prevendo_notas_enem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predizer a nota de matemática de um candidato do ENEM baseado nas outras notas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importação das bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leitura do arquivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13730, 167)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtragem das colunas importantes para um novo dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = pd.DataFrame()\n",
    "#dados['Q047'] = data['Q047'] #Tipo de escola que concluiu o EM, ALFANUMÉRICA, TRATAR\n",
    "#dados['Q046'] = data['Q046'] #Já concluiu o EM?, ALFANUMÉRICA, TRATAR\n",
    "#dados['Q006'] = data['Q006'] #RENDA MENSAL DA FAMILIA, ALFANUMÉRICA, TRATAR\n",
    "#dados['Q005'] = data['Q005'] #quantidade de pessoas na residencia\n",
    "#dados['IN_TREINEIRO'] = data['IN_TREINEIRO']\n",
    "##dados['TP_ENSINO'] = data['TP_ENSINO']\n",
    "#dados['TP_ESCOLA'] = data['TP_ESCOLA']\n",
    "#dados['TP_ST_CONCLUSAO'] = data['TP_ST_CONCLUSAO']\n",
    "#dados['TP_COR_RACA'] = data['TP_COR_RACA']\n",
    "#dados['NU_IDADE'] = data['NU_IDADE']\n",
    "#dados['CO_UF_RESIDENCIA'] = data['CO_UF_RESIDENCIA']\n",
    "dados['NU_NOTA_CN'] = data['NU_NOTA_CN']\n",
    "dados['NU_NOTA_CH'] = data['NU_NOTA_CH']\n",
    "dados['NU_NOTA_LC'] = data['NU_NOTA_LC']\n",
    "dados['NU_NOTA_REDACAO'] = data['NU_NOTA_REDACAO']\n",
    "dados['NU_NOTA_MT'] = data['NU_NOTA_MT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remoção das linhas com elementos faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_NOTA_CN         3389\n",
       "NU_NOTA_CH         3389\n",
       "NU_NOTA_LC         3597\n",
       "NU_NOTA_REDACAO    3597\n",
       "NU_NOTA_MT         3597\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_NOTA_CN         0\n",
       "NU_NOTA_CH         0\n",
       "NU_NOTA_LC         0\n",
       "NU_NOTA_REDACAO    0\n",
       "NU_NOTA_MT         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = dados.dropna()\n",
    "dados.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movendo a coluna de \"notas de matemática\" para outro dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "matematica = pd.DataFrame()\n",
    "matematica['NU_NOTA_MT'] = dados['NU_NOTA_MT']\n",
