hackaturing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Grupo Airmid</h1> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Airmid](airmid.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 align=\"center\">(Airmid é a deusa céltica da cura. Nada mais apropriado do que esse nome para um desafio de healthcare)</h4> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introdução"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hoje, há um problema de considerável tamanho no ambiente médico-hospitalar em que médicos pedem equipamentos ou medicamentos em demasia aos operadores de planos de saúde. Os equipamentos podem acabar não sendo utilizados, e os medicamentos podem acabar sendo repassados ao paciente sem a necessidade (o que é potencialmente mais arriscado, pois pode induzir o paciente a uma overdose de medicamentos). \n",
    "\n",
    "Com o objetivo de reduzir este desperdício, o grupo criou neste Hackaturing uma maneira de as operadoras de planos de saúde terem informações a respeito de, com base no código do item requisitado e no tipo de tratamento que está sendo feito, elas saberem se é mais provável que o pedido seja pago em sua totalidade ou não.\n",
    "\n",
    "Isso permite às empresas saber quais famílias de produtos possuem as maiores diferenças entre valor total requisitado e valor efetivamente pago. Um delta alto entre esses valores indica que (i) os médicos estão requisitando um determinado produto em demasia ou (ii) os planos de saúde possuem políticas inadequadas de liberação desses itens que podem ser bastante necessários para o médico e o paciente. \n",
    "\n",
    "De qualquer maneira, essa inteligência de dados ajudará as empresas de planos de saúde reduzirem ineficiências e tomarem melhores decisões."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Initial setup\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.read_table(\"hackaturing.dsv\", sep='|', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establishing backup to avoid long loading times in case of mistakes\n",
    "df_backup = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2928945fbe0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E//3f/+Hy5cvIzs7G888/j6+//trWtRERETVK0hHZJ598gs2bN8PT0xMAMHv2bDzxxBOYPHmyTYsjIiJqjKQjMkEQzCEGAF5eXlAoFDYrioiISCpJR2S33347Xn31VfMR2Ndff42+ffvatDAiIiIpJB2RJSQkwMnJCTExMVi8eDHUajXi4+NtXRsREVGjJB2RabVaLFy40Na1EBERNZmkILvnnnuQl5cHd3d3AEBJSQnc3d3RrVs3JCQksCs+ERHZjaQgGzJkCMaOHYv77rsPALB//3589913iIyMxCuvvIIvv/zSpkUSERE1RNI1svPnz5tDDLhxhHbu3Dn0798flZWVNiuOiIioMZKCzN3dHV9++SWuX7+OsrIyfPHFF+jYsSN+++03CIJg6xqJiIgaJCnI3njjDRw+fBghISEYNWoUUlNT8dprr+Hw4cN46aWXbF0jERFRgyRdI+vSpQvee++9WtsqKioQGRlpk6KIiIikkhRke/bswTvvvIPr169DFEUIggC9Xo+jR4/auj4iIiKrJAXZypUrsXz5cnzyySeYPXs2du/eDb1eb+vaHEKFwYiikkp4ujtDq5G82ACRw+JnglqbpL8iNzc3DBs2DD/99BNKS0uxYMECjBs3zta1yZrJJGDD9gwcTc9BfrEenTxcMCywK6aHBUCl4jJw1P7wM0G2IumvR6vV4uLFi+jduzeOHTsGg8GAqqoqW9cmaxu2ZyDp4AXkFekhikBekR5JBy9gw/YMe5dGZBf8TJCtSAqyefPm4Z133sG9996LI0eO4G9/+1utcWVUW4XBiKPpORbvO5qegwqD8SZXRGRf/EyQLUk6tRgcHIzg4GAUFxfjX//6FwRBQMeOHa0+pqqqCjExMbhy5QoMBgOee+453HrrrYiOjoZCoUCfPn0QHx8PpVKJtWvXYt++fVCr1YiJicHAgQNx6dIlyW3bmqKSSuQXW76GWFCsR1FJJbp689oAtR/8TJAtSfrLOXv2LBYuXIjc3FyIogh/f3+8/vrr6NGjR4OPSUpKgoeHB1avXo2ioiJMmjQJffv2xbx58zB06FDExcUhOTkZvr6+OHbsGDZv3oycnBxERUVhy5YtWLlypeS2bY2nuzM6ebggr6j+B9fbwwWe7s52qIrIfviZIFuSdGoxJiYGL7zwAlJTU3Hs2DHMmDED0dHRVh8zduxY/P3vfzffVqlUyMjIQHBwMABgxIgROHz4ME6cOIHhw4dDoVDA19cXJpMJhYWFTWrb1mg1agwL7GrxvmGBXdlTi9odfibIliQFmSiKuPfee823R48ejevXr1t9jKurK3Q6HcrKyjB37lzMmzcPoiiaV5Z2dXVFaWkpysrKoNPpaj2utLS0SW3boulhAZgQ4o/Oni5QKoDOni6YEOKP6WEB9i6NyC74mSBbkfQ16K9//SvWrVuHRx99FCqVCt9++y169+6N7OxsAICvr6/Fx+Xk5OD555/H1KlTERYWhtWrV5vvKy8vh7u7O3Q6HcrLy2ttd3Nzg1KplNy2LVKplJgVPgCR4/pxzAwR+Jkg25F0RLZz5058/fXXePTRR/Hwww/j448/xsmTJzFt2rQGp6kqKCjA9OnTsWDBAkyePBkA0L9/f6SmpgIADhw4gKCgIAwePBiHDh2CIAjIzs6GIAjw8vJqUtu2TKtRo6u3Kz+wRH/gZ4Jam+QpqhrS0FpkH3zwAUpKSrBu3TqsW7cOAPDyyy8jISEBb731Fvz9/TFmzBioVCoEBQUhIiICgiAgLi4OALBo0SLExsZKaktERO2XQhRFsSVPMGnSJGzbtq216mlVWVlZCA0NRXJyMrp162bvcoiIbK497vdaPC9MC3OQiIioRVocZNU9C4mIiOyBM3USEZGsMciIiEjWJAXZ2bNnG7yvrY7jIiKi9kFSkL3wwgsN3vfpp5+2WjFERERNJWkc2a233oq1a9fijjvugFarNW8fMmSIzQojIiKSQlKQFRcXIzU11TzTBnCjtyKPxoiIyN4kBdnGjRsBAGVlZRAEAe7u7jYtioiISCpJQXb58mW88MILuHz5MkRRhK+vL9555x306tXLxuURERFZJ6mzR1xcHGbOnGlej+yZZ55BbGysrWsjIiJqlKQgKyoqwtixY823x40bh+LiYpsVRUREJJWkINNoNMjIyDDfTk9Ph4uLi82KIqK2pcJgRE5BOSoMRnuXQlSPpGtkMTExiIqKgoeHB0RRxLVr1/DWW2/ZujYisjOTScCG7Rk4mp6D/GI9Onm4YFhgV0wPC4BKxYmBqG2QFGSDBg3Crl27kJmZCUEQ4OfnB41GY+vaiMjONmzPQNLBC+bbeUV68+1Z4QPsVRZRLVaDbPHixVYfvHLlylYthojajgqDEUfTcyzedzQ9B5Hj+nGVZ2oTrP4VBgcH36w6iKiNKSqpRH6x3uJ9BcV6FJVUoqs3g4zsz+pf4aRJk8z/Li4uhl6vhyiKMJlMyMrKsnlxRGQ/nu7O6OThgryi+mHm7eECT3dnO1RFVJ+kr1Nr1qzBv/71LxiNRnh6eiI3NxeBgYHYvHmzresjIjvRatQYFti11jWyasMCu/K0IrUZkrodbdu2Dfv378e4cePw6aefYv369fD09LR1bURkZ9PDAjAhxB+dPV2gVACdPV0wIcQf08MC7F0akZmkr1SdO3eGTqdDnz59cPbsWdx///148803bV0bEdmZSqXErPABiBzXD0UllfB0d+aRGLU5kv4idTodEhMTERAQgM8++wydO3dGRUWFrWsjojZCq1GzYwe1WZJOLb766qsoLCzE0KFD8Ze//AVxcXGYN2+erWsjIiJqlKSvWN999x0mTpwIAIiOjrZpQURERE0h6Yjs6tWreOSRRzBz5kwkJSVBr7c8toSIiOhmkxRkixYtwp49ezB79mykpaUhPDwcCxcutHVtREREjZI866coiqiqqkJVVRUUCgWcnJxsWRcREZEkkq6RJSQk4IcffkC/fv0wYcIELFmyBM7OHNVPRET2JynIevbsiW3btsHLy8vW9RARETWJpFOLkZGR9UKs5kKbRERE9tLslfHefffd1qyDiIioWZodZB9++GFr1kFERNQskq6RJSYm1tum1Wrh7++P2267rdWLIiIikkpSkCUnJ+P06dO47777AAD79u1D586dcf36dYSFheGpp56yZY1EREQNkhRk+fn52LZtG9zd3QEAUVFRmD17Nr766is89NBDDDIiIrIbSdfIioqK4Orqar7t7OyMa9euQa1WQ6FQ2Kw4IiKixkg6Irv//vvx5JNP4oEHHoAgCPj+++8RGhqKxMREdOrUydY1EhERNUhSkL300kvYu3cvUlJSoFKpMHPmTNxzzz1IS0vjAptERGRXklfK69atG8aOHQtRFAEAx48fx5AhQ2xWGBERkRSSguyVV17B3r170b17d/M2hUKBTz/91GaFERERSSEpyFJSUvDdd99Bq9U26cl//vlnvPHGG9i4cSMyMjIwe/Zs9OrVCwDw2GOPYdy4cVi7di327dsHtVqNmJgYDBw4EJcuXUJ0dDQUCgX69OmD+Ph4KJVKi22JiKh9kxRk3bt3N59SlOqjjz5CUlISXFxcAACnT5/G008/jenTp5vbZGRk4NixY9i8eTNycnIQFRWFLVu2YOXKlZg3bx6GDh2KuLg4JCcnw9fX12JbIiJq3yQFWceOHfHggw/izjvvhEajMW9fuXJlg4/p0aMH1qxZY16AMz09HRcvXkRycjJ69uyJmJgYnDhxAsOHD4dCoYCvry9MJhMKCwuRkZGB4OBgAMCIESOQkpICPz8/i205Iz8RUfsmKchCQkIQEhLSpCceM2YMsrKyzLcHDhyIRx55BIGBgVi/fj3ef/99uLm5wcPDw9zG1dUVpaWlEEXRPD6teltZWZnFtgwyIqL2zWqQ5efno1OnThg6dGiLX2j06NHmmUFGjx6N5cuXIzQ0FOXl5eY25eXlcHNzg1KprLXN3d0dOp3OYlsiImrfrM7ssWTJEgDAtGnTEBkZWe//TTFjxgycOnUKAHDkyBEEBARg8ODBOHToEARBQHZ2NgRBgJeXF/r374/U1FQAwIEDBxAUFNRgWyIiat+sHpH94x//AADExsbi3nvvbdELLV26FMuXL4eTkxO8vb2xfPly6HQ6BAUFISIiAoIgIC4uDgCwaNEixMbG4q233oK/vz/GjBkDlUplsS2R3FQYjCgqqYSnuzO0GslDOYmoAQpRQnfEBx98EP/5z39uRj2tKisrC6GhoUhOTka3bt3sXQ61cyaTgA3bM3A0PQf5xXp08nDBsMCumB4WAJWq2UsDEtXSHvd7krvfL168GHfccUetsWTh4eE2K4zI0WzYnoGkgxfMt/OK9Obbs8IH2KssItmTFGSenp4AbgxwrolBRiRNhcGIo+k5Fu87mp6DyHH9eJqRqJkkfXKsjRcjosYVlVQiv1hv8b6CYj2KSirR1ZtBRtQckj45o0aNsrjuWHJycqsXROSIPN2d0cnDBXlF9cPM28MFnu7OdqiKyDFICrKNGzea/200GvHDDz/AYDDYrCgiR6PVqDEssGuta2TVhgV25WlFohaQ1FXqL3/5i/m/nj17YubMmdi9e7etayNyKNPDAjAhxB+dPV2gVACdPV0wIcQf08MC7F0akaxJ+hp4/Phx879FUcT58+dRWVlps6KIHJFKpcSs8AGIHNeP48iIWpGkT9F7771n/rdCoYCnpydWrVpls6KIHJlWo2bHDqJWJOnTFBsbi9tuu63WtrS0NJsURERE1BRWg+zEiRMQBAFLlizBq6++al6TzGg0YunSpdi1a9dNKZKIiKghVoPs8OHDOHbsGPLy8vDuu+/++SC1GhERETYvjoiIqDFWgywqKgoAkJiYyFk8iIioTZJ0jWzQoEFISEjA9evXIYoiBEFAVlYWPv/8c1vXR2RTnImeSP4kfXJffPFFjBw5EidOnMCkSZPwww8/oE+fPraujchmWjITPcOPqG2R9CmsqqrC3LlzYTQa0b9/fzz66KN4+OGHbV0bkc00ZyZ6LsNC1DZJ+vS5uLjAYDCgV69eyMjIqLWUC5HcNDYTfYXBaPG+6vDLK9JDFP8Mvw3bM2xZLhE1QlKQTZgwAbNnz8bIkSPx2WefYebMmejSpYutayNqdRUGI85dKrI4eS/w50z0lh7XnPAjItuTdGpx2rRpCA8Ph06nw8aNG/HLL79g+PDhtq6NqNXUPS2oVAKCUL9dQzPRcxkWorZL0hHZG2+8AZ1OBwDw8fGBWq3Ggw8+aNPCiFpT3dOClkIMuDETPQDkFJSjwmBEhcGInIJydNCq0cnDxeJjuAwLkX1J+gr5+++/Y9WqVZg5cyaWL1+OX3/9lXMtkmxYOy2oVAKiAHTydEFwgA8EUcTzr+9BfrH+jx6JIvSVJnT2dIHOxcniKUkuw0JkX5KOyN555x2UlJQgNDQUffv2RWJiIoKDg21dG1GrsHZaECKwfPZf8f7CUVAqFNhx6KL5qE1faYS+0gTgRseOC9kl6NXVjcuwELUxVr9Grl271vzvrl27QqfT4fTp0/jHP/4BAJgzZ45tqyNqBY2tznx7T08AaPCorabfc0sxekgPhI+8Fd4eLjwSI2oDJA9+USgUeOyxx3D77bfbsh6iVle9OrMl1acFrR611SAIwK7U37HzcCZDjKiNsPpJrD7iMhqN2L9/P0JDQ1FYWIg9e/ZwQDTJSvXpv6PpOSgo1sO7xmBmwPpRmyVH03MQOa4fw4yoDZC8HpkgCAgNDQUApKam4tSpU1i2bJlNiyNqLY2tzlx91FZztg9r2OWeqO2Q9ClMT0/H9u3bAQBeXl5YvXo1wsLCbFoYkS1YW5257lGbs0Zl7uxRF7vcE7UdkoJMEATk5eWhc+fOAID//e9/UCo5txw5FktHbZ9sz8C3hzPrtWWXe6K2Q9Incfbs2Zg0aRLuuusuAMDPP/+MmJgYmxZGZC81j9qeCR8AtUrZ4LU1IrI/SUEWFhaG4OBgpKWlQa1WY8mSJeajMyJH1ti1NSKyP0mfyMLCQuzcuRPl5eUQRREZGRnIysrC66+/buv6iNoEa9fWiMi+JF3omjdvHs6cOYOkpCTo9Xrs2rWALqI3AAAgAElEQVSL18iIiKhNkJRGeXl5eO211zBq1Cjcf//9+Oyzz3D69Glb10bUoOrJfKUsn9KUtkQkP5LOlXTs2BEA4Ofnh7Nnz+KOO+6waVFEDWnKKs1c0ZmofZAUZMOGDcPcuXOxaNEiTJ8+natEk91UL8dSrXqVZgCYFT6g2W2JSL4kfS194YUXMH/+fPzlL3/BW2+9BX9//1oTChPdDE1ZpZkrOhO1H5KCrKqqChcvXkRiYiLOnz8PDw8PHD582Na1EdUiZZXm5rQlInmTdGrx73//O/Lz89G7d28oFArz9vDwcJsVRlRXY8ux1JwyqiltiUjeJAXZhQsX8N1339m6FiKrrE3sW3fKqKa0JSJ5k/Rp7tGjB7Kzs+Hr62vreoisamw5lua2JSL5shpkkZGRUCgUKCwsRFhYGPr27QuVSmW+/9NPP7X65D///DPeeOMNbNy4EZcuXUJ0dDQUCgX69OmD+Ph4KJVKrF27Fvv27YNarUZMTAwGDhzYpLZ0o2NDe5k+qSlTRnF6KaL2weqnOioqqtEnyM/PR6dOnept/+ijj5CUlAQXFxcAwMqVKzFv3jwMHToUcXFxSE5Ohq+vL44dO4bNmzcjJycHUVFR2LJlS5PatmfteZxUU6aM4vRSRI7N6qc7ODi40Sd45plnsG3btnrbe/TogTVr1mDhwoUAgIyMDPPzjRgxAikpKfDz88Pw4cOhUCjg6+sLk8mEwsLCJrX18vJq8pt2FBwnRUQksfu9NaIoWtw+ZswYqNXqWu2qezy6urqitLQUZWVl0Ol05jbV25vStr3iOCkiohtaHGQ1u+NbfaEakwyXl5fD3d0dOp0O5eXltba7ubk1qW17xXFSREQ33LQLKf3790dqaioA4MCBAwgKCsLgwYNx6NAhCIKA7OxsCIIALy+vJrVtr6rHSVnCcVJE1J7ctCvgixYtQmxsrHmKqzFjxkClUiEoKAgREREQBAFxcXFNbttetadxUtfKKpGZU4JeXd3RUceAJqLaFGJDF7kkCg8PR2JiYmvV06qysrIQGhqK5ORkdOvWzd7ltLqavRbrjpNyhF6LBoMRC9YcRObVEggCoFQCvXzcsToqBBoHCmqi1uTo+z1LJO8NsrKy8OuvvyIkJATZ2dno3r07AGDJkiU2K46sc/RxUgvWHMSF7BLzbUEALmSXYMGag3j3pXvtWBkRtSWSvrZ/++23eO6555CQkIDi4mJMmTIF33zzDQAgKCjIpgVS426Mk3J1qBC7VlaJzKslFu/LvFqCa2XszEJEN0gKso8++ghffPEFdDodbrnlFmzbtg0ffvihrWujdiwz58bpREsE4cb9RESAxCBTKpW1xnB17ty5Vhd5otbWq6s7GvoTUypv3E9EBEi8RtanTx989tlnMBqNOHPmDP7973+jb9++tq6N2rGOOmf08nGvdY2sWi8f9l4koj9JOqyKi4tDbm4unJ2dERMTA51Oh/j4eFvXRu3c6qgQ+Pv+eWSmVAL+vjd6LRIRVZN0RNahQwdERUXhpZdeQmZmJjIzM9GhQwdb10YOrrFZ+zUaNd596V6OIyMiqyQF2dq1a3HhwgXMnz8f06ZNQ58+fXDo0CF2vadmaeqs/R11zrijT/0VFoiIAImnFvfs2YMVK1Zgx44dCAsLwyeffIKffvrJ1rWRg6qetT+vSA9R/HPW/g3bM+xdGhHJkKQgEwQBWq0We/fuxciRIyEIAvR6yxPWEllzrawSKT9nW7yPs/YTUXNIOrV49913Y/z48dBqtRgyZAimTZuGe+/lzAokXfXpxEM/X0FhAzPzV8/az0UwiagpJO0xFi1ahMjISHTp0gVKpRKxsbHo16+frWsjB1J3EVBLOGs/ETWHpCDLzMzEZ599huvXr0MURQiCgKysLHz++ee2ro8cgLVFQGsKDvBxqGm2iOjmkHSN7MUXX4S7uzvOnDmDfv36ITs7G3369LF1beQgrC0CSkTUUpKCrKqqCnPnzkVISAj69++Pjz76CMePH7d1beQgrC0CWtOxjKvs7EFETSYpyFxcXGAwGNCrVy9kZGRAq9Xaui6SsQqDETkF5eZQ0mrUCOrXpdHHVXf2ICJqCkkXJCZMmIDZs2fjjTfeQEREBA4ePIguXRrfMVH7Ym2gc1iIP749nGn18ezsQUTNISnIHn30UQiCgPj4eHTt2hV33303IiIibF0byUzdnonVA50BIHJcP3T2dEFeUcPXyoYFdmVnDyJqMkmnFpcvX460tDRMmjQJTzzxBNLS0vDuu+/aujaSEWs9E6u3DwvsavF+F2c1JoT4Y3pYgM3qIyLHJenrb1paGrZv326+PWrUKEycONFmRZH8WOuZWH3tqzqojqbnoKBYj1s6ajHw1k54JjwQHVw0N7NcInIgkoKsS5cuuHz5Mrp37w4AyMvLQ6dOnMSVYJ6ZvustrujkYfnUYfW1L5VKiVnhAxA5rp/VWe+JiJrC6l4kMjISCoUCRUVFmDBhAoYMGQKVSoUTJ05wHFk7VHPZFSWABWsOIvNqCQThxlphugaOqupe+9Jq1JyGiohajdW9SVRUlMXtTz/9tE2KobbJUm/ECoMJJeUGcxtBAErKDVAAEP/Y5uKsQuiQHrz2RUQ2ZTXIgoODb1Yd1IZZ6o3YELHGv/WVJigVCotrjBERtRbuYcgqqfMkNoRLsxCRrTHIyKqWzpPI2TqIyNYYZGSV1HkSG8LZOojI1hhkZJVWo25wILNCwuM5WwcR2Rr3MA6sZnf5loRJ3YHMzho19JXGWh07AMDf1x1l+ioUFOvhXWOeRSIiW2KQOSBrk/c2pwdhzYHMV/9XjmX/PAp9Zf0OHGX6Krw17x5crzBysDMR3TTc0zgga5P3zgof0OjjGzqS02rUcHZSo+BahcXHFRTrcb3CiK7eri18B0RE0jHIHExjk/dGjuvX4JFSY0dyFQYjKquM8O6oRX5x/TBjxw4isgcGmYORMnlvQ9NDNXQkJ4gilAqFOeAaCkJ27CAie+Bex8FUd5e3NnlvXRUGI67+rxxHfsm2+Jy7j/2OCoPJfLv6+piLswqVBhM7dhCRXTHIHEx1d/maR1bVAnt717ptMgn4MPEXHE3PQaGVQcs1Q6wmtw4avB41DD63dOCRGBHZDfc+DshSd3lAxJ4fLyP9twIMC+yKJ8f1w4I1B3Ehu6TZr1NQrIezk4ohRkR2xT2QA6rZXf6DLaeQ/ONl833V171+PJuL7PzyFr0OO3cQUVvAmT0c3C+/FVjc3tIQA9i5g4jaBu6FHFD1OLDKKmOLJvytS6kEIIKdO4ioTWGQOZC648C8O2qh/WM6qdYwdlgvhN9zK2ftIKI25abvjcLDw+Hm5gYA6NatGyIiIvDqq69CpVJh+PDhmDNnDgRBwNKlS3Hu3DloNBokJCSgZ8+eSEtLq9eW/lR3HJilQcvN4eKsxujgHs2e4oqIyJZuapBVVt7o4r1x40bztokTJ2LNmjXo3r07nnnmGWRkZODKlSswGAz46quvkJaWhlWrVmH9+vWIj4+v1zYggKe3gBunExsaB9YULs5q6FzU+N+1CtzSUYuBt3bCM+GB6OCiaYUqidq31prIm2q7qT/Js2fPQq/XY/r06TAajYiKioLBYECPHj0AAMOHD8eRI0eQn5+PkJAQAMCgQYOQnp6OsrIyi20ZZDdOKa7fcqpVjsAqDUYsiLwLnTw6cHwYUStp7Ym8qbabupfSarWYMWMGHnnkEWRmZmLWrFlwd3c33+/q6orLly+jrKwMOp3OvF2lUtXbVt22LbtZ3742bM/Anh9b6WehAJb9MxWdPflBI2otLZ3Im6y7qUHm5+eHnj17QqFQwM/PD25ubiguLjbfX15eDnd3d1RUVKC8/M/u4YIgQKfT1dpW3bYtupnfvqxNEtwQBYChgT44mn613n2CcOP//KARtY6WTORN0tzUr9pff/01Vq1aBQDIzc2FXq9Hhw4d8Pvvv0MURRw6dAhBQUEYPHgwDhw4AABIS0vDbbfdBp1OBycnp3pt26Lqb195RXqI4p+hsGF7Rqu/lrVJghvi6e6MeVPuxIQQf3T2dIFS8UfXeguOpuegwtA6vR6J2iMpE3lTy9zUrwGTJ0/G4sWL8dhjj0GhUGDFihVQKpWYP38+TCYThg8fjjvuuAMDBgxASkoKpkyZAlEUsWLFCgDAK6+8Uq9tW3Ozv31ZmyS4IcMCu8LVRWOe/ePcpSIs+eCwxbaNzZhPRNY1ZyJvapqbunfSaDR48803623ftGlTrdtKpRLLli2r127QoEH12rY1LVlGpTm0GjWC+nXBt4czJbXv1lmHZ2qcKtRq1Li9pyc6e/KDRmQL1iby5uw4rYNX8VuZp7szvDtqLd7XGqFQYTAip6DcfLqvwmDE4L6dJT1Wq1Hhjbkh9a7TVX/QLOEHjajlpocF1DqV39nTBRNC/Dk7TivhHqoVmUwCNn57BmV6y9eUWhIK1R1IjqTnoKBID28PF7h1cELpdQMKJHa7v39oT7g2MB6s7oz5nIaKqPXUnMib48haH3+SrahuF9tqLs4qjA7u2aJQ+GdSOnYcumi+nV+sl9zJo5OHFncP8LX6+vygEdmeVqPm9WYb4E+0lVjr5OHWQYPIcf1qndKrOcYMgMXwqG7TQatG8vHmjRMbeVc3PD/5DsmhxA8aEckN91itRGonj7pjzLR/LHqprzShk6cLBvT2xsyw/vhy93kcTc9BXpEeHXWaJk/8W/MojAOaiciRMchaidQutnVPP9YMqPwiPfb8eBn7f8qCSRDN26+VGSTX0dnTBbEzhnF6KSJqN/hVvZVI6fkndRaOmiHWVMMCu6JXV3eGGBG1G9zbtaLGev41ZxYOqTx0Goy4sxt7GRJRu8Mga0WN9fxrziwcUigVwJt/vwedvTqgwmBEXpGevQ6JqN3gns4GLPX8q+6B2JRZOKTq1dUdt3TU4qPEX7hMBBG1OwwyG6vbS9G7oxb+vu4o01ehoFgPZ40ahiojTELznr+Hjw6ro0K4TAQRtVsMMhurGzD5xRXIL67AuL/2Qvg9t8LT3RmXc0vw4jsHm/X8S54eBgGQNFExV6clIkfEvZkNNTYT/tQxfaHVqLHryKVmPX8nDy083Z2R97/yBq+7FRTrUVCsx87DmTztSEQOiUFmQ9Z6KRaWVGLuG3vhrtPg96ulzXr+uwf4wkmlRPS6lAbbeHu4YPvBC7Wuy/G0IxE5En4dt6HqXooNKSytRGZOKZozbKyXjxse+GsvrN10EqXXqxpsN/DWTvjxTK7F+7hoJhE5AgaZDVkbJN1Sv+eV4v9e34PkE1lW2wX438LVaYnIoTHIbKx6HSJPN8vLpzSXIACieOO/higVwMBbvRs8KuSimUTkCBhkNlK9AGalwQijSQCgaPJzeLo5w7tj84Omh48bOnt14KKZROTQuBdrZXXHjakUChibOXfiHX284e7qbHGNs8a4u2rw5twRALhoJhE5NgZZK6s7bsxo7dxfI9J/K8CwAb4YP9wPxzKuIr9ID4XyxmnFupR/bPfQaTA0wAfPPXyHuWs9F80kIkfGvVkrkjq7vVQF1yqx49BFTAjxx/sLR6GopBKJ+3+1OMXV2GF/DrBuKKS4aCYROSJeI2slFQYjfvmtAPmtPCEw8OesHZ7uzggL8ce4v/ZCZ08XKBU31h+bEOKPZ8IHoKu3K4+0iKjd4V6vARUGI67+7zoAET63NBwQJpOAfyalI/n45Sav4ixVfpEe67ecQvpvBeaZOYL6dUFYiD+8PVwYXkTUrnEPWMefwfQ79JUmAIBWo8K9d3XDs5MGmq87Vc9b2NCpvtakdVZhz4+XzbfzivT49nAm1H9c+yIias8YZHVs2J6BHYcu1tpWYTBh55FLOJNZiPnT7kLivt/w45lcFJcZblJVlrvu15wQmIioveI1shoqDEYc+SW7wfszc0oxZ/U+7D5+udVCTKkAQod0M1/zcnFWw8VZBQVuXP8KDere4DRSnJmDiIhHZLXcmOS34qa+piACx0/n4YOFo1BWYTTPtFHdTR4AfvmtwOLs9pyZg4iIR2S13JjkV3vTX7ek3ICY9SnmXoc3usn/+W/OzEFE1DAGWQ1ajRp3D/C1y2tfyi3FtTLLpwmr52us2+Ve6swc1dNlcaZ7InJE/Dpfx+Njbseuo5morLIwfYYNiSKQmVOCO/p0qndfc2fmqDtdFhfUJCJHxCCr41p51U0PMeDGFFO9urpbbdPUmTnqTpfV2gtqVg9B4JRXRGRP3PvU0UGrNs9beDP18nFHR13rddy4VlaJlJ8t98Bsabd9HukRtX37f8rC49262buMm4JBVsf1CqNNQkylBLp3dsOlq6WoOY2wUnHjSGx1VEirvE51yBz6+QoKG+iaX91tv7nzLtr6SI+IqCn49bkGk0lA4v5fobTBT0WEApl1QgwA/jawK16LCoGmlU7NVYdMQyEGtKzbvrWJkY+m57BDCRHddAyyGjZsz8C3hzNtckQmNLAm2cGfc/B/r+/BR4m/wGRq2QtLnX2/Jd32b4y1szwxMgdoE5E9MMj+0NpLsDRF/h+n5jZsz2jR81gLGQC4paO2Sd32Lbkx1s7F4n0coE1E9sAg+0NjIXAztPTUnLWQ8XJ3xutzQjB+uD+qWnDkxwHaRNTWcK/zB093Z7i5qFBy3WS3GlraCaM6ZGp2xKjmoXPG4nWHWqWXYfUR3dH0HBQU6+Fd4/mIqO347kimvUtosbF392q0DYPsD1qN2uYhptWo4NbBqcH5HL09XNBBq0ZOQXmzx2ZZChmdixMuZJeY27S0l2FzB2g3B8eqEVFjuGf4Q17hdZu/xqig7nh8zO14ZuVulFfUD02dixNefGe/xaMmqTv0KpOA8cP98eh9t+F6hREdtGq8+M5+i22bMp7M0us3dYB2UzR3rBqDj6j9kd0nXRAELF26FOfOnYNGo0FCQgJ69uzZ4udduzmtFaprmL+vO2aEBeDphB8shphGrbR41CSIIpQKRaM79IZ2/A/8tVejvQythZG9Bj83dawaB2kTtV+yC7Ldu3fDYDDgq6++QlpaGlatWoX169e3+HlP/je/Faqz7IG7e+LZSQOxfusplJRbXsfMYLTcAaPmStVAwzv0hnb8RpOATh4uzV4Gxh6Dnxsbq2bpKJKDtInaL9l9VT1x4gRCQm7MgjFo0CCkp6fbuaKGKRXAhiX34f8mD0KVSUBq+tUmP0fNEKupZg9Hazv+H8/kIqhfF4v3NdbL0F6Dn5s6Vo2DtInaN9kdkZWVlUGn05lvq1QqGI1GqNVt762IAIx/5FBRSSWulbfeYOGapwUb2/GHhfhDrVI2uZehlECxxTWy6mEEUo8i7VUnUVt2z+Bu6Ma5FtsmnU6H8vJy821BENpkiAFApxo7XWs7ZwBw6+CE0utV9ba7OKuhr6x/ROEt8bm9PVzg7eHSrF6GTQ2U1mJtGIGlo0h71UlEbYPsTi0OHjwYBw4cAACkpaXhtttua5Xn3f7mxFZ5nppq7nStDSR2d9Xg45j7LC6eGTqke4ueu2676pWnpbDn4OemLCbKQdpE7ZvsPuGjR49GSkoKpkyZAlEUsWLFCrvWo+ugxt8G+uLAySvm61kuzmqEDuleb6dbd4xXR50zggN88NxDAxscm2UyCeZei9ZOC9pqkLK9Bj83dawaB2kTtV8KURQtz2brALKyshAaGork5OQmnSsOe+kb87+93J3wwDA/GEwmpP1agLv7+8CzowsqK00Y0t8Hnb06ALjR4eDq/64DEOFzi/WjnuaMdZL6GFuNo5LL+Cy51ElkK83d78kZP+kWNHSa8Qkrj9Fq1I2u8FyzbVM7H0h9jK0GKdty8HNrkkudRNR6ZHeNjIiIqCYGGRERyRqDjIiIZI1BRkREssYgIyIiWWOQERGRrDHIiIhI1hx6wI3JdGOmjatXmz7rPBFRW+Pj49Nm55a1J4f+ieTn31hj7PHHH7dzJURELdeeZutoCoeeoqqiogLp6eno1KkTVCqVvcshImoRKUdkRqMRV69ebVdHbw4dZERE5PjY2YOIiGSNQUZERLLGICMiIlljkBERkay1jy4tEgmCgKVLl+LcuXPQaDRISEhAz5497V2WJOHh4XBzcwMAdOvWDREREXj11VehUqkwfPhwzJkzp8H3l5aWVq+tvf3888944403sHHjRly6dAnR0dFQKBTo06cP4uPjoVQqsXbtWuzbtw9qtRoxMTEYOHBgk9ra8z1lZGRg9uzZ6NWrFwDgsccew7hx42T1nqqqqhATE4MrV67AYDDgueeew6233ir735Wl9+Xj4yP735dDE8ls165d4qJFi0RRFMWTJ0+Ks2fPtnNF0lRUVIgTJ06stW3ChAnipUuXREEQxJkzZ4rp6ekNvj9Lbe3pww8/FMePHy8+8sgjoiiK4rPPPisePXpUFEVRjI2NFb///nsxPT1djIyMFAVBEK9cuSI+9NBDTW5rz/e0adMm8eOPP67VRm7v6euvvxYTEhJEURTFwsJC8Z577nGI35Wl9+UIvy9HxlOLNZw4cQIhISEAgEGDBiE9Pd3OFUlz9uxZ6PV6TJ8+HU888QSOHz8Og8GAHj16QKFQYPjw4Thy5IjF91dWVmaxrT316NEDa9asMd/OyMhAcHAwAGDEiBE4fPgwTpw4geHDh0OhUMDX1xcmkwmFhYVNamvP95Seno59+/bh8ccfR0xMDMrKymT3nsaOHYu///3v5tsqlcohfleW3pcj/L4cGYOshrKyMuh0OvNtlUoFo9Fox4qk0Wq1mDFjBj7++GO88sorWLx4MVxcXMz3u7q6orS01OL7q7utuq09jRkzptZATlEUoVAoADT8Xqq3N6XtzVT3PQ0cOBALFy7E559/ju7du+P999+X3XtydXWFTqdDWVkZ5s6di3nz5jnE78rS+3KE35cjY5DVoNPpUF5ebr4tCIIsRsb7+flhwoQJUCgU8PPzg5ubG4qLi833l5eXw93d3eL7q7utum1bolT++Wfa0HspLy+Hm5tbk9ra0+jRoxEYGGj+9+nTp2X5nnJycvDEE09g4sSJCAsLc5jfVd335Si/L0fFIKth8ODBOHDgAAAgLS0Nt912m50rkubrr7/GqlWrAAC5ubnQ6/Xo0KEDfv/9d4iiiEOHDiEoKMji+9PpdHBycqrXti3p378/UlNTAQAHDhwwv5dDhw5BEARkZ2dDEAR4eXk1qa09zZgxA6dOnQIAHDlyBAEBAbJ7TwUFBZg+fToWLFiAyZMnA3CM35Wl9+UIvy9Hximqaqju1fff//4XoihixYoV6N27t73LapTBYMDixYuRnZ0NhUKB+fPnQ6lUYsWKFTCZTBg+fDheeOGFBt9fWlpavbb2lpWVhRdffBGbNm3CxYsXERsbi6qqKvj7+yMhIQEqlQpr1qzBgQMHIAgCFi9ejKCgoCa1ted7ysjIwPLly+Hk5ARvb28sX74cOp1OVu8pISEBO3fuhL+/v3nbyy+/jISEBFn/riy9r3nz5mH16tWy/n05MgYZERHJGk8tEhGRrDHIiIhI1hhkREQkawwyIiKSNQYZERHJGoOMmiQ1NRWRkZGyfM01a9bUmiaqMe+99x5+/PHHJr3GrFmzkJub29TSWmTr1q2Ijo5uleeqrv/y5cuIiYlplecksjUGGVEDjh8/DpPJ1KTHfPTRR+jSpYuNKrK96vqzs7Nx+fJle5dDJEnbn3+J2pyioiLMmDEDeXl5GDhwIOLj47Fp0yZ888030Ov1cHJywptvvgl/f3+89tprSElJgVKpxH333Yc5c+agvLwcy5Ytw/nz52EymTBr1iyMHz/e6msWFhZi1qxZ+P333+Hn54f33nsPGo0Gb7/9No4cOYJr166hc+fOePvtt+Ht7Y3t27dj/fr1UCgUGDBgAJYvX25+LpPJhBdeeAHdunXDwoUL8dlnn9Wr/dSpU0hPT8eSJUuwdu1aJCQkYM6cORg6dCiysrLwxBNPYM+ePYiOjkZxcTEuXbqEBQsWICEhAZ9++imOHTuGgwcP4tq1a7h8+TL+9re/YenSpQCAN998E7t27YKnpyc6deqEUaNG4aGHHjLX9+mnn+LSpUuIjY0FAKxatQo+Pj544IEHEBMTg9LSUuTl5WHSpEm1JrcFYF6Sp7KyEp6enli2bBl69uyJyMhIdOzYEefPn8c777yDfv36Wfw5jxo1Cp9++ikSEhKQlZWFV155BfHx8fjwww+xc+dO86D5BQsW4MqVK3j++efh7++PX3/9Ff3798edd96Jbdu24dq1a3j//fdlMaEAyR+PyKjJsrKyEBsbi6SkJJSXl+OLL77A7t27sXHjRuzYsQMjR47E559/jitXruDAgQNISkrCF198gV9//RWVlZVYv349AgICsHXrVnz++ef44IMPGv32n52djbi4OOzcuRMFBQU4fPgwLl26hAsXLuDLL7/Erl270LVrVyQlJSE3NxcrV67Ehg0b8J///Acmkwn79+8HcGMC4iVLlsDHxwcLFy5EWVmZxdrDw8MRGBiIhIQE3H777VZr8/DwwM6dOzFq1Kha20+ePIn33nsPSUlJ2Lt3L86dO4c9e/bgxIkT2LFjBz788EOcPn263vONHz8eP/zwA0wmE0RRxPfff48HH3wQO3bswPjx47Fp0yZs374d/+///b9aM6gbDAa8+OKL5t/NlClT8OKLL5rvv/3227Fr164GQ6ymJUuWIDAwEPHx8Thw4ADS09Px9ddfIzExEbm5uUhKSgIAnDt3DrNmzcI333yDn376CVeuXMFXX32F8ePH46uvvmr0dYhaA4/IqMmCgoLMCwyGhYVh69atePPNN/Gf//wHmZmZOHjwIPr164cuXbrA2dkZU6ZMwb333ov58+fD2dkZhw8fRkVFBbZs2QIAuH79Os6fP4/u3bs3+Jp9+/Y139+7d28UFRVh5P8vyOgAAASpSURBVMiRWLRoETZv3oyLFy8iLS0NPXr0wMmTJzF48GD4+PgAAFavXg0AOHPmDL788kuUlpYiOTkZwI2Joi3V3hQNLZB45513mmc87969O65du4bDhw/jgQcegEajgUajwX333VfvcV5eXujbty9SU1Ph5OQEPz8/dOrUCTNmzMDRo0fx8ccf4/z586iqqoJerzc/LjMzE+7u7uZ6HnjgAcTFxZlnWW/uQo5HjhzBqVOnzEeNFRUV8PX1xV133QVvb2/0798fAODj44O7774bAODr64usrKxmvR5RUzHIqMnqLrFSUlKCiIgITJs2DSNGjIC3tzfOnDkDtVqNzZs349ixYzhw4ACmTJmCjRs3QhAErF69GgEBAQBuTNLasWNHya+pUCggiiLS09Px0ksv4amnnsKYMWOgVCohiiLUarV5GQ0AtY5a7rzzTvTv3x8JCQl47733kJOTg8jIyHq1W1I9m1vdpX20Wq3F9s7OzvVqViqVEATB6nsFgIkTJ+Lbb7+Fk5MTwsLCANw4xXj58mWMHz8e9913Hw4fPoyaM8xZel5RFM3X+RqqszEmkwlPPvkknn76aQBASUkJVCoVioqKoNFoarVVqVTNeg2iluCpRWqyEydOmGfwTkxMxIgRI9CzZ0889dRTGDBgAHbv3g2TyYTTp09j2rRpGDJkCBYtWoTevXvj4sWLGDZsGL744gsAQF5eHiZMmICcnJwm13H8+HEEBwfjscceQ69evbBv3z6YTCYMGDAAaWlpyM/PBwCsWLHCfATWt29fzJo1C+fPn8eePXvwyy+/WKwduLFTrv63p6cnfv31VwDA7t27m/2z++tf/4rvv/8eBoMBZWVl2LdvX63QrRYaGorjx48jJSUFo0ePBgCkpKRgxowZeOCBB3Dx4kXk5ubWCi9/f38UFxebZ2n/9ttv4evrCw8PjybXWXMtvmHDhuGbb75BeXk5jEYjnn/+eezatas5b5/IJnhERk126623IiYmBvn5+Rg2bBimTJmCw4cPY9y4cRBFEUOGDMH58+fRv39/DBo0COPHj4eLiwsGDx6MESNGIDg4GEuXLsX48eNhMpmwYMEC9OjRo8l1jBs3DnPmzDEfsQQGBiIrKwtdunTByy+/jBkzZkAQBAwaNAgPPfQQ1q1bBwDQaDRYunQpoqOjsWPHDnzxxRf1ageAkJAQxMfH47XXXsPMmTMRHR2NLVu2IDQ0tNk/u5EjR+LkyZOYNGkSOnbsiM6dO5uP3GbNmoW5c+diwIAB0Gq1GDx4MAwGA1xdXQEAzz77LBYuXAitVgsfHx/z+61W3fll+fLl0Ov16NixI95+++1m1dm7d2+UlpZiwYIFWL16Nc6ePYtHH30UJpMJISEhmDRpEq5cudLsnwNRa+Ls90Q30cmTJ5GZmYlJkyahqqoKERERWLFiBfr27Wvv0ohki0FGbcKPP/5Yq4t8TR9++KGsx2bVVFxcjJdeegn5+fkQRRHh4eGYMWPGTa0hMjISJSUl9bZPmTIFjz322E2thag1MMiIiEjW2NmD/n97dUACAAAAIOj/63YEekKANZEBsCYyANZEBsCayABYExkAawENLQZ1Kd2LzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2928945fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "aggregation_functions = {'base_hackaturing.valor_item': 'mean', 'base_hackaturing.valor_pago': 'mean'}\n",
    "df_new = df.groupby(df['base_hackaturing.servico']).aggregate(aggregation_functions)\n",
    "df_new = df_new.dropna(subset=['base_hackaturing.valor_item', 'base_hackaturing.valor_pago'])\n",
    "\n",
    "# Joint Distribution Plot\n",
    "sns.jointplot(x='base_hackaturing.valor_item', y='base_hackaturing.valor_pago', data=df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliers\n",
    "\n",
    "df = df[df['base_hackaturing.valor_item'] < 20000]\n",
    "df = df[df['base_hackaturing.valor_pago'] < 20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2928155e710>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292bf8b4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution Plot (a.k.a. Histogram)\n",
    "sns.distplot(df_new['base_hackaturing.valor_pago'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29281d94908>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29281a3b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "corr = df.corr()\n",
    " \n",
    "# Heatmap\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data cleaning and manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O racional de data cleaning e feature engineering foi o seguinte: iríamos eliminar features que fossem ligadas ao customer e ao médico, e deixar somente features que fossem ligadas ao tipo de tratamento e ao produto/medicamento/equiapamento em questão. A target variable seria se houve ou não pagamento total de tudo que foi solicitado no tratamento (0 se \"nao foi pago certinho\" e 1 se \"foi pago certinho\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "base_hackaturing.cnpj                   660848\n",
