y, cv=10, scoring = 'accuracy')\n",
    "print(\"LogisticRegression accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()*2))\n",
    "y_pred = cross_val_predict(LogisticClassifier, scaledData, Y, cv=10)\n",
    "conf_mat = confusion_matrix(Y, y_pred)\n",
    "print(classification_report(Y,y_pred))\n",
    "\n",
    "clf1 = RandomForestClassifier()\n",
    "scores = cross_val_score(clf1, scaledData, Y, cv=10, scoring = 'accuracy')\n",
    "print(\"Random Forest accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()*2))\n",
    "y_pred1 = cross_val_predict(clf1, scaledData,Y, cv=10)\n",
    "conf_mat1 = confusion_matrix(Y, y_pred1)\n",
    "\n",
    "clf3 = LinearSVC()\n",
    "scores = cross_val_score(clf3, scaledData, Y, cv=10, scoring = 'accuracy')\n",
    "print(\"LinearSVC accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()*2))\n",
    "y_pred2 = cross_val_predict(clf3, scaledData, Y, cv=10)\n",
    "conf_mat2 = confusion_matrix(Y, y_pred2)\n",
    "\n",
    "#Trying XGBoost\n",
    "xgBoost = xgb.XGBClassifier()\n",
    "scores = cross_val_score(xgBoost, scaledData, Y, cv=10, scoring = 'accuracy')\n",
    "print(\"XGBoost accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()*2))\n",
    "y_pred3 = cross_val_predict(xgBoost, scaledData, Y, cv=10)\n",
    "conf_mat3 = confusion_matrix(Y, y_pred3)\n",
    "\n",
    "#Trying SVM \n",
    "svm = SVC(gamma=0.001,C=1, decision_function_shape='ovo')\n",
    "scores = cross_val_score(svm, scaledData, Y, cv=10)\n",
    "print(\"SVC accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()*2))\n",
    "y_pred4 = cross_val_predict(svm, scaledData, Y, cv=10)\n",
    "conf_mat4 = confusion_matrix(Y, y_pred4)\n",
    "print(classification_report(Y,y_pred4))\n",
    "\n",
    "#solver = 'newton-cg', multi_class= 'multinomial'\n",
    "#gamma='scale', decision_function_shape='ovo'\n",
    "\n",
    "#Printing confusion matrix:Performance measurement for machine learning classification\n",
    "\n",
    "print(conf_mat)\n",
    "\n",
    "print(conf_mat1)\n",
    "\n",
    "print(conf_mat2)\n",
    "\n",
    "print(conf_mat3)\n",
    "\n",
    "print(conf_mat4)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "model = pickle.load(open(r\"C:\\Users\\Mlej\\futbol-prediction-bot\\LogisticRegression.pkl\",\"rb\"))\n",
    "def predict(data):\n",
    "    predictData = data[['HAS', 'HDS', 'HTYCI', 'HTRCI', 'HTSOTI', 'HTFI', 'HTSI',\n",
    "        'HTCKI', 'HTCR', 'AAS', 'ADS', 'ATYCI', 'ATRCI', 'ATSOTI', 'ATFI',\n",
    "         'ATSI', 'ATCKI', 'ATCR']]\n",
    "    #scale data\n",
    "    scaler = StandardScaler()\n",
    "    scaledData = scaler.fit_transform(predictData)\n",
    "    prediction = model.predict(scaledData)\n",
    "    return prediction[0]\n",
    "\n",
    "predict(hey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the model and the weights \n",
    "import pickle\n",
    "pickle.dump(LogisticClassifier, open(\"LogisticRegression.pkl\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([ 0.61468569,  0.06659381,  0.03186509,  0.40705636,  1.81429065,\n",
      "        0.59983312,  0.55340795,  0.93645766,  0.52339022,  0.12291809,\n",
      "       -0.18359265, -1.11546321,  1.82019201,  0.47786744, -1.1301506 ,\n",
      "        0.26692319,  0.92540318,  0.02673912])]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "#test predicting \n",
    "\n",
    "bot = [scaledData[0]]\n",
    "# bot\n",
    "print(bot)\n",
    "model = pickle.load(open(\"LogisticRegression.pkl\",\"rb\"))\n",
    "prediction = model.predict(bot)\n",
    "print(prediction[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.625000 using {'C': 0.1, 'dual': False, 'max_iter': 2}\n"
     ]
    }
   ],
   "source": [
    "#Hyperparameter Tuning for logistic regression...\n",
    "#Using KFold cross validation technique \n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "kfold = KFold(n_splits=10)\n",
