estudantes_ia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## **🚀 Projeto - Desempenho Acadêmico**\n",
    "\n",
    "### **Inteligência Artificial e Previsões**:  \n",
    "**Criar um modelo que consiga ler as informações dos estudantes e dizer automaticamente a classificação de suas notas: A, B, C, D, F**\n",
    "\n",
    "Analisar o dataframe e com base na análise criar o modelo de Inteligência Artificial e Previsões.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Passos do Projeto ##\n",
    "\n",
    "Passo 1 - Importar o DataFrame\n",
    "\n",
    "Passo 2 - Visualizar o DataFrame\n",
    "\n",
    "Passo 3 - Corrigir erros no DataFrame, modelar e entender os dados\n",
    "\n",
    "Passo 4 - Preparar o dataframe para a Inteligência Artificial\n",
    "\n",
    "Passo 5 - Treinar a Inteligência Artificial\n",
    "\n",
    "Passo 6 - Escolher o melhor modelo\n",
    "\n",
    "Passo 7 - Usar o melhor modelo\n",
    "\n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Passo 1 - Importar o DataFrame\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StudentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>ParentalEducation</th>\n",
       "      <th>StudyTimeWeekly</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      StudentID  Age  Gender  Ethnicity  ParentalEducation  StudyTimeWeekly  \\\n",
       "0          1001   17       1          0                  2        19.833723   \n",
       "1          1002   18       0          0                  1        15.408756   \n",
       "2          1003   15       0          2                  3         4.210570   \n",
       "3          1004   17       1          0                  3        10.028829   \n",
       "4          1005   17       1          0                  2         4.672495   \n",
       "...         ...  ...     ...        ...                ...              ...   \n",
       "2387       3388   18       1          0                  3        10.680555   \n",
       "2388       3389   17       0          0                  1         7.583217   \n",
       "2389       3390   16       1          0                  2         6.805500   \n",
       "2390       3391   16       1          1                  0        12.416653   \n",
       "2391       3392   16       1          0                  2        17.819907   \n",
       "\n",
       "      Absences  Tutoring  ParentalSupport  Extracurricular  Sports  Music  \\\n",
       "0            7         1                2                0       0      1   \n",
       "1            0         0                1                0       0      0   \n",
       "2           26         0                2                0       0      0   \n",
       "3           14         0                3                1       0      0   \n",
       "4           17         1                3                0       0      0   \n",
       "...        ...       ...              ...              ...     ...    ...   \n",
       "2387         2         0                4                1       0      0   \n",
       "2388         4         1                4                0       1      0   \n",
       "2389        20         0                2                0       0      0   \n",
       "2390        17         0                2                0       1      1   \n",
       "2391        13         0                2                0       0      0   \n",
       "\n",
       "      Volunteering       GPA  GradeClass  \n",
       "0                0  2.929196         2.0  \n",
       "1                0  3.042915         1.0  \n",
       "2                0  0.112602         4.0  \n",
       "3                0  2.054218         3.0  \n",
       "4                0  1.288061         4.0  \n",
       "...            ...       ...         ...  \n",
       "2387             0  3.455509         0.0  \n",
       "2388             0  3.279150         4.0  \n",
       "2389             1  1.142333         2.0  \n",
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       "2391             1  2.140014         1.0  \n",
       "\n",
       "[2392 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importando as bibliotecas necessárias\n",
    "import pandas as pd\n",
    "\n",
    "# Importando o dataset\n",
    "df_dados_estudantes = pd.read_csv(\"dados_estudantes.csv\", sep= ',')\n",
    "\n",
    "# Verificando as primeiras linhas do dataset\n",
    "display(df_dados_estudantes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
       "      <th>Faltas</th>\n",
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       "      <th>Apoio_Pais</th>\n",
       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
       "      <th>Musica</th>\n",
       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_Aluno  Idade  Genero  Etnia  Escolaridade_Pais  Horas_Estudo_Semanais  \\\n",
       "0      1001     17       1      0                  2              19.833723   \n",
       "1      1002     18       0      0                  1              15.408756   \n",
       "2      1003     15       0      2                  3               4.210570   \n",
       "3      1004     17       1      0                  3              10.028829   \n",
       "4      1005     17       1      0                  2               4.672495   \n",
       "\n",
       "   Faltas  Aulas_Particulares  Apoio_Pais  Atividades_Extracurriculares  \\\n",
       "0       7                   1           2                             0   \n",
       "1       0                   0           1                             0   \n",
       "2      26                   0           2                             0   \n",
       "3      14                   0           3                             1   \n",
       "4      17                   1           3                             0   \n",
       "\n",
       "   Esportes  Musica  Voluntariado  Media_Notas  Classificacao_Notas  \n",
       "0         0       1             0     2.929196                  2.0  \n",
       "1         0       0             0     3.042915                  1.0  \n",
       "2         0       0             0     0.112602                  4.0  \n",
       "3         0       0             0     2.054218                  3.0  \n",
       "4         0       0             0     1.288061                  4.0  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traducao_colunas = {\n",
    "    \"StudentID\": \"ID_Aluno\",\n",
    "    \"Age\": \"Idade\",\n",
    "    \"Gender\": \"Genero\",\n",
    "    \"Ethnicity\": \"Etnia\",\n",
    "    \"ParentalEducation\": \"Escolaridade_Pais\",\n",
    "    \"StudyTimeWeekly\": \"Horas_Estudo_Semanais\",\n",
    "    \"Absences\": \"Faltas\",\n",
    "    \"Tutoring\": \"Aulas_Particulares\",\n",
    "    \"ParentalSupport\": \"Apoio_Pais\",\n",
    "    \"Extracurricular\": \"Atividades_Extracurriculares\",\n",
    "    \"Sports\": \"Esportes\",\n",
    "    \"Music\": \"Musica\",\n",
    "    \"Volunteering\": \"Voluntariado\",\n",
    "    \"GPA\": \"Media_Notas\",\n",
    "    \"GradeClass\": \"Classificacao_Notas\"\n",
    "}\n",
    "\n",
    "# Renomear as colunas\n",
    "df_dados_estudantes = df_dados_estudantes.rename(columns=traducao_colunas)\n",
    "\n",
    "df_dados_estudantes.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passo 2 - Visualizar o DataFrame\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2392, 15)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usando shape para conferir as dimensões do dataframe\n",
    "df_dados_estudantes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID_Aluno                          int64\n",
       "Idade                             int64\n",
       "Genero                            int64\n",
       "Etnia                             int64\n",
       "Escolaridade_Pais                 int64\n",
       "Horas_Estudo_Semanais           float64\n",
       "Faltas                            int64\n",
       "Aulas_Particulares                int64\n",
       "Apoio_Pais                        int64\n",
       "Atividades_Extracurriculares      int64\n",
       "Esportes                          int64\n",
       "Musica                            int64\n",
       "Voluntariado                      int64\n",
       "Media_Notas                     float64\n",
       "Classificacao_Notas             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conferindo os tipos de dados utilizados no dataframe com dtypes\n",
    "df_dados_estudantes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2392 entries, 0 to 2391\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   ID_Aluno                      2392 non-null   int64  \n",
