{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NBA Matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objetivo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considerando o crescente uso de ciência dos dados no mercardo esportivo e de especulação, nesta semana vocês farão parte de uma startup que quer quebrar os sites de apostas da NBA!\n",
    "\n",
    "O mercado online de apostas foi avaliado em US$85.047 no ano de 2019 e pode ter um crescimento ainda maior nos próximos anos levando em consideração a posição favorável de alguns governos com a legalização das plataformas e pagamento de impostos. [1]\n",
    "\n",
    "Com isso, a startup de vocês, RodaRodaBet, após um estudo inicial sobre o mercado de apostas americano e dos dados disponíveis online sobre a NBA [2], está buscando a construção de um modelo que possa indicar se os times da casa irão ganhar ou perder em cada rodada da liga.\n",
    "\n",
    "Neste desafio, vocês irão utilizar dados raspados da NBA & ABA League Index, que contém informações sobre os times que jogam em cada rodada da NBA, para prever se determinado time da casa vai ganhar ou perder (Win or Lose).\n",
    "\n",
    "References:\n",
    "\n",
    "1 - https://www.globenewswire.com/news-release/2020/08/31/2086041/0/en/Global-Sports-Betting-Market-Worth-85-Billion-in-2019-Industry-Assessment-and-Forecasts-Throughout-2020-2025.html\n",
    "\n",
    "2 - https://towardsdatascience.com/predicting-the-2020-nba-playoffs-bracket-with-machine-learning-2030f80fa62c\n",
    "\n",
    "3 - https://www.basketball-reference.com/leagues/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qBkzj_ReB62_"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "np.random.seed(2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ZrqBFe7oCIe_"
   },
   "outputs": [],
   "source": [
    "df_test= pd.read_csv(\"test_without_label.csv\")\n",
    "df_train = pd.read_csv(\"train_full.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gjQImiZs5bwq"
   },
   "source": [
    "## Entendendo os dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SZj_XZj65ZmP"
   },
   "source": [
    "Pelo fato das variáveis serem as estatísticas dos jogos e por termos bastantes variáveis nesse sentido, optamos por não criar novas variáveis a partir delas. Decidimos investir na variável data. Observamos que a variável dia do ano foi bastante importante para os modelos testados e a partir disso, criamos outras variações, como dias da semana, dias do mês, entre outros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iooNWloS5ep1"
   },
   "source": [
    "### Porcentagem de dados jogos vencidos e jogos perdidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "KlYqn12M7cLh"
   },
   "outputs": [],
   "source": [
    "# remove espaço nos nomes das colunas\n",
    "df_train.columns = df_train.columns.str.strip()\n",
    "df_test.columns = df_test.columns.str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IQ_vZPfXwjfw",
    "outputId": "6eaccb10-7de1-409b-8350-eee1941e9e60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "L    654\n",
       "W    352\n",
       "Name: WinOrLose, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.WinOrLose.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "_oUeIsBkxRDS",
    "outputId": "1207675e-9b92-4dee-e68b-5343bdf5ad75"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df_train.WinOrLose.value_counts()/df_train.WinOrLose.value_counts().sum() #frequencia absoluta\n",
    "plt.bar(['L','W'],y)\n",
    "plt.title('Frequencia absoluta de Vitorias e derrotas')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uWTDXElo5_F5"
   },
   "source": [
    "Temos uma grande maioria de jogos perdidos, portanto eh necessario uma analise estratificada quando for treinar os modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "id": "b08pBpIgDjIn",
    "outputId": "943d4897-4d94-4c39-db86-c71c53a7b8d1"
   },
   "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>Game</th>\n",
       "      <th>Data</th>\n",
       "      <th>H_Team</th>\n",
       "      <th>H_Wins</th>\n",
       "      <th>H_Loss</th>\n",
       "      <th>H_W/D %</th>\n",
       "      <th>H_SRS</th>\n",
       "      <th>H_Games</th>\n",
       "      <th>H_TotalPoints</th>\n",
       "      <th>H_AvgPointsPerGame</th>\n",
       "      <th>...</th>\n",
       "      <th>A_TS%</th>\n",
       "      <th>A_eFG%</th>\n",
       "      <th>A_TOV%</th>\n",
       "      <th>A_ORB%</th>\n",
       "      <th>A_FT/FGA</th>\n",
       "      <th>A_OeFG%</th>\n",
       "      <th>A_OTOV%</th>\n",
       "      <th>A_DRB%</th>\n",
       "      <th>A_OFT/FGA</th>\n",
       "      <th>WinOrLose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Thu, June 8</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>52</td>\n",
       "      <td>30</td>\n",
       "      <td>0.634</td>\n",
       "      <td>3.59</td>\n",
       "      <td>82</td>\n",
       "      <td>8191</td>\n",
       "      <td>99.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.495</td>\n",
       "      <td>13.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.475</td>\n",
       "      <td>13.7</td>\n",
       "      <td>72.2</td>\n",
       "      <td>0.257</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Sun, June 11</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>52</td>\n",
       "      <td>30</td>\n",
       "      <td>0.634</td>\n",
       "      <td>3.59</td>\n",
       "      <td>82</td>\n",
       "      <td>8191</td>\n",
       "      <td>99.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.495</td>\n",
       "      <td>13.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.475</td>\n",
       "      <td>13.7</td>\n",
       "      <td>72.2</td>\n",
       "      <td>0.257</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Tue, June 13</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>0.732</td>\n",
       "      <td>5.96</td>\n",
       "      <td>82</td>\n",
       "      <td>8130</td>\n",
       "      <td>99.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.517</td>\n",
       "      <td>13.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.477</td>\n",
       "      <td>12.4</td>\n",
       "      <td>76.4</td>\n",
       "      <td>0.251</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Thu, June 15</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>0.732</td>\n",
       "      <td>5.96</td>\n",
       "      <td>82</td>\n",
       "      <td>8130</td>\n",
       "      <td>99.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.517</td>\n",
       "      <td>13.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.477</td>\n",
       "      <td>12.4</td>\n",
       "      <td>76.4</td>\n",
