titanic-machinelearning-from-disaster.ipynb

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    "![Titanic](https://urbanian.mundodeportivo.com/p/943f/11817_este_video_de_3_horas_muestra_en_tiempo_real_el_hundimiento_del_titanic__thumb_fb.jpg?cb=8859627)\n",
    "\n",
    "# Titanic - Aprendizado de Máquina com Desastre\n",
    "\n",
    "O famosa desafio do Titanic do Kaggle, nós propoêm criação de um model de algoritmo de machine learning para prever com alta taxa de acerto, quais passageiros sobreviverão ao naufrágio do Titanic.\n",
    "\n",
    "### História do Titanic:\n",
    "\n",
    "O RMS Titanic foi um dos navios mais famosos e luxuosos já construídos na época. Ele foi projetado como um navio de passageiros de classe mundial e foi construído no início do século 20. O Titanic foi operado pela White Star Line e era aclamado como \"inafundável\" devido ao seu design inovador e tecnologia de segurança.\n",
    "\n",
    "Em sua viagem inaugural, o Titanic partiu de Southampton, Inglaterra, em 10 de abril de 1912, com destino a Nova York. No entanto, em 14 de abril de 1912, o navio colidiu com um iceberg no Atlântico Norte, a cerca de 600 km ao sul da Terra Nova. A colisão danificou seriamente o casco do navio, resultando em seu naufrágio.\n",
    "\n",
    "O naufrágio do Titanic é um dos desastres mais conhecidos da história. Devido à falta de botes salva-vidas suficientes e à má preparação para uma evacuação de emergência, mais de 1.500 pessoas perderam a vida. O navio afundou na madrugada de 15 de abril de 1912, e apenas cerca de 700 sobreviventes foram resgatados por navios que vieram em seu socorro.\n",
    "\n",
    "### Sobre os dados:\n",
    "\n",
    "Nesta competição, teremos acesso a dois conjuntos de dados semelhantes que incluem informações sobre os passageiros, como nome, idade, sexo, classe socioeconômica etc. Um conjunto de dados é intitulado train.csv e o outro é intitulado test.csv.\n",
    "\n",
    "O train.csv conterá os detalhes de um subconjunto dos passageiros a bordo (891, para ser exato) e, o que é mais importante, revelará se eles sobreviveram ou não, também conhecido como \"verdade básica\".\n",
    "\n",
    "O conjunto de dados test.csv contém informações semelhantes, mas não revela a \"verdade básica\" de cada passageiro. Sua tarefa é prever esses resultados.\n",
    "\n",
    "### Objetivo: \n",
    "\n",
    "Usando os padrões encontrados nos dados train.csv, devemos prever se os outros 418 passageiros a bordo (encontrados em test.csv) sobreviveram.\n",
    "\n",
    "\n",
    "Fonte desafio: [Titanic - Machine Learning from Disaster](https://www.kaggle.com/code/startupsci/titanic-data-science-solutions)"
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    "# Para engenharia de recursos com expressões regulares.\n",
    "import re\n",
    "\n",
    "# Para leitura, manipulação e análise dos dados.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Para visualização e análise dos dados.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.offline as pyo\n",
    "\n",
    "# Para padronização dos dados.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Para o treinando dos modelos\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Para análise dos modelo\n",
    "from sklearn.model_selection import learning_curve, LearningCurveDisplay, ShuffleSplit, cross_val_score"
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   },
   "source": [
    "# Coleta dos Dados"
   ]
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    "#### **Sobre os dados:**\n",
    "\n",
    "**PassengerId**: Um identificador único para cada passageiro.\n",
    "\n",
    "**Survived**: Indica se o passageiro sobreviveu (1) ou não (0) ao naufrágio. Esta é geralmente a variável alvo que deseja-se prever.\n",
    "\n",
    "**Pclass (Classe):** A classe da passagem do passageiro (1ª, 2ª ou 3ª classe).\n",
    "\n",
    "**Name**: O nome do passageiro.\n",
    "\n",
    "**Sex**: O sexo do passageiro.\n",
    "\n",
    "**Age**: A idade do passageiro.\n",
    "\n",
    "**SibSp**: Número de irmãos/cônjuges a bordo.\n",
    "\n",
    "**Parch**: Número de pais/filhos a bordo.\n",
    "\n",
    "**Ticket**: O número do bilhete.\n",
    "\n",
    "**Fare**: A tarifa paga pelo passageiro.\n",
    "\n",
    "**Cabin**: O número da cabine do passageiro.\n",
    "\n",
    "**Embarked**: O porto de embarque do passageiro (C = Cherbourg, Q = Queenstown, S = Southampton)."
   ]
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    "# Carregandos os dados.\n",
    "dftest = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "dftrain = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "dfgendersubmission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')"
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   "source": [
    "# **Limpeza e Preparação do Dados**"
   ]
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   "source": [
    "## Primeiras Imprensões"
   ]
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       "     PassengerId  Survived  Pclass  \\\n",
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       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
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       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
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       "..     ...               ...      ...   ...      ...  \n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       "1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       "2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       "3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       "..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416    male   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2a57607",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:40.971150Z",
