football-match-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Winning Football Team\n",
    "\n",
    "- Sports betting is a 500 billion dollar market (Sydney Herald)\n",
    "\n",
    "- Football is played by 250 million players in over 200 countries (most popular sport globally).\n",
    "- The English Premier League is the most popular domestic team in the world.\n",
    "- Design a predictive model capable of accurately predicting if the home team will win a football match? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Discription of Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A prediction system was built to predict whether a home team will win it's match or not.**\n",
    "\n",
    "Key to results data:\n",
    "* Div = League Division\n",
    "* Date = Match Date (dd/mm/yy)\n",
    "* Time = Time of match kick-off\n",
    "* HomeTeam = Home Team\n",
    "* Away team = Away Team\n",
    "* FTHG and HG = Full Time Home Team Goals\n",
    "* FTAG and AG = Full-Time Away Team Goals\n",
    "* FTR and Res = Full-Time Result (H=Home Win, D=Draw, A=Away Win)\n",
    "* HTHG = Half Time Home Team Goals\n",
    "* HTAG = Half Time Away Team Goals\n",
    "* HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)\n",
    "\n",
    "Match Statistics (where available)\n",
    "* Attendance = Crowd Attendance\n",
    "* Referee = Match Referee\n",
    "* HS = Home Team Shots\n",
    "* AS = Away Team Shots\n",
    "* HST = Home Team Shots on Target\n",
    "* AST = Away Team Shots on Target\n",
    "* HHW = Home Team Hit Woodwork\n",
    "* AHW = Away Team Hit Woodwork\n",
    "* HC = Home Team Corners\n",
    "* AC = Away Team Corners\n",
    "* HF = Home Team Fouls Committed\n",
    "* AF = Away Team Fouls Committed\n",
    "* HFKC = Home Team Free Kicks Conceded\n",
    "* AFKC = Away Team Free Kicks Conceded\n",
    "* HO = Home Team Offsides\n",
    "* AO = Away Team Offsides\n",
    "* HY = Home Team Yellow Cards\n",
    "* AY = Away Team Yellow Cards\n",
    "*HR = Home Team Red Cards\n",
    "AR = Away Team Red Cards\n",
    "HBP = Home Team Bookings Points (10 = yellow, 25 = red)\n",
    "ABP = Away Team Bookings Points (10 = yellow, 25 = red)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from datetime import datetime as dt\n",
    "import itertools\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from the CSV into a dataframe\n",
    "folder='Datasets/'\n",
    "raw_data_1 = pd.read_csv(folder +'2000-01.csv')\n",
    "raw_data_2 = pd.read_csv(folder +'2001-02.csv')\n",
    "raw_data_3 = pd.read_csv(folder +'2002-03.csv')\n",
    "raw_data_4 = pd.read_csv(folder +'2003-04.csv')\n",
    "raw_data_5 = pd.read_csv(folder +'2004-05.csv')\n",
    "raw_data_6 = pd.read_csv(folder +'2005-06.csv')\n",
    "raw_data_7 = pd.read_csv(folder +'2006-07.csv')\n",
    "raw_data_8 = pd.read_csv(folder +'2007-08.csv')\n",
    "raw_data_9 = pd.read_csv(folder +'2008-09.csv')\n",
    "raw_data_10 = pd.read_csv(folder +'2009-10.csv')\n",
    "raw_data_11 = pd.read_csv(folder +'2010-11.csv')\n",
    "raw_data_12 = pd.read_csv(folder +'2011-12.csv')\n",
    "raw_data_13 = pd.read_csv(folder +'2012-13.csv')\n",
    "raw_data_14 = pd.read_csv(folder +'2013-14.csv')\n",
    "raw_data_15 = pd.read_csv(folder +'2014-15.csv')\n",
    "raw_data_16 = pd.read_csv(folder +'2015-16.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Gets all the statistics related to gameplay\n",
    "                      \n",
    "columns_req = ['Date','HomeTeam','AwayTeam','FTHG','FTAG','FTR']\n",
    "\n",
    "playing_statistics_1 = raw_data_1[columns_req]                      \n",
    "playing_statistics_2 = raw_data_2[columns_req]\n",
    "playing_statistics_3 = raw_data_3[columns_req]\n",
    "playing_statistics_4 = raw_data_4[columns_req]\n",
    "playing_statistics_5 = raw_data_5[columns_req]\n",
    "playing_statistics_6 = raw_data_6[columns_req]\n",
    "playing_statistics_7 = raw_data_7[columns_req]\n",
    "playing_statistics_8 = raw_data_8[columns_req]\n",
    "playing_statistics_9 = raw_data_9[columns_req]\n",
    "playing_statistics_10 = raw_data_10[columns_req]\n",
    "playing_statistics_11 = raw_data_11[columns_req]   \n",
    "playing_statistics_12 = raw_data_12[columns_req]\n",
    "playing_statistics_13 = raw_data_13[columns_req]\n",
    "playing_statistics_14 = raw_data_14[columns_req]\n",
    "playing_statistics_15 = raw_data_15[columns_req]\n",
    "playing_statistics_16 = raw_data_16[columns_req]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GOALS SCORED AND CONCEDED AT THE END OF MATCHWEEK, ARRANGED BY TEAMS AND MATCHWEEK**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-717daf1bfaea>:68: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['HTGS'] = HTGS\n",
      "<ipython-input-56-717daf1bfaea>:69: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['ATGS'] = ATGS\n",
      "<ipython-input-56-717daf1bfaea>:70: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['HTGC'] = HTGC\n",
      "<ipython-input-56-717daf1bfaea>:71: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['ATGC'] = ATGC\n"
     ]
    }
   ],
   "source": [
    "# Gets the goals scored agg arranged by teams and matchweek\n",
    "def get_goals_scored(playing_stat):\n",
    "    # Create a dictionary with team names as keys\n",
    "    teams = {}\n",
    "    for i in playing_stat.groupby('HomeTeam').mean().T.columns:\n",
    "        teams[i] = []\n",
    "    \n",
    "    # the value corresponding to keys is a list containing the match location.\n",
    "    for i in range(len(playing_stat)):\n",
