decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instalando as Dependencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn\n",
      "  Using cached scikit_learn-1.1.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (31.2 MB)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /home/vitoria/.local/lib/python3.8/site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.17.3 in /home/vitoria/.local/lib/python3.8/site-packages (from scikit-learn) (1.23.3)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=1.3.2 in /home/vitoria/.local/lib/python3.8/site-packages (from scikit-learn) (1.9.3)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=1.0.0 in /home/vitoria/.local/lib/python3.8/site-packages (from scikit-learn) (1.2.0)\n",
      "Installing collected packages: scikit-learn\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 0.24.1\n",
      "    Uninstalling scikit-learn-0.24.1:\n",
      "      Successfully uninstalled scikit-learn-0.24.1\n",
      "Successfully installed scikit-learn-1.1.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydotplus in /home/vitoria/.local/lib/python3.8/site-packages (2.0.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in /home/vitoria/.local/lib/python3.8/site-packages (from pydotplus) (3.0.9)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
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       "       Unnamed: 0  nivelbairro  serie  genero  resultado  IN_AGUA_FILTRADA  \\\n",
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     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caminhoBase = \"data/database/data.csv\"\n",
    "df = pd.read_csv(caminhoBase)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "tipos = df.dtypes\n",
    "atributos = df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inserindo cada atributo da tabela resultante em um vetor para melhor manipulação dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "vetor_nivelbairro = np.array(df.nivelbairro)\n",
    "vetor_serie = np.array(df.serie)\n",
    "vetor_genero = np.array(df.genero)\n",
    "vetor_resultado = np.array(df.resultado)\n",
    "vetor_IN_AGUA_FILTRADA = np.array(df.IN_AGUA_FILTRADA)\n",
    "vetor_IN_ENERGIA_REDE_PUBLICA = np.array(df.IN_ENERGIA_REDE_PUBLICA)\n",
    "vetor_IN_AGUA_FILTRADA = np.array(df.IN_AGUA_FILTRADA)\n",
    "vetor_IN_LABORATORIO_INFORMATICA = np.array(df.IN_LABORATORIO_INFORMATICA)\n",
    "vetor_IN_QUADRA_ESPORTES_COBERTA = np.array(df.IN_QUADRA_ESPORTES_COBERTA)\n",
    "vetor_IN_COZINHA = np.array(df.IN_COZINHA)\n",
    "vetor_IN_REFEITORIO = np.array(df.IN_REFEITORIO)\n",
    "vetor_IN_INTERNET = np.array(df.IN_INTERNET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo um novo dataframe vazio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo as colunas do novo dataframe e atribuindo para cada coluna, seu respectivo vetor de dados registrado anteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLUNAS = [\n",
    "'nivelbairro',\n",
    "'serie',\n",
    "'genero',\n",
    "'resultado',\n",
    "'IN_AGUA_FILTRADA',\n",
    "'IN_ENERGIA_REDE_PUBLICA',\n",
    "'IN_LABORATORIO_INFORMATICA',\n",
    "'IN_QUADRA_ESPORTES_COBERTA',\n",
    "'IN_COZINHA',\n",
    "'IN_REFEITORIO',\n",
    "'IN_INTERNET',\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(columns = COLUNAS)\n",
    "\n",
    "df.nivelbairro = vetor_nivelbairro \n",
    "df.serie = vetor_serie\n",
    "df.genero = vetor_genero \n",
    "df.resultado = vetor_resultado\n",
    "df.IN_AGUA_FILTRADA = vetor_IN_AGUA_FILTRADA \n",
    "df.IN_ENERGIA_REDE_PUBLICA = vetor_IN_ENERGIA_REDE_PUBLICA \n",
    "df.IN_LABORATORIO_INFORMATICA = vetor_IN_LABORATORIO_INFORMATICA \n",
    "df.IN_QUADRA_ESPORTES_COBERTA = vetor_IN_QUADRA_ESPORTES_COBERTA \n",
    "df.IN_COZINHA = vetor_IN_COZINHA \n",
    "df.IN_REFEITORIO = vetor_IN_REFEITORIO\n",
    "df.IN_INTERNET = vetor_IN_INTERNET\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90741</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90742</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90743</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90744</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90745</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90746 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nivelbairro  serie  genero  resultado  IN_AGUA_FILTRADA  \\\n",
       "0                1      1       2          0               1.0   \n",
       "1                1      1       2          0               1.0   \n",
       "2                1      1       1          0               1.0   \n",
       "3                1      1       1          0               1.0   \n",
       "4                1      1       1          0               1.0   \n",
       "...            ...    ...     ...        ...               ...   \n",
       "90741            4      1       2          0               1.0   \n",
       "90742            4      1       2          0               1.0   \n",
       "90743            4      1       2          0               1.0   \n",
       "90744            4      1       2          0               1.0   \n",