       "      <td>0.251</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sun, June 18</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>0.732</td>\n",
       "      <td>5.96</td>\n",
       "      <td>82</td>\n",
       "      <td>8130</td>\n",
       "      <td>99.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.517</td>\n",
       "      <td>13.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.477</td>\n",
       "      <td>12.4</td>\n",
       "      <td>76.4</td>\n",
       "      <td>0.251</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Game          Data            H_Team  H_Wins  H_Loss  H_W/D %  H_SRS  \\\n",
       "0     0   Thu, June 8        Miami Heat      52      30    0.634   3.59   \n",
       "1     1  Sun, June 11        Miami Heat      52      30    0.634   3.59   \n",
       "2     2  Tue, June 13  Dallas Mavericks      60      22    0.732   5.96   \n",
       "3     3  Thu, June 15  Dallas Mavericks      60      22    0.732   5.96   \n",
       "4     4  Sun, June 18  Dallas Mavericks      60      22    0.732   5.96   \n",
       "\n",
       "   H_Games  H_TotalPoints  H_AvgPointsPerGame  ...  A_TS%  A_eFG%  A_TOV%  \\\n",
       "0       82           8191                99.9  ...  0.550   0.495    13.1   \n",
       "1       82           8191                99.9  ...  0.550   0.495    13.1   \n",
       "2       82           8130                99.1  ...  0.556   0.517    13.9   \n",
       "3       82           8130                99.1  ...  0.556   0.517    13.9   \n",
       "4       82           8130                99.1  ...  0.556   0.517    13.9   \n",
       "\n",
       "   A_ORB%  A_FT/FGA  A_OeFG%  A_OTOV%  A_DRB%  A_OFT/FGA  WinOrLose  \n",
       "0    31.8     0.285    0.475     13.7    72.2      0.257          L  \n",
       "1    31.8     0.285    0.475     13.7    72.2      0.257          L  \n",
       "2    26.7     0.254    0.477     12.4    76.4      0.251          L  \n",
       "3    26.7     0.254    0.477     12.4    76.4      0.251          L  \n",
       "4    26.7     0.254    0.477     12.4    76.4      0.251          L  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "usBZn40Lp3pm"
   },
   "source": [
    "## Pre-processamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LOkLsohQklNd"
   },
   "source": [
    "### Tratando as datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "kWj53FtbkyOT"
   },
   "outputs": [],
   "source": [
    "treino = df_train\n",
    "teste  = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pabwacq8eGsj",
    "outputId": "f84d2ef5-ee98-4699-d6ad-836b13735d48"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\msini\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1637: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  self._setitem_single_block(indexer, value, name)\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Na base de teste\n",
    "for i in range(0, teste.shape[0]):\n",
    "  teste['Data'].iloc[i] = datetime.strptime(teste['Data'].iloc[i], '%a, %B %d')\n",
    "  teste['Data'].iloc[i] = datetime.strftime(teste['Data'].iloc[i], '%m-%d')\n",
    "\n",
    "teste['Data'] = pd.to_datetime(teste['Data'], format=\"%m-%d\", errors='raise')\n",
    "\n",
    "#base de treino\n",
    "for i in range(0, treino.shape[0]):\n",
    "  treino['Data'].iloc[i] = datetime.strptime(treino['Data'].iloc[i], '%a, %B %d')\n",
    "  treino['Data'].iloc[i] = datetime.strftime(treino['Data'].iloc[i], '%m-%d')\n",
    "\n",
    "treino['Data'] = pd.to_datetime(treino['Data'], format=\"%m-%d\", errors='raise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0x3XBiTnA2_r"
   },
   "source": [
    "## Criacao de algumas features com data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TVGAVLxKA5bf",
    "outputId": "cbcab5d6-af70-459c-cf40-0f0a1089d202"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-c7af2d33b494>:10: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  teste['weekofyear'] = teste.Data.dt.weekofyear\n",
      "<ipython-input-9-c7af2d33b494>:11: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  treino['weekofyear'] = treino.Data.dt.weekofyear\n"
     ]
    }
   ],
   "source": [
    "teste['Dia'] = teste.Data.dt.day\n",
    "treino['Dia'] = treino.Data.dt.day\n",
    "\n",
    "teste['Dia'] = teste.Data.dt.day\n",
    "treino['Dia'] = treino.Data.dt.day\n",
    "\n",
    "teste['weekday'] = teste.Data.dt.weekday\n",
    "treino['weekday'] = treino.Data.dt.weekday\n",
    "\n",
    "teste['weekofyear'] = teste.Data.dt.weekofyear\n",
    "treino['weekofyear'] = treino.Data.dt.weekofyear\n",
    "\n",
    "teste['Dia do Ano'] = teste.Data.dt.dayofyear\n",
    "treino['Dia do Ano'] = treino.Data.dt.dayofyear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IBk3ed5-p9tv"
   },
   "source": [
    "### Análise exploratória (Variáveis de datas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917
    },
    "id": "GMDe9Dd99I2J",
    "outputId": "735f3c79-cbb0-430e-84f9-2650eac9e6f0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vars_dias = ['Dia', 'weekday', 'weekofyear']\n",
    "\n",
    "for i in vars_dias:\n",
    "\n",
    "  sns.set(style=\"ticks\")\n",
    "\n",
    "  x = treino[i]\n",
    "  coluna = i\n",
    "  mu = round(x.mean(),2) # mean of distribution\n",
    "  sigma = round(x.std(),2)  # standard deviation of distribution\n",
    "\n",
    "  f, (ax_box, ax_hist) = plt.subplots(2)\n",
    "\n",
    "  sns.boxplot(x=x, ax=ax_box)\n",
    "  sns.histplot(x=x, ax=ax_hist)\n",
    "\n",
    "  ax_box.set(yticks=[])\n",
    "  sns.despine(ax=ax_hist)\n",
    "  sns.despine(ax=ax_box, left=True)\n",
    "  ax_box.set_title('Boxplot e Histograma de {}\\n $\\mu={}$, $\\sigma={}$'.format(coluna, mu,sigma))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q5tSiZuI98Ik"
   },
   "source": [
    "Weekofyear e Dia do ano possuem um formato de distribuição próximo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZD2ho0R6Sigu"
   },
   "source": [
    "### Gráfico de barras (variáveis de datas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "Bx1958LUS1Qy",