     "iopub.status.busy": "2023-11-04T18:20:40.970785Z",
     "iopub.status.idle": "2023-11-04T18:20:40.980018Z",
     "shell.execute_reply": "2023-11-04T18:20:40.978950Z"
    },
    "papermill": {
     "duration": 0.02912,
     "end_time": "2023-11-04T18:20:40.982755",
     "exception": false,
     "start_time": "2023-11-04T18:20:40.953635",
     "status": "completed"
    },
    "tags": []
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         1\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfgendersubmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd5ee32d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.018427Z",
     "iopub.status.busy": "2023-11-04T18:20:41.018026Z",
     "iopub.status.idle": "2023-11-04T18:20:41.026464Z",
     "shell.execute_reply": "2023-11-04T18:20:41.024616Z"
    },
    "papermill": {
     "duration": 0.029841,
     "end_time": "2023-11-04T18:20:41.029171",
     "exception": false,
     "start_time": "2023-11-04T18:20:40.999330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número total de linhas nos dados de treinamento  891\n",
      "Número total de colunas nos dados de treinamento  12\n",
      "Número total de linhas nos dados de teste  418\n",
      "Número total de colunas nos dados de teste  11\n"
     ]
    }
   ],
   "source": [
    "print(\"Número total de linhas nos dados de treinamento \", dftrain.shape[0])\n",
    "print(\"Número total de colunas nos dados de treinamento \", dftrain.shape[1])\n",
    "print(\"Número total de linhas nos dados de teste \", dftest.shape[0])\n",
    "print(\"Número total de colunas nos dados de teste \", dftest.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e5b4677",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.065156Z",
     "iopub.status.busy": "2023-11-04T18:20:41.064521Z",
     "iopub.status.idle": "2023-11-04T18:20:41.182752Z",
     "shell.execute_reply": "2023-11-04T18:20:41.181171Z"
    },
    "papermill": {
     "duration": 0.139109,
     "end_time": "2023-11-04T18:20:41.185348",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.046239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "dftrain.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "faae4679",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.221638Z",
     "iopub.status.busy": "2023-11-04T18:20:41.220633Z",
     "iopub.status.idle": "2023-11-04T18:20:41.233467Z",
     "shell.execute_reply": "2023-11-04T18:20:41.231682Z"
    },
    "papermill": {
     "duration": 0.033769,
     "end_time": "2023-11-04T18:20:41.236122",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.202353",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "dftest.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2810716",
   "metadata": {
    "papermill": {
     "duration": 0.016917,
     "end_time": "2023-11-04T18:20:41.269530",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.252613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Trantandos valores nulos:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6ae96b",
   "metadata": {
    "papermill": {
     "duration": 0.015991,
     "end_time": "2023-11-04T18:20:41.303355",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.287364",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Dados de teste:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bae8ea5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.337681Z",
     "iopub.status.busy": "2023-11-04T18:20:41.337341Z",
     "iopub.status.idle": "2023-11-04T18:20:41.346528Z",
     "shell.execute_reply": "2023-11-04T18:20:41.345008Z"
    },
    "papermill": {
     "duration": 0.029494,
     "end_time": "2023-11-04T18:20:41.349116",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.319622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a2b016d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.385681Z",
     "iopub.status.busy": "2023-11-04T18:20:41.385308Z",
     "iopub.status.idle": "2023-11-04T18:20:41.395204Z",
     "shell.execute_reply": "2023-11-04T18:20:41.394125Z"
    },
    "papermill": {
     "duration": 0.029991,
     "end_time": "2023-11-04T18:20:41.397133",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.367142",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftest['Cabin'].fillna('Missing', inplace=True)\n",
    "\n",
    "dftest['Fare'].fillna(dftest['Fare'].mean(), inplace = True)\n",
    "\n",
    "dftest['Age'].fillna(dftest['Age'].mean(), inplace=True)\n",
    "\n",
    "dftest.rename(columns={'Parch': 'ParCh'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06d910df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.431962Z",
     "iopub.status.busy": "2023-11-04T18:20:41.431618Z",
     "iopub.status.idle": "2023-11-04T18:20:41.438280Z",
     "shell.execute_reply": "2023-11-04T18:20:41.437589Z"
    },
    "papermill": {
     "duration": 0.026196,
     "end_time": "2023-11-04T18:20:41.439939",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.413743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "ParCh          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b442afe5",
   "metadata": {
    "papermill": {
     "duration": 0.016752,
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     "start_time": "2023-11-04T18:20:41.457025",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Dados de treinamento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74d4f564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.509531Z",
     "iopub.status.busy": "2023-11-04T18:20:41.509189Z",
     "iopub.status.idle": "2023-11-04T18:20:41.520054Z",
     "shell.execute_reply": "2023-11-04T18:20:41.519114Z"
    },
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     "duration": 0.031921,