      " 1   Idade                         2392 non-null   int64  \n",
      " 2   Genero                        2392 non-null   int64  \n",
      " 3   Etnia                         2392 non-null   int64  \n",
      " 4   Escolaridade_Pais             2392 non-null   int64  \n",
      " 5   Horas_Estudo_Semanais         2392 non-null   float64\n",
      " 6   Faltas                        2392 non-null   int64  \n",
      " 7   Aulas_Particulares            2392 non-null   int64  \n",
      " 8   Apoio_Pais                    2392 non-null   int64  \n",
      " 9   Atividades_Extracurriculares  2392 non-null   int64  \n",
      " 10  Esportes                      2392 non-null   int64  \n",
      " 11  Musica                        2392 non-null   int64  \n",
      " 12  Voluntariado                  2392 non-null   int64  \n",
      " 13  Media_Notas                   2392 non-null   float64\n",
      " 14  Classificacao_Notas           2392 non-null   float64\n",
      "dtypes: float64(3), int64(12)\n",
      "memory usage: 280.4 KB\n"
     ]
    }
   ],
   "source": [
    "# Visualizando as informaçoes do dataframe com info\n",
    "df_dados_estudantes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID_Aluno                        0\n",
       "Idade                           0\n",
       "Genero                          0\n",
       "Etnia                           0\n",
       "Escolaridade_Pais               0\n",
       "Horas_Estudo_Semanais           0\n",
       "Faltas                          0\n",
       "Aulas_Particulares              0\n",
       "Apoio_Pais                      0\n",
       "Atividades_Extracurriculares    0\n",
       "Esportes                        0\n",
       "Musica                          0\n",
       "Voluntariado                    0\n",
       "Media_Notas                     0\n",
       "Classificacao_Notas             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conferir se existe valores ausentes no dataframe\n",
    "df_dados_estudantes.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
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       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2196.500000</td>\n",
       "      <td>16.468645</td>\n",
       "      <td>0.510870</td>\n",
       "      <td>0.877508</td>\n",
       "      <td>1.746237</td>\n",
       "      <td>9.771992</td>\n",
       "      <td>14.541388</td>\n",
       "      <td>0.301421</td>\n",
       "      <td>2.122074</td>\n",
       "      <td>0.383361</td>\n",
       "      <td>0.303512</td>\n",
       "      <td>0.196906</td>\n",
       "      <td>0.157191</td>\n",
       "      <td>1.906186</td>\n",
       "      <td>2.983696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>690.655244</td>\n",
       "      <td>1.123798</td>\n",
       "      <td>0.499986</td>\n",
       "      <td>1.028476</td>\n",
       "      <td>1.000411</td>\n",
       "      <td>5.652774</td>\n",
       "      <td>8.467417</td>\n",
       "      <td>0.458971</td>\n",
       "      <td>1.122813</td>\n",
       "      <td>0.486307</td>\n",
       "      <td>0.459870</td>\n",
       "      <td>0.397744</td>\n",
       "      <td>0.364057</td>\n",
       "      <td>0.915156</td>\n",
       "      <td>1.233908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1598.750000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.043079</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.174803</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2196.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.705363</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.893393</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2794.250000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.408410</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.622216</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3392.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19.978094</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID_Aluno        Idade       Genero        Etnia  Escolaridade_Pais  \\\n",
       "count  2392.000000  2392.000000  2392.000000  2392.000000        2392.000000   \n",
       "mean   2196.500000    16.468645     0.510870     0.877508           1.746237   \n",
       "std     690.655244     1.123798     0.499986     1.028476           1.000411   \n",
       "min    1001.000000    15.000000     0.000000     0.000000           0.000000   \n",
       "25%    1598.750000    15.000000     0.000000     0.000000           1.000000   \n",
       "50%    2196.500000    16.000000     1.000000     0.000000           2.000000   \n",
       "75%    2794.250000    17.000000     1.000000     2.000000           2.000000   \n",
       "max    3392.000000    18.000000     1.000000     3.000000           4.000000   \n",
       "\n",
       "       Horas_Estudo_Semanais       Faltas  Aulas_Particulares   Apoio_Pais  \\\n",
       "count            2392.000000  2392.000000         2392.000000  2392.000000   \n",
       "mean                9.771992    14.541388            0.301421     2.122074   \n",
       "std                 5.652774     8.467417            0.458971     1.122813   \n",
       "min                 0.001057     0.000000            0.000000     0.000000   \n",
       "25%                 5.043079     7.000000            0.000000     1.000000   \n",
       "50%                 9.705363    15.000000            0.000000     2.000000   \n",
       "75%                14.408410    22.000000            1.000000     3.000000   \n",
       "max                19.978094    29.000000            1.000000     4.000000   \n",
       "\n",
       "       Atividades_Extracurriculares     Esportes       Musica  Voluntariado  \\\n",
       "count                   2392.000000  2392.000000  2392.000000   2392.000000   \n",
       "mean                       0.383361     0.303512     0.196906      0.157191   \n",
       "std                        0.486307     0.459870     0.397744      0.364057   \n",
       "min                        0.000000     0.000000     0.000000      0.000000   \n",
       "25%                        0.000000     0.000000     0.000000      0.000000   \n",
       "50%                        0.000000     0.000000     0.000000      0.000000   \n",
       "75%                        1.000000     1.000000     0.000000      0.000000   \n",
       "max                        1.000000     1.000000     1.000000      1.000000   \n",
       "\n",
       "       Media_Notas  Classificacao_Notas  \n",
       "count  2392.000000          2392.000000  \n",
       "mean      1.906186             2.983696  \n",
       "std       0.915156             1.233908  \n",
       "min       0.000000             0.000000  \n",
       "25%       1.174803             2.000000  \n",
       "50%       1.893393             4.000000  \n",
       "75%       2.622216             4.000000  \n",
       "max       4.000000             4.000000  "
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Por fim fazer uma análise descritiva do dataframe\n",
    "df_dados_estudantes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passo 3 - Corrigir erros no DataFrame, modelar e entender os dados\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para seguir os padrões apresentados na Descrição das Colunas do DataFrame, irei alterar o tipo de dados de algumas colunas, seguindo o padrão descrito abaixo:\n",
    "\n",
    "## **📊 Descrição das Colunas do DataFrame**\n",
    "\n",
    "### **Identificação do Estudante**  \n",
    "- **ID_Aluno**: Identificador único para cada aluno (de 1001 a 3392).\n",
    "\n",
    "### **👥 Detalhes Demográficos**  \n",
    "- **Idade**: Idade dos estudantes, variando de 15 a 18 anos.  \n",
    "- **Genero**: Gênero do estudante:\n",
    "  - 0: Masculino\n",
    "  - 1: Feminino  \n",
    "- **Etnia**: Etnia dos estudantes:\n",
    "  - 0: Caucasiano\n",
    "  - 1: Afrodescendente\n",
    "  - 2: Asiático\n",
    "  - 3: Outra\n",
    "\n",
    "### **👨‍👩‍👧 Educação dos Pais**  \n",
    "- **Escolaridade_Pais**: Nível de escolaridade dos pais:\n",
    "  - 0: Nenhum\n",
    "  - 1: Ensino Médio\n",
    "  - 2: Alguma Faculdade\n",
    "  - 3: Graduação\n",
    "  - 4: Pós-Graduação ou Superior\n",
    "\n",
    "### **📚 Hábitos de Estudo**  \n",
    "- **Horas_Estudo_Semanais**: Tempo de estudo semanal em horas (0 a 20).  \n",
    "- **Faltas**: Número de faltas no ano letivo (0 a 30).  \n",
    "- **Aulas_Particulares**: Participação em aulas particulares:\n",
    "  - 0: Não\n",
