enem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "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": "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13730, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q047                0\n",
       "Q046                0\n",
       "Q006                0\n",
       "Q005                0\n",
       "IN_TREINEIRO        0\n",
       "TP_ESCOLA           0\n",
       "TP_ST_CONCLUSAO     0\n",
       "TP_COR_RACA         0\n",
       "NU_IDADE            0\n",
       "CO_UF_RESIDENCIA    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.isnull().sum().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q047                0\n",
       "Q046                0\n",
       "Q006                0\n",
       "Q005                0\n",
       "IN_TREINEIRO        0\n",
       "TP_ESCOLA           0\n",
       "TP_ST_CONCLUSAO     0\n",
       "TP_COR_RACA         0\n",
       "NU_IDADE            0\n",
       "CO_UF_RESIDENCIA    0\n",
       "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = dados.dropna()\n",
    "dados.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10097, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = pd.get_dummies(dados, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10097, 34)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_NOTA_MT    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matematica.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dadosValues = dados.values\n",
    "matematicaValues = matematica.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_treino = dadosValues[:822, :]\n",
    "dados_teste = dadosValues[822: , :]\n",
    "matematica_treino = matematicaValues[ :822]\n",
    "matematica_teste = matematicaValues[ 822: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = linear_model.LinearRegression()\n",
    "modelo.fit(dados_treino, matematica_treino)\n",
    "predicao = modelo.predict(dados_teste)"
   ]
  }
 ],
 "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
}