       "base_hackaturing.prestador              660848\n",
       "base_hackaturing.uf                     660848\n",
       "base_hackaturing.id_beneficiario        660848\n",
       "base_hackaturing.sexo                   660770\n",
       "base_hackaturing.data_nascimento        660770\n",
       "base_hackaturing.id_conta               660848\n",
       "base_hackaturing.cid                         7\n",
       "base_hackaturing.crm_solicitante        608420\n",
       "base_hackaturing.cbos_solicitante            0\n",
       "base_hackaturing.cbos_executante             0\n",
       "base_hackaturing.data_entrada           660674\n",
       "base_hackaturing.data_saida             660674\n",
       "base_hackaturing.data_item              660848\n",
       "base_hackaturing.senha                  660848\n",
       "base_hackaturing.tipo_guia              660848\n",
       "base_hackaturing.tipo_item              660848\n",
       "base_hackaturing.carater_atendimento    660848\n",
       "base_hackaturing.servico                660848\n",
       "base_hackaturing.descricao_despesa      660848\n",
       "base_hackaturing.quantidade             660848\n",
       "base_hackaturing.valor_item             660848\n",
       "base_hackaturing.valor_cobrado          660848\n",
       "base_hackaturing.valor_pago             660848\n",
       "base_hackaturing.ano_mes                660848\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['base_hackaturing.crm_solicitante'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "base_hackaturing.cnpj                   660848\n",
       "base_hackaturing.prestador              660848\n",
       "base_hackaturing.uf                     660848\n",
       "base_hackaturing.id_beneficiario        660848\n",
       "base_hackaturing.sexo                   660770\n",
       "base_hackaturing.data_nascimento        660770\n",
       "base_hackaturing.id_conta               660848\n",
       "base_hackaturing.cid                         7\n",
       "base_hackaturing.crm_solicitante        608420\n",
       "base_hackaturing.cbos_solicitante            0\n",
       "base_hackaturing.cbos_executante             0\n",
       "base_hackaturing.data_entrada           660674\n",
       "base_hackaturing.data_saida             660674\n",
       "base_hackaturing.data_item              660848\n",
       "base_hackaturing.senha                  660848\n",
       "base_hackaturing.tipo_guia              660848\n",
       "base_hackaturing.tipo_item              660848\n",
       "base_hackaturing.carater_atendimento    660848\n",
       "base_hackaturing.servico                660848\n",
       "base_hackaturing.descricao_despesa      660848\n",
       "base_hackaturing.quantidade             660848\n",
       "base_hackaturing.valor_item             660848\n",
       "base_hackaturing.valor_cobrado          660848\n",
       "base_hackaturing.valor_pago             660848\n",
       "base_hackaturing.ano_mes                660848\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping columns without valor_item or valor_pago\n",
    "\n",
    "df = df.dropna(subset = ['base_hackaturing.valor_item',\n",
    "                         'base_hackaturing.valor_pago'])\n",
    "\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>base_hackaturing.uf</th>\n",
       "      <th>base_hackaturing.sexo</th>\n",
       "      <th>base_hackaturing.data_nascimento</th>\n",
       "      <th>base_hackaturing.crm_solicitante</th>\n",
       "      <th>base_hackaturing.tipo_guia</th>\n",
       "      <th>base_hackaturing.tipo_item</th>\n",
       "      <th>base_hackaturing.carater_atendimento</th>\n",
       "      <th>base_hackaturing.servico</th>\n",
       "      <th>base_hackaturing.quantidade</th>\n",
       "      <th>base_hackaturing.valor_item</th>\n",
       "      <th>base_hackaturing.valor_cobrado</th>\n",
       "      <th>base_hackaturing.valor_pago</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1936-07-05</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>55851.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.9840</td>\n",
       "      <td>5.9840</td>\n",
       "      <td>5.9840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>1963-03-04</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>0.6035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1945-01-15</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MATERIAIS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>256200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1963-08-22</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MATERIAIS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>280022.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>15.5805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>1957-01-18</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>0.6035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1970-05-10</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61006.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2870</td>\n",
       "      <td>0.5270</td>\n",
       "      <td>0.5270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>2011-04-29</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61007.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.3325</td>\n",
       "      <td>0.6290</td>\n",
       "      <td>0.6290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>2013-02-22</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MATERIAIS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>280022.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>15.5805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1945-01-15</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61007.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.6650</td>\n",
       "      <td>1.2665</td>\n",
       "      <td>1.2665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>1957-01-18</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>0.6035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  base_hackaturing.uf base_hackaturing.sexo base_hackaturing.data_nascimento  \\\n",
       "0                  SP                     F                       1936-07-05   \n",
       "1                  SP                     M                       1963-03-04   \n",
       "2                  SP                     F                       1945-01-15   \n",
       "3                  SP                     F                       1963-08-22   \n",
       "4                  SP                     M                       1957-01-18   \n",
       "5                  SP                     F                       1970-05-10   \n",
       "6                  SP                     M                       2011-04-29   \n",
       "7                  SP                     M                       2013-02-22   \n",
       "8                  SP                     F                       1945-01-15   \n",
       "9                  SP                     M                       1957-01-18   \n",
       "\n",
       "   base_hackaturing.crm_solicitante base_hackaturing.tipo_guia  \\\n",
       "0  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "1  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "2  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "3  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "4  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "5  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "6  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "7  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "8  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "9  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "\n",