    "  - 1: Sim\n",
    "\n",
    "### **👨‍👩‍👧 Envolvimento Parental**  \n",
    "- **Apoio_Pais**: Nível de apoio dos pais:\n",
    "  - 0: Nenhum\n",
    "  - 1: Baixo\n",
    "  - 2: Moderado\n",
    "  - 3: Alto\n",
    "  - 4: Muito Alto\n",
    "\n",
    "### **🏫 Atividades Extracurriculares**  \n",
    "- **Atividades_Extracurriculares**: Participação em atividades extracurriculares:\n",
    "  - 0: Não\n",
    "  - 1: Sim  \n",
    "- **Esportes**: Participação em esportes:\n",
    "  - 0: Não\n",
    "  - 1: Sim  \n",
    "- **Musica**: Participação em atividades musicais:\n",
    "  - 0: Não\n",
    "  - 1: Sim  \n",
    "- **Voluntariado**: Participação em atividades de voluntariado:\n",
    "  - 0: Não\n",
    "  - 1: Sim\n",
    "\n",
    "### **📈 Desempenho Acadêmico**  \n",
    "- **Media_Notas**: Média das notas (GPA), variando de 2.0 a 4.0.  \n",
    "- **Classificacao_Notas**: Classificação do aluno com base na média das notas:\n",
    "  - 0: 'A' (Média >= 3.5)\n",
    "  - 1: 'B' (3.0 <= Média < 3.5)\n",
    "  - 2: 'C' (2.5 <= Média < 3.0)\n",
    "  - 3: 'D' (2.0 <= Média < 2.5)\n",
    "  - 4: 'F' (Média < 2.0)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando a coluna Media_Notas para float com 1 casa decimal\n",
    "df_dados_estudantes[[\"Media_Notas\"]] = df_dados_estudantes[[\"Media_Notas\"]].astype(float).round(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID_Aluno                          int64\n",
       "Idade                             int64\n",
       "Genero                            int64\n",
       "Etnia                             int64\n",
       "Escolaridade_Pais                 int64\n",
       "Horas_Estudo_Semanais             int64\n",
       "Faltas                            int64\n",
       "Aulas_Particulares                int64\n",
       "Apoio_Pais                        int64\n",
       "Atividades_Extracurriculares      int64\n",
       "Esportes                          int64\n",
       "Musica                            int64\n",
       "Voluntariado                      int64\n",
       "Media_Notas                     float64\n",
       "Classificacao_Notas               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alterando as colunas Horas_Estudo_Semanais e Classificacao_Notas para Int\n",
    "df_dados_estudantes[[\"Horas_Estudo_Semanais\", \n",
    "                     \"Classificacao_Notas\"]] = df_dados_estudantes[[\"Horas_Estudo_Semanais\", \n",
    "                                                                    \"Classificacao_Notas\"]].astype(int)\n",
    "\n",
    "# Visualizando a alteração das 3 colunas\n",
    "df_dados_estudantes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_Aluno  Idade  Genero  Etnia  Escolaridade_Pais  Horas_Estudo_Semanais  \\\n",
       "0      1001     17       1      0                  2                     19   \n",
       "1      1002     18       0      0                  1                     15   \n",
       "2      1003     15       0      2                  3                      4   \n",
       "3      1004     17       1      0                  3                     10   \n",
       "4      1005     17       1      0                  2                      4   \n",
       "\n",
       "   Faltas  Aulas_Particulares  Apoio_Pais  Atividades_Extracurriculares  \\\n",
       "0       7                   1           2                             0   \n",
       "1       0                   0           1                             0   \n",
       "2      26                   0           2                             0   \n",
       "3      14                   0           3                             1   \n",
       "4      17                   1           3                             0   \n",
       "\n",
       "   Esportes  Musica  Voluntariado  Media_Notas  Classificacao_Notas  \n",
       "0         0       1             0          2.9                    2  \n",
       "1         0       0             0          3.0                    1  \n",
       "2         0       0             0          0.1                    4  \n",
       "3         0       0             0          2.1                    3  \n",
       "4         0       0             0          1.3                    4  "
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando o dataframe após as alterações\n",
    "df_dados_estudantes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O ID_Aluno está sendo contando de 1001 à 3392, para alterar a contagem para 1 à 2392 usarei o método apply lambda para \n",
    "# fazer essa alteração.\n",
    "\n",
    "# Alterando os valores do ID_Aluno para contar do número 1 em diante\n",
    "df_dados_estudantes[\"ID_Aluno\"] = df_dados_estudantes[\"ID_Aluno\"].apply(lambda x: x - 1000)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para alterar os valores numéricos pelos seus respectivos valores informados na descrição, usarei o método replace \n",
    "# para reverter os números cadastrados em seus devidos valores informados.\n",
    "\n",
    "# Alterando os valores da descrição da coluna Gênero\n",
    "df_dados_estudantes[\"Genero\"] = df_dados_estudantes[\"Genero\"].replace({\n",
    "     0: \"Masculino\", \n",
    "     1: \"Feminino\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Etnia\n",
    "df_dados_estudantes[\"Etnia\"] = df_dados_estudantes[\"Etnia\"].replace({\n",
    "    0: \"Caucasiano\", \n",
    "    1: \"Afrodescendente\", \n",
    "    2: \"Asiático\", \n",
    "    3: \"Outra\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Escolaridade dos Pais\n",
    "df_dados_estudantes[\"Escolaridade_Pais\"] = df_dados_estudantes[\"Escolaridade_Pais\"].replace({\n",
    "     0: \"Nenhum\", \n",
    "     1: \"Ensino Médio\", \n",
    "     2: \"Alguma Faculdade\", \n",
    "     3: \"Graduação\", \n",
    "     4: \"Pós-Graduação ou Superior\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Aulas Particulares\n",
    "df_dados_estudantes[\"Aulas_Particulares\"] = df_dados_estudantes[\"Aulas_Particulares\"].replace({\n",
    "    0: \"Não\", \n",
    "    1: \"Sim\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Apoio dos Pais\n",
    "df_dados_estudantes[\"Apoio_Pais\"] = df_dados_estudantes[\"Apoio_Pais\"].replace({\n",
    "    0: \"Nenhum\", \n",
    "    1: \"Baixo\", \n",
    "    2: \"Moderado\", \n",
    "    3: \"Alto\", \n",
    "    4: \"Muito Alto\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Atividades Extracurriculares\n",
    "df_dados_estudantes[\"Atividades_Extracurriculares\"] = df_dados_estudantes[\"Atividades_Extracurriculares\"].replace({\n",
    "    0: \"Não\",\n",
    "    1: \"Sim\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Esportes\n",
    "df_dados_estudantes[\"Esportes\"] = df_dados_estudantes[\"Esportes\"].replace({\n",
    "     0: \"Não\", \n",
    "     1: \"Sim\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Musica\n",
    "df_dados_estudantes[\"Musica\"] = df_dados_estudantes[\"Musica\"].replace({\n",
    "    0: \"Não\", \n",
    "    1: \"Sim\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Voluntariado\n",
    "df_dados_estudantes[\"Voluntariado\"] = df_dados_estudantes[\"Voluntariado\"].replace({\n",
    "    0: \"Não\", \n",
    "    1: \"Sim\"\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando os valores da descrição da coluna Classificacao_Notas\n",
    "df_dados_estudantes[\"Classificacao_Notas\"] = df_dados_estudantes[\"Classificacao_Notas\"].replace({\n",
    "    0: \"A\", \n",
    "    1: \"B\", \n",
    "    2: \"C\", \n",
    "    3: \"D\", \n",
    "    4: \"F\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "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>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Aulas_Particulares</th>\n",
       "      <th>Apoio_Pais</th>\n",
       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
       "      <th>Musica</th>\n",
       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.9</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Não</td>\n",
       "      <td>Baixo</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Asiático</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>0.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>Não</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.3</td>\n",
       "      <td>F</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>2388</td>\n",
       "      <td>18</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Não</td>\n",
       "      <td>Muito Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>2389</td>\n",