    "outputId": "46e1bf9a-9543-4c0c-c06d-46420cff249a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAFgCAYAAADjF51eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAc2UlEQVR4nO3de1TUdf7H8dfIxcsqWS6jbrhadrF1S8xtdUTBK6AyKuCeVEqs3LTUWis9Lrm5tVlmF067LGfbMg9H7WKJiSxRLbYUQhrkBnavVQyOg5jJZCjX+f3BcX6RlmMyl48+H+dwDt8v3+933nBOPZ3vzHy/FpfL5RIAAIbo5O8BAAA4E4QLAGAUwgUAMArhAgAYhXABAIxifLiam5tVVVWl5uZmf48CAPAB48PlcDg0fvx4ORwOf48CAPAB48MFADi/EC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIzi1XAdPXpUCQkJqqqqkiQVFxfLbrcrNjZW6enp7u0++ugjJSUlKS4uTvfeey8fJgYA/CCvhev999/XrFmztG/fPknS8ePHlZaWpszMTOXl5WnPnj0qLCyUJC1dulT33XefXnvtNblcLm3atMlbYwEADOe1cG3atEkrV66U1WqVJJWXl6t///7q16+fgoODZbfblZ+fr+rqah0/flyRkZGSpKSkJOXn53trLACA4YK9deBVq1a1Wz548KDCw8Pdy1arVTU1NSetDw8PV01NzSmP6XQ65XQ6263jUk8AcH7xWri+r7W1VRaLxb3scrlksVh+cP2pZGVlKSMjw+uzAgACl8/C1adPH9XW1rqXa2trZbVaT1p/6NAh9+nF70tNTVViYmK7dQ6HQykpKd4ZGgAQcHwWriFDhmjv3r2qrKxURESEcnNzlZycrIsvvlidO3dWWVmZhg0bpq1btyo6OvqUxwgLC1NYWJivRgYABCCfhatz585avXq1Fi9erIaGBsXExCg+Pl6S9Nhjj2nFihU6evSoBg8erDlz5vhqLMDnGptaFBoS5PdjAKayuFwul7+HOBtVVVUaP368CgoKFBER4e9xAI/MXrbxrPZ/bg2nx3H+4soZAACjnJPhamxqCYhjAAA6ns9e4/Kl0JAgTsUAwDnqnHzGBQA4dxEuAIBRCBcAwCiECwBgFMLlR7z7EQDO3Dn5rkJT8O5HADhzPOMCABiFcAEAjEK4AABGIVwAAKMQLgCAUQgXAMAohAsAYBTCBcBofJD//MMHkAEYjQ/yn394xgUAMArhwmmZcirGlDkBnB1OFeK0TDkVY8qcAM4Oz7gAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QJwSo1NLQFxDOD7gv09AIDAFBoSpNnLNp7VMZ5bk9JB0wD/j2dcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIzil3Bt3bpVU6ZM0ZQpU/TII49IkoqLi2W32xUbG6v09HR/jAUAMIDPw3Xs2DGtWrVK69ev19atW1VaWqrt27crLS1NmZmZysvL0549e1RYWOjr0QAABvD5lTNaWlrU2tqqY8eOqVu3bmpublb37t3Vv39/9evXT5Jkt9uVn5+vmJiYdvs6nU45nc526xwOh89mBwD4n8/D1b17d915552aNGmSunbtquuuu04HDx5UeHi4exur1aqampqT9s3KylJGRoYvxwUABBifh+vjjz/W5s2b9eabb6pHjx665557tG/fPlksFvc2Lper3fIJqampSkxMbLfO4XAoJYXroQHA+cLn4SoqKpLNZlOvXr0kSUlJSVq7dq2CgoLc29TW1spqtZ60b1hYmMLCwnw2KwAg8Pj8zRmDBg1ScXGx6uvr5XK5tH37dg0ZMkR79+5VZWWlWlpalJubq+joaF+PBgAwgM+fcY0aNUoffvihkpKSFBISoquvvlqLFy9WVFSUFi9erIaGBsXExCg+Pt7XowEADOCX+3HdeuutuvXWW9uts9lsysnJ8cc4AACDcOUMAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFMIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuAIBRCBcAwCiECwBgFL+Ea/v27UpKStKkSZP04IMPSpKKi4tlt9sVGxur9PR0f4wFADCAz8P15ZdfauXKlcrMzFROTo4+/PBDFRYWKi0tTZmZmcrLy9OePXtUWFjo69EAAAbwebjeeOMNTZ48WX369FFISIjS09PVtWtX9e/fX/369VNwcLDsdrvy8/N9PRoAwADBvn7AyspKhYSEaMGCBTpw4IDGjBmjyy+/XOHh4e5trFarampqTtrX6XTK6XS2W+dwOLw+MwAgcPg8XC0tLSotLdX69evVrVs33XbbberSpYssFot7G5fL1W75hKysLGVkZPhyXABAgPF5uH7+85/LZrPpoosukiRNmDBB+fn5CgoKcm9TW1srq9V60r6pqalKTExst87hcCglJcW7QwMAAobPX+MaO3asioqK5HQ61dLSorffflvx8fHau3evKisr1dLSotzcXEVHR5+0b1hYmCIiItp99enTx9e/AgDAj3z+jGvIkCGaN2+eZs+eraamJkVFRWnWrFm69NJLtXjxYjU0NCgmJkbx8fG+Hg0AYACfh0uSZsyYoRkzZrRbZ7PZlJOT449xAAAG4coZAACjEC4AXtPa3BQQx8C5xS+nCgGcHzoFh6hszbyzOsawZc900DQ4V/CMCwBgFMIFADAK4QIAGMWjcJ3quoGff/55hw8DAMDp/Gi4jhw5oiNHjuj3v/+96urq3MuHDh3SokWLfDUj4DO8Cw4IfD/6rsK7775bO3bskCQNHz78/3cKDlZcXJx3JwP8gHfBAYHvR8O1du1aSdIf//hHPfzwwz4ZCACAH+PR57gefvhhVVdXq66uTi6Xy71+8ODBXhsMAIBT8Shcf/3rX7V27Vr16tXLvc5isaigoMBrgwEApFtuuUXR0dFKTU2VJO3du1fx8fGaP3++7rrrLknSV199pZiYGA0dOlQrV67UZZdd9qPHPHz4sJ544gnt3LlTXbt2VadOnZSQkKCbbrqp3S2mTqiqqpLdbtfu3bs7/hf8CTx6V+Err7yi119/Xdu3b3d/ES0A8L7o6Gjt3LnTvfzmm29q7Nix7f4f/M477+jaa6/V+vXrTxstp9OpWbNmacCAAXr11VeVk5OjrKwsVVRUaNmyZV77PTqSR+Hq27evevfu7e1ZAADfEx0drdLSUrW2tkpqC9ett96qb7/9Vvv375cklZSUaMyYMRo3bpwqKiq0c+dOzZw5U0uXLtX06dOVkJCgsrIySdLzzz+vq666SvPmzVNwcNtJtwsuuEBr1qxRSUmJysvLtXPnTk2dOlUzZ86U3W5XY2PjD873zTff6J577lFCQoLsdrvWrFmj5uZmSW1n6+x2u5KSknTLLbfo4MGDkqQvvvhCN998s5KSkjRt2jS9/PLLZ/Q38ShcNptNa9asUVlZmT744AP3FwDAuy655BKFhYXpk08+UV1dnfbu3avIyEhFR0dr+/btktrCFRMT026/8vJy3XzzzXrllVeUlJSk9PR0SdLu3bt13XXXnfQ4nTt31rBhw/Tee+9Jkj777DM9/vjj2rZtm0JDQ39wvgcffFA9e/bUtm3btHnzZn3yySd69tlndeDAAWVlZWnz5s3Kzs5WVFSUysvL1dzcrDvuuEN33323srOztWHDBj377LP673//6/HfxKPXuLKzsyVJ+fn57nW8xgUAvnHidGGvXr00cuRIderUSWPHjtXGjRs1YcIEWSwWDRw4sN0+v/jFL3TVVVdJkn71q19py5Yt7p81NZ36s4bffWbVt29fXXzxxaed7a233tLzzz8vi8Wi0NBQzZw5U1lZWZo3b54GDRqkxMRERUdHKzo6WjabTZ9//rn279+vtLQ09zGOHz+uDz/8UJGRkR79PTwK14mqAwB8Lzo6Wi+//LI6d+6s8ePHS2o7E7ZixQr3acLv69Kli/t7i8Xifkf4tddeq127dmnu3Lnttv/2229VUVGhhQsX6tixY+rWrZtHs7W2tspisbRbbm5uVqdOnbRhwwZVVFSopKREDz30kEaPHq1p06apR48e2rp1q3ufQ4cOqUePHp7+OTw7Vbhu3bpTfgEAvG/48OH66KOPtGvXLo0ePVpSW5gGDx6sDRs2nHSa8MfMnj1bX3zxhf75z3+qpaVFklRXV6fly5frN7/5ja655pozmm3UqFHasGGDXC6XGhsbtWnTJo0cOVIff/yxEhISNHDgQM2fP19z585VRUWFLrnkEnXp0sUdrgMHDighIUF79uzx+DE9esb16aefur9vbGzUu+++K5vNdka/HADgp+natasGDBigpqamds9MYmJi9Oijj7a7stHpdO/eXS+++KKefPJJTZ48WSEhIbJYLEpISNDNN9/8g/vV19dr6NCh7da98MILWrFihR588EHZ7XY1NTVp9OjRWrBggUJDQzVp0iQlJyerW7du6tKli1asWKHQ0FBlZmZq1apVeuaZZ9Tc3Kw777xTw4YN8/h38PgDyN9VU1Oje++91+MHgfe0NjepU3CI348BwLtOXMnou1JSUpSSkuJe/u7LOrm5ue7vhw8f3m65Z8+eWrly5Q8+1ve3j4iI0CeffPKD2z/++OOnXL9o0aJTXtd20KBBWr9+/Q8e73R+0h2Qe/furerq6p/8oOg4XFsPwPnGo3B99/Usl8ulPXv2tLuKBgAAvnLGr3FJbW+TNOUT1gCAc8sZvcZVXV2t5uZm9e/f36tDAQDwQzwKV2VlpW6//XYdPHhQra2tuvDCC/XUU0+d9IE3AAC8zaPPcT3wwAOaN2+e3n33XZWVlem2227T/fff7+3ZAAA4iUfh+uqrr5SYmOheTk5O1tdff+21oQAAnmtsavHrcXfu3Kkbb7zRKzOcikenCltaWnTkyBH17NlTUtu9XAAAgSE0JEizl23s8OM+tybl9Bv5gUfhuuGGG3T99ddr0qRJslgsysvLc9/UDPAEH5QG0FE8CldMTIyeffZZNTU16csvv1RNTY0mTpzo7dlwDuGD0gA6ikfhWr58uVJSUjRnzhw1NDTo+eefV1pamp5++mlvzwcAQDsevTnj66+/1pw5cyS13Wxs7ty5qq2t9epg/tbafOr71fj6GAC8j//ezeLxmzNqamrUu3dvSW33Tjlxb5dzFae2gPMH/72bxaNwzZ07V9OnT9fo0aNlsVhUXFzMJZ8AAG6lpaXtbntit9v1wAMPeOWxPArXjBkz9Otf/1rvvPOOgoKCdMstt+iKK67wykAAgDPT2NTilbeuNza1KDQk6LTbnbjRpa94fFuTQYMGadCgQd6cBQDwE3gSl0A67tny6M0ZAAAECsIFADAK4QIAGIVwAQCMQrgAAEYhXAAAoxAuADCcty435clxb7rpJv373/92Lz/yyCMaOnSoGhsb3etGjRqlqqqqDpvL489xAQACU0dcsupUPLmM1YgRI1RWVqYJEyZIkoqLixUZGamysjLZbDZVVlaqW7duioiI6LC5eMYFAPjJbDabdu/eLUmqqalRaGio4uLiVFRUJKntUlBRUVEd+piECwDwkw0ePFj79+9XQ0ODioqKFBUVpaioKMIFAAhMQUFBGjJkiCoqKlRUVKRRo0apX79+On78uOrq6rR7926NGDGiQx+TcAEAzsqIESP03nvvqby8XJGRkZLaTiEWFBTowgsvVPfu3Tv08QgXAOCs2Gw2bd26VVdccYWCg9ve8xcVFaV169Z1+GlCiXcVAoDxWpubvHIjy9bmJnUKDjntdldccYWOHDmi2bNnu9eNGDFCf/jDHzRy5MgOn4twAYDhPImLt4+7Y8eOdss9evTQBx980NEjSeJUIQDAMH4N1yOPPKLly5dLavvQmt1uV2xsrNLT0/05FhDwOuJKCd662gLgbX47VVhSUqItW7ZozJgxOn78uNLS0rR+/Xr17dtX8+fPV2FhoWJiYvw1HhDQOuJKCd54TQTwBb884zpy5IjS09O1YMECSVJ5ebn69++vfv36KTg4WHa7Xfn5+Sft53Q6VVVV1e7L4XD4enwAgB/55RnXfffdpyVLlujAgQOSpIMHDyo8PNz9c6vVqpqampP2y8rKUkZGhs/mBAAEHp+H66WXXlLfvn1ls9mUnZ0tSWptbZXFYnFv43K52i2fkJqaqsTExHbrHA6HUlJSvDs0ACBg+DxceXl5qq2t1bRp01RXV6f6+npVV1crKCjIvU1tba2sVutJ+4aFhSksLMyX4wIAAozPw7Vu3Tr399nZ2dq1a5fuv/9+xcbGqrKyUhEREcrNzVVycrKvRwMAGCAgPoDcuXNnrV69WosXL1ZDQ4NiYmIUHx/v77EAAAHIr+FKSkpSUlKSpLZrXeXk5PhzHACAAbhyBgDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMIpfwpWRkaEpU6ZoypQpWrNmjSSpuLhYdrtdsbGxSk9P98dYAAAD+DxcxcXFKioq0pYtW/TKK6/ogw8+UG5urtLS0pSZmam8vDzt2bNHhYWFvh4NAGAAn4crPDxcy5cvV2hoqEJCQjRw4EDt27dP/fv3V79+/RQcHCy73a78/HxfjwYAMECwrx/w8ssvd3+/b98+vfrqq7rhhhsUHh7uXm+1WlVTU3PSvk6nU06ns906h8PhvWEBAAHH5+E64bPPPtP8+fO1bNkyBQUFad++fe6fuVwuWSyWk/bJyspSRkaGD6cEAAQav4SrrKxMd9xxh9LS0jRlyhTt2rVLtbW17p/X1tbKarWetF9qaqoSExPbrXM4HEpJSfH6zACAwODzcB04cEALFy5Uenq6bDabJGnIkCHau3evKisrFRERodzcXCUnJ5+0b1hYmMLCwnw9MgAggPg8XGvXrlVDQ4NWr17tXjdz5kytXr1aixcvVkNDg2JiYhQfH+/r0QAABvB5uFasWKEVK1ac8mc5OTk+ngYAYBqunAEAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARiFcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAYhXABAIxCuAAARgmocG3btk2TJ09WbGysNm7c6O9xAAABKNjfA5xQU1Oj9PR0ZWdnKzQ0VDNnztTw4cN12WWX+Xs0AEAACZhwFRcXa8SIEerZs6ckKS4uTvn5+Vq0aJF7G6fTKafT2W6/6upqSZLD4Wi3vqH+yFnNU1VVpdpvjp/1MU6HOc/sGKfDnGd2jNNhzjM7xvf16dNHwcEB87/Zc4bF5XK5/D2EJD311FOqr6/XkiVLJEkvvfSSysvL9Ze//MW9zd/+9jdlZGT4a0QAOCMFBQWKiIjw9xjnnID5p0Bra6ssFot72eVytVuWpNTUVCUmJrZb19jYqC+//FIDBgxQUFBQh8zicDiUkpKijRs3qk+fPh1yTG9gzo7FnB2LORXQv7fJAiZcffr0UWlpqXu5trZWVqu13TZhYWEKCws7ad9LL73UazOZ8K8l5uxYzNmxmBMdLWDeVThy5EiVlJTo8OHDOnbsmF5//XVFR0f7eywAQIAJmGdcvXv31pIlSzRnzhw1NTVpxowZuuaaa/w9FgAgwARMuCTJbrfLbrf7ewwAQAALmFOFgSQsLEyLFi065etpgYQ5OxZzdizmhLcEzNvhAQDwBM+4AABGIVwAAKMQLgCAUQgXAMAohAsAYBTCBQAwCuEC1HZLiqFDh552u4qKCo0bN84HEwH4IYQLAGAUwgXjTJs2TSUlJZKk3NxcXX311Tp+vO0mgPfee6+ysrL00EMPKTExUVOnTtXy5ct19OhRSW132l64cKGSkpJkt9v1j3/846Tjf/HFFxo3bpzeeOMNSdJzzz2nuLg4JScn67nnnnNvd+jQId1+++26/vrrNW7cON1444366quvVFZWpjFjxqi1tVWSdOzYMdlsNh0+fNirfxfgfEG4YJyJEyfqrbfekiS9/fbbuuCCC1RaWiqXy6XCwkJ98803CgoKUnZ2tnJycmS1WvXYY49JkpYuXark5GRlZ2fr5ZdfVnFxsfLy8tzH/vTTT7VgwQKtWrVKEydO1EcffaSMjAxt2LBBmzdvVkhIiHvbf/3rX4qMjNSLL76ogoICdenSRVu3btWwYcN0wQUX6O2333ZvZ7PZdNFFF/nwrwScuwLqIruAJyZOnKi77rpLy5YtU2lpqebOnasdO3boZz/7mX75y1/qP//5j7755hsVFxdLkpqamtSrVy/V19fr3XffVV1dnZ588klJUn19vT7++GNdc801amxs1Jw5c/Tb3/5WNptNklRSUqKoqCiFh4dLkq6//noVFRVJaruxaWlpqdatW6d9+/bps88+05AhQyRJKSkp2rRpk2JiYvTiiy9q2bJlvv4zAecswgXjXHnllWpqalJBQYEGDBigsWPHasmSJQoODlZcXJy2bt2qtLQ0xcTESJK+/fZbNTQ0qLW1VS6XSy+88IK6du0qSTp8+LA6d+6sr7/+WpL097//XcuWLdNrr72muLg4SW134z7hu3fZfvTRR1VeXq7k5GQNHz5czc3N7m3tdrueeOIJvfPOO6qvr9d1113nk78NcD7gVCGMNGHCBD3++OOKiorSwIEDdfToUW3btk2xsbEaNWqUNm7cqMbGRrW2tupPf/qTnnjiCXXv3l2RkZFat26dJMnpdGrWrFkqKCiQJIWGhmrYsGF66KGH9Oc//1m1tbWKiorSjh075HA4JElbtmxxz1BUVKTU1FRNnz5dvXr1UnFxsVpaWiRJXbt21dSpU5WWlqaZM2f6+K8DnNsIF4w0ceJE/e9//9PIkSMltd1BOzw8XH379tXtt9+uiy++WImJiZo8ebJcLpeWL18uSXrsscf0/vvvy26363e/+50SEhI0derUdscePny4pkyZorS0NF155ZVaunSpUlNTlZSUpIaGBvd2Cxcu1Jo1a2S323Xbbbfp2muv1f79+90/T0pK0uHDhzV9+nTv/0GA8wi3NQG8wOVy6emnn1Z1dbXuv/9+f48DnFN4jQvwgvHjx8tqtSozM9PfowDnHJ5xAQCMwmtcAACjEC4AgFEIFwDAKIQLAGAUwgUAMArhAgAY5f8AJ2TKDRClu5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 428.8x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visu = sns.catplot(x = 'weekday', data = treino, hue ='WinOrLose', kind = 'count', margin_titles = True)\n",
    "visu.set(xticklabels=[])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "rpAOz1QVS8sb",
    "outputId": "6747269e-83c8-4e88-9141-233471344eaa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 428.8x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visu = sns.catplot(x = 'weekofyear', data = treino, hue ='WinOrLose', kind = 'count', margin_titles = True)\n",
    "visu.set(xticklabels=[])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "NeiKCbQt7k7R",
    "outputId": "b651a0e3-2a92-4263-e842-27b26774e18e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 428.8x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visu = sns.catplot(x = 'Dia do Ano', data = treino, hue ='WinOrLose', kind = 'count', margin_titles = True)\n",
    "visu.set(xticklabels=[])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "8xzrkG-_SuAM",
    "outputId": "8c34df70-e133-417a-e749-05bea9e8546c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 428.8x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visu = sns.catplot(x = 'Dia', data = treino, hue ='WinOrLose', kind = 'count', margin_titles = True)\n",
    "visu.set(xticklabels=[])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A9IkEHPKsX9I"
   },
   "source": [
    "### Criacao da feature season (estacao do ano: Primavera, verão, outono e inverno)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "udH9QRqB87wH"
   },
   "source": [
    "lembrar que nos EUA as estacoes do ano sao diferentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "agzwZrQCWtkq"
   },
   "outputs": [],
   "source": [
    "teste['Season'] = teste.Data.dt.month%12 // 3 + 1\n",
    "treino['Season'] = treino.Data.dt.month%12 // 3 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zZeIBfY26dHT",
    "outputId": "e88291d5-60e3-49b1-a3ec-3906a6bb00a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    77\n",
      "3    47\n",
      "4    41\n",
      "Name: Season, dtype: int64\n",
      "2    924\n",
      "3     82\n",
      "Name: Season, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(teste['Season'].value_counts())\n",
    "print(treino['Season'].value_counts())\n",
    "\n",
    "teste_total = teste.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 551
    },
    "id": "mMBWKTBP6hD6",
    "outputId": "f565c2d8-0412-4d4e-8582-673c8869eac7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = teste['Season'].value_counts()/teste['Season'].value_counts().sum() #frequencia absoluta\n",
    "plt.bar(['2','3','4'],y)\n",
    "plt.title('Frequencia absoluta de Season dataframe de teste')\n",
    "plt.show()\n",
    "\n",
    "y = treino['Season'].value_counts()/treino['Season'].value_counts().sum() #frequencia absoluta\n",
    "plt.bar(['2','3'],y)\n",
    "plt.title('Frequencia absoluta de Season dataframe de treino')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YveXFYgi7Caq"
   },
   "source": [
    "Note que os jogos acontecem exclusivamente nas seasons 2, 3 e 4 e veja que no treino temos quase que exclusivamente os jogos acontecendo na season 2, indicando que essa variável talvez não seja muito interessantes para os modelos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "au-2gRjSvJSW"
   },
   "source": [
    "### Retirando a coluna Game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Cl4tgncqs4BA"
   },
   "outputs": [],
   "source": [
    "#teste\n",
    "Id = teste.Game #sera utilizado para prever depois\n",
    "teste = teste.iloc[:,1:]\n",
    "\n",
    "#treino\n",
    "treino = treino.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1jasMqNS_Sw3"
   },
   "source": [
    "### Retirando a coluna Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "x9ABHuf0_Fpv"
   },
   "outputs": [],
   "source": [
    "teste.drop('Data', axis=1, inplace= True)\n",
    "\n",
    "treino.drop('Data', axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fy40XdxDwSiJ"
   },
   "source": [
    "### Transformando os dados do tipo object 'O' para tipo int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "0a0v1dXAvTcw"
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "#base de treino\n",
    "for i in range(0, len(treino.columns.values)):\n",
    "  if treino.dtypes[i] == 'O':\n",
    "    treino.iloc[:, i] = le.fit_transform(treino.iloc[:, i]).astype('int')\n",
    "\n",
    "#Na base de test\n",
    "for i in range(0, len(teste.columns.values)):\n",
    "  if teste.dtypes[i] == 'O':\n",
    "    teste.iloc[:, i] = le.fit_transform(teste.iloc[:, i]).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LCXN9TPixHkb"
   },
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cQjRbcbJBVun"
   },
   "source": [
    "### Feature Importance (Random Forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "MoAu6PEz9p3y"
   },
   "outputs": [],
   "source": [
    "#Divide the features into Independent and Dependent Variable\n",