    "dual = [False]\n",
    "max_iter = [1,2,3,4,5,6,7,8,9,10,20,30,40,50,60,70,80,90,100,110,120,130,140,145,170,200]\n",
    "C = [0.0001,0.01,0.1, 0.5, 0.75,1.0,2,3,4,5,6,7,8,9,10,15,20,50,100]\n",
    "#solver = ['newton-cg','lbfgs','liblinear','sag', 'saga']\n",
    "\n",
    "param_grid = dict(max_iter = max_iter,C=C,dual = dual)\n",
    "\n",
    "grid = GridSearchCV(estimator=LogisticClassifier, param_grid=param_grid, cv = 10, n_jobs = -1)\n",
    "\n",
    "grid_result = grid.fit(scaledData,Y)\n",
    "\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.641447 using {'C': 3, 'gamma': 0.001}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "Cs = [0.001, 0.01, 0.1, 1, 2,3,5,10,12,15,17,20]\n",
    "gammas = [0.001, 0.01, 0.1, 1, 'scale']\n",
    "param_grid = dict(C=Cs,gamma=gammas)\n",
    "grid_search = GridSearchCV(svm, param_grid, cv=10, n_jobs = -1)\n",
    "grid_search.fit(scaledData, Y)\n",
    "print(\"Best: %f using %s\" % (grid_search.best_score_, grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.001}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mlej\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "def svc_param_selection(X, y, nfolds):\n",
    "   \n",
    "    Cs = [0.0001,0.001, 0.01, 0.1, 1, 10]\n",
    "    gammas = [0.0001,0.001, 0.01, 0.1, 1]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "    grid_search = GridSearchCV(svm, param_grid, cv=nfolds)\n",
    "    grid_search.fit(X, y)\n",
    "    grid_search.best_params_\n",
    "    return grid_search.best_params_\n",
    "\n",
    "print(svc_param_selection(X_train, y_train, 10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed =7\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 54.26% (7.96%)\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn import metrics\n",
    "from keras.layers.core import Activation \n",
    "\n",
    "def model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(8, input_dim=18, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(16,activation = 'relu'))\n",
    "    model.add(Dense(16,activation = 'relu'))  \n",
    "    model.add(Dense(3, activation='softmax'))\n",
    "    # Compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience = 10,verbose = 0,mode = 'auto')\n",
    "    checkpointer = ModelCheckpoint(filepath = \"best_weights.hdf5\", verbose = 0,save_best_only=True)\n",
    "                            \n",
    "    return model\n",
    "\n",
    "\n",
    "estimator = KerasClassifier(build_fn=model, epochs=200, batch_size=5, verbose=0)\n",
    "kfold = KFold(n_splits=10, shuffle=False, random_state=seed)\n",
    "results = cross_val_score(estimator, scaledData, y_one_hot_encoded, cv=kfold)\n",
    "print(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'iloc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-116-c52af60f1d87>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m# bot[['HAS', 'HDS', 'HTYCI', 'HTRCI', 'HTSOTI', 'HTFI', 'HTSI',\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#         'HTCKI', 'HTCR', 'AAS', 'ADS', 'ATYCI', 'ATRCI', 'ATSOTI', 'ATFI',\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#          'ATSI', 'ATCKI', 'ATCR']]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# bot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'iloc'"
     ]
    }
   ],
   "source": [
    "bot = bot.iloc[0]\n",
    "# bot[['HAS', 'HDS', 'HTYCI', 'HTRCI', 'HTSOTI', 'HTFI', 'HTSI',\n",
    "#         'HTCKI', 'HTCR', 'AAS', 'ADS', 'ATYCI', 'ATRCI', 'ATSOTI', 'ATFI',\n",
    "#          'ATSI', 'ATCKI', 'ATCR']]\n",
    "# bot\n",
    "print(bot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the model and the weights \n",
    "# import pickle\n",
    "# pickle.dump(dt_clf_gini, open(\".../model.pkl\",\"wb\"))\n",
    "# pickle.dump(regressor, open('model.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}