       "      <td>17</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Muito Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.3</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>2390</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>1.1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>2391</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Afrodescendente</td>\n",
       "      <td>Nenhum</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>2392</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>2.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Aluno  Idade     Genero            Etnia Escolaridade_Pais  \\\n",
       "0            1     17   Feminino       Caucasiano  Alguma Faculdade   \n",
       "1            2     18  Masculino       Caucasiano      Ensino Médio   \n",
       "2            3     15  Masculino         Asiático         Graduação   \n",
       "3            4     17   Feminino       Caucasiano         Graduação   \n",
       "4            5     17   Feminino       Caucasiano  Alguma Faculdade   \n",
       "...        ...    ...        ...              ...               ...   \n",
       "2387      2388     18   Feminino       Caucasiano         Graduação   \n",
       "2388      2389     17  Masculino       Caucasiano      Ensino Médio   \n",
       "2389      2390     16   Feminino       Caucasiano  Alguma Faculdade   \n",
       "2390      2391     16   Feminino  Afrodescendente            Nenhum   \n",
       "2391      2392     16   Feminino       Caucasiano  Alguma Faculdade   \n",
       "\n",
       "      Horas_Estudo_Semanais  Faltas Aulas_Particulares  Apoio_Pais  \\\n",
       "0                        19       7                Sim    Moderado   \n",
       "1                        15       0                Não       Baixo   \n",
       "2                         4      26                Não    Moderado   \n",
       "3                        10      14                Não        Alto   \n",
       "4                         4      17                Sim        Alto   \n",
       "...                     ...     ...                ...         ...   \n",
       "2387                     10       2                Não  Muito Alto   \n",
       "2388                      7       4                Sim  Muito Alto   \n",
       "2389                      6      20                Não    Moderado   \n",
       "2390                     12      17                Não    Moderado   \n",
       "2391                     17      13                Não    Moderado   \n",
       "\n",
       "     Atividades_Extracurriculares Esportes Musica Voluntariado  Media_Notas  \\\n",
       "0                             Não      Não    Sim          Não          2.9   \n",
       "1                             Não      Não    Não          Não          3.0   \n",
       "2                             Não      Não    Não          Não          0.1   \n",
       "3                             Sim      Não    Não          Não          2.1   \n",
       "4                             Não      Não    Não          Não          1.3   \n",
       "...                           ...      ...    ...          ...          ...   \n",
       "2387                          Sim      Não    Não          Não          3.5   \n",
       "2388                          Não      Sim    Não          Não          3.3   \n",
       "2389                          Não      Não    Não          Sim          1.1   \n",
       "2390                          Não      Sim    Sim          Não          1.8   \n",
       "2391                          Não      Não    Não          Sim          2.1   \n",
       "\n",
       "     Classificacao_Notas  \n",
       "0                      C  \n",
       "1                      B  \n",
       "2                      F  \n",
       "3                      D  \n",
       "4                      F  \n",
       "...                  ...  \n",
       "2387                   A  \n",
       "2388                   F  \n",
       "2389                   C  \n",
       "2390                   B  \n",
       "2391                   B  \n",
       "\n",
       "[2392 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Com a alteração de todos os valores necessários para suas devidas descrições, é hora de visualizar o resultado em \n",
    "# um novo dataframe.\n",
    "\n",
    "#Visualização do novo dataframe após a alteração das descrições das colunas\n",
    "novo_df_estudantes = df_dados_estudantes\n",
    "\n",
    "display(novo_df_estudantes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao analisar o novo DataFrame, observei que existem inconsistências nos valores em relação a coluna Media_Notas e Classificacao_Notas. Para corrigir esse problema e não haver divergências entre as análises, irei corrigir os valores usando o método apply com lambda de acordo com as informações descritas na Descrição do Desempenho Acadêmico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
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       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
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       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "      <th>Classificacao_Notas_Lambda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
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       "      <td>7</td>\n",
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       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Baixo</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Asiático</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>Não</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
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       "      <td>Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.3</td>\n",
       "      <td>F</td>\n",
       "      <td>F</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>2388</td>\n",
       "      <td>18</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Não</td>\n",
       "      <td>Muito Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.5</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>2389</td>\n",
       "      <td>17</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Muito Alto</td>\n",
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       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.3</td>\n",
       "      <td>F</td>\n",
       "      <td>B</td>\n",
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       "      <th>2389</th>\n",
       "      <td>2390</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>1.1</td>\n",
       "      <td>C</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>2391</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Afrodescendente</td>\n",
       "      <td>Nenhum</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.8</td>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>2392</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>2.1</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Aluno  Idade     Genero            Etnia Escolaridade_Pais  \\\n",
       "0            1     17   Feminino       Caucasiano  Alguma Faculdade   \n",
       "1            2     18  Masculino       Caucasiano      Ensino Médio   \n",
       "2            3     15  Masculino         Asiático         Graduação   \n",
       "3            4     17   Feminino       Caucasiano         Graduação   \n",
       "4            5     17   Feminino       Caucasiano  Alguma Faculdade   \n",
       "...        ...    ...        ...              ...               ...   \n",
       "2387      2388     18   Feminino       Caucasiano         Graduação   \n",
       "2388      2389     17  Masculino       Caucasiano      Ensino Médio   \n",
       "2389      2390     16   Feminino       Caucasiano  Alguma Faculdade   \n",
       "2390      2391     16   Feminino  Afrodescendente            Nenhum   \n",
       "2391      2392     16   Feminino       Caucasiano  Alguma Faculdade   \n",
       "\n",
       "      Horas_Estudo_Semanais  Faltas Aulas_Particulares  Apoio_Pais  \\\n",
       "0                        19       7                Sim    Moderado   \n",
       "1                        15       0                Não       Baixo   \n",
       "2                         4      26                Não    Moderado   \n",
       "3                        10      14                Não        Alto   \n",
       "4                         4      17                Sim        Alto   \n",
       "...                     ...     ...                ...         ...   \n",
       "2387                     10       2                Não  Muito Alto   \n",