       "  base_hackaturing.tipo_item base_hackaturing.carater_atendimento  \\\n",
       "0               MEDICAMENTOS                              ELETIVO   \n",
       "1               MEDICAMENTOS                              ELETIVO   \n",
       "2                  MATERIAIS                              ELETIVO   \n",
       "3                  MATERIAIS                              ELETIVO   \n",
       "4               MEDICAMENTOS                              ELETIVO   \n",
       "5               MEDICAMENTOS                              ELETIVO   \n",
       "6               MEDICAMENTOS                              ELETIVO   \n",
       "7                  MATERIAIS                              ELETIVO   \n",
       "8               MEDICAMENTOS                              ELETIVO   \n",
       "9               MEDICAMENTOS                              ELETIVO   \n",
       "\n",
       "   base_hackaturing.servico  base_hackaturing.quantidade  \\\n",
       "0                   55851.0                          2.0   \n",
       "1                   61005.0                          1.0   \n",
       "2                  256200.0                        100.0   \n",
       "3                  280022.0                          1.0   \n",
       "4                   61005.0                          1.0   \n",
       "5                   61006.0                          1.0   \n",
       "6                   61007.0                          1.0   \n",
       "7                  280022.0                          1.0   \n",
       "8                   61007.0                          2.0   \n",
       "9                   61005.0                          1.0   \n",
       "\n",
       "   base_hackaturing.valor_item  base_hackaturing.valor_cobrado  \\\n",
       "0                       5.9840                          5.9840   \n",
       "1                       6.1030                          0.6035   \n",
       "2                       0.8500                          0.8500   \n",
       "3                      15.5805                         15.5805   \n",
       "4                       6.1030                          0.6035   \n",
       "5                       5.2870                          0.5270   \n",
       "6                       6.3325                          0.6290   \n",
       "7                      15.5805                         15.5805   \n",
       "8                      12.6650                          1.2665   \n",
       "9                       6.1030                          0.6035   \n",
       "\n",
       "   base_hackaturing.valor_pago  \n",
       "0                       5.9840  \n",
       "1                       0.6035  \n",
       "2                       0.8500  \n",
       "3                      15.5805  \n",
       "4                       0.6035  \n",
       "5                       0.5270  \n",
       "6                       0.6290  \n",
       "7                      15.5805  \n",
       "8                       1.2665  \n",
       "9                       0.6035  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping unnecessary columns\n",
    "\n",
    "df = df.drop(['base_hackaturing.prestador',\n",
    "         'base_hackaturing.cnpj',\n",
    "         'base_hackaturing.id_conta', \n",
    "         'base_hackaturing.data_entrada',\n",
    "         'base_hackaturing.data_saida',\n",
    "         'base_hackaturing.cbos_executante',\n",
    "         'base_hackaturing.cbos_solicitante',\n",
    "         'base_hackaturing.id_beneficiario', \n",
    "         'base_hackaturing.cid',\n",
    "         'base_hackaturing.data_item',\n",
    "         'base_hackaturing.descricao_despesa',\n",
    "         'base_hackaturing.senha',\n",
    "         'base_hackaturing.ano_mes'], axis=1)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>base_hackaturing.uf</th>\n",
       "      <th>base_hackaturing.sexo</th>\n",
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       "      <th>base_hackaturing.pago_ou_nao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1936-07-05</td>\n",
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       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>5.9840</td>\n",
       "      <td>5.9840</td>\n",
       "      <td>5.9840</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>1963-03-04</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1945-01-15</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MATERIAIS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>256200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SP</td>\n",
       "      <td>F</td>\n",
       "      <td>1963-08-22</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MATERIAIS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>280022.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SP</td>\n",
       "      <td>M</td>\n",
       "      <td>1957-01-18</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>Internacao</td>\n",
       "      <td>MEDICAMENTOS</td>\n",
       "      <td>ELETIVO</td>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>0.6035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  base_hackaturing.uf base_hackaturing.sexo base_hackaturing.data_nascimento  \\\n",
       "0                  SP                     F                       1936-07-05   \n",
       "1                  SP                     M                       1963-03-04   \n",
       "2                  SP                     F                       1945-01-15   \n",
       "3                  SP                     F                       1963-08-22   \n",
       "4                  SP                     M                       1957-01-18   \n",
       "\n",
       "   base_hackaturing.crm_solicitante base_hackaturing.tipo_guia  \\\n",
       "0  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "1  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "2  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "3  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "4  d41d8cd98f00b204e9800998ecf8427e                 Internacao   \n",
       "\n",
       "  base_hackaturing.tipo_item base_hackaturing.carater_atendimento  \\\n",
       "0               MEDICAMENTOS                              ELETIVO   \n",
       "1               MEDICAMENTOS                              ELETIVO   \n",
       "2                  MATERIAIS                              ELETIVO   \n",
       "3                  MATERIAIS                              ELETIVO   \n",
       "4               MEDICAMENTOS                              ELETIVO   \n",
       "\n",
       "   base_hackaturing.servico  base_hackaturing.quantidade  \\\n",
       "0                   55851.0                          2.0   \n",
       "1                   61005.0                          1.0   \n",
       "2                  256200.0                        100.0   \n",
       "3                  280022.0                          1.0   \n",
       "4                   61005.0                          1.0   \n",
       "\n",
       "   base_hackaturing.valor_item  base_hackaturing.valor_cobrado  \\\n",
       "0                       5.9840                          5.9840   \n",
       "1                       6.1030                          0.6035   \n",
       "2                       0.8500                          0.8500   \n",