    "dados = dados.drop('NU_NOTA_MT', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dados = pd.get_dummies(dados, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10097, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_NOTA_MT    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matematica.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "np.random.shuffle(matematica.values)\n",
    "np.random.shuffle(dados.values)\n",
    "dadosValues = dados.values\n",
    "matematicaValues = matematica.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação dos datasets de treino e testes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_treino = dadosValues[:897, :]\n",
    "dados_teste = dadosValues[897: , :]\n",
    "matematica_treino = matematicaValues[ :897]\n",
    "matematica_teste = matematicaValues[897: ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação do modelo de regressão linear, seguido do treinamento e da predição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = linear_model.LinearRegression()\n",
    "modelo.fit(dados_treino, matematica_treino)\n",
    "predicao = modelo.predict(dados_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10012.298951916775"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.score(dados_teste, predicao)\n",
    "metrics.mean_squared_error(matematica_teste, predicao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.4194832826788"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(matematica_teste, predicao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(matematica_teste, predicao);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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43A/0hxzjwKdqkLeytABVOvT15BQMZE4VF/T9a5aFrpZafIcfVpZs2bl/xmY+45OeqAM5bVI5A7na/VFlbuU0wkjmSGlBXzqyKGyPhbCyJGoiXCt3IKcyGNRi2JfMHTUVyVwoXa+ooFyTclRZErWMSlt3IDejdhz2lXYaWSSlIlasnrXCekVJZgpHlRkT7m03YjGVwaAdh32l2W9vf7HV+95kFuqxfmHxsNE4y0lElRnFM6TbZcRiKpuJ4nQKSXMYGBqesRKlSD0dDvZiDusLGPzhUZ5+ZWSq/+Cy5d08tns4tCxptxGLqd0DWaOJ0mHV5l3qPJY5VZjbEvZ3Z8wcIbog28H8bIbR4+OpK0u0BzKaZ5AWCgRSrQ6L3hwnTLm1jsLSj49P4hj3rlvZ1mVKaoOBagbNb2BoOPROTCSJL31iZaLRaLP5eyvuayguVy5b3j2tWamVy5lUBoOBoWH6H32B8Yn81z48Okb/oy8AmmfQTLbs3K9AIFUpNPnMxY55pXMMCovrlb4OrVnOpHI00d3f3DcVCArGJ5y7v7mvQTmSMBrqK9UwYOmZndy2bc+cbJ0aZ9e1Vt43JZXBoHgZ3Djp0hga6ivVcOBvSpa4rpcku6616k1OKoOBpMNly7s12UyqUm0g6MxmWFBhy9VFC7KJdl1r1ZucVPYZSPMbGBrmsd3D6jOQhrrwnNP57qtvhr62aEGWOz++Ylr7f7l1iQCyHa27jHUqg8GiBdnQJqFFC7INyI2ECVvzRWSu/c0/Hg2d6dzVmWXoc5dPSyte1C5qSPR7Tp3Xkp3HkNJmojs/viJ044k7P76iQTmSUq3arirpEtUNcGwsvH+xrydX9s5/dBb9knGWxWgGqawZVFqWVhovalMRkWZQrt2/3GihsPeVm/OUpuX2YwcDM8sAg8Cwu19lZh8FtpCvXfwY+FV3PxBsg/k14CLgX4B17v5arTOuGcjN7bLl3dPGaIs0i0rrmJWr1favWTat8D+9M8tb756YNuepuLBP03L7SZqJbgVeLnp+P/DL7r4S+DPgjiD9FuBNd/8QcC/whVpkVNLl6VdGGp0FkRmiVh8tbsrpiFhbu6sz3ye5aftehoMlL0bHxmfMeSqei5Cm5fZjBQMzWwJcCTxQlOzAe4PHpwOHg8dXAw8Gjx8FPmpW65XLpdk14x+7tLdcVyfPblwdGgiKC/iw+Qad2Qx3rV0Re2BE4e8/Tcvtx60Z3AfcDhSvRfxrwJNmdgj4FWBzkJ4DDgK4+wngGHBm6QnNbIOZDZrZ4MiI7iJbTTP+sUt7e+udE6Gdt5UK+OLaRNybnMLff/+aZZGb5DRbx3LFPgMzuwo44u67zewjRS/dBlzh7s+bWT/wJfIBIqwWMCPUuvtWYCvkl7BOmvE7BvbyjecPMuFOxowbLjmb3+s7P+lppE7C9pwQaaTRsfGp9nw4OQClXOFTmFdQqE3EGRhR3CcRNdgFaLqO5Yr7GZjZPeTv/E8Ap5JvGnoaWO7u5wbHnAN8290/bGY7gbvc/W/NbB7wT0C3l/lFSfczuGNgb2jn5I2XnqOA0EQGhob57e0vanMbaSpdJZ2+lRSalyBf9nz9udenBZBsh/GeU+cl2g8hap+P4t8VRy33M6jYTOTum9x9ibsvBdYDu8j3C5xuZj8ZHPYLnOxc3gHcFDy+DthVLhDMxjeeP5goXRqjryenGcjSdMI6fcspNA1Fzaq/+AOLGPrc5by6+crQPoly54ybPhdmNeks6Av4b8BjZvYC+ZpDf/DyV4EzzewA8BlgYy0yWixqQam5WNlQ4hsYGmZMtQJJuUL7f1TfwrM/OJq4vb8ZO5YTBQN3f8bdrwoeP+7u57v7Be7+EXf/xyD9bXe/3t0/5O4XF9JrKRMxOCkqXRqjVZf6lfZRvBZRubv2u3YkWz6/XMdyo6RyOYobLjk7Ubo0hoaXStptuf6CaZ3HUUYjlreI0teTm1op1Yie/zCXUrkcRaGTWKOJmpuWpJA0y3V1Tiuc+9csS7T9ZiXNtopCKoMB5AOCCv/mdtny7hkjL0TSIKzJpq8nx+88vpe33p3Zb9AKKyansplImp/2M5C0Kmx2E3bX/vlfOn/GismZDsOdppk8NluprRlIc9N+BjLXMh3GxGTy24+MGZPuseYIlE4i61qQ5cdvn5jqM2iGyWOzldpgUG7ZWGk8dR5Lvcyf18E7J2YOWT5t/jzMovdCz3YYGNPmGHRmM4k7bovb+ldt3jXj9zXrqqSVpDIYpGmN8HalzmOpl7BAAPkRPeWGnW+5/gKg+n1Qim9Eo+ohabwZSmUwSNMa4e1KaxNJI0RNPJ10nyobqikjSm9Eo6RxocZUBoNmnMot05W2raojWaJ0GCRp6u/qzPLOiclENxq1Kpzj9IU1evLYbKVyNFEzTuWWmfp6cjy7cTX3rlvZ6KxIg5VbGyAqEGQ6LN/OX6Swr0BhwlYctSycy91wNsvksdlKZc0grAkirdG4HSSdqi+tZzY1wy9WaOPv68mx8u6nQmf/JhkhlERUX1jS1UabUSqDQdQa4WmMxu0g6VR9keLZv1H/rweGhnnr3RMz0rMdNm0ZiVpq5RvRVAYDaL6p3CLtKpuxREtCVxK3cN2yc3/o733PqfPqVja08o1oaoOBpMeiBdnIsd/SPDqAL61bmWj9nYwZ637mbJ5+ZYTDo2N0mEWO6OnqzJadB1A43z3X5JeZWbV5V9kCN6r9frTOf2uteiOayg5kSZc7P76i0VmQGE5fkKWvJ0eSleAn3Hls9zD9a5bx6uYr+eInLiDTMfME2Q7jrrUrGPrc5dxXZkDBZBBIijeoL8wjKl3mQQNJait2MDCzjJkNmdkTwfP/Z2Z7gp/DZjYQpJuZfdnMDpjZi2Z2Yb0yL+nQ15PjxkvPaXQ2WkpXZ35htFru4FG4o066R1Rhjk9BWKGy7uKzp/UBRI0EWtzVWXYeUbFm3BMgzZLUDG7l5NaWuPvPu/tKd18J/C2wPXjpY8B5wc8G4P4a5VVS7Pf6zldAqBEz2HPn5eS6OhOP0unMZqYCSanCHXXcIZvFCk02W3buZzxkrOg3nj847c6+XEEe1fxTOoqnGfcESLNYwcDMlgBXAg+EvHYasBoYCJKuBr7mec8BXWZ2Vo3yKylVWMVUqle4c086yTJjxrUX5bhr7Yqyd9RhBXUlhUASlacJ92lNPeUK8qhmHoMZTUWFuSxJ9h+WcHFrBvcBtwNhi4L8EvAdd//X4HkOKN6Z/lCQJm0sauZmSPOyVFBYfydp23ihfR8oe0ddWlAvWpCdMfmrWHEgKZen0qaeqIK8f82y0OYvp7m3Uh0YGmbV5l2pXcq64mgiM7sKOOLuu83sIyGH3MD0GkPU91h63g3km5E45xw1H7S6qDtGd1h17hk8+4Ojc5yjyhZkOzg+Hr4oWiMVRuvMZv2nQoFc6S66dMRM8eJspwejgkaPj88Y6VMpT3FqM309ucgRTc265EwrLJ4ZZ2jpKmCtmV0BnAq818wecvcbzexM4GLytYOCQ0DxZsRLgMOlJ3X3rcBWgN7eXi1d0+KiZm4u7urktX9pvv/gndkMv3/N+WzZuX9OV181Ks/WLbTpl455Lzess1jcAnU2y8QXXv/sIy+E5iVubSZX5u+lGbXC4pkVm4ncfZO7L3H3pcB6YJe73xi8fD3whLu/XfSWHcAng1FFlwLH3P2NWmdc0mU2HYaNVGg2qefIlK7OLDdees605pp7160s24Gb7TCOv3tiqikCmGpq+eInLojV1h+nQC3c6VYa3hmmrycXmpckI33SNlKoFRbPrHbS2Xpgc0nak8AVwAHgOHBzlb9DWkC5mZtJ774XLcjiXr9lLkqXQrj7m/tqOmlu0YIsd358Rdk7xrCmls5sBycmfSovpU0RhfOVy2+S2b3V3OlWO1M3bTN9y9V808I86aDiOujt7fXBwcFGZ0Ma5I6BvTz03Ouxji1eEKznd5+q+czmsJ2vBoaGE83KjZJkMbOwJpqooBl23sL7h0fHyATNR7kEBeoHNn4rtLnKgFc3X1k2n81aYNdT2D4Hs9lFLSkz2+3uvbU4l5ajkIZ7+pWRWMeV3tVe+dNnlQ0icdrfIT+iyZ3IwqyvJ8ddO/bFrolEba+YpIkjbMmD2xJ0qla7ZEKcO91W6DStlbTVZMIoGEjDxWlXLaxZU/yfq1wQyXV1ctnybrb93cGyi6hlM8aW6yqvcHnX2hWRo2SyGWPhKfM4NnZydA3UvmCYy6aIOKtztkKnaS2lfc0iBQNpuEr7JUdVt6OCiMFUs0nvT5wxrQ19QbaDU+ZlphXcSdvA4za91LpgmMvlk+Pc6bZCp6mcpGAgDRdWyBWaeMoVtnHulGt5t9boO7+5boqodL2t0GkqJykYSMPNtpBr5Y1GojQ6IBVrx8+/lSkYSFOYTSHXCp12aabPv7VoaKmISErVcmipNrcREREFAxERUTAQEREUDEREBAUDERFBwUBERFAwEBERFAxERIQEwcDMMmY2ZGZPBM/NzD5vZt8zs5fN7DeL0r9sZgfM7EUzu7BemRcRkdpIshzFrcDLwHuD579Kfq/j5e4+aWbvC9I/BpwX/FwC3B/8KyIiTSpWzcDMlgBXAg8UJf868LvuPgng7keC9KuBr3nec0CXmZ1VwzyLiEiNxW0mug+4HZgsSjsXWGdmg2b2l2Z2XpCeAw4WHXcoSBMRkSZVMRiY2VXAEXffXfLSfODtYJGk/wP8UeEtIaeZsRqemW0IAsngyEi8bQ9FRKQ+4tQMVgFrzew14GFgtZk9RP6O/7HgmMeBnw4eHyLfl1CwBDhcelJ33+ruve7e293dPcvsi4hILVQMBu6+yd2XuPtSYD2wy91vBAaA1cFh/xn4XvB4B/DJYFTRpcAxd3+j9lkXEZFaqWZzm83A183sNuDHwK8F6U8CVwAHgOPAzVXlUERE6i5RMHD3Z4Bngsej5EcYlR7jwKdqkDcREZkjmoEsIiIKBiIiomAgIiIoGIiICAoGIiJCdUNLRZrKwNAwW3bu5/DoGIu7Oulfs4y+Hq2EUiv6fFubgoG0hIGhYTZt38vY+AQAw6NjbNq+F0AFVg3o8219aiaSlrBl5/6pgqpgbHyCLTv3NyhHrUWfb+tTMJCWcHh0LFG6JKPPt/UpGEhLWNzVmShdktHn2/oUDKQl9K9ZRmc2My2tM5uhf82yBuWotejzbX3qQJaWUOjEbIbRLq046qaZPl+pD8uvK9dYvb29Pjg42OhsSJuoZ2FdOuoG8nfQ91xzvgpOqTkz2x1sMFY1NRNJWykU1sOjYzgnh0gODA3X5PwadSNppWAgbaXehbVG3UhaKRhIW6l3Ya1RN5JWsYOBmWXMbMjMngie/4mZvWpme4KflUG6mdmXzeyAmb1oZhfWK/MiSdW7sNaoG0mrJDWDW4GXS9L63X1l8LMnSPsYcF7wswG4v/psitRGvQvrvp4c91xzPrmuTgzIdXWq81hSIdbQUjNbQn6Ly88Dn6lw+NXA14LtL58zsy4zO8vd36guqyLVm4shkn09ORX+kjpx5xncB9wOnFaS/nkz+xzwHWCju78D5ICDRcccCtIUDKQpqLAWmaliM5GZXQUccffdJS9tApYDPwOcAfxW4S0hp5kxmcHMNpjZoJkNjoyMJMu1iIjUVJw+g1XAWjN7DXgYWG1mD7n7G573DvDHwMXB8YeAs4vevwQ4XHpSd9/q7r3u3tvd3V3VRYiISHUqBgN33+TuS9x9KbAe2OXuN5rZWZAfPQT0AS8Fb9kBfDIYVXQpcEz9BSIiza2atYm+bmbd5JuF9gD/PUh/ErgCOAAcB26uKociIlJ3iYKBuz8DPBM8Xh1xjAOfqjZjIiIydzQDWUREFAxERETBQEREUDAQERG005lI3bXizmfSehQMROqodOezwmY6gAKCNBU1E4nUkXY+k7RQMBCpI+18JmmhYCBSR9r5TNJCwUCkjrTzmaSFOpBF6mguNtMRqQUFA5E602Y6kgZqJhIREQUDERFRMBARERIEAzPLmNmQmT1Rkv4VM/tx0fP5ZrbNzA6Y2fNmtrR22RURkXpIUjO4FXi5OMHMeoGukuNuAd509w8B9wJfqCqHIiJSd7GCgZkrWWbwAAAH00lEQVQtAa4EHihKywBbgNtLDr8aeDB4/Cjw0WCfZBERaVJxawb3kS/0J4vSfgPYEbLZfQ44CODuJ4BjwJlV5lNEROqoYjAws6uAI+6+uyhtMXA98JWwt4Skech5N5jZoJkNjoyMJMiyiIjUWpyawSpgrZm9BjwMrAb2AR8CDgTpC8zsQHD8IeBsADObB5wOHC09qbtvdfded+/t7u6u9jpERKQKFYOBu29y9yXuvhRYD+xy90Xu/u/dfWmQfjzoMAbYAdwUPL4uOH5GzUBERJpHPZaj+Crwp0FN4Sj5ACIiIk0sUTBw92eAZ0LS31P0+G3y/QkiIpISmoEsIiIKBiIiomAgIiIoGIiICAoGIiKCgoGIiKBgICIiKBiIiAgKBiIigoKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiIoGIiICAmCgZllzGzIzJ4Inn/VzF4wsxfN7FEze0+QPt/MtpnZATN73syW1ifrIiJSK0lqBrcCLxc9v83dL3D3nwZeB34jSL8FeDPYE/le4As1yamIiNRNrGBgZkuAK4EHCmnu/q/BawZ0AoVN768GHgwePwp8NDhGRESaVNyawX3A7cBkcaKZ/THwT8By4CtBcg44CODuJ4BjwJmlJzSzDWY2aGaDIyMjs8u9iIjURMVgYGZXAUfcfXfpa+5+M7CYfPPRusJbQk7jMxLct7p7r7v3dnd3J8u1iIjUVJyawSpgrZm9BjwMrDazhwovuvsEsA24Nkg6BJwNYGbzgNOBozXMs4iI1FjFYODum9x9ibsvBdYDu4BfMbMPwVSfwceBV4K37ABuCh5fB+xy9xk1AxERaR7zZvk+Ax40s/cGj18Afj147avAn5rZAfI1gvVV51JEROoqUTBw92eAZ4KnqyKOeRu4vqpciYjInNIMZBERUTAQEREFAxERQcFARESY/WgikbY1MDTMlp37OTw6xuKuTvrXLKOvJ9fobIlURcFAJIGBoWE2bd/L2PgEAMOjY2zavhdAAUFSTc1EIgls2bl/KhAUjI1PsGXn/gblSKQ2FAxEEjg8OpYoXSQtFAxEEljc1ZkoXSQtFAxEEuhfs4zObGZaWmc2Q/+aZQ3KkUhtqANZJIFCJ7FGE0mrUTAQSaivJ6fCX1qOmolERETBQEREFAxERAQFAxERQcFAREQAa4btic1sBPjhLN/+74Af1TA7aaPr1/Xr+tvXMnc/rRYnaoqhpe7ePdv3mtmgu/fWMj9pouvX9ev62/v6a3UuNROJiIiCgYiItEYw2NroDDSYrr+96frbW82uvyk6kEVEpLFaoWYgIiJVSnUwMLNfNLP9ZnbAzDY2Oj/1YGZnm9nTZvayme0zs1uD9DPM7K/M7PvBv4uCdDOzLwefyYtmdmFjr6B6ZpYxsyEzeyJ4/gEzez649m1mdkqQPj94fiB4fWkj810rZtZlZo+a2SvB38HPtsv3b2a3BX/3L5nZN8zs1Fb//s3sj8zsiJm9VJSW+Ps2s5uC479vZjdV+r2pDQZmlgH+F/Ax4MPADWb24cbmqi5OAJ919/8AXAp8KrjOjcB33P084DvBc8h/HucFPxuA++c+yzV3K/By0fMvAPcG1/4mcEuQfgvwprt/CLg3OK4V/AHwbXdfDlxA/rNo+e/fzHLAbwK97v