       "2388                      7       4                Sim  Muito Alto   \n",
       "2389                      6      20                Não    Moderado   \n",
       "2390                     12      17                Não    Moderado   \n",
       "2391                     17      13                Não    Moderado   \n",
       "\n",
       "     Atividades_Extracurriculares Esportes Musica Voluntariado  Media_Notas  \\\n",
       "0                             Não      Não    Sim          Não          2.9   \n",
       "1                             Não      Não    Não          Não          3.0   \n",
       "2                             Não      Não    Não          Não          0.1   \n",
       "3                             Sim      Não    Não          Não          2.1   \n",
       "4                             Não      Não    Não          Não          1.3   \n",
       "...                           ...      ...    ...          ...          ...   \n",
       "2387                          Sim      Não    Não          Não          3.5   \n",
       "2388                          Não      Sim    Não          Não          3.3   \n",
       "2389                          Não      Não    Não          Sim          1.1   \n",
       "2390                          Não      Sim    Sim          Não          1.8   \n",
       "2391                          Não      Não    Não          Sim          2.1   \n",
       "\n",
       "     Classificacao_Notas Classificacao_Notas_Lambda  \n",
       "0                      C                          C  \n",
       "1                      B                          B  \n",
       "2                      F                          F  \n",
       "3                      D                          D  \n",
       "4                      F                          F  \n",
       "...                  ...                        ...  \n",
       "2387                   A                          A  \n",
       "2388                   F                          B  \n",
       "2389                   C                          F  \n",
       "2390                   B                          F  \n",
       "2391                   B                          D  \n",
       "\n",
       "[2392 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aplicando o lambda para corrigir os valores da Classificacao_Notas em relação a Média_Notas\n",
    "novo_df_estudantes[\"Classificacao_Notas_Lambda\"] = novo_df_estudantes[\"Media_Notas\"].apply(lambda x: \n",
    "                                                                                           \"A\" if x >= 3.5 else \n",
    "                                                                                           \"B\" if x >= 3.0 else \n",
    "                                                                                           \"C\" if x >= 2.5 else \n",
    "                                                                                           \"D\" if x >= 2.0 else \n",
    "                                                                                           \"F\")\n",
    "\n",
    "display(novo_df_estudantes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como eu havia notado, os valores estavam divergentes. Isso pode ser observado no dataframe comparando-se os valores entre Classificacao_Notas e Classificacao_Notas_Lambda que eu criei."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
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       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas_Lambda</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Não</td>\n",
       "      <td>Baixo</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Asiático</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>0.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>Não</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.3</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_Aluno  Idade     Genero       Etnia Escolaridade_Pais  \\\n",
       "0         1     17   Feminino  Caucasiano  Alguma Faculdade   \n",
       "1         2     18  Masculino  Caucasiano      Ensino Médio   \n",
       "2         3     15  Masculino    Asiático         Graduação   \n",
       "3         4     17   Feminino  Caucasiano         Graduação   \n",
       "4         5     17   Feminino  Caucasiano  Alguma Faculdade   \n",
       "\n",
       "   Horas_Estudo_Semanais  Faltas Aulas_Particulares Apoio_Pais  \\\n",
       "0                     19       7                Sim   Moderado   \n",
       "1                     15       0                Não      Baixo   \n",
       "2                      4      26                Não   Moderado   \n",
       "3                     10      14                Não       Alto   \n",
       "4                      4      17                Sim       Alto   \n",
       "\n",
       "  Atividades_Extracurriculares Esportes Musica Voluntariado  Media_Notas  \\\n",
       "0                          Não      Não    Sim          Não          2.9   \n",
       "1                          Não      Não    Não          Não          3.0   \n",
       "2                          Não      Não    Não          Não          0.1   \n",
       "3                          Sim      Não    Não          Não          2.1   \n",
       "4                          Não      Não    Não          Não          1.3   \n",
       "\n",
       "  Classificacao_Notas_Lambda  \n",
       "0                          C  \n",
       "1                          B  \n",
       "2                          F  \n",
       "3                          D  \n",
       "4                          F  "
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exluindo a coluna Classificacao_Notas (com os valores errados)\n",
    "novo_df_estudantes = novo_df_estudantes.drop(columns=[\"Classificacao_Notas\"])\n",
    "\n",
    "novo_df_estudantes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Aulas_Particulares</th>\n",
       "      <th>Apoio_Pais</th>\n",
       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
       "      <th>Musica</th>\n",
       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.9</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Não</td>\n",
       "      <td>Baixo</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Asiático</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>0.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>Não</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.3</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_Aluno  Idade     Genero       Etnia Escolaridade_Pais  \\\n",
       "0         1     17   Feminino  Caucasiano  Alguma Faculdade   \n",
       "1         2     18  Masculino  Caucasiano      Ensino Médio   \n",
       "2         3     15  Masculino    Asiático         Graduação   \n",
       "3         4     17   Feminino  Caucasiano         Graduação   \n",
       "4         5     17   Feminino  Caucasiano  Alguma Faculdade   \n",
       "\n",
       "   Horas_Estudo_Semanais  Faltas Aulas_Particulares Apoio_Pais  \\\n",
       "0                     19       7                Sim   Moderado   \n",
       "1                     15       0                Não      Baixo   \n",
       "2                      4      26                Não   Moderado   \n",
       "3                     10      14                Não       Alto   \n",
       "4                      4      17                Sim       Alto   \n",
       "\n",
       "  Atividades_Extracurriculares Esportes Musica Voluntariado  Media_Notas  \\\n",
       "0                          Não      Não    Sim          Não          2.9   \n",
       "1                          Não      Não    Não          Não          3.0   \n",
       "2                          Não      Não    Não          Não          0.1   \n",
       "3                          Sim      Não    Não          Não          2.1   \n",
       "4                          Não      Não    Não          Não          1.3   \n",
       "\n",
       "  Classificacao_Notas  \n",
       "0                   C  \n",
       "1                   B  \n",
       "2                   F  \n",
       "3                   D  \n",
       "4                   F  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renomeando a coluna Classificacao_Notas_Lambda para o nome original Classificacao_Notas\n",
    "novo_df_estudantes = novo_df_estudantes.rename(columns={\"Classificacao_Notas_Lambda\": \"Classificacao_Notas\"})\n",
    "\n",