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     "exception": false,
     "start_time": "2023-11-04T18:20:41.490550",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5eb6e29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.557723Z",
     "iopub.status.busy": "2023-11-04T18:20:41.557361Z",
     "iopub.status.idle": "2023-11-04T18:20:41.569854Z",
     "shell.execute_reply": "2023-11-04T18:20:41.568523Z"
    },
    "papermill": {
     "duration": 0.033186,
     "end_time": "2023-11-04T18:20:41.572548",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.539362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "ParCh          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain['Age'].fillna(dftrain['Age'].mean(), inplace=True)\n",
    "\n",
    "dftrain['Cabin'].fillna('missing', inplace=True)\n",
    "\n",
    "dftrain['Embarked'].fillna('S', inplace=True)\n",
    "\n",
    "dftrain.rename(columns={'Parch': 'ParCh'}, inplace=True)\n",
    "\n",
    "dftrain.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56943813",
   "metadata": {
    "papermill": {
     "duration": 0.016706,
     "end_time": "2023-11-04T18:20:41.606783",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.590077",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Análise Descritiva e Visualização\n",
    "\n",
    "A análise descritiva e a visualização de dados são passos vitais na exploração e compreensão de conjuntos de dados. A análise descritiva oferece estatísticas como média e mediana, ajudando a entender como os dados estão distribuídos. A visualização de dados, por meio de gráficos e gráficos, ajuda a encontrar tendências, relações e padrões nos dados e a comunicar resultados de maneira eficaz. Essas etapas iniciais são fundamentais para formular hipóteses, planejar a modelagem e tomar decisões informadas em projetos de ciência de dados e aprendizado de máquina."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf0e9036",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.642216Z",
     "iopub.status.busy": "2023-11-04T18:20:41.641816Z",
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     "shell.execute_reply": "2023-11-04T18:20:41.669682Z"
    },
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     "duration": 0.049621,
     "end_time": "2023-11-04T18:20:41.673029",
     "exception": false,
     "start_time": "2023-11-04T18:20:41.623408",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
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       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.002015</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</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>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            ParCh        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02d1abde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:41.710299Z",
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     "start_time": "2023-11-04T18:20:41.690850",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "hist = go.Histogram(x=dftrain['Age'], name='Age')\n",
    "\n",
    "layout = go.Layout(\n",
    "    title=\"Age Frequency in the Set\",\n",
    "    xaxis=dict(title=\"Values\"),\n",
    "    yaxis=dict(title=\"Frequency\"),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[hist], layout=layout)\n",
    "\n",
    "pyo.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4327f806",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:43.231582Z",
     "iopub.status.busy": "2023-11-04T18:20:43.231058Z",
     "iopub.status.idle": "2023-11-04T18:20:43.262585Z",
     "shell.execute_reply": "2023-11-04T18:20:43.261522Z"
    },
    "papermill": {
     "duration": 0.052486,
     "end_time": "2023-11-04T18:20:43.264679",
     "exception": false,
     "start_time": "2023-11-04T18:20:43.212193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "hist = go.Histogram(x=dftrain['Fare'], name='Fare')\n",
    "\n",
    "layout = go.Layout(\n",
    "    title=\"Fare Frequency in the Set\",\n",
    "    xaxis=dict(title=\"Values\"),\n",
    "    yaxis=dict(title=\"Frequency\"),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[hist], layout=layout)\n",
    "\n",
    "pyo.iplot(fig)"
   ]
  },
  {
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   "id": "00369b80",
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   ],
   "source": [
    "hist = go.Histogram(x=dftrain['Pclass'], name='Pclass')\n",
    "\n",
    "layout = go.Layout(\n",
    "    title=\"Pclass Frequency in the Set\",\n",
    "    xaxis=dict(title=\"Values\"),\n",
    "    yaxis=dict(title=\"Frequency\"),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[hist], layout=layout)\n",
    "\n",
    "pyo.iplot(fig)"
   ]
  },
  {
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   "execution_count": 18,
   "id": "585f0d74",
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Frequency in the Set\"},\"xaxis\":{\"title\":{\"text\":\"Values\"}},\"yaxis\":{\"title\":{\"text\":\"Frequency\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = go.Histogram(x=dftrain['SibSp'], name='SibSp')\n",
    "\n",
    "layout = go.Layout(\n",
    "    title=\"SibSp Frequency in the Set\",\n",
    "    xaxis=dict(title=\"Values\"),\n",
    "    yaxis=dict(title=\"Frequency\"),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[hist], layout=layout)\n",
    "\n",
    "pyo.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67c672b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:43.443142Z",
     "iopub.status.busy": "2023-11-04T18:20:43.442819Z",
     "iopub.status.idle": "2023-11-04T18:20:43.468243Z",
     "shell.execute_reply": "2023-11-04T18:20:43.467201Z"
    },
    "papermill": {
     "duration": 0.046155,
     "end_time": "2023-11-04T18:20:43.470119",
     "exception": false,