    "X = treino.drop('WinOrLose' , axis =1)\n",
    "X_completo = X\n",
    "y = treino['WinOrLose']\n",
    "y_completo = y.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "JgPGXF95ltfa"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "colunas = X.columns\n",
    "scaler_train = StandardScaler()\n",
    "#scaler_train = MinMaxScaler()\n",
    "X = scaler_train.fit_transform(X)\n",
    "\n",
    "#Nao precisa padronizar o teste pq estamos apenas vendo as features de importancia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D8iDOZfRBaZG",
    "outputId": "5aa0617b-c1fe-4efa-e564-cc25b6e2d7f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y, test_size=0.25)\n",
    "model  = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "ld7rw-Ukjgx4"
   },
   "outputs": [],
   "source": [
    "def plot_feature_importance(importance,names,model_type):\n",
    "    \n",
    "    #Create arrays from feature importance and feature names\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "    \n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "    \n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "    \n",
    "    #Define size of bar plot\n",
    "    plt.figure(figsize=(10,25))\n",
    "    #Plot Searborn bar chart\n",
    "    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "    #Add chart labels\n",
    "    plt.title(model_type + 'FEATURE IMPORTANCE')\n",
    "    plt.xlabel('FEATURE IMPORTANCE')\n",
    "    plt.ylabel('FEATURE NAMES')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vYUfN1xfiakz",
    "outputId": "346a2329-b005-47d1-d304-032c3e6c3e87"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(model.feature_importances_,colunas,'Random Forest ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Es5F6bnyoN0Z"
   },
   "source": [
    "### Selecionando k colunas por ordem de importancia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "WTdH2pdFkLb7"
   },
   "outputs": [],
   "source": [
    "    #Create arrays from feature importance and feature names\n",
    "    importance = model.feature_importances_\n",
    "    names = colunas\n",
    "\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "    \n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "    \n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "    #Resetando os index para poder selecionar as colunas desejadas\n",
    "    fi_df.reset_index(inplace=True)\n",
    "    #Selecionando o numero de colunas que deseja, por ordem de importancia\n",
    "    select_colunas = fi_df.feature_names[0:14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JZSPUCJpmgOs",
    "outputId": "73309b62-f469-4065-b6ff-ebb534e2abd7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dia',\n",
       " 'Dia do Ano',\n",
       " 'weekday',\n",
       " 'weekofyear',\n",
       " 'A_MOV',\n",
       " 'A_SRS',\n",
       " 'H_Wins',\n",
       " 'A_W/D %',\n",
       " 'A_FG%',\n",
       " 'H_Loss',\n",
       " 'H_eFG%',\n",
       " 'H_TS%',\n",
       " 'H_W/D %',\n",
       " 'A_Loss']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(select_colunas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "Jfz3VFdNnz7C"
   },
   "outputs": [],
   "source": [
    "treino_completo = treino.copy()\n",
    "teste_completo = teste.copy()\n",
    "\n",
    "treino = treino[select_colunas]\n",
    "\n",
    "teste = teste[select_colunas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "ju2M5O7XoXeO",
    "outputId": "fc320bc2-1c12-4045-ae41-1cd7fc1214cd"
   },
   "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>Dia</th>\n",
       "      <th>Dia do Ano</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>A_MOV</th>\n",
       "      <th>A_SRS</th>\n",
       "      <th>H_Wins</th>\n",
       "      <th>A_W/D %</th>\n",
       "      <th>A_FG%</th>\n",
       "      <th>H_Loss</th>\n",
       "      <th>H_eFG%</th>\n",
       "      <th>H_TS%</th>\n",
       "      <th>H_W/D %</th>\n",
       "      <th>A_Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>159</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.96</td>\n",
       "      <td>52</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.462</td>\n",
       "      <td>30</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.634</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.96</td>\n",
       "      <td>52</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.462</td>\n",
       "      <td>30</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.634</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>164</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.59</td>\n",
       "      <td>60</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.478</td>\n",
       "      <td>22</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.732</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>166</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.59</td>\n",
       "      <td>60</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.478</td>\n",
       "      <td>22</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.732</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.59</td>\n",
       "      <td>60</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.478</td>\n",
       "      <td>22</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.732</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dia  Dia do Ano  weekday  weekofyear  A_MOV  A_SRS  H_Wins  A_W/D %  A_FG%  \\\n",
       "0    8         159        4          23   6.07   5.96      52    0.732  0.462   \n",
       "1   11         162        0          24   6.07   5.96      52    0.732  0.462   \n",
       "2   13         164        2          24   3.87   3.59      60    0.634  0.478   \n",
       "3   15         166        4          24   3.87   3.59      60    0.634  0.478   \n",
       "4   18         169        0          25   3.87   3.59      60    0.634  0.478   \n",
       "\n",
       "   H_Loss  H_eFG%  H_TS%  H_W/D %  A_Loss  \n",
       "0      30   0.517  0.556    0.634      22  \n",
       "1      30   0.517  0.556    0.634      22  \n",
       "2      22   0.495  0.550    0.732      30  \n",
       "3      22   0.495  0.550    0.732      30  \n",
       "4      22   0.495  0.550    0.732      30  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treino.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JmT10ZR7pxYs"
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xZ0xt4ROqF_H"
   },
   "source": [
    "### Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 461
    },
    "id": "wX4DOtnVovcA",
    "outputId": "f5b54574-bc68-4517-ab3c-cc5888741e9d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-4aa79b6928d7>:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.triu(np.ones_like(treino.corr(), dtype=np.bool))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "# define the mask to set the values in the upper triangle to True\n",