    "novo_df_estudantes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora que todas as colunas e seus valores foram remodelados e corrigidos, temos uma visão melhorada e precisa das informações, irei preparar o dataframe para a inteligência artifical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "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>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Aulas_Particulares</th>\n",
       "      <th>Apoio_Pais</th>\n",
       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
       "      <th>Musica</th>\n",
       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.9</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Não</td>\n",
       "      <td>Baixo</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Asiático</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>0.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>Não</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.3</td>\n",
       "      <td>F</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>2388</td>\n",
       "      <td>18</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Não</td>\n",
       "      <td>Muito Alto</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>2389</td>\n",
       "      <td>17</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Ensino Médio</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Muito Alto</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>3.3</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>2390</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>1.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>2391</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Afrodescendente</td>\n",
       "      <td>Nenhum</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Sim</td>\n",
       "      <td>Não</td>\n",
       "      <td>1.8</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>2392</td>\n",
       "      <td>16</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Caucasiano</td>\n",
       "      <td>Alguma Faculdade</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>Não</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Não</td>\n",
       "      <td>Sim</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Aluno  Idade     Genero            Etnia Escolaridade_Pais  \\\n",
       "0            1     17   Feminino       Caucasiano  Alguma Faculdade   \n",
       "1            2     18  Masculino       Caucasiano      Ensino Médio   \n",
       "2            3     15  Masculino         Asiático         Graduação   \n",
       "3            4     17   Feminino       Caucasiano         Graduação   \n",
       "4            5     17   Feminino       Caucasiano  Alguma Faculdade   \n",
       "...        ...    ...        ...              ...               ...   \n",
       "2387      2388     18   Feminino       Caucasiano         Graduação   \n",
       "2388      2389     17  Masculino       Caucasiano      Ensino Médio   \n",
       "2389      2390     16   Feminino       Caucasiano  Alguma Faculdade   \n",
       "2390      2391     16   Feminino  Afrodescendente            Nenhum   \n",
       "2391      2392     16   Feminino       Caucasiano  Alguma Faculdade   \n",
       "\n",
       "      Horas_Estudo_Semanais  Faltas Aulas_Particulares  Apoio_Pais  \\\n",
       "0                        19       7                Sim    Moderado   \n",
       "1                        15       0                Não       Baixo   \n",
       "2                         4      26                Não    Moderado   \n",
       "3                        10      14                Não        Alto   \n",
       "4                         4      17                Sim        Alto   \n",
       "...                     ...     ...                ...         ...   \n",
       "2387                     10       2                Não  Muito Alto   \n",
       "2388                      7       4                Sim  Muito Alto   \n",
       "2389                      6      20                Não    Moderado   \n",
       "2390                     12      17                Não    Moderado   \n",
       "2391                     17      13                Não    Moderado   \n",
       "\n",
       "     Atividades_Extracurriculares Esportes Musica Voluntariado  Media_Notas  \\\n",
       "0                             Não      Não    Sim          Não          2.9   \n",
       "1                             Não      Não    Não          Não          3.0   \n",
       "2                             Não      Não    Não          Não          0.1   \n",
       "3                             Sim      Não    Não          Não          2.1   \n",
       "4                             Não      Não    Não          Não          1.3   \n",
       "...                           ...      ...    ...          ...          ...   \n",
       "2387                          Sim      Não    Não          Não          3.5   \n",
       "2388                          Não      Sim    Não          Não          3.3   \n",
       "2389                          Não      Não    Não          Sim          1.1   \n",
       "2390                          Não      Sim    Sim          Não          1.8   \n",
       "2391                          Não      Não    Não          Sim          2.1   \n",
       "\n",
       "     Classificacao_Notas  \n",
       "0                      C  \n",
       "1                      B  \n",
       "2                      F  \n",
       "3                      D  \n",
       "4                      F  \n",
       "...                  ...  \n",
       "2387                   A  \n",
       "2388                   B  \n",
       "2389                   F  \n",
       "2390                   F  \n",
       "2391                   D  \n",
       "\n",
       "[2392 rows x 15 columns]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novo_df_estudantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passo 4 - Preparar o dataframe para a Inteligência Artifical\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2392 entries, 0 to 2391\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   ID_Aluno                      2392 non-null   int64  \n",
      " 1   Idade                         2392 non-null   int64  \n",
      " 2   Genero                        2392 non-null   object \n",
      " 3   Etnia                         2392 non-null   object \n",
      " 4   Escolaridade_Pais             2392 non-null   object \n",
      " 5   Horas_Estudo_Semanais         2392 non-null   int64  \n",
      " 6   Faltas                        2392 non-null   int64  \n",
      " 7   Aulas_Particulares            2392 non-null   object \n",
      " 8   Apoio_Pais                    2392 non-null   object \n",
      " 9   Atividades_Extracurriculares  2392 non-null   object \n",
      " 10  Esportes                      2392 non-null   object \n",
      " 11  Musica                        2392 non-null   object \n",
      " 12  Voluntariado                  2392 non-null   object \n",
      " 13  Media_Notas                   2392 non-null   float64\n",
      " 14  Classificacao_Notas           2392 non-null   object \n",
      "dtypes: float64(1), int64(4), object(10)\n",
      "memory usage: 280.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Visualizar as informações do dataframe\n",
    "novo_df_estudantes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2392 entries, 0 to 2391\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   ID_Aluno                      2392 non-null   int64  \n",
      " 1   Idade                         2392 non-null   int64  \n",
      " 2   Genero                        2392 non-null   int64  \n",
      " 3   Etnia                         2392 non-null   int64  \n",
      " 4   Escolaridade_Pais             2392 non-null   int64  \n",
      " 5   Horas_Estudo_Semanais         2392 non-null   int64  \n",
      " 6   Faltas                        2392 non-null   int64  \n",
      " 7   Aulas_Particulares            2392 non-null   int64  \n",
      " 8   Apoio_Pais                    2392 non-null   int64  \n",
      " 9   Atividades_Extracurriculares  2392 non-null   int64  \n",
      " 10  Esportes                      2392 non-null   int64  \n",
      " 11  Musica                        2392 non-null   int64  \n",
      " 12  Voluntariado                  2392 non-null   int64  \n",
      " 13  Media_Notas                   2392 non-null   float64\n",
      " 14  Classificacao_Notas           2392 non-null   object \n",
      "dtypes: float64(1), int64(13), object(1)\n",