       "3                      15.5805                         15.5805   \n",
       "4                       6.1030                          0.6035   \n",
       "\n",
       "   base_hackaturing.valor_pago  base_hackaturing.pago_ou_nao  \n",
       "0                       5.9840                             1  \n",
       "1                       0.6035                             1  \n",
       "2                       0.8500                             1  \n",
       "3                      15.5805                             1  \n",
       "4                       0.6035                             1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding new columns\n",
    "\n",
    "df['base_hackaturing.intermediate'] = df['base_hackaturing.valor_cobrado'] - df['base_hackaturing.valor_pago']\n",
    "df['base_hackaturing.pago_ou_nao'] = [1 if x == 0 else 0 for x in df['base_hackaturing.intermediate']]\n",
    "df = df.drop(['base_hackaturing.intermediate'], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>base_hackaturing.servico</th>\n",
       "      <th>base_hackaturing.quantidade</th>\n",
       "      <th>base_hackaturing.valor_item</th>\n",
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       "      <td>6.1030</td>\n",
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       "      <td>1</td>\n",
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       "      <td>280022.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>1</td>\n",
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       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   base_hackaturing.servico  base_hackaturing.quantidade  \\\n",
       "0                   55851.0                          2.0   \n",
       "1                   61005.0                          1.0   \n",
       "2                  256200.0                        100.0   \n",
       "3                  280022.0                          1.0   \n",
       "4                   61005.0                          1.0   \n",
       "5                   61006.0                          1.0   \n",
       "6                   61007.0                          1.0   \n",
       "7                  280022.0                          1.0   \n",
       "8                   61007.0                          2.0   \n",
       "9                   61005.0                          1.0   \n",
       "\n",
       "   base_hackaturing.valor_item  base_hackaturing.pago_ou_nao  \\\n",
       "0                       5.9840                             1   \n",
       "1                       6.1030                             1   \n",
       "2                       0.8500                             1   \n",
       "3                      15.5805                             1   \n",
       "4                       6.1030                             1   \n",
       "5                       5.2870                             1   \n",
       "6                       6.3325                             1   \n",
       "7                      15.5805                             1   \n",
       "8                      12.6650                             1   \n",
       "9                       6.1030                             1   \n",
       "\n",
       "   base_hackaturing.tipo_guia_Internacao  \\\n",
       "0                                      1   \n",
       "1                                      1   \n",
       "2                                      1   \n",
       "3                                      1   \n",
       "4                                      1   \n",
       "5                                      1   \n",
       "6                                      1   \n",
       "7                                      1   \n",
       "8                                      1   \n",
       "9                                      1   \n",
       "\n",
       "   base_hackaturing.tipo_item_GASES MEDICINAIS  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "7                                            0   \n",
       "8                                            0   \n",
       "9                                            0   \n",
       "\n",
       "   base_hackaturing.tipo_item_MATERIAIS  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     1   \n",
       "3                                     1   \n",
       "4                                     0   \n",
       "5                                     0   \n",
       "6                                     0   \n",
       "7                                     1   \n",
       "8                                     0   \n",
       "9                                     0   \n",
       "\n",
       "   base_hackaturing.tipo_item_MEDICAMENTOS  base_hackaturing.tipo_item_OPME  \\\n",
       "0                                        1                                0   \n",
       "1                                        1                                0   \n",
       "2                                        0                                0   \n",
       "3                                        0                                0   \n",
       "4                                        1                                0   \n",
       "5                                        1                                0   \n",
       "6                                        1                                0   \n",
       "7                                        0                                0   \n",
       "8                                        1                                0   \n",
       "9                                        1                                0   \n",
       "\n",
       "   base_hackaturing.tipo_item_PROCEDIMENTO  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "7                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   base_hackaturing.tipo_item_TAXAS DIVERSAS  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "5                                          0   \n",
       "6                                          0   \n",
       "7                                          0   \n",
       "8                                          0   \n",
       "9                                          0   \n",
       "\n",
       "   base_hackaturing.carater_atendimento_ELETIVO  \\\n",
       "0                                             1   \n",
       "1                                             1   \n",
       "2                                             1   \n",
       "3                                             1   \n",
       "4                                             1   \n",
       "5                                             1   \n",
       "6                                             1   \n",
       "7                                             1   \n",
       "8                                             1   \n",
       "9                                             1   \n",
       "\n",
       "   base_hackaturing.carater_atendimento_URGENCIA  \n",
       "0                                              0  \n",
       "1                                              0  \n",
       "2                                              0  \n",
       "3                                              0  \n",
       "4                                              0  \n",
       "5                                              0  \n",
       "6                                              0  \n",
       "7                                              0  \n",