       "90745            4      1       1          0               1.0   \n",
       "\n",
       "       IN_ENERGIA_REDE_PUBLICA  IN_LABORATORIO_INFORMATICA  \\\n",
       "0                          1.0                         0.0   \n",
       "1                          1.0                         0.0   \n",
       "2                          1.0                         0.0   \n",
       "3                          1.0                         0.0   \n",
       "4                          1.0                         0.0   \n",
       "...                        ...                         ...   \n",
       "90741                      1.0                         0.0   \n",
       "90742                      1.0                         0.0   \n",
       "90743                      1.0                         0.0   \n",
       "90744                      1.0                         0.0   \n",
       "90745                      1.0                         0.0   \n",
       "\n",
       "       IN_QUADRA_ESPORTES_COBERTA  IN_COZINHA  IN_REFEITORIO  IN_INTERNET  \n",
       "0                             0.0         1.0            0.0          1.0  \n",
       "1                             0.0         1.0            0.0          1.0  \n",
       "2                             0.0         1.0            0.0          1.0  \n",
       "3                             0.0         1.0            0.0          1.0  \n",
       "4                             0.0         1.0            0.0          1.0  \n",
       "...                           ...         ...            ...          ...  \n",
       "90741                         0.0         1.0            0.0          0.0  \n",
       "90742                         0.0         1.0            0.0          0.0  \n",
       "90743                         0.0         1.0            0.0          0.0  \n",
       "90744                         0.0         1.0            0.0          0.0  \n",
       "90745                         0.0         1.0            0.0          0.0  \n",
       "\n",
       "[90746 rows x 11 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impressão da coluna da situacao dos alunos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "90741    0\n",
       "90742    0\n",
       "90743    0\n",
       "90744    0\n",
       "90745    0\n",
       "Name: resultado, Length: 90746, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resultado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Informações do dataframe\n",
    "\n",
    "Atributos, elementos não nulos, Tipo das variáveis da coluna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90746 entries, 0 to 90745\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   nivelbairro                 90746 non-null  int64  \n",
      " 1   serie                       90746 non-null  int64  \n",
      " 2   genero                      90746 non-null  int64  \n",
      " 3   resultado                   90746 non-null  int64  \n",
      " 4   IN_AGUA_FILTRADA            90746 non-null  float64\n",
      " 5   IN_ENERGIA_REDE_PUBLICA     90746 non-null  float64\n",
      " 6   IN_LABORATORIO_INFORMATICA  90746 non-null  float64\n",
      " 7   IN_QUADRA_ESPORTES_COBERTA  90746 non-null  float64\n",
      " 8   IN_COZINHA                  90746 non-null  float64\n",
      " 9   IN_REFEITORIO               90746 non-null  float64\n",
      " 10  IN_INTERNET                 90746 non-null  float64\n",
      "dtypes: float64(7), int64(4)\n",
      "memory usage: 7.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando os elementos do dataframe por resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resultado\n",
       "0    88799\n",
       "1     1947\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='resultado').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importando os métodos de árvore de decisão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criação de um dataframe reserva para possíveis conclusões"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nivelbairro</th>\n",
       "      <th>serie</th>\n",
       "      <th>genero</th>\n",
       "      <th>resultado</th>\n",
       "      <th>IN_AGUA_FILTRADA</th>\n",
       "      <th>IN_ENERGIA_REDE_PUBLICA</th>\n",
       "      <th>IN_LABORATORIO_INFORMATICA</th>\n",
       "      <th>IN_QUADRA_ESPORTES_COBERTA</th>\n",
       "      <th>IN_COZINHA</th>\n",
       "      <th>IN_REFEITORIO</th>\n",
       "      <th>IN_INTERNET</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <th>90741</th>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>90742</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>90743</th>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>90744</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>90745</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90746 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nivelbairro  serie  genero  resultado  IN_AGUA_FILTRADA  \\\n",
       "0                1      1       2          0               1.0   \n",
       "1                1      1       2          0               1.0   \n",
       "2                1      1       1          0               1.0   \n",