      "memory usage: 280.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Codificar as colunas para tipo numérico para a Inteligência Artificial\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Codificar coluna Genero\n",
    "codificador_genero = LabelEncoder()\n",
    "novo_df_estudantes[\"Genero\"] = codificador_genero.fit_transform(novo_df_estudantes[\"Genero\"])\n",
    "\n",
    "# Codificar coluna Etnia\n",
    "codificado_etnia = LabelEncoder()\n",
    "novo_df_estudantes[\"Etnia\"] = codificado_etnia.fit_transform(novo_df_estudantes[\"Etnia\"])\n",
    "\n",
    "# Codificar coluna Escolaridade_Pais\n",
    "codificador_escola_pais = LabelEncoder()\n",
    "novo_df_estudantes[\"Escolaridade_Pais\"] = codificador_escola_pais.fit_transform(novo_df_estudantes[\"Escolaridade_Pais\"])\n",
    "\n",
    "# Codificar coluna Aulas Particulares\n",
    "codificador_particualares = LabelEncoder()\n",
    "novo_df_estudantes[\"Aulas_Particulares\"] = codificador_particualares.fit_transform(novo_df_estudantes[\"Aulas_Particulares\"])\n",
    "\n",
    "# Codificar coluna Apoio_Pais\n",
    "codificador_apoio = LabelEncoder()\n",
    "novo_df_estudantes[\"Apoio_Pais\"] = codificador_apoio.fit_transform(novo_df_estudantes[\"Apoio_Pais\"])\n",
    "\n",
    "# Codificar coluna Ativiadades Extracurriculares\n",
    "codificador_atividade = LabelEncoder()\n",
    "novo_df_estudantes[\"Atividades_Extracurriculares\"] = codificador_atividade.fit_transform(novo_df_estudantes[\"Atividades_Extracurriculares\"])\n",
    "\n",
    "# Codificar coluna Esportes\n",
    "codificador_esporte = LabelEncoder()\n",
    "novo_df_estudantes[\"Esportes\"] = codificador_esporte.fit_transform(novo_df_estudantes[\"Esportes\"])\n",
    "\n",
    "# Codificar coluna Musica\n",
    "codificador_musica = LabelEncoder()\n",
    "novo_df_estudantes[\"Musica\"] = codificador_musica.fit_transform(novo_df_estudantes[\"Musica\"])\n",
    "\n",
    "# Codificar coluna Voluntariado\n",
    "codificador_voluntariado = LabelEncoder()\n",
    "novo_df_estudantes[\"Voluntariado\"] = codificador_voluntariado.fit_transform(novo_df_estudantes[\"Voluntariado\"])\n",
    "\n",
    "novo_df_estudantes.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "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>ID_Aluno</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Aulas_Particulares</th>\n",
       "      <th>Apoio_Pais</th>\n",
       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
       "      <th>Musica</th>\n",
       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "      <th>Classificacao_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</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>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>14</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>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>F</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>2388</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>2389</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>2390</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>2391</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>2392</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Aluno  Idade  Genero  Etnia  Escolaridade_Pais  \\\n",
       "0            1     17       0      2                  0   \n",
       "1            2     18       1      2                  1   \n",
       "2            3     15       1      1                  2   \n",
       "3            4     17       0      2                  2   \n",
       "4            5     17       0      2                  0   \n",
       "...        ...    ...     ...    ...                ...   \n",
       "2387      2388     18       0      2                  2   \n",
       "2388      2389     17       1      2                  1   \n",
       "2389      2390     16       0      2                  0   \n",
       "2390      2391     16       0      0                  3   \n",
       "2391      2392     16       0      2                  0   \n",
       "\n",
       "      Horas_Estudo_Semanais  Faltas  Aulas_Particulares  Apoio_Pais  \\\n",
       "0                        19       7                   1           2   \n",
       "1                        15       0                   0           1   \n",
       "2                         4      26                   0           2   \n",
       "3                        10      14                   0           0   \n",
       "4                         4      17                   1           0   \n",
       "...                     ...     ...                 ...         ...   \n",
       "2387                     10       2                   0           3   \n",
       "2388                      7       4                   1           3   \n",
       "2389                      6      20                   0           2   \n",
       "2390                     12      17                   0           2   \n",
       "2391                     17      13                   0           2   \n",
       "\n",
       "      Atividades_Extracurriculares  Esportes  Musica  Voluntariado  \\\n",
       "0                                0         0       1             0   \n",
       "1                                0         0       0             0   \n",
       "2                                0         0       0             0   \n",
       "3                                1         0       0             0   \n",
       "4                                0         0       0             0   \n",
       "...                            ...       ...     ...           ...   \n",
       "2387                             1         0       0             0   \n",
       "2388                             0         1       0             0   \n",
       "2389                             0         0       0             1   \n",
       "2390                             0         1       1             0   \n",
       "2391                             0         0       0             1   \n",
       "\n",
       "      Media_Notas Classificacao_Notas  \n",
       "0             2.9                   C  \n",
       "1             3.0                   B  \n",
       "2             0.1                   F  \n",
       "3             2.1                   D  \n",
       "4             1.3                   F  \n",
       "...           ...                 ...  \n",
       "2387          3.5                   A  \n",
       "2388          3.3                   B  \n",
       "2389          1.1                   F  \n",
       "2390          1.8                   F  \n",
       "2391          2.1                   D  \n",
       "\n",
       "[2392 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualização do dataframe após a codificação\n",
    "display(novo_df_estudantes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparar o dataframe para o treino\n",
    "\n",
    "# y é a coluna do dataframe a ser prevista\n",
    "y = novo_df_estudantes[\"Classificacao_Notas\"]\n",
    "\n",
    "# x são as colunas do dataframe que serão usadas para a previsão\n",
    "x = novo_df_estudantes.drop(columns=[\"Classificacao_Notas\", \"ID_Aluno\"])\n",
    "\n",
    "# Separar os dados de treino e os dados de teste\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passo 5 - Treinar a Inteligência Artificial\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criar o medelo -> Classificação da Nota: A, B, C, D, F\n",
    "\n",
    "# Arvore de Decisão -> RandonForest\n",
    "# Nearest Neighbors -> KNN -> Vizinhos Próximos\n",
    "\n",
    "# Importar a Inteligência Artificial (IA)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Cria a IA\n",
    "modelo_arvore_decisao = RandomForestClassifier()\n",
    "modelo_knn = KNeighborsClassifier()\n",
    "\n",
    "# Treinar a IA\n",
    "modelo_arvore_decisao.fit(x_treino, y_treino)\n",
    "modelo_knn.fit(x_treino, y_treino)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passo 6 - Escolher o melhor modelo\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.97014925373134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "71.46268656716418"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testar os modelos\n",
    "previsao_arvore_decisao = modelo_arvore_decisao.predict(x_teste)\n",
    "previsao_knn = modelo_knn.predict(x_teste)\n",
    "\n",
    "# Verificar Acurácia\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "display(accuracy_score(y_teste, previsao_arvore_decisao) * 100)\n",
    "display(accuracy_score(y_teste, previsao_knn) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Passo 7 - Usar melhor modelo\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "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>Idade</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Etnia</th>\n",