    "        HTGS = playing_stat.iloc[i]['FTHG']\n",
    "        ATGS = playing_stat.iloc[i]['FTAG']\n",
    "        teams[playing_stat.iloc[i].HomeTeam].append(HTGS)\n",
    "        teams[playing_stat.iloc[i].AwayTeam].append(ATGS)\n",
    "    \n",
    "    # Create a dataframe for goals scored where rows are teams and cols are matchweek.\n",
    "    GoalsScored = pd.DataFrame(data=teams, index = [i for i in range(1,39)]).T\n",
    "    GoalsScored[0] = 0\n",
    "    # Aggregate to get uptil that point\n",
    "    for i in range(2,39):\n",
    "        GoalsScored[i] = GoalsScored[i] + GoalsScored[i-1]\n",
    "    return GoalsScored\n",
    "\n",
    "\n",
    "\n",
    "# Gets the goals conceded agg arranged by teams and matchweek\n",
    "def get_goals_conceded(playing_stat):\n",
    "    # Create a dictionary with team names as keys\n",
    "    teams = {}\n",
    "    for i in playing_stat.groupby('HomeTeam').mean().T.columns:\n",
    "        teams[i] = []\n",
    "    \n",
    "    # the value corresponding to keys is a list containing the match location.\n",
    "    for i in range(len(playing_stat)):\n",
    "        ATGC = playing_stat.iloc[i]['FTHG']\n",
    "        HTGC = playing_stat.iloc[i]['FTAG']\n",
    "        teams[playing_stat.iloc[i].HomeTeam].append(HTGC)\n",
    "        teams[playing_stat.iloc[i].AwayTeam].append(ATGC)\n",
    "    \n",
    "    # Create a dataframe for goals scored where rows are teams and cols are matchweek.\n",
    "    GoalsConceded = pd.DataFrame(data=teams, index = [i for i in range(1,39)]).T\n",
    "    GoalsConceded[0] = 0\n",
    "    # Aggregate to get uptil that point\n",
    "    for i in range(2,39):\n",
    "        GoalsConceded[i] = GoalsConceded[i] + GoalsConceded[i-1]\n",
    "    return GoalsConceded\n",
    "\n",
    "def get_gss(playing_stat):\n",
    "    GC = get_goals_conceded(playing_stat)\n",
    "    GS = get_goals_scored(playing_stat)\n",
    "   \n",
    "    j = 0\n",
    "    HTGS = []\n",
    "    ATGS = []\n",
    "    HTGC = []\n",
    "    ATGC = []\n",
    "\n",
    "    for i in range(380):\n",
    "        ht = playing_stat.iloc[i].HomeTeam\n",
    "        at = playing_stat.iloc[i].AwayTeam\n",
    "        HTGS.append(GS.loc[ht][j])\n",
    "        ATGS.append(GS.loc[at][j])\n",
    "        HTGC.append(GC.loc[ht][j])\n",
    "        ATGC.append(GC.loc[at][j])\n",
    "        \n",
    "        if ((i + 1)% 10) == 0:\n",
    "            j = j + 1\n",
    "        \n",
    "    playing_stat['HTGS'] = HTGS\n",
    "    playing_stat['ATGS'] = ATGS\n",
    "    playing_stat['HTGC'] = HTGC\n",
    "    playing_stat['ATGC'] = ATGC\n",
    "    \n",
    "    return playing_stat\n",
    "\n",
    "\n",
    "# Apply to each dataset\n",
    "playing_statistics_1 = get_gss(playing_statistics_1)\n",
    "playing_statistics_2 = get_gss(playing_statistics_2)\n",
    "playing_statistics_3 = get_gss(playing_statistics_3)\n",
    "playing_statistics_4 = get_gss(playing_statistics_4)\n",
    "playing_statistics_5 = get_gss(playing_statistics_5)\n",
    "playing_statistics_6 = get_gss(playing_statistics_6)\n",
    "playing_statistics_7 = get_gss(playing_statistics_7)\n",
    "playing_statistics_8 = get_gss(playing_statistics_8)\n",
    "playing_statistics_9 = get_gss(playing_statistics_9)\n",
    "playing_statistics_10 = get_gss(playing_statistics_10)\n",
    "playing_statistics_11 = get_gss(playing_statistics_11)\n",
    "playing_statistics_12 = get_gss(playing_statistics_12)\n",
    "playing_statistics_13 = get_gss(playing_statistics_13)\n",
    "playing_statistics_14 = get_gss(playing_statistics_14)\n",
    "playing_statistics_15 = get_gss(playing_statistics_15)\n",
    "playing_statistics_16 = get_gss(playing_statistics_16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GET RESPECTIVE POINTS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-e2e68d60602f>:54: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['HTP'] = HTP\n",
      "<ipython-input-57-e2e68d60602f>:55: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['AST'] = AST\n"
     ]
    }
   ],
   "source": [
    "def get_points(result):\n",
    "    if result == 'W':\n",
    "        return 3\n",
    "    elif result == 'D':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "\n",
    "def get_cuml_points(matchres):\n",
    "    matchres_points = matchres.applymap(get_points)\n",
    "    for i in range(2,39):\n",
    "        matchres_points[i] = matchres_points[i] + matchres_points[i-1]\n",
    "        \n",
    "    matchres_points.insert(column =0, loc = 0, value = [0*i for i in range(20)])\n",
    "    return matchres_points\n",
    "\n",
    "\n",
    "def get_matchres(playing_stat):\n",
    "    # Create a dictionary with team names as keys\n",
    "    teams = {}\n",
    "    for i in playing_stat.groupby('HomeTeam').mean().T.columns:\n",
    "        teams[i] = []\n",
    "\n",
    "    # the value corresponding to keys is a list containing the match result\n",
    "    for i in range(len(playing_stat)):\n",
    "        if playing_stat.iloc[i].FTR == 'H':\n",
    "            teams[playing_stat.iloc[i].HomeTeam].append('W')\n",
    "            teams[playing_stat.iloc[i].AwayTeam].append('L')\n",
    "        elif playing_stat.iloc[i].FTR == 'A':\n",
    "            teams[playing_stat.iloc[i].AwayTeam].append('W')\n",
    "            teams[playing_stat.iloc[i].HomeTeam].append('L')\n",
    "        else:\n",
    "            teams[playing_stat.iloc[i].AwayTeam].append('D')\n",
    "            teams[playing_stat.iloc[i].HomeTeam].append('D')\n",