5TQAZYT+t//38C/GJJWqLv28zOAO4ELgEuBu4sBJBI7p7KH+BngZ1FzzcBmxqdrzm47r8AfgHYD5wVpJ0F7A8e/yFwQ9HxU8el8QdYEvzxrwaeAIz8JKN5pX8HwE7gZ4PH84LjrNHXUOX1vxd4tfQ62uH7B3LAQeCM4Pt8AljTDt8/sBR4abbfN3AD8IdF6dOOC/tJbc2Ak38oBYeCtJYVVHt7gOeB97v7GwDBv+8LDmu1z+U+4HZgMnh+JjDq7ieC58XXN3XtwevHguPT7IPACPDHQVPZA2a2kDb4/t19GPifwOvAG+S/z9201/dfkPT7Tvx3kOZgYCFpLTs0yszeAzwGfNrd/7XcoSFpqfxczOwq4Ii77y5ODjnUY7yWVvOAC4H73b0HeIuTTQRhWuYzCJo1rgY+ACwGFpJvFinVyt9/JVHXnPizSHMwOAScXfR8CXC4QXmpKzPLkg8EX3f37UHyP5vZWcHrZwFHgvRW+lxWAWvN7DXgYfJNRfcBXWZWWEql+Pqmrj14/XTg6FxmuA4OAYfc/fng+aPkg0M7fP//BXjV3UfcfRzYDvwc7fX9FyT9vhP/HaQ5GPwdcF4wsuAU8h1LOxqcp5ozMwO+Crzs7l8qemkHUBghcBP5voRC+ieDUQaXAscK1cu0cfdN7r7E3ZeS/353ufsvA08D1wWHlV574TO5Ljg+1XeG7v5PwEEzWxYkfRT4B9rg+yffPHSpmS0I/h8Urr1tvv8iSb/vncDlZrYoqGFdHqRFa3RHSZWdLFcA3wN+APxOo/NTp2v8j+Srdy8Ce4KfK8i3hX4H+H7w7xnB8UZ+lNUPgL3kR2I0/Dpq8Dl8BHgiePxB4LvAAeDPgflB+qnB8wPB6x9sdL5rdO0rgcHgb2AAWNQu3z9wN/AK8BLwp8D8Vv/+gW+Q7yMZJ3+Hf8tsvm/gvwafxQHg5kq/VzOQRUQk1c1EIiJSIwoGIiKiYCAiIgoGIiKCgoGIiKBgICIiKBiIiAgKBiIiAvx/z7CVuIkHT6oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(matematica_teste, predicao);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relação entre dados e a nota de matemática"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must be the same size",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-ccde59d8463e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdados_teste\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatematica_teste\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3468\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3469\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3470\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3471\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3472\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1853\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1855\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4241\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4242\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4243\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dados_teste, matematica_teste, c='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relação entre dados e a predição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(dados_teste, predicao, c='r')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}