       "      <th>Escolaridade_Pais</th>\n",
       "      <th>Horas_Estudo_Semanais</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Aulas_Particulares</th>\n",
       "      <th>Apoio_Pais</th>\n",
       "      <th>Atividades_Extracurriculares</th>\n",
       "      <th>Esportes</th>\n",
       "      <th>Musica</th>\n",
       "      <th>Voluntariado</th>\n",
       "      <th>Media_Notas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>10.182268</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.067764</td>\n",
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       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.949078</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.018906</td>\n",
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       "      <th>3</th>\n",
       "      <td>17</td>\n",
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       "      <td>12.233114</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.526868</td>\n",
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       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11.314946</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.058287</td>\n",
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       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.945826</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.253452</td>\n",
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       "      <th>254</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.736954</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.534509</td>\n",
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       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>11.389648</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3.191046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17.222270</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.815421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.566764</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.157122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Idade  Genero  Etnia  Escolaridade_Pais  Horas_Estudo_Semanais  Faltas  \\\n",
       "0       18       0      0                  1               5.344198      26   \n",
       "1       15       0      2                  1              10.182268      21   \n",
       "2       15       1      1                  1               2.949078       3   \n",
       "3       17       1      2                  1              12.233114      21   \n",
       "4       17       1      3                  2              11.314946      23   \n",
       "..     ...     ...    ...                ...                    ...     ...   \n",
       "253     18       1      0                  0               7.945826      15   \n",
       "254     17       1      0                  2               6.736954      27   \n",
       "255     15       1      0                  2              11.389648       3   \n",
       "256     17       1      2                  2              17.222270      11   \n",
       "257     16       0      1                  1               1.566764      10   \n",
       "\n",
       "     Aulas_Particulares  Apoio_Pais  Atividades_Extracurriculares  Esportes  \\\n",
       "0                     1           1                             0         0   \n",
       "1                     1           1                             0         1   \n",
       "2                     1           1                             1         1   \n",
       "3                     1           3                             1         1   \n",
       "4                     1           1                             0         0   \n",
       "..                  ...         ...                           ...       ...   \n",
       "253                   1           0                             0         1   \n",
       "254                   0           4                             0         0   \n",
       "255                   1           3                             1         0   \n",
       "256                   0           3                             0         0   \n",
       "257                   0           3                             0         0   \n",
       "\n",
       "     Musica  Voluntariado  Media_Notas  \n",
       "0         0             0     0.218285  \n",
       "1         0             0     1.067764  \n",
       "2         0             0     3.018906  \n",
       "3         0             0     1.526868  \n",
       "4         0             0     1.058287  \n",
       "..      ...           ...          ...  \n",
       "253       0             0     1.253452  \n",
       "254       0             0     0.534509  \n",
       "255       0             0     3.191046  \n",
       "256       1             0     2.815421  \n",
       "257       0             0     2.157122  \n",
       "\n",
       "[258 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(['F', 'F', 'B', 'F', 'F', 'C', 'C', 'C', 'A', 'C', 'C', 'F', 'D',\n",
       "       'C', 'C', 'D', 'F', 'F', 'F', 'F', 'F', 'F', 'C', 'F', 'F', 'D',\n",
       "       'D', 'D', 'B', 'B', 'F', 'F', 'F', 'B', 'C', 'F', 'C', 'F', 'F',\n",
       "       'C', 'F', 'F', 'F', 'D', 'F', 'F', 'F', 'C', 'B', 'A', 'B', 'F',\n",
       "       'D', 'F', 'F', 'F', 'F', 'A', 'D', 'B', 'F', 'B', 'A', 'C', 'A',\n",
       "       'C', 'D', 'F', 'D', 'B', 'B', 'C', 'B', 'D', 'D', 'F', 'C', 'D',\n",
       "       'B', 'F', 'F', 'D', 'D', 'C', 'A', 'D', 'F', 'C', 'C', 'F', 'F',\n",
       "       'F', 'B', 'C', 'F', 'C', 'F', 'F', 'B', 'F', 'A', 'C', 'D', 'F',\n",
       "       'D', 'D', 'D', 'F', 'F', 'F', 'D', 'C', 'F', 'F', 'C', 'F', 'C',\n",
       "       'B', 'B', 'F', 'D', 'B', 'B', 'F', 'B', 'C', 'F', 'F', 'F', 'D',\n",
       "       'D', 'D', 'C', 'F', 'B', 'F', 'F', 'F', 'F', 'F', 'B', 'D', 'F',\n",
       "       'D', 'F', 'F', 'F', 'F', 'C', 'F', 'B', 'F', 'D', 'F', 'F', 'C',\n",
       "       'F', 'F', 'F', 'D', 'D', 'C', 'F', 'F', 'F', 'F', 'F', 'B', 'F',\n",
       "       'F', 'F', 'D', 'F', 'C', 'B', 'F', 'F', 'F', 'B', 'F', 'C', 'D',\n",
       "       'F', 'D', 'F', 'F', 'F', 'C', 'F', 'D', 'C', 'B', 'F', 'D', 'B',\n",
       "       'C', 'F', 'A', 'D', 'F', 'F', 'F', 'F', 'D', 'F', 'F', 'F', 'F',\n",
       "       'F', 'F', 'D', 'F', 'D', 'C', 'F', 'F', 'D', 'F', 'F', 'B', 'F',\n",
       "       'D', 'B', 'F', 'F', 'F', 'C', 'D', 'F', 'F', 'B', 'C', 'B', 'F',\n",
       "       'B', 'D', 'C', 'F', 'F', 'F', 'F', 'D', 'B', 'D', 'D', 'B', 'F',\n",
       "       'F', 'F', 'B', 'F', 'F', 'C', 'F', 'F', 'B', 'C', 'D'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melhor modelo é Modelo Arvore de Decisão\n",
    "\n",
    "#Importar os novos estudantes para fazer a previsão\n",
    "df_novos_dados = pd.read_csv(\"novos_dados_estudantes.csv\")\n",
    "\n",
    "display(df_novos_dados)\n",
    "\n",
    "nova_previsao = modelo_arvore_decisao.predict(df_novos_dados)\n",
    "\n",
    "display(nova_previsao)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Quais as caracteristicas mais importantes para definir a Classificação das Notas?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      0\n",
      "Idade                          1.952668\n",
      "Genero                         1.104757\n",
      "Etnia                          1.995017\n",
      "Escolaridade_Pais              2.187444\n",
      "Horas_Estudo_Semanais          4.960194\n",
      "Faltas                        25.524121\n",
      "Aulas_Particulares             1.088561\n",
      "Apoio_Pais                     2.600279\n",
      "Atividades_Extracurriculares   1.295464\n",
      "Esportes                       0.921468\n",
      "Musica                         0.899870\n",
      "Voluntariado                   0.839665\n",
      "Media_Notas                   54.630491\n"
     ]
    }
   ],
   "source": [
    "# Características mais importantes par definir a Classificação das Notas\n",
    "\n",
    "colunas = list(x_teste.columns)\n",
    "importancia = pd.DataFrame(index=colunas, data=modelo_arvore_decisao.feature_importances_)\n",
    "importancia = importancia * 100\n",
    "print(importancia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "As características mais importantes são as médias das notas com 54% e faltas com 25% e não menos importante as horas de estudos semanais com 4.9%.\n",
    "\n",
    "---"
   ]
  }
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