    "            \n",
    "    return pd.DataFrame(data=teams, index = [i for i in range(1,39)]).T\n",
    "\n",
    "def get_agg_points(playing_stat):\n",
    "    matchres = get_matchres(playing_stat)\n",
    "    cum_pts = get_cuml_points(matchres)\n",
    "    HTP = []\n",
    "    AST = []\n",
    "    j = 0\n",
    "    for i in range(380):\n",
    "        ht = playing_stat.iloc[i].HomeTeam\n",
    "        at = playing_stat.iloc[i].AwayTeam\n",
    "        HTP.append(cum_pts.loc[ht][j])\n",
    "        AST.append(cum_pts.loc[at][j])\n",
    "\n",
    "        if ((i + 1)% 10) == 0:\n",
    "            j = j + 1\n",
    "            \n",
    "    playing_stat['HTP'] = HTP\n",
    "    playing_stat['AST'] = AST\n",
    "    return playing_stat\n",
    "    \n",
    "# Apply to each dataset\n",
    "playing_statistics_1 = get_agg_points(playing_statistics_1)\n",
    "playing_statistics_2 = get_agg_points(playing_statistics_2)\n",
    "playing_statistics_3 = get_agg_points(playing_statistics_3)\n",
    "playing_statistics_4 = get_agg_points(playing_statistics_4)\n",
    "playing_statistics_5 = get_agg_points(playing_statistics_5)\n",
    "playing_statistics_6 = get_agg_points(playing_statistics_6)\n",
    "playing_statistics_7 = get_agg_points(playing_statistics_7)\n",
    "playing_statistics_8 = get_agg_points(playing_statistics_8)\n",
    "playing_statistics_9 = get_agg_points(playing_statistics_9)\n",
    "playing_statistics_10 = get_agg_points(playing_statistics_10)\n",
    "playing_statistics_11 = get_agg_points(playing_statistics_11)\n",
    "playing_statistics_12 = get_agg_points(playing_statistics_12)\n",
    "playing_statistics_13 = get_agg_points(playing_statistics_13)\n",
    "playing_statistics_14 = get_agg_points(playing_statistics_14)\n",
    "playing_statistics_15 = get_agg_points(playing_statistics_15)\n",
    "playing_statistics_16 = get_agg_points(playing_statistics_16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GET TEAM FORM:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-58-2542280789b9>:31: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['HM' + str(num)] = h\n",
      "<ipython-input-58-2542280789b9>:32: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  playing_stat['AM' + str(num)] = a\n"
     ]
    }
   ],
   "source": [
    "def get_form(playing_stat,num):\n",
    "    form = get_matchres(playing_stat)\n",
    "    form_final = form.copy()\n",
    "    for i in range(num,39):\n",
    "        form_final[i] = ''\n",
    "        j = 0\n",
    "        while j < num:\n",
    "            form_final[i] += form[i-j]\n",
    "            j += 1           \n",
    "    return form_final\n",
    "\n",
    "def add_form(playing_stat,num):\n",
    "    form = get_form(playing_stat,num)\n",
    "    h = ['M' for i in range(num * 10)]  # since form is not available for n MW (n*10)\n",
    "    a = ['M' for i in range(num * 10)]\n",
    "    \n",
    "    j = num\n",
    "    for i in range((num*10),380):\n",
    "        ht = playing_stat.iloc[i].HomeTeam\n",
    "        at = playing_stat.iloc[i].AwayTeam\n",
    "        \n",
    "        past = form.loc[ht][j]               # get past n results\n",
    "        h.append(past[num-1])                    # 0 index is most recent\n",
    "        \n",
    "        past = form.loc[at][j]               # get past n results.\n",
    "        a.append(past[num-1])                   # 0 index is most recent\n",
    "        \n",
    "        if ((i + 1)% 10) == 0:\n",
    "            j = j + 1\n",
    "\n",
    "    playing_stat['HM' + str(num)] = h                 \n",
    "    playing_stat['AM' + str(num)] = a\n",
    "\n",
    "    \n",
    "    return playing_stat\n",
    "\n",
    "\n",
    "def add_form_df(playing_statistics):\n",
    "    playing_statistics = add_form(playing_statistics,1)\n",
    "    playing_statistics = add_form(playing_statistics,2)\n",
    "    playing_statistics = add_form(playing_statistics,3)\n",
    "    playing_statistics = add_form(playing_statistics,4)\n",
    "    playing_statistics = add_form(playing_statistics,5)\n",
    "    return playing_statistics    \n",
    "    \n",
    "# Make changes to df\n",
    "playing_statistics_1 = add_form_df(playing_statistics_1)\n",
    "playing_statistics_2 = add_form_df(playing_statistics_2)\n",
    "playing_statistics_3 = add_form_df(playing_statistics_3)\n",
    "playing_statistics_4 = add_form_df(playing_statistics_4)\n",
    "playing_statistics_5 = add_form_df(playing_statistics_5)\n",
    "playing_statistics_6 = add_form_df(playing_statistics_6)\n",
    "playing_statistics_7 = add_form_df(playing_statistics_7)\n",
    "playing_statistics_8 = add_form_df(playing_statistics_8)\n",
    "playing_statistics_9 = add_form_df(playing_statistics_9)\n",
    "playing_statistics_10 = add_form_df(playing_statistics_10)\n",
    "playing_statistics_11 = add_form_df(playing_statistics_11)\n",
    "playing_statistics_12 = add_form_df(playing_statistics_12)\n",
    "playing_statistics_13 = add_form_df(playing_statistics_13)\n",
    "playing_statistics_14 = add_form_df(playing_statistics_14)\n",
    "playing_statistics_15 = add_form_df(playing_statistics_15)\n",