     "start_time": "2023-11-04T18:20:43.423964",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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in the Set\"},\"xaxis\":{\"title\":{\"text\":\"Values\"}},\"yaxis\":{\"title\":{\"text\":\"Frequency\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = go.Histogram(x=dftrain['ParCh'], name='ParCh')\n",
    "\n",
    "layout = go.Layout(\n",
    "    title=\"ParChFrequency in the Set\",\n",
    "    xaxis=dict(title=\"Values\"),\n",
    "    yaxis=dict(title=\"Frequency\"),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[hist], layout=layout)\n",
    "\n",
    "pyo.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0fadb570",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:43.509513Z",
     "iopub.status.busy": "2023-11-04T18:20:43.508614Z",
     "iopub.status.idle": "2023-11-04T18:20:43.977589Z",
     "shell.execute_reply": "2023-11-04T18:20:43.975819Z"
    },
    "papermill": {
     "duration": 0.490885,
     "end_time": "2023-11-04T18:20:43.979715",
     "exception": false,
     "start_time": "2023-11-04T18:20:43.488830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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Class and Gender Distribution Treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e17a0d3a-6596-439d-9929-c02c254e0792');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_map = px.treemap(dftrain, path=[\"Age\", \"Pclass\", 'Sex'], color='Sex', \n",
    "                      color_discrete_map={'male': 'blue', 'female': 'pink'},\n",
    "                      labels={'Age': 'Age', 'Pclass': 'Classe', 'Sex': 'Gender'}\n",
    "                    )\n",
    "\n",
    "tree_map.update_layout(title='Age, Class and Gender Distribution Treemap')\n",
    "\n",
    "tree_map.update_traces(textinfo=\"label+value\", \n",
    "                       textfont=dict(size=12, family=\"Arial\"))\n",
    "\n",
    "tree_map.update_layout(margin=dict(l=10, r=10, t=60, b=10))\n",
    "\n",
    "tree_map.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5da2bdf2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:44.019004Z",
     "iopub.status.busy": "2023-11-04T18:20:44.018305Z",
     "iopub.status.idle": "2023-11-04T18:20:44.101015Z",
     "shell.execute_reply": "2023-11-04T18:20:44.099668Z"
    },
    "papermill": {
     "duration": 0.105071,
     "end_time": "2023-11-04T18:20:44.103456",
     "exception": false,
     "start_time": "2023-11-04T18:20:43.998385",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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   "source": [
    "fig = px.parallel_categories(dftrain, dimensions=[\"Pclass\", \"Sex\"], labels={\"Pclass\": \"Passenger Class\", \"Sex\": \"Sex\"})\n",
    "fig.update_traces(line=dict(color=\"purple\")) \n",
    "fig.update_layout(\n",
    "    title=\"Relation between Passenger Class and Sex\",\n",
    "    showlegend=True\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
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   "source": [
    "values_count_sex = dftrain[\"Sex\"].value_counts()\n",
    "\n",
    "graph = px.bar(values_count_sex, values_count_sex.index,\\\n",
    "               values_count_sex.values, labels={\"x\":\"Sex\", \"y\":\"Counts\"})\n",
    "graph.update_layout(title=\"Count by Sex\")\n",
    "graph.show();"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values_count = dftrain[\"Pclass\"].value_counts()\n",
    "\n",
    "graph = px.bar(values_count, values_count.index,\\\n",
    "               values_count.values, labels={\"x\":\"Class\", \"y\":\"Counts\"})\n",
    "graph.update_layout(title=\"Count by Class\")\n",
    "graph.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abf1b40d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:44.385046Z",
     "iopub.status.busy": "2023-11-04T18:20:44.384717Z",
     "iopub.status.idle": "2023-11-04T18:20:44.441995Z",
     "shell.execute_reply": "2023-11-04T18:20:44.440415Z"
    },
    "papermill": {
     "duration": 0.083376,
     "end_time": "2023-11-04T18:20:44.444453",
     "exception": false,
     "start_time": "2023-11-04T18:20:44.361077",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values_count = dftrain[\"Embarked\"].value_counts()\n",
    "\n",
    "graph = px.bar(values_count, values_count.index,\\\n",
    "               values_count.values, labels={\"x\":\"Embarked\", \"y\":\"Counts\"})\n",
    "graph.update_layout(title=\"Count by Embarked\")\n",
    "graph.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1d7c3193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:44.484305Z",
     "iopub.status.busy": "2023-11-04T18:20:44.483893Z",
     "iopub.status.idle": "2023-11-04T18:20:45.575541Z",
     "shell.execute_reply": "2023-11-04T18:20:45.573744Z"
    },
    "papermill": {
     "duration": 1.114529,
     "end_time": "2023-11-04T18:20:45.578109",
     "exception": false,
     "start_time": "2023-11-04T18:20:44.463580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning:\n",
      "\n",
      "The figure layout has changed to tight\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(dftrain, col=\"Sex\", row=\"Survived\", margin_titles=True)\n",
    "\n",
    "g.map(plt.hist, \"Age\", color=\"Teal\")\n",
    "\n",
    "g.set_axis_labels(\"Age\", \"Frequency\")\n",
    "g.set_titles(col_template=\"Sex: {col_name}\", row_template=\"Survived: {row_name}\")\n",
    "\n",
    "\n",
    "g.fig.subplots_adjust(wspace=0.2, hspace=0.2)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2583ad42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:45.621939Z",
     "iopub.status.busy": "2023-11-04T18:20:45.621547Z",
     "iopub.status.idle": "2023-11-04T18:20:46.655577Z",
     "shell.execute_reply": "2023-11-04T18:20:46.654187Z"
    },
    "papermill": {
     "duration": 1.059349,