       "8                                              0  \n",
       "9                                              0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping more columns\n",
    "\n",
    "df_crm = df['base_hackaturing.crm_solicitante']\n",
    "\n",
    "df = df.drop(['base_hackaturing.uf',\n",
    "         'base_hackaturing.sexo',\n",
    "         'base_hackaturing.valor_pago',\n",
    "         'base_hackaturing.data_nascimento',\n",
    "         'base_hackaturing.crm_solicitante',\n",
    "         'base_hackaturing.valor_cobrado'], axis=1)\n",
    "\n",
    "df = pd.get_dummies(df, drop_first=True)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['base_hackaturing.servico', 'base_hackaturing.quantidade',\n",
       "       'base_hackaturing.valor_item', 'base_hackaturing.tipo_guia_Internacao',\n",
       "       'base_hackaturing.tipo_item_GASES MEDICINAIS',\n",
       "       'base_hackaturing.tipo_item_MATERIAIS',\n",
       "       'base_hackaturing.tipo_item_MEDICAMENTOS',\n",
       "       'base_hackaturing.tipo_item_OPME',\n",
       "       'base_hackaturing.tipo_item_PROCEDIMENTO',\n",
       "       'base_hackaturing.tipo_item_TAXAS DIVERSAS',\n",
       "       'base_hackaturing.carater_atendimento_ELETIVO',\n",
       "       'base_hackaturing.carater_atendimento_URGENCIA',\n",
       "       'base_hackaturing.crm_solicitante', 'base_hackaturing.pago_ou_nao'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moving the pago_ou_nao target column to the end of the DataFrame\n",
    "\n",
    "df_last = df['base_hackaturing.pago_ou_nao']\n",
    "df = df.drop(['base_hackaturing.pago_ou_nao'], axis=1)\n",
    "df = pd.concat([df, df_crm, df_last], axis=1)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "base_hackaturing.servico                         660848\n",
       "base_hackaturing.quantidade                      660848\n",
       "base_hackaturing.valor_item                      660848\n",
       "base_hackaturing.tipo_guia_Internacao            660848\n",
       "base_hackaturing.tipo_item_GASES MEDICINAIS      660848\n",
       "base_hackaturing.tipo_item_MATERIAIS             660848\n",
       "base_hackaturing.tipo_item_MEDICAMENTOS          660848\n",
       "base_hackaturing.tipo_item_OPME                  660848\n",
       "base_hackaturing.tipo_item_PROCEDIMENTO          660848\n",
       "base_hackaturing.tipo_item_TAXAS DIVERSAS        660848\n",
       "base_hackaturing.carater_atendimento_ELETIVO     660848\n",
       "base_hackaturing.carater_atendimento_URGENCIA    660848\n",
       "base_hackaturing.crm_solicitante                 608420\n",
       "base_hackaturing.pago_ou_nao                     660848\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>base_hackaturing.servico</th>\n",
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       "      <th>base_hackaturing.tipo_guia_Internacao</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>256200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>280022.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   base_hackaturing.servico  base_hackaturing.quantidade  \\\n",
       "0                   55851.0                          2.0   \n",
       "1                   61005.0                          1.0   \n",
       "2                  256200.0                        100.0   \n",
       "3                  280022.0                          1.0   \n",
       "4                   61005.0                          1.0   \n",
       "\n",
       "   base_hackaturing.valor_item  base_hackaturing.tipo_guia_Internacao  \\\n",
       "0                       5.9840                                      1   \n",
       "1                       6.1030                                      1   \n",
       "2                       0.8500                                      1   \n",
       "3                      15.5805                                      1   \n",
       "4                       6.1030                                      1   \n",
       "\n",
       "   base_hackaturing.tipo_item_GASES MEDICINAIS  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   base_hackaturing.tipo_item_MATERIAIS  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     1   \n",
       "3                                     1   \n",
       "4                                     0   \n",
       "\n",
       "   base_hackaturing.tipo_item_MEDICAMENTOS  base_hackaturing.tipo_item_OPME  \\\n",
       "0                                        1                                0   \n",
       "1                                        1                                0   \n",
       "2                                        0                                0   \n",
       "3                                        0                                0   \n",
       "4                                        1                                0   \n",
       "\n",
       "   base_hackaturing.tipo_item_PROCEDIMENTO  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   base_hackaturing.tipo_item_TAXAS DIVERSAS  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   base_hackaturing.carater_atendimento_ELETIVO  \\\n",
       "0                                             1   \n",
       "1                                             1   \n",
       "2                                             1   \n",
       "3                                             1   \n",
       "4                                             1   \n",
       "\n",
       "   base_hackaturing.carater_atendimento_URGENCIA  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   base_hackaturing.crm_solicitante  base_hackaturing.pago_ou_nao  \n",
       "0  d41d8cd98f00b204e9800998ecf8427e                             1  \n",
       "1  d41d8cd98f00b204e9800998ecf8427e                             1  \n",
       "2  d41d8cd98f00b204e9800998ecf8427e                             1  \n",
       "3  d41d8cd98f00b204e9800998ecf8427e                             1  \n",
       "4  d41d8cd98f00b204e9800998ecf8427e                             1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting intermediate base as csv to Desktop\n",
    "\n",
    "export_csv = df.to_csv(r'C:\\Users\\Bruno Koba\\Desktop\\export_dataframe.csv', index = None, header=True) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para a parte de modelagem, vamos tentar diferentes classifiers e observar quais oferecem a maior acurácia possível."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>base_hackaturing.servico</th>\n",
       "      <th>base_hackaturing.quantidade</th>\n",
       "      <th>base_hackaturing.valor_item</th>\n",
       "      <th>base_hackaturing.tipo_guia_Internacao</th>\n",
       "      <th>base_hackaturing.tipo_item_GASES MEDICINAIS</th>\n",
       "      <th>base_hackaturing.tipo_item_MATERIAIS</th>\n",
       "      <th>base_hackaturing.tipo_item_MEDICAMENTOS</th>\n",
       "      <th>base_hackaturing.tipo_item_OPME</th>\n",
       "      <th>base_hackaturing.tipo_item_PROCEDIMENTO</th>\n",
       "      <th>base_hackaturing.tipo_item_TAXAS DIVERSAS</th>\n",
       "      <th>base_hackaturing.carater_atendimento_ELETIVO</th>\n",
       "      <th>base_hackaturing.carater_atendimento_URGENCIA</th>\n",
       "      <th>base_hackaturing.pago_ou_nao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55851.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.9840</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>256200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>280022.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5805</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   base_hackaturing.servico  base_hackaturing.quantidade  \\\n",