    "mask = np.triu(np.ones_like(treino.corr(), dtype=np.bool))\n",
    "heatmap = sns.heatmap(treino.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
    "heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B8bjnVG0o8AI",
    "outputId": "ceba86dc-381d-4228-c5f9-14146cd53096"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1006, 14)\n",
      "(1006,)\n"
     ]
    }
   ],
   "source": [
    "print(treino.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_zwttFl68Es"
   },
   "source": [
    "Através das 14 primeiras variáveis selecionadas (apenas com a variável dia do ano, sem as outras derivações da Data), utilizamos o método de seleção forward para encontrar o melhor subconjunto de forma a obtermos o melhor resultado, considerando a métrica curva roc. Após observarmos qual foi o melhor subconjunto, fomos eliminando aquelas variáveis que estavam correlacionadas com alguma outra variável. Fizemos isso considerando apenas a variável Dia do Ano na hora de rodar o modelo randon forest (eliminando as outras variações da variável data) e obtivemos como melhores características, através do modelo Naive Bayes, as seguintes variáveis: \n",
    "'Dia do Ano', 'A_W/D %', 'A_FG%', 'H_MOV', 'H_eFG%', 'A_3P%', 'A_FT%'. Resultando no Score do Kaggle 0.727"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x8KzWdAu77Qz"
   },
   "source": [
    "Da mesma forma, fizemos o mesmo procedimento testando as outras variações das variáveis a partir da Data, eliminando a variável Dia do Ano, e obtivemos como melhores variáveis para o modelo de Naive Bayes: 'Dia', 'weekday', 'weekofyear', 'H_eFG%','A_W/D %', 'A_SRS'. E essas variáveis resultaram no melhor score do Kaggle: 0.729"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5VtIVDz7_WSE"
   },
   "source": [
    "Será reproduzido os resultados para a melhor acurácia que obtivemos no teste e que resultou na melhor classificação do kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gb4g8LWPqpQi"
   },
   "source": [
    "## Padronização e Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "kjhdJrDr_m6a"
   },
   "outputs": [],
   "source": [
    "#As 14 melhores variáveis escolhidas pelo modelo random forest sem a variável dia do Ano\n",
    "#col = ['Dia', 'weekday', 'weekofyear', 'A_Loss', 'H_eFG%', 'H_MOV', 'A_W/D %', 'H_SRS', 'A_MOV', 'A_Wins', 'A_SRS', 'H_TS%', 'H_W/D %', 'H_Loss']\n",
    "\n",
    "treino = X_completo[select_colunas]\n",
    "teste = teste_completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "ntviq1u3q92a"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler_train = StandardScaler()\n",
    "#scaler_train = MinMaxScaler()\n",
    "X = scaler_train.fit_transform(treino)\n",
    "\n",
    "#Vamos padronizar o teste tbm\n",
    "scaler_train = StandardScaler()\n",
    "#scaler_train = MinMaxScaler()\n",
    "teste = scaler_train.fit_transform(teste[select_colunas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "ZmZfFaRFq923"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y, test_size=0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cvLY564NB1lH",
    "outputId": "914c1358-0cc0-4c67-d0cf-ee76ca77536f"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector\n",
    "\n",
    "feature_selector = SequentialFeatureSelector(RandomForestClassifier(n_jobs=-1),\n",
    "           k_features = 14,\n",
    "           forward = True,\n",
    "           verbose = 2,\n",
    "           scoring = 'roc_auc',\n",
    "           cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "njLH1-efB9a1",
    "outputId": "1a40f4fe-fce0-471b-8985-736160c95d06"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:   18.2s finished\n",
      "\n",
      "[2021-10-09 17:31:45] Features: 1/14 -- score: 0.5970483694203371[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:   13.1s finished\n",
      "\n",
      "[2021-10-09 17:31:58] Features: 2/14 -- score: 0.5919707650839727[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:   12.4s finished\n",
      "\n",
      "[2021-10-09 17:32:10] Features: 3/14 -- score: 0.5784259204407453[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:   12.4s finished\n",
      "\n",
      "[2021-10-09 17:32:23] Features: 4/14 -- score: 0.5912465566778236[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:   10.2s finished\n",
      "\n",
      "[2021-10-09 17:32:33] Features: 5/14 -- score: 0.6063153490714137[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    9.1s finished\n",
      "\n",
      "[2021-10-09 17:32:42] Features: 6/14 -- score: 0.6132538283818607[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    8.0s finished\n",
      "\n",
      "[2021-10-09 17:32:51] Features: 7/14 -- score: 0.61611956103196[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    7.3s finished\n",
      "\n",
      "[2021-10-09 17:32:58] Features: 8/14 -- score: 0.6224219513639999[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    6.1s finished\n",
      "\n",
      "[2021-10-09 17:33:04] Features: 9/14 -- score: 0.6082610112259709[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    5.2s finished\n",
      "\n",
      "[2021-10-09 17:33:09] Features: 10/14 -- score: 0.6064708539438998[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    4.4s finished\n",
      "\n",
      "[2021-10-09 17:33:14] Features: 11/14 -- score: 0.5931933295814698[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    3.3s finished\n",
      "\n",
      "[2021-10-09 17:33:17] Features: 12/14 -- score: 0.5899106957732295[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    2.2s finished\n",
      "\n",
      "[2021-10-09 17:33:19] Features: 13/14 -- score: 0.5786436272622256[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s finished\n",
      "\n",
      "[2021-10-09 17:33:21] Features: 14/14 -- score: 0.5741880424158052"
     ]
    }
   ],
   "source": [
    "# o subconjunto formado por 8 variáveis foi o escolhido: score: 0.615483\n",
    "features = feature_selector.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s9zL4RZnB-Av",
    "outputId": "9bc5ed36-66ba-4109-caca-5d102bfbc25d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: {'feature_idx': (7,),\n",
       "  'cv_scores': array([0.55236812, 0.67404698, 0.64054678, 0.5790335 , 0.53924647]),\n",
       "  'avg_score': 0.5970483694203371,\n",
       "  'feature_names': ('7',)},\n",
       " 2: {'feature_idx': (7, 13),\n",
       "  'cv_scores': array([0.54331921, 0.65941471, 0.64372353, 0.57787832, 0.53551805]),\n",
       "  'avg_score': 0.5919707650839727,\n",
       "  'feature_names': ('7', '13')},\n",
       " 3: {'feature_idx': (1, 7, 13),\n",
       "  'cv_scores': array([0.57691567, 0.5883712 , 0.61676935, 0.60271467, 0.50735871]),\n",