       "3                1      1       1          0               1.0   \n",
       "4                1      1       1          0               1.0   \n",
       "...            ...    ...     ...        ...               ...   \n",
       "90741            4      1       2          0               1.0   \n",
       "90742            4      1       2          0               1.0   \n",
       "90743            4      1       2          0               1.0   \n",
       "90744            4      1       2          0               1.0   \n",
       "90745            4      1       1          0               1.0   \n",
       "\n",
       "       IN_ENERGIA_REDE_PUBLICA  IN_LABORATORIO_INFORMATICA  \\\n",
       "0                          1.0                         0.0   \n",
       "1                          1.0                         0.0   \n",
       "2                          1.0                         0.0   \n",
       "3                          1.0                         0.0   \n",
       "4                          1.0                         0.0   \n",
       "...                        ...                         ...   \n",
       "90741                      1.0                         0.0   \n",
       "90742                      1.0                         0.0   \n",
       "90743                      1.0                         0.0   \n",
       "90744                      1.0                         0.0   \n",
       "90745                      1.0                         0.0   \n",
       "\n",
       "       IN_QUADRA_ESPORTES_COBERTA  IN_COZINHA  IN_REFEITORIO  IN_INTERNET  \n",
       "0                             0.0         1.0            0.0          1.0  \n",
       "1                             0.0         1.0            0.0          1.0  \n",
       "2                             0.0         1.0            0.0          1.0  \n",
       "3                             0.0         1.0            0.0          1.0  \n",
       "4                             0.0         1.0            0.0          1.0  \n",
       "...                           ...         ...            ...          ...  \n",
       "90741                         0.0         1.0            0.0          0.0  \n",
       "90742                         0.0         1.0            0.0          0.0  \n",
       "90743                         0.0         1.0            0.0          0.0  \n",
       "90744                         0.0         1.0            0.0          0.0  \n",
       "90745                         0.0         1.0            0.0          0.0  \n",
       "\n",
       "[90746 rows x 11 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reserva = result_df\n",
    "reserva"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processo de separação de treine/teste\n",
    "\n",
    "Proporções estabelecidas:\n",
    "\n",
    "70% treino\n",
    "\n",
    "30% teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(result_df.drop('resultado', axis=1), result_df['resultado'], test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Número de amostras para cada processo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63522, 10), (27224, 10))"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Número de targets para cada processo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63522,), (27224,))"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Criação do classifcador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treinamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nivelbairro</th>\n",
       "      <th>serie</th>\n",
       "      <th>genero</th>\n",
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      "text/plain": [
       "       nivelbairro  serie  genero  resultado  IN_AGUA_FILTRADA  \\\n",
       "0                1      1       2          0               1.0   \n",
       "1                1      1       2          0               1.0   \n",
       "2                1      1       1          0               1.0   \n",
       "3                1      1       1          0               1.0   \n",
       "4                1      1       1          0               1.0   \n",
       "...            ...    ...     ...        ...               ...   \n",
       "90741            4      1       2          0               1.0   \n",
       "90742            4      1       2          0               1.0   \n",
       "90743            4      1       2          0               1.0   \n",
       "90744            4      1       2          0               1.0   \n",
       "90745            4      1       1          0               1.0   \n",
       "\n",
       "       IN_ENERGIA_REDE_PUBLICA  IN_LABORATORIO_INFORMATICA  \\\n",
       "0                          1.0                         0.0   \n",
       "1                          1.0                         0.0   \n",
       "2                          1.0                         0.0   \n",
       "3                          1.0                         0.0   \n",
       "4                          1.0                         0.0   \n",
       "...                        ...                         ...   \n",