    "playing_statistics_16 = add_form_df(playing_statistics_16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['ATP'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-f8f85a114eb2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m         'HM4', 'HM5', 'AM1', 'AM2', 'AM3', 'AM4', 'AM5' ]\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mplaying_statistics_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplaying_statistics_1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcols\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      6\u001b[0m \u001b[0mplaying_statistics_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplaying_statistics_2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplaying_statistics_3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplaying_statistics_3\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3028\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3029\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3030\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\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   3031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3032\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1264\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1266\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\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   1267\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1314\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1315\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\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[1;32m-> 1316\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{not_found} not in index\"\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   1317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1318\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['ATP'] not in index\""
     ]
    }
   ],
   "source": [
    "# Rearranging columns\n",
    "cols = ['Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTGS', 'ATGS', 'HTGC', 'ATGC', 'HTP', 'ATP', 'HM1', 'HM2', 'HM3',\n",
    "        'HM4', 'HM5', 'AM1', 'AM2', 'AM3', 'AM4', 'AM5' ]\n",
    "\n",
    "playing_statistics_1 = playing_statistics_1[cols]\n",
    "playing_statistics_2 = playing_statistics_2[cols]\n",
    "playing_statistics_3 = playing_statistics_3[cols]\n",
    "playing_statistics_4 = playing_statistics_4[cols]\n",
    "playing_statistics_5 = playing_statistics_5[cols]\n",
    "playing_statistics_6 = playing_statistics_6[cols]\n",
    "playing_statistics_7 = playing_statistics_7[cols]\n",
    "playing_statistics_8 = playing_statistics_8[cols]\n",
    "playing_statistics_9 = playing_statistics_9[cols]\n",
    "playing_statistics_10 = playing_statistics_10[cols]\n",
    "playing_statistics_11 = playing_statistics_11[cols]\n",
    "playing_statistics_12 = playing_statistics_12[cols]\n",
    "playing_statistics_13 = playing_statistics_13[cols]\n",
    "playing_statistics_14 = playing_statistics_14[cols]\n",
    "playing_statistics_15 = playing_statistics_15[cols]\n",
    "playing_statistics_16 = playing_statistics_16[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get MatchWeek:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mw(playing_stat):\n",
    "    j = 1\n",
    "    MatchWeek = []\n",
    "    for i in range(380):\n",
    "        MatchWeek.append(j)\n",
    "        if ((i + 1)% 10) == 0:\n",
    "            j = j + 1\n",
    "    playing_stat['MW'] = MatchWeek\n",
    "    return playing_stat\n",
    "\n",
    "playing_statistics_1 = get_mw(playing_statistics_1)\n",
    "playing_statistics_2 = get_mw(playing_statistics_2)\n",
    "playing_statistics_3 = get_mw(playing_statistics_3)\n",
    "playing_statistics_4 = get_mw(playing_statistics_4)\n",
    "playing_statistics_5 = get_mw(playing_statistics_5)\n",
    "playing_statistics_6 = get_mw(playing_statistics_6)\n",
    "playing_statistics_7 = get_mw(playing_statistics_7)\n",
    "playing_statistics_8 = get_mw(playing_statistics_8)\n",
    "playing_statistics_9 = get_mw(playing_statistics_9)\n",
    "playing_statistics_10 = get_mw(playing_statistics_10)\n",
    "playing_statistics_11 = get_mw(playing_statistics_11)\n",
    "playing_statistics_12 = get_mw(playing_statistics_12)\n",
    "playing_statistics_13 = get_mw(playing_statistics_13)\n",
    "playing_statistics_14 = get_mw(playing_statistics_14)\n",
    "playing_statistics_15 = get_mw(playing_statistics_15)\n",
    "playing_statistics_16 = get_mw(playing_statistics_16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**FINAL DATAFRAME**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "playing_stat = pd.concat([playing_statistics_1,\n",
    "                          playing_statistics_2,\n",
    "                          playing_statistics_3,\n",
    "                          playing_statistics_4,\n",
    "                          playing_statistics_5,\n",
    "                          playing_statistics_6,\n",
    "                          playing_statistics_7,\n",
    "                          playing_statistics_8,\n",
    "                          playing_statistics_9,\n",
    "                          playing_statistics_10,\n",
    "                          playing_statistics_11,\n",
    "                          playing_statistics_12,\n",
    "                          playing_statistics_13,\n",
    "                          playing_statistics_14,\n",
    "                          playing_statistics_15,\n",
    "                          playing_statistics_16,\n",
    "                          ], ignore_index=True)\n",
    "\n",
    "\n",
    "# Gets the form points.\n",
    "def get_form_points(string):\n",
    "    sum = 0\n",
    "    for letter in string:\n",
    "        sum += get_points(letter)\n",
    "    return sum\n",
    "\n",
    "playing_stat['HTFormPtsStr'] = playing_stat['HM1'] + playing_stat['HM2'] + playing_stat['HM3'] + playing_stat['HM4'] + playing_stat['HM5']\n",