     "end_time": "2023-11-04T18:20:46.657780",
     "exception": false,
     "start_time": "2023-11-04T18:20:45.598431",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning:\n",
      "\n",
      "The figure layout has changed to tight\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 972.111x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(dftrain, hue=\"Survived\", col=\"Pclass\", margin_titles=True,\n",
    "                  palette={1:\"Orange\", 0:\"Indigo\"}\n",
    ")\n",
    "                  \n",
    "g=g.map(plt.scatter, \"Fare\", \"Age\", edgecolor=\"w\").add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40304113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:46.704583Z",
     "iopub.status.busy": "2023-11-04T18:20:46.704233Z",
     "iopub.status.idle": "2023-11-04T18:20:47.310108Z",
     "shell.execute_reply": "2023-11-04T18:20:47.308716Z"
    },
    "papermill": {
     "duration": 0.629965,
     "end_time": "2023-11-04T18:20:47.313052",
     "exception": false,
     "start_time": "2023-11-04T18:20:46.683087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning:\n",
      "\n",
      "The figure layout has changed to tight\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 672.111x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(dftrain, hue=\"Survived\", col=\"Sex\", margin_titles=True,\n",
    "                palette=\"deep\", hue_kws=dict(marker=[\"^\", \"v\"])\n",
    ")\n",
    "\n",
    "g.map(plt.scatter, \"Fare\", \"Age\", edgecolor=\"w\").add_legend()\n",
    "\n",
    "plt.subplots_adjust(top=0.8)\n",
    "\n",
    "g.fig.suptitle('Survival by gender, age and rate');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f7978770",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:47.357997Z",
     "iopub.status.busy": "2023-11-04T18:20:47.356655Z",
     "iopub.status.idle": "2023-11-04T18:20:47.653608Z",
     "shell.execute_reply": "2023-11-04T18:20:47.652374Z"
    },
    "papermill": {
     "duration": 0.321561,
     "end_time": "2023-11-04T18:20:47.656045",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.334484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e9vUvvoTETHh52BXZHwCsLIygpydCQlKmUnlCYiZsC/1qNDOVwsxUCncXK3g3dESrd9fi+PnH6O1b/JeddwNHrN5yBTk5eZBIqu7YqpW5BHpiERKSlRPk+GQZbK2Kn+Fua2mosn62LB/LfrmHlQGt0PnvKzA83S1w/0kKNu5/pJQ8pGfmYeycSzAx0sePAS1hoK97H4Nd2teFd0MnxeOcnL/fi4kZsLc1VZQnJGbAq65Dkf0BwMrSuOC9mFj4vZgBW2vTIvWPnrqP7Oxc9HunsSYOoUqwMtaDnhhISFf+QRefngdbM70S9ioQfD4Z688mY+NHNeBZ43WvmJWxHvTFgIe98pyQOvYSXH9auqEQXaB7fzWVq9SfwJ06dVL8u3PnzuV60YCAAPj7+yuV/WDRolxtUvmZGktg+q9JYoIgwM7aGKHXn6N+3YIvp/QMGW7di8awfsV/wEoM9NCwnj1Crz+Hb4eC2epyuYDQG88x4v0mJb+4IEAQXicsxbn3MA4WZtIqnTgAgMRAjIZ1LBB6Ox6+bWoAKDgHl27HY3ivWsXu09TTCqG34zGq7+sZ/n+ExaGpZ8GlnXn5cuTmCRAXytnFeiLFL0GgoMfBb84lSAzEWD2zFaQS1V8QVZWpiRSmJq+/sARBgJ2NCUKvPUX9egXJQnqGDLfuRmHY+82LbUNioIeGno4Ivf4Uvp3qAfj7vXjtL4wYUPTz5tfDt9GlfV1YFxpy02USfREaOkkR+igTvg0Kkne5XMClx1kY3taixP02nEvC2tPJ2DCmBho7Kye8En0RGjlLERmfo1T+ND4XTpZV+2+TNKdM/6fr1q2L4cOHY/jw4ahbt67a+0ulUkilyuO7VXHIwsDEGNYeryf+WdVyhoO3F7ISU5D6/JUWI6scIpEIIwc1w5qtV+DmbAnnGuZYHhwKexsT+LZ//SU36otf0a2DB0YMKJhkNmZwc0wPOoZGnvZoUt8RW/bcRFZWLvr3agAAeB6VgpBTD/BWS1dYWxohOjYdP227BkOpPjq1cQcAnLr4BAlJmfBu4AipRB8Xrz3Dul+u4qOhxX9RVDWj36uDGctvopGHBZrULbhUMys7H/19XQAA05fegL2NIaaMLDgnH/apjZFfX8TG/Y/Q2ccBR86/RPjjZMwbX3BOTY0N0LKRDX7YfBdSiR5q2hvhyp0EHDj9HDM+agjg78Qh8BKyZHn4YXIrpGfmIT0zDwBgbS6Fnp7uXkYnEokwcnBLrNnyB9xcrOHsZIHlP52Dva0ZfDvWU9QbNXE7unWqhxEDfQAAY4a2wvRvDqORlyOaNCi4VDMrOxf9eysnsn+9SMTVsGf4aXHFD49UttHtLTHj/2LRyFmKJs6G2HIxBVk5Avo3L5icPH1PDOzN9TGlR8HcmfVnk7DiRCIWDXFATSt9xKUVvIeMJWKYSAs+p/06WMJ/Zwx8ahmhdW0jnH+QidP3M7B1rFPxQeigqveNVLWUKXn47LPPsH37dsyfPx/NmzfHiBEjMGTIEDg6Or55Zx3i5NMIo8/8rHjcY+lXAICwzXtxYEyAtsKqVB9/0AJZ2bmYvegkUtNlaNHYCRsW9VO6ZPJ5VAqSUl5PvurVtR4Sk7OwYuMlxCVmor6HLTYs6qcYtpBI9HDt1kts2XMTqWky2FgZw8e7JnasHqyYaKmvL8a2fbfx7cpzAADXmhaYMb4jBvdpVIlHX3a9OtREYmoOVm6PQFySDPVrmWN9YBvYWhb8iouKz4LoX90IzetbY9GUFlj2yz0s/fk+3J1M8GNAK8UaDwCw5MsWWLL1HqYuuYGU9Bw42RnjixH1MbSnOwAg/HEKbj0ouDqj+/+UFzM78ZMvnB10+xf1xyPaFLwXv/sNqenZaNHEBRuWDFZ+L75MRlLyv96Lvg2QmJyJFevPIy4xA/Xr2mPDksFFhtB+PXwbjvbmaN+qdqUdT2Xp1cQUiRn5WHkiCXFpeahfQ4r1Y2rA1qzgvEUl5ykNQ++4nIrcfGDS9hildsZ3scJE34Kh624NTTHnPTl+OpuMBYfiUcvOACs+cEQL9+ox3wFg8vAmIqEc1749ePAA27Ztw44dOxAZGYm3334bI0aMwMiRI9Vua65I9yZ1VRWB0ZO0HYLOEpIi31yJSiSya6jtEHSWcGa3tkPQaaIBIRXa/m8a/E56R4jQWFtVRbmSq3r16mHu3Ll48OABzp8/j7i4OIwZM0ZTsREREWmFSINbdVTu2S1XrlzB9u3bsWvXLqSmpmLQoEGaiIuIiEhrOGyhWpmSh8LDFV26dMF3332H/v37w9S06CVQREREuoTJg2plSh68vLzQsmVLjB8/HkOHDoWDQ/HXWRMREVH1o3bykJ+fj3Xr1mHgwIGwslJ9W2YiIiJdxJ4H1dQ+P3p6epg4cSKSk5MrIBwiIiLtE4k0t1VHZUquGjVqhCdPnmg6FiIiItIBZUoevvnmG3z55Zc4fPgwXr16hdTUVKWNiIhIl4lFgsa26qhMEyZ79eoFAOjbt6/SymSlvasmERFRVVZdhxs0pUzJw+nTpzUdBxEREemIMiUP/77DJhERUXXDjgfVypQ8nDt3TuXzHTt2LFMwREREVYGoms5V0JQyJQ+dO3cuUvbvuQ+c80BERFR9lelqi6SkJKUtNjYWR48eRcuWLXHs2DFNx0hERFSpuM6DamVKHiwsLJQ2W1tbdOvWDd999x2mTZum6RiJiIgqlTaTh1WrVsHd3R2GhoZo3bo1rly5orL+smXL4OnpCSMjI7i4uGDy5MnIzs4u45GXTrnvqvlvDg4OiIiofvctJyKi/xZtrc+wa9cu+Pv7Y+3atWjdujWWLVuGHj16ICIiAvb29kXqb9++HTNmzMDGjRvRrl07PHjwAKNHj4ZIJMKSJUsqLM4yJQ+3b99WeiwIAl69eoWFCxeiadOmmoiLiIjoP2fJkiX4+OOPMWbMGADA2rVrceTIEWzcuBEzZswoUv+PP/7AW2+9hQ8++AAA4O7ujmHDhuHy5csVGmeZkoemTZtCJBJBEJQzszZt2mDjxo0aCYyIiEhbNDlVQSaTQSaTKZVJpVJIpVKlspycHFy/fh0BAQGKMrFYDF9fX4SGhhbbdrt27fDLL7/gypUraNWqFZ48eYKQkBB8+OGHGjyCosqUPERGRio9FovFsLOzg6GhoUaCIiIi0iZNTnQMCgrC3LlzlcoCAwMxZ84cpbL4+Hjk5+fDwcFBqdzBwQH3798vtu0PPvgA8fHxaN++PQRBQF5eHv73v//hq6++0twBFEOtCZOhoaE4fPgw3NzcFNvZs2fRsWNHuLq64pNPPimSXREREf2XBQQEICUlRWn7d+9CeZw5cwbffvstVq9ejRs3bmDv3r04cuQI5s+fr5H2S6JWz8O8efPQuXNn9O7dGwDw559/ws/PD6NHj0b9+vXxww8/wMnJqUg2RUREpEs02fNQ3BBFcWxtbaGnp4eYmBil8piYGDg6Oha7z6xZs/Dhhx9i7NixAIDGjRsjIyMDn3zyCb7++muIxWW6qPKN1Go1LCwMXbt2VTzeuXMnWrdujfXr18Pf3x8rVqzA7t27NR4kERFRZRKJBI1tpSWRSNCiRQucPHlSUSaXy3Hy5Em0bdu22H0yMzOLJAh6enoAUGReoiap1fOQlJSkNBZz9uxZvPPOO4rHLVu2xPPnzzUXHRER0X+Iv78/Ro0aBR8fH7Rq1QrLli1DRkaG4uqLkSNHombNmggKCgIA9OnTB0uWLEGzZs3QunVrPHr0CLNmzUKfPn0USURFUCt5cHBwQGRkJFxcXJCTk4MbN24oTQJJS0uDgYGBxoMkIiKqTGItrQw5ZMgQxMXFYfbs2YiOjkbTpk1x9OhRxQ/3Z8+eKfU0zJw5EyKRCDNnzsTLly9hZ2eHPn36YMGCBRUap1rJQ69evTBjxgx899132L9/P4yNjdGhQwfF87dv30adOnU0HiQREVFl0uay0hMmTMCECROKfe7MmTNKj/X19REYGIjAwMBKiOxfr6tO5fnz56N///7o1KkTTE1NsWXLFkgkEsXzGzduRPfu3TUeJBEREVUdaiUPtra2OHfuHFJSUmBqalpkPGXPnj0wNTXVaIBERESVTQTekluVMi0SZWFhUWy5tbV1uYIhIiKqCqrr3TA1RaM3xiIiIqoOmDyoVjGrRxAREVG1xZ4HIiKiQrR1S25dweSBiIioEA5bqFZlkofA6EnaDkFnzXVcru0QdNasOZzkWx6iwKHaDkF3OZ7RdgREZVZlkgciIqKqgh0PqjF5ICIiKkSdG1r9F/FqCyIiIlILex6IiIgK4YRJ1Zg8EBERFaKtu2rqCg5bEBERkVrY80BERFQIJ0yqxuSBiIioEI5aqMbkgYiIqBBOmFSNcx6IiIhILex5ICIiKoRzHlRj8kBERFQIL9VUjcMWREREpBb2PBARERXCCZOqMXkgIiIqhMmDahy2ICIiIrWw54GIiKgQEXi1hSpMHoiIiArhsIVqHLYgIiIitZSr5yE2NhYREREAAE9PT9jb22skKCIiIm0ScaEHlcrU85CWloYPP/wQNWvWRKdOndCpUyfUrFkTI0aMQEpKiqZjJCIiqlQisea26qhMhzV27FhcvnwZhw8fRnJyMpKTk3H48GFcu3YNn376qaZjJCIiqlQikea26qhMwxaHDx/G77//jvbt2yvKevTogfXr16Nnz54aC46IiIiqnjIlDzY2NrCwsChSbmFhASsrq3IHRUREpFWc86BSmYYtZs6cCX9/f0RHRyvKoqOjMXXqVMyaNUtjwREREWkD5zyoVqaehzVr1uDRo0dwdXWFq6srAODZs2eQSqWIi4vDunXrFHVv3LihmUiJiIioSihT8tCvXz8Nh0FERFR1iKrrTEcNKVPyEBgYqOk4iIiIqozqOtygKeVenjo7Oxu7du1CRkYGunXrhrp162oiLiIiIqqi1Eoe/P39kZubi5UrVwIAcnJy0KZNG9y9exfGxsaYNm0ajh07hnbt2lVIsERERJWCwxYqqZU8HDt2DN9++63i8bZt2/Ds2TM8fPgQrq6u+Oijj7BgwQIcOXJE44GWhyAIWLHxEvYcuoPUdBmaN3bCHP+34e6i+rLSbXtvIXjndcQlZsKrji1mTeqMJg0cFc/P/uEk/rj+HLHx6TA2kqBZoxr48n9voY6btaKOZ8flRdpdEtgT73b11NwBVkGuHXzQbqofnFo0gpmTPXb2+wwRB05qOyztazkAonbDAVNrIPoRhN+WAFF337xfQ1+IB86HcP8shF0zXpd7dYLI532ghhdExhaQrx0JxDysuPi1SBAErFjxf9iz5zRSUzPQvHk9zJnzEdzda5S4z7p1B3Ds2FU8eRIFQ0MJmjWriy+/HIbatZ0UdWbP3oA//riD2NgkGBsbolmzevjyy6GoU6dmZRxWpdh2MhrBR6MQn5IDLxcTzBzujia1zYqt+/BlJlbsf47wpxmISpAhYKg7RnUveo5jkmRYtOcZzv2ZjOycfLjaG+LbjzzQuJZpRR9OpeCwhWpqnZ5nz56hQYMGisfHjh3DwIED4ebmBpFIhEmTJuHmzZsaD7K81m+/jp9/DcOcKV2we90QGBkawO/L/ZDJ8krcJ+TkAwStOo/xo1tj34Zh8PKwg9+X+5GQlKmo09DTHkEzuiHk55EIXtQPgiDAb8o+5OfLldoKCuiGC/vGKjbf9nUq7FirComJMWJuRSBk/Fxth1J1NOwKUffPIZwNhrBuNBDzEKIRSwHjN6yNYuEIUfeJEP4q5m9LYgTh2W0IJ1ZVSMhVyfr1h/Dzz79jzpyPsHv3fBgZGcLPbyFkspwS97ly5R6GD++G3bvnYdOmAOTl5cPPbyEyM7MVdRo2rIWgoE8RErIIwcEzCv6O/RYW+TvWVSFX4rFw11OM7+uMvYFN4OlijLFL7iEhNbfY+tk5crjYSTFloCvsLAyKrZOSkYdh34ZDX0+E9ZO9cOSbppg+xB0WJrxR83+FWsmDWCyGILy+x/mlS5fQpk0bxWNLS0skJSVpLjoNEAQBW/fcxLgPW8G3Qx141bHD9193R2xCBk5ceFzifpt238Dg3g0xoFdDeLjbYO6ULjA01MevR8IVdYb0bYyWTWvCuYY5Gnra44uP2+JVbDpeRqcqtWVuKoWdjYlik0qr/x/Yo6PncHrWMtzff0LboVQZojbDgBsHgbAjQPxTCIe/B3JlQLPeKnYSQ9R/LoQzG4CkqKLP3z4KnNsIPLlacYFXAYIgYOvWoxg3rh98fX3g5eWK778fh9jYZJw4ca3E/YKDZ6B//06oW9cZXl5uWLjwf4iKikd4eKSizpAhXdGyZX04O9uhYcNa+OKLwXj1KgEvX8ZVxqFVuM2/v8KgjvYY0MEeHjWNMXdkbRhKxPj1fGyx9RvXMsW0we54t7UtDPSL/4rYEPISNawlCPLzQJPaZnC2M0T7RpZwtTesyEOpVCKxSGNbdaRW8lC/fn0cOnQIABAeHo5nz57h7bffVjz/119/wcHBQbMRltOLV6mIS8xEOx9XRZmZqRTe9R1x8050sfvk5OYj/EGs0j5isQjtWrjiZnjx+2Rm5WJvyF041zCHo71yd+DcpafRus86DPxkJ/7vSLhSAkb/EWJ9wMkTgtKXvAA8uQqRc6MSdxN1+gjISAJuHqr4GKuwFy9iEReXjHbtXp8rMzNjeHvXwc2bpR+mSUsr6Dm0sCi+az0zMxt7956Fs7MdHB1tyhd0FZCTJ0f4X+lo18BSUSYWi9C2gSXCHqeVud1TYUlo5G6KSasj0G7SVbw/5xZ2n43RQMRVB+9toZpaP4GnTZuGoUOH4siRIwgPD0evXr1Qq1YtxfMhISFo1aqVxoMsj7iEDACAjZWxUrmNtTHiEzOK3ScpJQv5+UKx+zx5lqhUtm3fLSxaexGZWbmo5WqFTUveh8RAT/H8535t0Ka5C4yk+rhw9RnmLj2NzKxcjBzYVANHRzrD2BIisT6EDOX3DzISAVu34vdxaQI06wNh7ciKj6+Ki4sruFuvjY3ysvg2NhaIjy/dnXzlcjm+/fZnNG9eD/XquSg9t23bcSxatB2ZmTLUqlUDmzZ9BYlE93sIk9LykC8HbMyVhx9szQ0Q+SqrzO0+j8vGjtPRGN3DCZ++64w/I9OxYHskDPRFeP8t+/KGXSVwzoNqav11vP/++wgJCcHhw4fRvXt3TJw4Uel5Y2NjfPbZZ29sRyaTQSaTKZVJZbmQSosfX1PHwWP3Ebj4lOLxuu/6lrtNVfp288JbPq6IS8hE8M7r+CLwN+xYNUgxNDF+VGtF3Qb17JGVnYvgHdeZPJBqEmOI3g+EcCgIyPrv3eb+4MELCAwMVjxet25auducO3cTHj58ju3bi65T07fvW3jrrUaIi0tGcPARfPHFcuzYMQdSqaTcr1sdCQLQ0N0E/gMKemcbuJng4ctM7DwTU22SB1JN7dS6a9eu6Nq1a7HPlXbxqKCgIMydqzyRLnBKL8yZ+q664RTRpX1teP/rioic3HwAQEJSJuxtTRTlCYmZ8PKwK7YNKwsj6OmJlCZH/rOPrbWJUpmZqRRmplK4u1jBu6EjWr27FsfPP0Zv3+KvpvBu4IjVW64gJyevWvyyoVLKTIYgzwNMrJXLTayB9ISi9a1qQmTlBAz74XXZ3z+FRLPOQ/hxKJD0sgID1q4uXVrA29tD8Tgnp2Byc0JCCuztX08wTUhIgZdXCT03/zJv3iacOXMTv/wyu9jhCDMzY5iZGcPdvQa8veuiVauPcfz4NfTurduXnVuZ6UNPjCKTI+NTc2FbwmTI0rCzNICHk3LPbB0nIxy7Xsx7WVdV07kKmlLmb6+kpCQEBwfj3r17AArmQ3z00UewtrZ+w55AQEAA/P39lcqkyZvKGooSU2MJTI1f/1oQBAF21sYIvf4c9esWJAvpGTLcuheNYf0aF9uGxEAPDevZI/T6c/h2KLgyQi4XEHrjOUa836TkFxcECMLrhKU49x7GwcJMysThv0aeB0RFQFTbB0LEub8LRUBtHwhX/q9o/fi/IF89XKlI1OUTQGIC4ehSIKV6jS8XZmpqBFNTI8VjQRBgZ2eJ0NBw1K/vDgBIT8/ErVuPMWyYb4ntCIKA+fM34/jxa/j555lwcSnNr2IBgiAgJ6f4qxF0iURfjIZupgi9lwLf5gWfzXK5gEv3UjC8i+Mb9i5ZMw8zREYrD3s8jc6Gk420XPFWJdV1roKmlOkb7Ny5c+jTpw8sLCzg4+MDAFi5ciXmz5+PQ4cOoWPHjir3l0qlkEoLvcmyyj9kURyRSISRg5phzdYrcHO2hHMNcywPDoW9jYnSJZOjvvgV3Tp4YMQAbwDAmMHNMT3oGBp52qNJfUds2XMTWVm56N+r4FLV51EpCDn1AG+1dIW1pRGiY9Px07ZrMJTqo1MbdwDAqYtPkJCUCe8GjpBK9HHx2jOs++UqPhravEKOtSoxMDGGtcfrCadWtZzh4O2FrMQUpD5/pcXItEe4tAOifrOAqPvAy3CI2gwFDAyBsMMAAFG/2UBaHISTa4D8HCDuiXID2ekF//13uaE5YOEAmNkWPLb9+5ynJxTMp6gmRCIRRo7siTVr9sHNzRHOznZYvnwP7O0t4evro6g3atQCdOvmgxEjegAoGKo4fPgPrF49BSYmRoiLSwZQ0NNgaCjB8+cxCAm5hLfeagxra3NERyfip58OwtBQgk6dmmrhSDVvdI8amLHhERq5m6BJLVNsOf4KWbJ89G9f8GNq+vqHsLeSYMrAgh6cnDw5HkcVJAa5eXLEJMtw71kGjKViuDkUJHSjuzth2Ld3sPbwC7zT0ga3I9Ox+2wM5o2qrZ2DpEpXpuRh/PjxGDJkCNasWQM9vYLJgfn5+fjss88wfvx4/PnnnxoNsrw+/qAFsrJzMXvRSaSmy9CisRM2LOqndMnk86gUJKW8zqR7da2HxOQsrNh4CXGJmajvYYsNi/ophi0kEj1cu/USW/bcRGqaDDZWxvDxrokdqwcrJlrq64uxbd9tfLuy4Jema00LzBjfEYP7lDy7vrpw8mmE0Wd+VjzusfQrAEDY5r04MCZAW2FpV/hJCMZWEHUeC5jaANEPIWybXHA1BVCQBAhqri3g2R7ifrMUD8UDvwEACGc2QDgbXNJeOunjj/sgK0uG2bM3IDU1Ey1a1MOGDTOU5iU8fx6DpKTXVxHs2FFwqfCHH85Xaiso6FP0798JEokE167dx5YtvyE1NQM2Nhbw8fHCjh1zikzO1FW9WtkiMS0XK/c/R1xKLuq7mGD95PqwtSg4b1GJOUqXE8Ym5+D9ObcVjzcefYWNR1+hpac5fp7eEEDB5Zwrx3tiya9/YfXBF3C2M0TAMHf0aVv8ULAuqq6XWGqKSCjDdYNGRkYICwuDp6fyuH5ERASaNm2KrKwyzOKNWa3+PgQAmOtYdBVLKp1Zc948zEYlEwf+qO0QdJZwcaW