       "  'avg_score': 0.5784259204407453,\n",
       "  'feature_names': ('1', '7', '13')},\n",
       " 4: {'feature_idx': (1, 7, 10, 13),\n",
       "  'cv_scores': array([0.57653061, 0.55650751, 0.6380439 , 0.62726223, 0.55788854]),\n",
       "  'avg_score': 0.5912465566778236,\n",
       "  'feature_names': ('1', '7', '10', '13')},\n",
       " 5: {'feature_idx': (1, 2, 7, 10, 13),\n",
       "  'cv_scores': array([0.59655372, 0.53744705, 0.68569503, 0.61994609, 0.59193485]),\n",
       "  'avg_score': 0.6063153490714137,\n",
       "  'feature_names': ('1', '2', '7', '10', '13')},\n",
       " 6: {'feature_idx': (1, 2, 3, 7, 10, 13),\n",
       "  'cv_scores': array([0.62216018, 0.54688102, 0.68800539, 0.63573354, 0.57348901]),\n",
       "  'avg_score': 0.6132538283818607,\n",
       "  'feature_names': ('1', '2', '3', '7', '10', '13')},\n",
       " 7: {'feature_idx': (1, 2, 3, 5, 7, 10, 13),\n",
       "  'cv_scores': array([0.60117443, 0.59568733, 0.66316904, 0.6325568 , 0.5880102 ]),\n",
       "  'avg_score': 0.61611956103196,\n",
       "  'feature_names': ('1', '2', '3', '5', '7', '10', '13')},\n",
       " 8: {'feature_idx': (0, 1, 2, 3, 5, 7, 10, 13),\n",
       "  'cv_scores': array([0.60714286, 0.59145168, 0.66981132, 0.64882557, 0.59487834]),\n",
       "  'avg_score': 0.6224219513639999,\n",
       "  'feature_names': ('0', '1', '2', '3', '5', '7', '10', '13')},\n",
       " 9: {'feature_idx': (0, 1, 2, 3, 4, 5, 7, 10, 13),\n",
       "  'cv_scores': array([0.5732576 , 0.61532538, 0.6413169 , 0.62427801, 0.58712716]),\n",
       "  'avg_score': 0.6082610112259709,\n",
       "  'feature_names': ('0', '1', '2', '3', '4', '5', '7', '10', '13')},\n",
       " 10: {'feature_idx': (0, 1, 2, 3, 4, 5, 7, 10, 11, 13),\n",
       "  'cv_scores': array([0.57142857, 0.60329226, 0.63274933, 0.64276088, 0.58212323]),\n",
       "  'avg_score': 0.6064708539438998,\n",
       "  'feature_names': ('0', '1', '2', '3', '4', '5', '7', '10', '11', '13')},\n",
       " 11: {'feature_idx': (0, 1, 2, 3, 4, 5, 7, 8, 10, 11, 13),\n",
       "  'cv_scores': array([0.53475164, 0.60271467, 0.64767039, 0.61725067, 0.56357928]),\n",
       "  'avg_score': 0.5931933295814698,\n",
       "  'feature_names': ('0', '1', '2', '3', '4', '5', '7', '8', '10', '11', '13')},\n",
       " 12: {'feature_idx': (0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13),\n",
       "  'cv_scores': array([0.55217559, 0.59520601, 0.63659992, 0.60964575, 0.55592622]),\n",
       "  'avg_score': 0.5899106957732295,\n",
       "  'feature_names': ('0',\n",
       "   '1',\n",
       "   '2',\n",
       "   '3',\n",
       "   '4',\n",
       "   '5',\n",
       "   '7',\n",
       "   '8',\n",
       "   '9',\n",
       "   '10',\n",
       "   '11',\n",
       "   '13')},\n",
       " 13: {'feature_idx': (0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13),\n",
       "  'cv_scores': array([0.53696573, 0.59279938, 0.62283404, 0.60107817, 0.53954082]),\n",
       "  'avg_score': 0.5786436272622256,\n",
       "  'feature_names': ('0',\n",
       "   '1',\n",
       "   '2',\n",
       "   '3',\n",
       "   '4',\n",
       "   '5',\n",
       "   '7',\n",
       "   '8',\n",
       "   '9',\n",
       "   '10',\n",
       "   '11',\n",
       "   '12',\n",
       "   '13')},\n",
       " 14: {'feature_idx': (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13),\n",
       "  'cv_scores': array([0.52050443, 0.59279938, 0.62023489, 0.60512129, 0.53228022]),\n",
       "  'avg_score': 0.5741880424158052,\n",
       "  'feature_names': ('0',\n",
       "   '1',\n",
       "   '2',\n",
       "   '3',\n",
       "   '4',\n",
       "   '5',\n",
       "   '6',\n",
       "   '7',\n",
       "   '8',\n",
       "   '9',\n",
       "   '10',\n",
       "   '11',\n",
       "   '12',\n",
       "   '13')}}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.subsets_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "I5iAWvdjGWVF"
   },
   "outputs": [],
   "source": [
    "#O melhor subconjunto foi: ['Dia', 'weekday', 'weekofyear', 'A_Loss', 'H_eFG%', 'A_W/D %', 'A_Wins', 'A_SRS']\n",
    "cols = ['Dia', 'weekday', 'weekofyear', 'A_Loss', 'H_eFG%', 'A_W/D %', 'A_Wins', 'A_SRS'] \n",
    "treino = X_completo\n",
    "treino = treino[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "Oo9RQP84F5o9",
    "outputId": "3c14ef38-7575-4172-ba8b-8710c22c0ce2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-4aa79b6928d7>:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.triu(np.ones_like(treino.corr(), dtype=np.bool))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "# define the mask to set the values in the upper triangle to True\n",
    "mask = np.triu(np.ones_like(treino.corr(), dtype=np.bool))\n",
    "heatmap = sns.heatmap(treino.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
    "heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I6enQEhwG_ED"
   },
   "source": [
    "Através das correlações, optamos por eliminar as variáveis 'A_Loss' e 'A_Wins'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "cR5-uvAKHHsj"
   },
   "outputs": [],
   "source": [
    "col = ['Dia', 'weekday', 'weekofyear', 'H_eFG%','A_W/D %', 'A_SRS']\n",
    "treino = X_completo[col]\n",
    "teste = teste_completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "mOLtoPlWHd85"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler_train = StandardScaler()\n",
    "#scaler_train = MinMaxScaler()\n",
    "X = scaler_train.fit_transform(treino)\n",
    "\n",
    "#Vamos padronizar o teste tbm\n",
    "scaler_train = StandardScaler()\n",
    "#scaler_train = MinMaxScaler()\n",
    "teste = scaler_train.fit_transform(teste[col])\n",
    "\n",
    "#treino e validação\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y, test_size=0.25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2LTepUwVtLa_"
   },
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X6sE-PUYr9ll",
    "outputId": "0374b6ad-cdc8-4787-9683-a2b27ed6b4e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracia Naivy bayes: 0.632\n",
      "F1 score Naivy bayes: 0.655\n",
      "Precision Naivy bayes: 0.656\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
    "model_NB = GaussianNB()                       # 2. instantiate model\n",
    "model_NB.fit(X_train, y_train)                # 3. fit model to data\n",
    "y_predNB = model_NB.predict(X_test)            # 4. predict on new data\n",
    "\n",
    "# calcula a acuracia\n",
    "\n",
    "print('Acuracia Naivy bayes: {:.3f}'.format(balanced_accuracy_score(y_predNB, y_test)))\n",
    "print(\"F1 score Naivy bayes: {:.3f}\".format(f1_score(y_test, y_predNB, average = \"weighted\")))\n",
    "print(\"Precision Naivy bayes: {:.3f}\".format(precision_score(y_test, y_predNB, average = \"weighted\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "quJPe1bPtPVV",