    "playing_stat['ATFormPtsStr'] = playing_stat['AM1'] + playing_stat['AM2'] + playing_stat['AM3'] + playing_stat['AM4'] + playing_stat['AM5']\n",
    "\n",
    "playing_stat['HTFormPts'] = playing_stat['HTFormPtsStr'].apply(get_form_points)\n",
    "playing_stat['ATFormPts'] = playing_stat['ATFormPtsStr'].apply(get_form_points)\n",
    "\n",
    "# Identify Win/Loss Streaks if any.\n",
    "def get_3game_ws(string):\n",
    "    if string[-3:] == 'WWW':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def get_5game_ws(string):\n",
    "    if string == 'WWWWW':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def get_3game_ls(string):\n",
    "    if string[-3:] == 'LLL':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def get_5game_ls(string):\n",
    "    if string == 'LLLLL':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "playing_stat['HTWinStreak3'] = playing_stat['HTFormPtsStr'].apply(get_3game_ws)\n",
    "playing_stat['HTWinStreak5'] = playing_stat['HTFormPtsStr'].apply(get_5game_ws)\n",
    "playing_stat['HTLossStreak3'] = playing_stat['HTFormPtsStr'].apply(get_3game_ls)\n",
    "playing_stat['HTLossStreak5'] = playing_stat['HTFormPtsStr'].apply(get_5game_ls)\n",
    "\n",
    "playing_stat['ATWinStreak3'] = playing_stat['ATFormPtsStr'].apply(get_3game_ws)\n",
    "playing_stat['ATWinStreak5'] = playing_stat['ATFormPtsStr'].apply(get_5game_ws)\n",
    "playing_stat['ATLossStreak3'] = playing_stat['ATFormPtsStr'].apply(get_3game_ls)\n",
    "playing_stat['ATLossStreak5'] = playing_stat['ATFormPtsStr'].apply(get_5game_ls)\n",
    "\n",
    "playing_stat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Goal Difference\n",
    "playing_stat['HTGD'] = playing_stat['HTGS'] - playing_stat['HTGC']\n",
    "playing_stat['ATGD'] = playing_stat['ATGS'] - playing_stat['ATGC']\n",
    "\n",
    "# Diff in points\n",
    "playing_stat['DiffPts'] = playing_stat['HTP'] - playing_stat['ATP']\n",
    "playing_stat['DiffFormPts'] = playing_stat['HTFormPts'] - playing_stat['ATFormPts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale DiffPts , DiffFormPts, HTGD, ATGD by Matchweek.\n",
    "cols = ['HTGD','ATGD','DiffPts','DiffFormPts','HTP','ATP']\n",
    "playing_stat.MW = playing_stat.MW.astype(float)\n",
    "\n",
    "for col in cols:\n",
    "    playing_stat[col] = playing_stat[col] / playing_stat.MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def only_hw(string):\n",
    "    if string == 'H':\n",
    "        return 'H'\n",
    "    else:\n",
    "        return 'NH'\n",
    "    \n",
    "playing_stat['FTR'] = playing_stat.FTR.apply(only_hw)\n",
    "\n",
    "# Testing set (2015-16 season)\n",
    "playing_stat_test = playing_stat[5700:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the final dataset\n",
    "playing_stat.to_csv('final_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the test set\n",
    "playing_stat_test.to_csv(\"test_set.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the final dataset\n",
    "dataset = pd.read_csv('final_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix for dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "sns.heatmap(dataset.corr(), annot= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "POSITIVE CORRELATION: If an increase in feature A leads to increase in feature B, then they are positively correlated. A value 1 means perfect positive correlation.\n",
    "\n",
    "NEGATIVE CORRELATION: If an increase in feature A leads to decrease in feature B, then they are negatively correlated. A value -1 means perfect negative correlation.\n",
    "\n",
    "Now lets say that two features are highly or perfectly correlated, so the increase in one leads to increase in the other. This means that both the features are containing highly similar information and there is very little or no variance in information. This is known as MultiColinearity as both of them contains almost the same information.\n",
    "\n",
    "So do you think we should use both of them as one of them is redundant. While making or training models, we should try to eliminate redundant features as it reduces training time and many such advantages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove few column\n",
    "dataset2 = dataset.copy().drop(columns =['Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG',\n",
    "       'HTGS', 'ATGS', 'HTGC', 'ATGC',\n",
    "       'HM4', 'HM5','AM4', 'AM5', 'MW', 'HTFormPtsStr',\n",
    "       'ATFormPtsStr', 'HTFormPts', 'ATFormPts', 'HTWinStreak3',\n",
    "       'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5', 'ATWinStreak3',\n",
    "       'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5',\n",
    "       'DiffPts'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'FTR', 'HTP', 'ATP', 'HM1', 'HM2', 'HM3', 'AM1', 'AM2',\n",
       "       'AM3', 'HTGD', 'ATGD', 'DiffFormPts'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTP</th>\n",
       "      <th>ATP</th>\n",
       "      <th>HM1</th>\n",
       "      <th>HM2</th>\n",
       "      <th>HM3</th>\n",
       "      <th>AM1</th>\n",
       "      <th>AM2</th>\n",
       "      <th>AM3</th>\n",
       "      <th>HTGD</th>\n",
       "      <th>ATGD</th>\n",
       "      <th>DiffFormPts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 FTR  HTP  ATP HM1 HM2 HM3 AM1 AM2 AM3  HTGD  ATGD  DiffFormPts\n",