       "0                   55851.0                          2.0   \n",
       "1                   61005.0                          1.0   \n",
       "2                  256200.0                        100.0   \n",
       "3                  280022.0                          1.0   \n",
       "4                   61005.0                          1.0   \n",
       "\n",
       "   base_hackaturing.valor_item  base_hackaturing.tipo_guia_Internacao  \\\n",
       "0                       5.9840                                      1   \n",
       "1                       6.1030                                      1   \n",
       "2                       0.8500                                      1   \n",
       "3                      15.5805                                      1   \n",
       "4                       6.1030                                      1   \n",
       "\n",
       "   base_hackaturing.tipo_item_GASES MEDICINAIS  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   base_hackaturing.tipo_item_MATERIAIS  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     1   \n",
       "3                                     1   \n",
       "4                                     0   \n",
       "\n",
       "   base_hackaturing.tipo_item_MEDICAMENTOS  base_hackaturing.tipo_item_OPME  \\\n",
       "0                                        1                                0   \n",
       "1                                        1                                0   \n",
       "2                                        0                                0   \n",
       "3                                        0                                0   \n",
       "4                                        1                                0   \n",
       "\n",
       "   base_hackaturing.tipo_item_PROCEDIMENTO  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   base_hackaturing.tipo_item_TAXAS DIVERSAS  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   base_hackaturing.carater_atendimento_ELETIVO  \\\n",
       "0                                             1   \n",
       "1                                             1   \n",
       "2                                             1   \n",
       "3                                             1   \n",
       "4                                             1   \n",
       "\n",
       "   base_hackaturing.carater_atendimento_URGENCIA  base_hackaturing.pago_ou_nao  \n",
       "0                                              0                             1  \n",
       "1                                              0                             1  \n",
       "2                                              0                             1  \n",
       "3                                              0                             1  \n",
       "4                                              0                             1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crm = df['base_hackaturing.crm_solicitante']\n",
    "df = df.drop(['base_hackaturing.crm_solicitante'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zero = df[df['base_hackaturing.pago_ou_nao'] == 0]\n",
    "df_one = df[df['base_hackaturing.pago_ou_nao'] == 1]\n",
    "\n",
    "df_one = df_one.sample(n=15000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_csv = df.to_csv(r'C:\\Users\\Bruno Koba\\Desktop\\final_dataframe.csv', index = None, header=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing dataset into X and y portions\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.concat([df_zero, df_one])\n",
    "\n",
    "X = df.iloc[:, :-1]  \n",
    "y = df.iloc[:, -1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "KNeighborsClassifier\n",
      "****Results****\n",
      "Accuracy: 87.0716%\n",
      "Log Loss: 2.6070691099662344\n",
      "==============================\n",
      "RandomForestClassifier\n",
      "****Results****\n",
      "Accuracy: 88.9574%\n",
      "Log Loss: 0.8236767533121541\n",
      "==============================\n",
      "AdaBoostClassifier\n",
      "****Results****\n",
      "Accuracy: 82.2847%\n",
      "Log Loss: 0.6828759575204646\n",
      "==============================\n",
      "GradientBoostingClassifier\n",
      "****Results****\n",
      "Accuracy: 85.2947%\n",
      "Log Loss: 0.3879372518640811\n",
      "==============================\n",
      "LinearDiscriminantAnalysis\n",
      "****Results****\n",
      "Accuracy: 70.4442%\n",
      "Log Loss: 0.5888101992322531\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29288bce400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29288b0eac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training and evaluating 5 differente classifiers, and saving the best performer as best_classifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    RandomForestClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GradientBoostingClassifier(),\n",
    "    LinearDiscriminantAnalysis()]\n",
    "    \n",
    "best_classifier = 0\n",
    "record_accuracy = 0\n",
    "\n",
    "# Logging for Visual Comparison\n",
    "log_cols=[\"Classifier\", \"Accuracy\", \"Log Loss\"]\n",
    "log = pd.DataFrame(columns=log_cols)\n",
    "\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, y_train)\n",
    "    name = clf.__class__.__name__\n",
    "    \n",
    "    print(\"=\"*30)\n",
    "    print(name)\n",
    "    \n",
    "    print('****Results****')\n",
    "    train_predictions = clf.predict(X_test)\n",
    "    acc = accuracy_score(y_test, train_predictions)\n",
    "    print(\"Accuracy: {:.4%}\".format(acc))\n",
    "    if acc > record_accuracy:\n",
    "        record_accuracy = acc\n",
    "        best_classifier = clf\n",
    "    \n",
    "    train_predictions = clf.predict_proba(X_test)\n",
    "    ll = log_loss(y_test, train_predictions)\n",
    "    print(\"Log Loss: {}\".format(ll))\n",
    "    \n",
    "    log_entry = pd.DataFrame([[name, acc*100, ll]], columns=log_cols)\n",
    "    log = log.append(log_entry)\n",
    "    \n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"b\")\n",
    "\n",
    "plt.xlabel('Accuracy %')\n",
    "plt.title('Classifier Accuracy')\n",
    "plt.show()\n",
    "\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Log Loss', y='Classifier', data=log, color=\"g\")\n",
    "\n",
    "plt.xlabel('Log Loss')\n",
    "plt.title('Classifier Log Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   nao_pago       0.84      0.93      0.89      2537\n",
      "       pago       0.94      0.85      0.89      2978\n",
      "\n",
      "avg / total       0.89      0.89      0.89      5515\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "best_classifier.fit(X_train, y_train)\n",
    "y_pred = best_classifier.predict(X_test)\n",
    "\n",
    "target_names = ['nao_pago', 'pago']\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2362,  175],\n",
       "       [ 437, 2541]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8921390552132006"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x292893b1ac8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29281a424a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from string import ascii_letters\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = df.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}