    "outputId": "b8982f9d-a0af-4a3c-c1dd-53d88b3b1025"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.68421053 0.68421053 0.69736842 0.67105263 0.73333333 0.65333333\n",
      " 0.72       0.66666667 0.62666667 0.65333333]\n",
      "Media Cross-val accuracy: 0.679018\n",
      "Variância: 0.000930\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv_scores = cross_val_score(model_NB, X_train, y_train, cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(\"Media Cross-val accuracy: %f\" % cv_scores.mean())\n",
    "print(\"Variância: %f\" % cv_scores.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GnD3sRpN3bH1",
    "outputId": "9c783c60-172d-4fe3-a622-cbc0f6cacd32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.667950495049505\n",
      "0.6770484065712926\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "#cv = cross_validate(model_NB, X_train, y_train, return_train_score=True)\n",
    "cv = cross_validate(model_NB, X, y, return_train_score=True, cv=10)\n",
    "\n",
    "print(cv['test_score'].mean())\n",
    "print(cv['train_score'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xHT7hWo0tZ8Z"
   },
   "source": [
    "## SVM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rJHX0BEatUSJ",
    "outputId": "b5882fca-dcc4-45b1-f229-f75a170f3715"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia SVM: 0.574\n",
      "F1 score SVM: 0.629\n",
      "Precision SVM: 0.629\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "#Hiper parâmetros para otimizacao\n",
    "C = np.arange(1,30)\n",
    "gamma = [\"scale\", \"auto\"]\n",
    "decision_function_shape = [\"ovo\", \"ovr\"]\n",
    "k_fold = 10\n",
    "#GridSearch para achar a melhor combinação de valores dos hiper parâmetros.\n",
    "#   aplicando ainda uma validação cruzada com 10 folds.\n",
    "model_svm = GridSearchCV(SVC(), cv = k_fold,\n",
    "                     param_grid={\"C\": C, \"gamma\": gamma, \"decision_function_shape\": decision_function_shape})\n",
    "model_svm.fit(X_train, y_train)\n",
    "y_pred = model_svm.predict(X_test)\n",
    "\n",
    "\n",
    "\n",
    "#Mensurar a qualidade do modelo ajustado\n",
    "print(\"Acurácia SVM: {:.3f}\".format(balanced_accuracy_score(y_test, y_pred)))\n",
    "print(\"F1 score SVM: {:.3f}\".format(f1_score(y_test, y_pred, average = \"weighted\")))\n",
    "print(\"Precision SVM: {:.3f}\".format(precision_score(y_test, y_pred, average = \"weighted\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CBAXpLiZtauJ",
    "outputId": "44a3e805-18d7-40af-bee1-1db59f0538ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6252871287128713\n",
      "0.7168105813910943\n"
     ]
    }
   ],
   "source": [
    "#SVM \n",
    "from sklearn.model_selection import cross_validate\n",
    "cv = cross_validate(model_svm.best_estimator_, X, y, return_train_score=True, cv=10)\n",
    "print(cv['test_score'].mean())\n",
    "print(cv['train_score'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c6B0NBn35ONq"
   },
   "source": [
    "## Submetendo NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "WhWrgwDc3FzB"
   },
   "outputs": [],
   "source": [
    "y_pred = model_NB.predict(teste)\n",
    "y_pred = np.array(y_pred, dtype = int)\n",
    "\n",
    "prediction = pd.DataFrame()\n",
    "prediction['Game'] = Id\n",
    "prediction['WinOrLose'] = y_pred\n",
    "prediction['WinOrLose']\n",
    "\n",
    "d = {1: 'W', 0: 'L'}\n",
    "prediction['WinOrLose'].replace(d,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "BnPzCd9Z9-wF",
    "outputId": "345a37e1-162d-4e8d-87e9-4f772c72e536"
   },
   "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>Game</th>\n",
       "      <th>WinOrLose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Game WinOrLose\n",
       "0     0         W\n",
       "1     1         W\n",
       "2     2         L\n",
       "3     3         L\n",
       "4     4         W"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zaKLqjNG5lrh",
    "outputId": "b5a8a179-7b89-4fc2-ac9d-8b4bd3f05d59"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "L    130\n",
       "W     35\n",
       "Name: WinOrLose, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction['WinOrLose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "7IjZVLEy6j24",
    "outputId": "285d0daa-54e6-4b08-aff8-bc65ff419f78"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = prediction['WinOrLose'].value_counts()/prediction.WinOrLose.value_counts().sum()\n",
    "plt.bar(['L','W'],y)\n",
    "plt.title('Frequencia Absoluta Vitorias e derrotas')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "1tYP8s9F9m4h"
   },
   "outputs": [],
   "source": [
    "prediction.to_csv('NB.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Garx_iscABWj"
   },
   "source": [
    "**Score no Kaggle: 0.729**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JlyxWYQ6_B70"
   },
   "source": [
    "## Submetendo SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "PCPJqUsR_UwW"
   },
   "outputs": [],
   "source": [
    "y_pred = model_svm.predict(teste)\n",
    "y_pred = np.array(y_pred, dtype = int)\n",
    "\n",
    "prediction = pd.DataFrame()\n",
    "prediction['Game'] = Id\n",
    "prediction['WinOrLose'] = y_pred\n",
    "prediction['WinOrLose']\n",
    "\n",
    "d = {1: 'W', 0: 'L'}\n",
    "prediction['WinOrLose'].replace(d,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "3nCt_Hd4_UwX",
    "outputId": "9bdfc569-badb-41db-a8d2-fe3a70fb0f6f"
   },
   "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>Game</th>\n",
       "      <th>WinOrLose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Game WinOrLose\n",
       "0     0         L\n",
       "1     1         L\n",
       "2     2         L\n",
       "3     3         L\n",
       "4     4         L"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q5pu5SEB_Uwc",
    "outputId": "be851298-04bd-4c6d-906b-6f562628df17"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "L    136\n",
       "W     29\n",
       "Name: WinOrLose, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction['WinOrLose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "bo8iywf9_Uwd",
    "outputId": "4e1e34d2-880b-407a-9d77-6f77b0dbacea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = prediction['WinOrLose'].value_counts()/prediction.WinOrLose.value_counts().sum()\n",
    "plt.bar(['L','W'],y)\n",
    "plt.title('Frequencia Absoluta Vitorias e derrotas')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "S3vaqOXD_Uwf"
   },
   "outputs": [],
   "source": [
    "prediction.to_csv('SVM.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Final - Games.ipynb",
   "provenance": []
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}