       "0           0   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "1           1   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "2           2  NH  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "3           3  NH  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "4           4   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "5           5  NH  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "6           6   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "7           7   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "8           8   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0\n",
       "9           9   H  0.0  0.0   M   M   M   M   M   M   0.0   0.0          0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of matches: 6080\n",
      "Number of features: 12\n",
      "Number of matches won by home team: 2816\n",
      "Win rate of home team: 46.32%\n"
     ]
    }
   ],
   "source": [
    "#what is the win rate for the home team?\n",
    "\n",
    "# Total number of matches.\n",
    "n_matches = dataset2.shape[0]\n",
    "\n",
    "# Calculate number of features. -1 because we are saving one as the target variable (win/lose/draw)\n",
    "n_features = dataset2.shape[1] - 1\n",
    "\n",
    "# Calculate matches won by home team.\n",
    "n_homewins = len(dataset2[dataset2.FTR == 'H'])\n",
    "\n",
    "# Calculate win rate for home team.\n",
    "win_rate = (float(n_homewins) / (n_matches)) * 100\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of matches: {}\".format(n_matches))\n",
    "print (\"Number of features: {}\".format(n_features))\n",
    "print( \"Number of matches won by home team: {}\".format(n_homewins))\n",
    "print (\"Win rate of home team: {:.2f}%\".format(win_rate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='HTGD', ylabel='HTGD'>,\n",
       "        <AxesSubplot:xlabel='ATGD', ylabel='HTGD'>,\n",
       "        <AxesSubplot:xlabel='HTP', ylabel='HTGD'>,\n",
       "        <AxesSubplot:xlabel='ATP', ylabel='HTGD'>,\n",
       "        <AxesSubplot:xlabel='DiffFormPts', ylabel='HTGD'>],\n",
       "       [<AxesSubplot:xlabel='HTGD', ylabel='ATGD'>,\n",
       "        <AxesSubplot:xlabel='ATGD', ylabel='ATGD'>,\n",
       "        <AxesSubplot:xlabel='HTP', ylabel='ATGD'>,\n",
       "        <AxesSubplot:xlabel='ATP', ylabel='ATGD'>,\n",
       "        <AxesSubplot:xlabel='DiffFormPts', ylabel='ATGD'>],\n",
       "       [<AxesSubplot:xlabel='HTGD', ylabel='HTP'>,\n",
       "        <AxesSubplot:xlabel='ATGD', ylabel='HTP'>,\n",
       "        <AxesSubplot:xlabel='HTP', ylabel='HTP'>,\n",
       "        <AxesSubplot:xlabel='ATP', ylabel='HTP'>,\n",
       "        <AxesSubplot:xlabel='DiffFormPts', ylabel='HTP'>],\n",
       "       [<AxesSubplot:xlabel='HTGD', ylabel='ATP'>,\n",
       "        <AxesSubplot:xlabel='ATGD', ylabel='ATP'>,\n",
       "        <AxesSubplot:xlabel='HTP', ylabel='ATP'>,\n",
       "        <AxesSubplot:xlabel='ATP', ylabel='ATP'>,\n",
       "        <AxesSubplot:xlabel='DiffFormPts', ylabel='ATP'>],\n",
       "       [<AxesSubplot:xlabel='HTGD', ylabel='DiffFormPts'>,\n",
       "        <AxesSubplot:xlabel='ATGD', ylabel='DiffFormPts'>,\n",
       "        <AxesSubplot:xlabel='HTP', ylabel='DiffFormPts'>,\n",
       "        <AxesSubplot:xlabel='ATP', ylabel='DiffFormPts'>,\n",
       "        <AxesSubplot:xlabel='DiffFormPts', ylabel='DiffFormPts'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising distribution of data\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "#the scatter matrix is plotting each of the columns specified against each other column.\n",
    "#You would have observed that the diagonal graph is defined as a histogram, which means that in the \n",
    "#section of the plot matrix where the variable is against itself, a histogram is plotted.\n",
    "\n",
    "#Scatter plots show how much one variable is affected by another. \n",
    "#The relationship between two variables is called their correlation\n",
    "#negative vs positive correlation\n",
    "\n",
    "#HTGD - Home team goal difference\n",
    "#ATGD - away team goal difference\n",
    "#HTP - Home team points\n",
    "#ATP - Away team points\n",
    "#DiffFormPts Diff in points\n",
    "#DiffLP - Differnece in last years prediction\n",
    "\n",
    "scatter_matrix(dataset2[['HTGD','ATGD','HTP','ATP','DiffFormPts']], figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate into feature set and target variable\n",
    "#FTR = Full Time Result (H=Home Win, D=Draw, A=Away Win)\n",
    "X_all = dataset2.drop(['FTR'],1)\n",
    "y_all = dataset2['FTR']\n",
    "\n",
    "# Standardising the data.\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "#Center to the mean and component wise scale to unit variance.\n",
    "cols = [['HTGD','ATGD','HTP','ATP']]\n",
    "for col in cols:\n",
    "    X_all[col] = scale(X_all[col])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed feature columns (30 total features):\n",
      "['Unnamed: 0', 'HTP', 'ATP', 'HM1_D', 'HM1_L', 'HM1_M', 'HM1_W', 'HM2_D', 'HM2_L', 'HM2_M', 'HM2_W', 'HM3_D', 'HM3_L', 'HM3_M', 'HM3_W', 'AM1_D', 'AM1_L', 'AM1_M', 'AM1_W', 'AM2_D', 'AM2_L', 'AM2_M', 'AM2_W', 'AM3_D', 'AM3_L', 'AM3_M', 'AM3_W', 'HTGD', 'ATGD', 'DiffFormPts']\n"
     ]
    }
   ],
   "source": [
    "#last 3 wins for both sides\n",
    "X_all.HM1 = X_all.HM1.astype('str')\n",
    "X_all.HM2 = X_all.HM2.astype('str')\n",
    "X_all.HM3 = X_all.HM3.astype('str')\n",
    "X_all.AM1 = X_all.AM1.astype('str')\n",
    "X_all.AM2 = X_all.AM2.astype('str')\n",
    "X_all.AM3 = X_all.AM3.astype('str')\n",
    "\n",
