copa_america.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto de Deep Learning\n",
    "### 2021/2\n",
    "\n",
    "Predição de partidas de futebol da copa américa com base em dados históricos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sklearn tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pegando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Casa</th>\n",
       "      <th>Fora</th>\n",
       "      <th>Gols Casa</th>\n",
       "      <th>Gols Fora</th>\n",
       "      <th>Edição</th>\n",
       "      <th>Fase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-07-11</td>\n",
       "      <td>Equador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-07-11</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-07-14</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-07-14</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Equador</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-07-17</td>\n",
       "      <td>Equador</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Data      Casa       Fora  Gols Casa  Gols Fora  Edição     Fase\n",
       "0  2001-07-11   Equador      Chile        1.0        4.0    2001  Grupo A\n",
       "1  2001-07-11  Colombia  Venezuela        2.0        0.0    2001  Grupo A\n",
       "2  2001-07-14     Chile  Venezuela        1.0        0.0    2001  Grupo A\n",
       "3  2001-07-14  Colombia    Equador        1.0        0.0    2001  Grupo A\n",
       "4  2001-07-17   Equador  Venezuela        4.0        0.0    2001  Grupo A"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando Coluna objetivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212, 8)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\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>Data</th>\n",
       "      <th>Casa</th>\n",
       "      <th>Fora</th>\n",
       "      <th>Gols Casa</th>\n",
       "      <th>Gols Fora</th>\n",
       "      <th>Edição</th>\n",
       "      <th>Fase</th>\n",
       "      <th>Vencedor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-07-11</td>\n",
       "      <td>Equador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-07-11</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-07-14</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-07-14</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Equador</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-07-17</td>\n",
       "      <td>Equador</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Grupo A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Data      Casa       Fora  Gols Casa  Gols Fora  Edição     Fase  \\\n",
       "0  2001-07-11   Equador      Chile        1.0        4.0    2001  Grupo A   \n",
       "1  2001-07-11  Colombia  Venezuela        2.0        0.0    2001  Grupo A   \n",
       "2  2001-07-14     Chile  Venezuela        1.0        0.0    2001  Grupo A   \n",
       "3  2001-07-14  Colombia    Equador        1.0        0.0    2001  Grupo A   \n",
       "4  2001-07-17   Equador  Venezuela        4.0        0.0    2001  Grupo A   \n",
       "\n",
       "   Vencedor  \n",
       "0         0  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Vencedor\"] = df.apply(lambda row: 1 if row[\"Gols Casa\"] > row[\"Gols Fora\"] else 0, axis=1)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropando valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 8)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtrando features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: <class 'numpy.ndarray'>\n",
      "y: [[0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "X = df.iloc[:, 1:5].values\n",
    "y = df.iloc[:, -1:].values\n",
    "print(f\"X: {X[0])}\")\n",
    "print(f\"y: {y[:5]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformando features categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [5, 6, 1.0, 0.0],\n",
       "       [0, 5, 0.0, 0.0],\n",
       "       [1, 6, 0.0, 2.0],\n",
       "       [5, 1, 2.0, 0.0],\n",
       "       [0, 6, 3.0, 0.0],\n",
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       "       [7, 5, 0.0, 1.0],\n",
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       "       [4, 15, 0.0, 2.0],\n",
       "       [1, 0, 1.0, 4.0],\n",
       "       [17, 15, 1.0, 0.0],\n",
       "       [2, 18, 3.0, 0.0],\n",
       "       [5, 8, 1.0, 0.0],\n",
       "       [5, 18, 0.0, 0.0],\n",
       "       [2, 16, 4.0, 0.0],\n",
       "       [18, 8, 2.0, 2.0],\n",
       "       [5, 16, 1.0, 2.0],\n",
       "       [8, 16, 2.0, 2.0],\n",
       "       [2, 5, 2.0, 1.0],\n",
       "       [2, 8, 1.0, 1.0],\n",
       "       [18, 16, 0.0, 1.0]], dtype=object)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "X[:, 0] = le.fit_transform(X[:, 0])\n",
    "X[:, 1] = le.fit_transform(X[:, 1])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 4.0, 1.0, 4.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [4.0, 0.0, 4.0, 0.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [3.0, 3.0, 3.0, 3.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [3.0, 1.0, 3.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [0.0, 4.0, 0.0, 4.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [3.0, 1.0, 3.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [6.0, 1.0, 6.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [4.0, 2.0, 4.0, 2.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [4.0, 1.0, 4.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [1.0, 3.0, 1.0, 3.0],\n",
       "       [0.0, 4.0, 0.0, 4.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [0.0, 3.0, 0.0, 3.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [2.0, 3.0, 2.0, 3.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [5.0, 0.0, 5.0, 0.0],\n",
       "       [4.0, 1.0, 4.0, 1.0],\n",
       "       [1.0, 3.0, 1.0, 3.0],\n",
       "       [4.0, 2.0, 4.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 4.0, 1.0, 4.0],\n",
       "       [1.0, 6.0, 1.0, 6.0],\n",
       "       [6.0, 0.0, 6.0, 0.0],\n",
       "       [4.0, 0.0, 4.0, 0.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [0.0, 3.0, 0.0, 3.0],\n",
       "       [1.0, 3.0, 1.0, 3.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [3.0, 3.0, 3.0, 3.0],\n",
       "       [4.0, 2.0, 4.0, 2.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [4.0, 1.0, 4.0, 1.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [2.0, 3.0, 2.0, 3.0],\n",