       "90741                      1.0                         0.0   \n",
       "90742                      1.0                         0.0   \n",
       "90743                      1.0                         0.0   \n",
       "90744                      1.0                         0.0   \n",
       "90745                      1.0                         0.0   \n",
       "\n",
       "       IN_QUADRA_ESPORTES_COBERTA  IN_COZINHA  IN_REFEITORIO  IN_INTERNET  \n",
       "0                             0.0         1.0            0.0          1.0  \n",
       "1                             0.0         1.0            0.0          1.0  \n",
       "2                             0.0         1.0            0.0          1.0  \n",
       "3                             0.0         1.0            0.0          1.0  \n",
       "4                             0.0         1.0            0.0          1.0  \n",
       "...                           ...         ...            ...          ...  \n",
       "90741                         0.0         1.0            0.0          0.0  \n",
       "90742                         0.0         1.0            0.0          0.0  \n",
       "90743                         0.0         1.0            0.0          0.0  \n",
       "90744                         0.0         1.0            0.0          0.0  \n",
       "90745                         0.0         1.0            0.0          0.0  \n",
       "\n",
       "[90746 rows x 11 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = cls.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vetor com as importancias de cada atributo para a determinação do review_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13303779, 0.38392857, 0.05485469, 0.02609931, 0.00304726,\n",
       "       0.0825506 , 0.10105947, 0.04794994, 0.08714475, 0.08032761])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para tornar o modelo mais visual, criamos um laço para a impressão dos pesos de cada atributo para determinar o resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nivelbairro:13.3%\n",
      "serie:38.4%\n",
      "genero:5.5%\n",
      "resultado:2.6%\n",
      "IN_AGUA_FILTRADA:0.3%\n",
      "IN_ENERGIA_REDE_PUBLICA:8.3%\n",
      "IN_LABORATORIO_INFORMATICA:10.1%\n",
      "IN_QUADRA_ESPORTES_COBERTA:4.8%\n",
      "IN_COZINHA:8.7%\n",
      "IN_REFEITORIO:8.0%\n"
     ]
    }
   ],
   "source": [
    "for feature, importancia in zip(result_df.columns, cls.feature_importances_):\n",
    "    print(\"{}:{:.1f}%\".format(feature,((importancia*100))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vetor com predições para checagem do aprendizado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = cls.predict(X_test)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Representação das métricas de precisão e médias do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     26681\n",
      "           1       0.00      0.00      0.00       543\n",
      "\n",
      "    accuracy                           0.98     27224\n",
      "   macro avg       0.49      0.50      0.49     27224\n",
      "weighted avg       0.96      0.98      0.97     27224\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vitoria/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/vitoria/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/vitoria/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test,result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precisão total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9778816811788079"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "allScores = cross_val_score(cls, X_train, y_train , cv=10)\n",
    "allScores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treinamento utilizando o dataframe reserva. (apenas com os alunos que evadiram)\n",
    "\n",
    "Split do dataframe em treino e teste (70%, 30%, respectivamente)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(reserva.drop('resultado', axis=1), reserva['resultado'], test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantidade de amostras do treino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63522, 10), (27224, 10))"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classificador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importancia de cada atributo para determinar o nível de satisfação dos consumidores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11025712, 0.38332579, 0.04508569, 0.05193873, 0.01434466,\n",
       "       0.077097  , 0.07130966, 0.04845111, 0.09896214, 0.09922809])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nivelbairro:11.0%\n",
      "serie:38.3%\n",
      "genero:4.5%\n",
      "resultado:5.2%\n",
      "IN_AGUA_FILTRADA:1.4%\n",
      "IN_ENERGIA_REDE_PUBLICA:7.7%\n",
      "IN_LABORATORIO_INFORMATICA:7.1%\n",
      "IN_QUADRA_ESPORTES_COBERTA:4.8%\n",
      "IN_COZINHA:9.9%\n",
      "IN_REFEITORIO:9.9%\n"
     ]
    }
   ],
   "source": [
    "for feature, importancia in zip(reserva.columns, clf.feature_importances_):\n",
    "    print(\"{}:{:.1f}%\".format(feature,((importancia*100))))"
   ]
  }
 ],
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