    "#we want continous vars that are integers for our input data, so lets remove any categorical vars\n",
    "def preprocess_features(X):\n",
    "    ''' Preprocesses the football data and converts catagorical variables into dummy variables. '''\n",
    "    \n",
    "    # Initialize new output DataFrame\n",
    "    output = pd.DataFrame(index = X.index)\n",
    "\n",
    "    # Investigate each feature column for the data\n",
    "    for col, col_data in X.iteritems():\n",
    "\n",
    "        # If data type is categorical, convert to dummy variables\n",
    "        if col_data.dtype == object:\n",
    "            col_data = pd.get_dummies(col_data, prefix = col)\n",
    "                    \n",
    "        # Collect the revised columns\n",
    "        output = output.join(col_data)\n",
    "    \n",
    "    return output\n",
    "\n",
    "X_all = preprocess_features(X_all)\n",
    "print (\"Processed feature columns ({} total features):\\n{}\".format(len(X_all.columns), list(X_all.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>HTP</th>\n",
       "      <th>ATP</th>\n",
       "      <th>HM1_D</th>\n",
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       "      <th>AM3_M</th>\n",
       "      <th>AM3_W</th>\n",
       "      <th>HTGD</th>\n",
       "      <th>ATGD</th>\n",
       "      <th>DiffFormPts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-2.303259</td>\n",
       "      <td>-2.373125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-2.303259</td>\n",
       "      <td>-2.373125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-2.303259</td>\n",
       "      <td>-2.373125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>-2.303259</td>\n",
       "      <td>-2.373125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>-2.303259</td>\n",
       "      <td>-2.373125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>-2.303259</td>\n",
       "      <td>-2.373125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       HTP       ATP  HM1_D  HM1_L  HM1_M  HM1_W  HM2_D  HM2_L  \\\n",
       "0           0 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "1           1 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "2           2 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "3           3 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "4           4 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "5           5 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "6           6 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "7           7 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "8           8 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "9           9 -2.303259 -2.373125      0      0      1      0      0      0   \n",
       "\n",
       "   HM2_M  ...  AM2_L  AM2_M  AM2_W  AM3_D  AM3_L  AM3_M  AM3_W      HTGD  \\\n",
       "0      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "1      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "2      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "3      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "4      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "5      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "6      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "7      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "8      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "9      1  ...      0      1      0      0      0      1      0  0.014963   \n",
       "\n",
       "       ATGD  DiffFormPts  \n",
       "0 -0.022914          0.0  \n",
       "1 -0.022914          0.0  \n",
       "2 -0.022914          0.0  \n",
       "3 -0.022914          0.0  \n",
       "4 -0.022914          0.0  \n",
       "5 -0.022914          0.0  \n",
       "6 -0.022914          0.0  \n",
       "7 -0.022914          0.0  \n",
       "8 -0.022914          0.0  \n",
       "9 -0.022914          0.0  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spliting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Shuffle and split the dataset into training and testing set.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, \n",
    "                                                    test_size = 0.3,\n",
    "                                                    random_state = 2,\n",
    "                                                    stratify = y_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Logistic Regression to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "cm = confusion_matrix(y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True,fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           H       0.64      0.56      0.60       845\n",
      "          NH       0.66      0.73      0.69       979\n",
      "\n",
      "    accuracy                           0.65      1824\n",
      "   macro avg       0.65      0.65      0.65      1824\n",
      "weighted avg       0.65      0.65      0.65      1824\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the SVM to the training set\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf',random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting result\n",
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           H       0.00      0.00      0.00       845\n",
      "          NH       0.54      1.00      0.70       979\n",
      "\n",
      "    accuracy                           0.54      1824\n",
      "   macro avg       0.27      0.50      0.35      1824\n",
      "weighted avg       0.29      0.54      0.37      1824\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(min_samples_split=10, n_estimators=700, n_jobs=-1,\n",