       "       [3.0, 3.0, 3.0, 3.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [5.0, 0.0, 5.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 3.0, 1.0, 3.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [6.0, 1.0, 6.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [4.0, 0.0, 4.0, 0.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [2.0, 3.0, 2.0, 3.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [7.0, 1.0, 7.0, 1.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [4.0, 0.0, 4.0, 0.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [3.0, 1.0, 3.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [5.0, 0.0, 5.0, 0.0],\n",
       "       [4.0, 2.0, 4.0, 2.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [4.0, 1.0, 4.0, 1.0],\n",
       "       [0.0, 7.0, 0.0, 7.0],\n",
       "       [0.0, 4.0, 0.0, 4.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [1.0, 3.0, 1.0, 3.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [0.0, 5.0, 0.0, 5.0],\n",
       "       [1.0, 3.0, 1.0, 3.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [4.0, 0.0, 4.0, 0.0],\n",
       "       [0.0, 4.0, 0.0, 4.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [0.0, 1.0, 0.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [2.0, 0.0, 2.0, 0.0],\n",
       "       [0.0, 3.0, 0.0, 3.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [3.0, 1.0, 3.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [3.0, 1.0, 3.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [0.0, 2.0, 0.0, 2.0],\n",
       "       [1.0, 4.0, 1.0, 4.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [3.0, 0.0, 3.0, 0.0],\n",
       "       [1.0, 0.0, 1.0, 0.0],\n",
       "       [0.0, 0.0, 0.0, 0.0],\n",
       "       [4.0, 0.0, 4.0, 0.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [1.0, 2.0, 1.0, 2.0],\n",
       "       [2.0, 2.0, 2.0, 2.0],\n",
       "       [2.0, 1.0, 2.0, 1.0],\n",
       "       [1.0, 1.0, 1.0, 1.0],\n",
       "       [0.0, 1.0, 0.0, 1.0]], dtype=object)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# convertendo dados para ser usado pelo tensorflow\n",
    "\n",
    "X[:,0] = X[:,2].astype(np.float64)\n",
    "X[:,1] = X[:,3].astype(np.float64)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide o dado em Treino e Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação do modelo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "ann = tf.keras.models.Sequential()\n",
    "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))\n",
    "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))\n",
    "ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
    "ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treinando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "6/6 [==============================] - 1s 3ms/step - loss: 0.6744 - accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6605 - accuracy: 0.6627\n",
      "Epoch 3/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6472 - accuracy: 0.7771\n",
      "Epoch 4/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6348 - accuracy: 0.8313\n",
      "Epoch 5/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6229 - accuracy: 0.8313\n",
      "Epoch 6/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6127 - accuracy: 0.8313\n",
      "Epoch 7/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6039 - accuracy: 0.8313\n",
      "Epoch 8/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5953 - accuracy: 0.8313\n",
      "Epoch 9/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5863 - accuracy: 0.8313\n",
      "Epoch 10/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5770 - accuracy: 0.8313\n",
      "Epoch 11/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5682 - accuracy: 0.8313\n",
      "Epoch 12/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5591 - accuracy: 0.8313\n",
      "Epoch 13/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5497 - accuracy: 0.9036\n",
      "Epoch 14/15\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5401 - accuracy: 0.9096\n",
      "Epoch 15/15\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.5301 - accuracy: 0.9096\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0e3e74e450>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convertendo dados para o tensorflow\n",
    "X_train = np.asarray(X_train).astype(np.float32)\n",
    "y_train = np.asarray(y_train).astype(np.float32)\n",
    "\n",
    "ann.fit(X_train, y_train, batch_size = 32, epochs = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4136217 ],\n",
       "       [0.47106886],\n",
       "       [0.7985399 ],\n",
       "       [0.60073066],\n",
       "       [0.6600599 ],\n",
       "       [0.656196  ],\n",
       "       [0.4136217 ],\n",
       "       [0.5425551 ],\n",
       "       [0.4866838 ],\n",
       "       [0.60484254],\n",
       "       [0.60073066],\n",
       "       [0.71124506],\n",
       "       [0.31880432],\n",
       "       [0.6600599 ],\n",
       "       [0.3649038 ],\n",
       "       [0.5023248 ],\n",
       "       [0.5023248 ],\n",
       "       [0.71475923],\n",
       "       [0.28322434],\n",
       "       [0.3649038 ],\n",
       "       [0.5425551 ],\n",
       "       [0.60073066],\n",
       "       [0.4136217 ],\n",
       "       [0.60073066],\n",
       "       [0.4866838 ],\n",
       "       [0.5425551 ],\n",
       "       [0.4866838 ],\n",
       "       [0.47106886],\n",
       "       [0.27599788],\n",
       "       [0.60073066],\n",
       "       [0.31880432],\n",
       "       [0.60484254],\n",
       "       [0.83411425],\n",
       "       [0.1710051 ],\n",
       "       [0.60484254],\n",
       "       [0.60073066],\n",
       "       [0.5023248 ],\n",
       "       [0.4136217 ],\n",
       "       [0.27599788],\n",
       "       [0.38175678],\n",
       "       [0.5425551 ],\n",
       "       [0.60484254]], dtype=float32)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = np.asarray(X_test).astype(np.float32)\n",
    "\n",
    "y_pred = ann.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predizendo os dados de Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = [ i[0] for i in y_pred.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = [ i[0] for i in y_test.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsões = [1 if i > 0.5 else 0 for i in y_pred]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando matrix de confusão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18  3]\n",
      " [ 0 21]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, previsões)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Salvando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model/assets\n"
     ]
    }
   ],
   "source": [
    "ann.save(\"model\")"
   ]
  }
 ],
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