       "                       oob_score=True, random_state=1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the RANDOM FOREST to the training se\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier = RandomForestClassifier(criterion='gini', \n",
    "                             n_estimators=700,\n",
    "                             min_samples_split=10,\n",
    "                             min_samples_leaf=1,\n",
    "                             max_features='auto',\n",
    "                             oob_score=True,\n",
    "                             random_state=1,\n",
    "                             n_jobs=-1)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting result\n",
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           H       0.62      0.54      0.58       845\n",
      "          NH       0.64      0.71      0.67       979\n",
      "\n",
      "    accuracy                           0.63      1824\n",
      "   macro avg       0.63      0.63      0.63      1824\n",
      "weighted avg       0.63      0.63      0.63      1824\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=82,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=82,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting XGBoost to the Training set\n",
    "from xgboost import XGBClassifier\n",
    "classifier = XGBClassifier(seed=82)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True,fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           H       0.58      0.53      0.55       845\n",
      "          NH       0.62      0.66      0.64       979\n",
      "\n",
      "    accuracy                           0.60      1824\n",
      "   macro avg       0.60      0.60      0.60      1824\n",
      "weighted avg       0.60      0.60      0.60      1824\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clearly XGBoost seems like the best model as it has the highest F1 score and accuracy score on the test set.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning the parameters of XGBoost.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:36:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=0.8, gamma=0.4, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
      "              min_child_weight=3, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=40, n_jobs=4, num_parallel_tree=1, random_state=2,\n",
      "              reg_alpha=1e-05, reg_lambda=1, scale_pos_weight=1, seed=2,\n",
      "              subsample=0.8, tree_method='exact', validate_parameters=1,\n",
      "              verbosity=None)\n",
      "F1 score and accuracy score for training set: 0.6141 , 0.6725.\n",
      "F1 score and accuracy score for test set: 0.5823 , 0.6453.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n",
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV' and 'make_scorer'\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, f1_score\n",
    "import xgboost as xgb\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune\n",
    "parameters = { 'learning_rate' : [0.1],\n",
    "               'n_estimators' : [40],\n",
    "               'max_depth': [3],\n",
    "               'min_child_weight': [3],\n",
    "               'gamma':[0.4],\n",
    "               'subsample' : [0.8],\n",
    "               'colsample_bytree' : [0.8],\n",
    "               'scale_pos_weight' : [1],\n",
    "               'reg_alpha':[1e-5]\n",
    "             }  \n",
    "\n",
    "def predict_labels(clf, features, target):\n",
    "    ''' Makes predictions using a fit classifier based on F1 score. '''\n",
    "\n",
    "    y_pred = clf.predict(features)\n",
    "    \n",
    "    return f1_score(target, y_pred, pos_label='H'), sum(target == y_pred) / float(len(y_pred))\n",
    "\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = xgb.XGBClassifier(seed=2)\n",
    "\n",
    "# TODO: Make an f1 scoring function using 'make_scorer' \n",
    "f1_scorer = make_scorer(f1_score,pos_label='H')\n",
    "\n",
    "# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method\n",
    "grid_obj = GridSearchCV(clf,\n",
    "                        scoring=f1_scorer,\n",
    "                        param_grid=parameters,\n",
    "                        cv=5)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_obj = grid_obj.fit(X_train,y_train)\n",
    "\n",
    "# Get the estimator\n",
    "clf = grid_obj.best_estimator_\n",
    "print(clf)\n",
    "\n",
    "# Report the final F1 score for training and testing after parameter tuning\n",
    "f1, acc = predict_labels(clf, X_train, y_train)\n",
    "print( \"F1 score and accuracy score for training set: {:.4f} , {:.4f}.\".format(f1 , acc))\n",
    "    \n",
    "f1, acc = predict_labels(clf, X_test, y_test)\n",
    "print(\"F1 score and accuracy score for test set: {:.4f} , {:.4f}.\".format(f1 , acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Probably little best!!!!!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is not soo good but it can improved.\n",
    "\n",
    "Actually it only depend upon past year match dataset,we can improve the accuracy by putting twitter data related to match, sentiment analysis, chances of player to play a specific match,player performace in recent series,etc.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this kernel useful and enjoyable. If so please upVote\n",
    "\n",
    "Your comments and feedback are most welcome."
   ]
  }
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