2Q9Bporc2V2j7Ka083lyplCyuPNJYW1VFmS5Gad68uWKuw7/du3cP3t7e5Q6KiIjov2rVqlVwd3eHoaEhWrdujStXrqisn5ycjPHjx6NGjRqQSqWoV68eQkJCKjTGUg9b3L79uhvr888/x6RJk/Do0SPFCpOXLl3CqlWrsHDhQs1HSUREVIlEWpoxuWvXLvj7+2Pt2rVo3bo1li1bhh49eiAiIgL29kUn/Obk5KBbt26wt7fH//3f/6FmzZr466+/YGlpWaFxlnrYQiwWQyQSvXF1RJFIhPz8kq82KBGHLcqMwxZlx2GL8uGwRdlx2KJ8KnrYIrVtXY21ZR5a+lVQW7dujZYtW+LHHwv+tuRyOVxcXDBx4kTMmDGjSP21a9fihx9+wP3792FgUDEXHhSn1D0PkZGRb65ERERESopdGLGYqw5zcnJw/fp1BAS8nlQuFovh6+uL0NDQYts+ePAg2rZti/Hjx+PAgQOws7PDBx98gOnTpysuaKgIpU4e3NzevBALERFRdaDJUYtiF0YMDMScOXOUyuLj45Gfn1/kHlEODg64f/9+sW0/efIEp06dwvDhwxESEoJHjx7hs88+Q25ubqkXbiyLUicPBw8exDvvvAMDAwMcPHhQZd2+fSt2SWgiIqKKpMlLNYtdGLHwWkdlJJfLYW9vj59++gl6enpo0aIFXr58iR9++KFqJA/9+vVDdHQ07O3t0a9fvxLrlXnOAxERURWhyRtjFbswYjFsbW2hp6eHmBjlFWRjYmLg6Fj8iqA1atSAgYGB0hBF/fr1ER0djZycHEgkFXN/llKfnn+ym3/+XdLGxIGIiEh9EokELVq0wMmTJxVlcrkcJ0+eRNu2bYvd56233sKjR48gl79eYO7BgweoUaNGhSUOgJrrPISGhuLw4cNKZVu3bkWtWrVgb2+PTz75pMikECIiIp0jEmluU4O/vz/Wr1+PLVu24N69exg3bhwyMjIwZswYAMDIkSOVJlSOGzcOiYmJmDRpEh48eIAjR47g22+/xfjx4zV6OgpTa3nqefPmoXPnzujduzcA4M8//4Sfnx9Gjx6N+vXr44cffoCTk1ORSSBERES6RJPDFuoYMmQI4uLiMHv2bERHR6Np06Y4evSoYhLls2fPIBa/Ds7FxQW///47Jk+ejCZNmqBmzZqYNGkSpk+fXqFxqpU8hIWFYf7812vE79y5E61bt8b69esBFBxEcTNIiYiIqHQmTJiACRMmFPvcmTNnipS1bdsWly5dquColKmVPCQlJSldQnL27Fm88847isctW7bE8+fPNRcdERGRFvDGWKqp1THj4OCgWCwqJycHN27cUCxPDQBpaWmVusIVERFRRdDSlAedoVby0KtXL8yYMQPnz59HQEAAjI2N0aFDB8Xzt2/fRp06dTQeJBEREVUdag1bzJ8/H/3790enTp1gamqKLVu2KF0KsnHjRnTv3l3jQRIREVUmDluoplbyYGtri3PnziElJQWmpqZF1s3es2cPTE1NNRogERFRpWPuoJJaycM/LCwsii23tuYdComIiKq7MiUPRERE1Zm21nnQFUweiIiICuGcB9WYPBARERVSXS+x1BR2zBAREZFa2PNARERUCIctVGPyQEREVBj75VXi6SEiIiK1sOeBiIioMA5bqMTkgYiIqDD2y6vE00NERERqYc8DERFRYRy2UInJAxERUWHsl1eJyQMREVFh7HlQibkVERERqYU9D0RERIWx50GlKpM8CEmR2g5BZ82aY63tEHTW/DmJ2g5BpwUGpGg7BN2VkqztCEgV9surxNNDREREaqkyPQ9ERERVBoctVGLyQEREVBj75VXi6SEiIiK1sOeBiIioMA5bqMTkgYiIqDDmDipx2IKIiIjUwp4HIiKiwjhsoRKTByIiosKYPKjE5IGIiKgwDuqrxNNDREREamHPAxERUWEctlCJyQMREVEhIvbLq8TTQ0RERGphzwMREVFhHLZQickDERFRYeyXV4mnh4iIiNTCngciIqLCOGyhEpMHIiKiwpg8qMRhCyIiIlJLmXseHj58iNOnTyM2NhZyuVzpudmzZ5c7MCIiIq3hT2uVypQ8rF+/HuPGjYOtrS0cHR0hEr3u3hGJREweiIhIt3HYQqUyJQ/ffPMNFixYgOnTp2s6HiIiIu1jz4NKZTo9SUlJGDRokKZjISIiIh1QpuRh0KBBOHbsmKZjISIiqhrEIs1t1VCphy1WrFih+LeHhwdmzZqFS5cuoXHjxjAwMFCq+/nnn2suQiIiosrGYQuVSp08LF26VOmxqakpzp49i7NnzyqVi0QiJg9ERETVWKlzq8jIyFJtT548qch4iYiIKp4Why1WrVoFd3d3GBoaonXr1rhy5Uqp9tu5cydEIhH69eun9muqS+2OmdTU1CLrOgCAXC5HamqqRoIiIiLSKrEGNzXs2rUL/v7+CAwMxI0bN+Dt7Y0ePXogNjZW5X5Pnz7Fl19+iQ4dOqj3gmWk1mHt27cPPj4+yM7OLvJcVlYWWrZsiUOHDmksOCIiov+SJUuW4OOPP8aYMWPQoEEDrF27FsbGxti4cWOJ++Tn52P48OGYO3cuateuXSlxqrXOw5o1azBt2jQYGxsXec7ExATTp0/Hjz/+iD59+mgsQE3YdiQSwfsfIT5JBi93c8z8pDGa1LMqsf7Ri1FYvu0+XsZmws3JBF+ObIBOPg6K5zOy8rB4612cvByN5LQcONsb48PetTH0HXcAQHJaDlbuiMDFm7F4FZ8Fa3MpurZ2xKThXjAzMSjhVXVEywEQtRsOmFoD0Y8g/LYEiLr75v0a+kI8cD6E+2ch7JrxutyrE0Q+7wM1vCAytoB87Ugg5mHFxa8DXDv4oN1UPzi1aAQzJ3vs7PcZIg6c1HZYWicIAlasOow9v15AaloWmjetjTmzPoC7m32J+1y99hDBm4/jzt1niItLwapln8K3a1OlOhmZ2Vi8dD9OnLqF5JQMONe0wYfD38awwR0r+Igqz7YLiQg+lYj4tDx4OUkxs78jmrgZFVt3d2gSDlxNwcNoGQCgobMhJr9rr6ifmy9geUgczt5Lx4uEHJga6qFdPRP497aDg4WOf779mwavkpDJZJDJZEplUqkUUqlUqSwnJwfXr19HQEDA6zDEYvj6+iI0NLTE9ufNmwd7e3v4+fnh/PnzGotbFbV6Hu7cuYPOnTuX+HzHjh3x559/ljcmjQo5/xILN4Zj/BBP7F3SCZ61LDB2ziUkJMuKrX/jXiKmLLqOgb6u2Le0E3xb18CEoCt48NfrIZmFG8Nx4UYsvp/cHEd+7IKRfWtj/k9/4tTlaABAbGI2YhOzMW1MQxxa8TaCJjXF+Zux+HplWGUccsVp2BWi7p9DOBsMYd1oIOYhRCOWAsYlJ2IAAAtHiLpPhPDXzaLPSYwgPLsN4cSqCglZF0lMjBFzKwIh4+dqO5QqZf3GY/h5+2nMmfUBdm+bBiMjKfw+XQGZLLfEfTKzZPCsVxOBXw8tsc7C73/F+Yt38cPCMQg5EIhRI7pg/re7cPL0rYo4jEoXcjMVC/fHYnwPW+ydUgueToYYu+4ZEtLyiq1/5VEm3m1uji3j3bBzkjscrQzgt/YZYpILznN2jhx3X2Tjs262+HVKLawc44zIWBk+2/CiMg+r4mlwzkNQUBAsLCyUtqCgoCIvGR8fj/z8fDg4OCiVOzg4IDo6utgwL1y4gODgYKxfv75CTkNJ1EoekpKSkJdX/BsOAHJzc5GUlFTuoDRp84HHGNTdFQN8XeHhaoa545rAUKqHX088K7b+z4eeoH1ze/j190AdFzNMGu6FBrUtse1IpKJO2P1E9OvigtaNbeHsYIwhPdzhWcsctx8WHHs9N3OsnNESXVo5wrWGCdo0scPkEfVx+moM8vKLzhfRFaI2w4AbB4GwI0D8UwiHvwdyZUCz3ip2EkPUfy6EMxuApKiiz98+CpzbCDy5WnGB65hHR8/h9KxluL//hLZDqTIEQcDWX05h3CfvwLeLN7w8nfH9t6MRG5eCE6fCStyvU4dGmPz5e+hWqLfh327eeox+fdugdct6cK5pgyGDOsCrXk3c/vOpxo9DGzafScCgtpYY0NoSHo5SzB3kCEOJGL9eTi62/qIPa+KD9taoX9MQtR2k+GZIDcgFIPRhBgDAzEgPG8e54p1m5qhtL0VTdyPMGuCI8BfZiEoqOZH7LwsICEBKSorS9u/ehbJKS0vDhx9+iPXr18PW1lYDkZaeWsmDu7s7rl27VuLz165dg5ubW7mD0pScXDnCH6egnbedokwsFqGtty3CIopPcsIiktDOW/l/wlvN7JTqN/WyxqkrMYhJyIIgCLh0Ox5PX6bjrWYld5+mZeTC1Fgf+no6evGwWB9w8oSg9CUvAE+uQuTcqMTdRJ0+AjKSgJucC0Nl9+JFPOLiU9GujZeizMzMCN6Na+HmrUgVe75ZM+86OHXmNmJikgv+nq9EIPKvWLRv16C8YWtdTp6A8BfZaFfPRFEmFovQtq4Jwv7KKlUbWTly5MkFWBjrlVgnLUsOkQgwN9LRz7fiaHDCpFQqhbm5udJWeMgCAGxtbaGnp4eYmBil8piYGDg6Ohap//jxYzx9+hR9+vSBvr4+9PX1sXXrVhw8eBD6+vp4/Pixhk5GUWrNeejfvz++/vprdOvWrUi3SnR0NGbOnIkRI0a8sZ3ixn8kOXmQSsp8k89iJaXmIF8uwMZS+X+SraUUkS/Si90nPjm72PrxSa8nic76pBFmrbqFTh8dh76eCCKRCPPHe6NlQ5sS4pBhze4HGNy96iRWajO2hEisDyEjUbk8IxGwLeG4XJoAzfpAWDuy4uOjai0uoWDY0MbGXKncxsYM8fHlu8pr1leDMWvuNnT0DYC+vhgikRjfzBmOlj51y9VuVZCUkYd8OWBjpvzFb2umh8jY4oduC1t8OBb25vpKCci/yXLlWHQ4Fu82M4epYckJhs7RwsqQEokELVq0wMmTJxWXW8rlcpw8eRITJkwoUt/Ly6vIVIGZM2ciLS0Ny5cvh4uLS4XFqta39YwZM3DgwAHUrVsXI0aMgKenJwDg/v372LZtG1xcXDBjxow3tAIEBQVh7lzl8dzZ49tizoS31AlHa34+HIlbEUlY/XUr1LQ3wtXwRMxbdxv21oZo19ROqW56Zi4+nXcZdVzMMGGYp5Yi1gKJMUTvB0I4FARkpWg7GtIxBw9fQeC87YrH61Z9VmGv9fP2Mwi7HYk1K8fBqYY1rl1/hLkLdsLezgLt2tavsNfVBT+diEfIzVRsHe8GqUHRXoXcfAFfbHkJCALmDCr6y1inaakTxd/fH6NGjYKPjw9atWqFZcuWISMjA2PGjAEAjBw5EjVr1kRQUBAMDQ3RqJFyz6+lpSUAFCnXNLWSBzMzM1y8eBEBAQHYtWuXYn6DpaUlRowYgQULFsDMzOyN7QQEBMDf31+pTPI0UJ1QSsXKXAI9sajI5Mj4ZBlsrQyL3cfW0lBl/WxZPpb9cg8rA1qh899XYHi6W+D+kxRs3P9IKXlIz8zD2DmXYGKkjx8DWsJAX4e79DKTIcjzABNr5XITayA9oWh9q5oQWTkBw354XSYqOH7RrPMQfhwKJL2swIBJl3V5uwm8m7grHufkFMy1SkhIhb2dhaI8ISENXl7OZX6d7OwcLF1+AD8u/xSdOzYGAHh5OuNexHMEbzmh88mDlYk+9MRAQlq+Unl8Wj5szVV//AefTsD6kwnYOM4Vnk5FPy9z8wVM3vICUUm52PyZa/XqddCiIUOGIC4uDrNnz0Z0dDSaNm2Ko0ePKnr7nz17BrFY+98lao8TWFhYYMaMGVi8eDHS09MhCALs7OwgEpW+i6e4S1QEDQ9ZAIDEQIyGdSwQejsevm1qAADk8oI5CsN71Sp2n6aeVgi9HY9Rfesoyv4Ii0NTz4IrCvLy5cjNE4r0aIn1RJALrx+nZ+bCb84lSAzEWD2zFaQSHf/DkucBUREQ1faBEHHu70IRUNsHwpX/K1o//i/IVw9XKhJ1+QSQmEA4uhRIiSm6D9HfTE0MYWry+gtLEATY2Zoj9HIE6nsVdMWmp2fh1p+RGDak7Ivi5OXlIzcvv8jnl55YDOHff9A6SqIvQkNnQ4Q+yIBv44IfdnK5gEsPMzC8fclXSW04mYC1J+Kx4VNXNHYteknnP4nDX3G52DLeFVYmmv/81jo1vtM0bcKECcUOUwDAmTNnVO67efNmzQdUDLX/j8vlcnh4eCA8PBx161b9McHR79XBjOU30cjDAk3qWmHLoSfIys5Hf9+CD6DpS2/A3sYQU0YWTI76sE9tjPz6Ijbuf4TOPg44cv4lwh8nY954bwCAqbEBWjaywQ+b70Iq0UNNeyNcuZOAA6efY8ZHDQH8nTgEXkKWLA8/TG6F9Mw8pGcW/HKyNpdCT08377ImXNoBUb9ZQNR94GU4RG2GAgaGQNhhAICo32wgLQ7CyTVAfg4QV2ip8uy/55n8u9zQHLBwAMz+nqRq61rw3/SEgvkU/0EGJsaw9nBVPLaq5QwHby9kJaYg9fkrLUamPSKRCCNHdMGadSFwc7WDc01bLP/xEOztLODbpami3qixy9CtS1OM+KAzgII1HJ49i1M8/+JlAu7dfw4LCxM41bCGqakRWvnUxQ9L9sLQUAKnGta4eu0h9h+6jBlTB1TyUVaM0Z1tMGN7FBq5GKKJmxG2nE1EVo4c/VtbAgCmb4uCvYU+pvQumPC9/mQ8VvwWj0UfOqGmtQHiUgs+u4ylYphIxcjNFzBp8wvcfZGNtWNdkC+Hoo6FsR4k+rr5+VZENTmMiqJ28iAWi1G3bl0kJCToRPLQq0NNJKbmYOX2CMQlyVC/ljnWB7aBrWXBr5qo+CyI/tWN0Ly+NRZNaYFlv9zD0p/vw93JBD8GtEI9t9cTtZZ82QJLtt7D1CU3kJKeAyc7Y3wxoj6G9nQHAIQ/TsGtBwVDOt3/p7y4z4mffOHsUHSRLZ0QfhKCsRVEnccCpjZA9EMI2yYXXE0BFCQBgpqXonq2h7jfLMVD8cBvAADCmQ0QzgZrKHDd4uTTCKPP/Kx43GPpVwCAsM17cWBM+S/v0lUff9QdWVk5mD13O1LTMtGiWR1sWDsRUunrhYmeP49DUvLrydB3wp9h5Eevb+oX9ENBL9n7fdtg4YJRAIAlP/hhybID+HLGRqSkZMKphjUmT+xbbRaJ6tXMHInpeVh5NA5xqfmoX1OK9Z+6wtas4OM/KilX6Uf2jovJfycIysOK43vYYmJPO8Sk5OLUnYJz3G+R8pUuW8a7orVH8RMrqXoRCYKgdt/coUOH8P3332PNmjUam5Qh3J+qkXb+i4RdF7Qdgs6aP+e/2buhKYGyNdoOQWcJJ1ZoOwSdJuq1v0Lbl6/opLG2xJ+ffXMlHVOmgaqRI0ciMzMT3t7ekEgkMDJSHhNLTOQHMhER6TAOW6hUpuRh2bJlGg6DiIiIdEWZkodRo0ZpOg4iIqKqQ4tXW+iCcl9fk52djZycHKUyc3PzEmoTERHpAO0vpVCllen0ZGRkYMKECbC3t4eJiQmsrKyUNiIiIqq+ypQ8TJs2DadOncKaNWsglUqxYcMGzJ07F05OTti6daumYyQiIqpcIpHmtmqoTMMWhw4dwtatW9G5c2eMGTMGHTp0gIeHB9zc3LBt2zYMHz78zY0QERFVVdXzO19jytTzkJiYiNq1awMomN/wz6WZ7du3x7lz51TtSkREVPWx50GlMiUPtWvXRmRkwcpiXl5e2L17N4CCHol/7uhFRERE1VOZkocxY8bg1q1bAApu071q1SoYGhpi8uTJmDqVK0USEZGOE2lwq4bUmvMgl8vxww8/4ODBg8jJyUFUVBQCAwNx//59XL9+HR4eHmjSpElFxUpERFQ5qulwg6aolTwsWLAAc+bMga+vL4yMjLB8+XLExsZi48aNcHNzq6gYiYiIqApRa9hi69atWL16NX7//Xfs378fhw4dwrZt2yCXq3knRSIioqpMrMGtGlLrsJ49e4ZevXopHvv6+kIkEiEqKkrjgREREWkNr7ZQSa3kIS8vD4aGhkplBgYGyM3N1WhQREREVHWpNedBEASMHj0aUqlUUZadnY3//e9/MDExUZTt3btXcxESERFVturZYaAxaiUPxd1Nc8SIERoLhoiIqEqopsMNmqJW8rBp06aKioOIiIh0RLlvyU1ERFTdsONBNSYPREREhTF7UInJAxERUWHMHVSqpstXEBERUUVhzwMREVFhYnY9qMLkgYiIqDDmDipx2IKIiIjUwp4HIiKiwni1hUpMHoiIiApj7qAShy2IiIhILex5ICIiKozDFioxeSAiIiqMuYNKHLYgIiIitVSZngeRXUNth6CzRIFDtR2CzgoMSNF2CDptrnSctkPQWbPDe2s7BFKFi0SpVGWSByIioiqDuYNKTB6IiIgK44RJlTjngYiIiNTCngciIqLC2PGgEpMHIiKiwjhsoRKHLYiIiEgt7HkgIiIqjB0PKjF5ICIiKozrPKjEYQsiIiJSC3seiIiICuOESZWYPBARERXG5EElDlsQERGRWtjzQEREVBh7HlRi8kBERFSYiB3zqvDsEBERFSYWaW5T06pVq+Du7g5DQ0O0bt0aV65cKbHu+vXr0aFDB1hZWcHKygq+vr4q62sKkwciIqIqYteuXfD390dgYCBu3LgBb29v9OjRA7GxscXWP3PmDIYNG4bTp08jNDQULi4u6N69O16+fFmhcTJ5ICIiKkwk0tymhiVLluDjjz/GmDFj0KBBA6xduxbGxsbYuHFjsfW3bduGzz77DE2bNoWXlxc2bNgAuVyOkydPauIslIhzHoiIiArT4JwHmUwGmUymVCaVSiGVSpXKcnJycP36dQQEBCjKxGIxfH19ERoaWqrXyszMRG5uLqytrcsfuArseSAiIqpAQUFBsLCwUNqCgoKK1IuPj0d+fj4cHByUyh0cHBAdHV2q15o+fTqcnJzg6+urkdhLwp4HIiKiwjR4qWZAQAD8/f2Vygr3OmjCwoULsXPnTpw5cwaGhoYab//fmDwQEREVpsEbYxU3RFEcW1tb6OnpISYmRqk8JiYGjo6OKvddtGgRFi5ciBMnTqBJkyblirc0OGxBRERUBUgkErRo0UJpsuM/kx/btm1b4n7ff/895s+fj6NHj8LHx6cyQmXPAxERURFaWiTK398fo0aNgo+PD1q1aoVly5YhIyMDY8aMAQCMHDkSNWvWVMyZ+O677zB79mxs374d7u7uirkRpqamMDU1rbA4mTwQEREVpqXlqYcMGYK4uDjMnj0b0dHRaNq0KY4ePaqYRPns2TOIxa8TmzVr1iAnJwcDBw5UaicwMBBz5sypsDiZPBAREVUhEyZMwIQJE4p97syZM0qPnz59WvEBFYPJAxERUWG8MZZKZR7UycnJQUREBPLy8jQZDxERkfaJxJrbqiG1jyozMxN+fn4wNjZGw4YN8ezZMwDAxIkTsXDhQo0HSEREVOm0eGMsXaB28hAQEIBbt24VWYTC19cXu3bt0mhwREREVPWoPedh//792LVrF9q0aQPRv8aEGjZsiMePH2s0OCIiIq3gnAeV1E4e4uLiYG9vX6Q8IyNDKZkgIiLSWdV0roKmqJ08+Pj44MiRI5g4cSIAKBKGDRs2qFwBqyoRBAErNpzHnoNhSE2ToXkTZ8yZ2gPuLqrvQrbt1+sI3nYZcYnp8PKwxyz/7mjSwAkA8OJVMroOWFPsfsu+6Yd3utTX+HFoiyAIWLHi/7Bnz2mkpmagefN6mDPnI7i71yhxn3XrDuDYsat48iQKhoYSNGtWF19+OQy1azsp6syevQF//HEHsbFJMDY2RLNm9fDll0NRp07NyjisSiEIAlasOow9v15AaloWmjetjTmzPoC7W9GE/B9Xrz1E8ObjuHP3GeLiUrBq2afw7dpUqU5GZjYWL92PE6duITklA841bfDh8LcxbHDHCj6iqse1gw/aTfWDU4tGMHOyx85+nyHiQMXenriq2RbyFMH7HyM+WQYvd3PMHNsQTepZlVj/6MUoLN8RgZexWXCrYYIvR3qhU4vXN2eKT5Zh0dZ7uBgWh7SMXPg0tMHMsQ3h7lSwCFFyWg5W7nyAi2FxeBWfBWtzCbq2dsSkYZ4wMzGo8OOlyqd2avXtt9/iq6++wrhx45CXl4fly5eje/fu2LRpExYsWFARMWrc+l8u4ec91zBnak/s3jAKRoYG8Ju8CzJZyVeOhJy4i6AVJzH+o/bYt+kjeHk4wG/yLiQkZgAAatib48KhiUrbxLEdYGwsQcc2dSrr0CrF+vWH8PPPv2POnI+we/d8GBkZws9vIWSynBL3uXLlHoYP74bdu+dh06YA5OXlw89vITIzsxV1GjashaCgTxESsgjBwTMgCAL8/BYiP19eGYdVKdZvPIaft5/GnFkfYPe2aTAyksLv0xWQyXJL3CczSwbPejUR+PXQEuss/P5XnL94Fz8sHIOQA4EYNaIL5n+7CydP36qIw6jSJCbGiLkVgZDxc7UdilaEXIjCwk13MX5IPexd3AGe7uYYO+8KEpJlxda/cT8RU5bcxMCurti3uAN8WztiwsJrePBXKoCChHd80FW8iMnE6oCW2LukI5zsjPDRnMvIzC74zIxNzEZsYjamjW6AQ8s6IWhiU5y/EYevV+nw+08k0txWDamdPLRv3x5hYWHIy8tD48aNcezYMdjb2yM0NBQtWrSoiBg1ShAEbN19FeNGvwXfjvXg5WGP72f3Rmx8Gk6ce1Difpt2XsHgvt4Y0LsJPGrZYu60njCU6uPXw7cBAHp6YtjZmCptJ84+wDtdvGBiLKmsw6twgiBg69ajGDeuH3x9feDl5Yrvvx+H2NhknDhxrcT9goNnoH//Tqhb1xleXm5YuPB/iIqKR3h4pKLOkCFd0bJlfTg726Fhw1r44ovBePUqAS9fxlXGoVU4QRCw9ZdTGPfJO/Dt4g0vT2d8/+1oxMal4MSpsBL369ShESZ//h66Fept+Lebtx6jX982aN2yHpxr2mDIoA7wqlcTt/98qvHjqOoeHT2H07OW4f7+E9oORSs2H3yCQd1cMKCrCzxczDD3f41hKBXj15PPi63/8+FItG9mB7/366COixkmfeCJBrUtsC3kKQDgaVQGbj1IRuCnjdG4riVq1zTFnE8bI1uWjyPnowAA9dzMsXK6D7q0dIBrDRO0aWKLycM9cfpqLPJ0Nfnn1RYqlWlQp06dOli/fj2uXLmCu3fv4pdffkHjxo01HVuFeBGVjLiEDLTzcVeUmZkawruBE27eeVnsPjm5+QiPiEY7n1qKMrFYhHYt3Uvc5879V7j3MAYD+3hrNH5te/EiFnFxyWjXrpGizMzMGN7edXDz5sNSt5OWlgkAsLAofu31zMxs7N17Fs7OdnB0tClf0FXEixfxiItPRbs2XooyMzMjeDeuhZu3IlXs+WbNvOvg1JnbiIlJhiAIuHQlApF/xaJ9uwblDZt0SE6uHOGPU9DO205RJhaL0LaJHcIikordJywiCe28bZXK3mpqh7AHBfVz8gq+/KUGr78uxGIRJAZiXL+XWGIsaZl5MDXWh74e5w5UR2rPeUhNTS22XCQSQSqVQiJ5869smUwGmUy5C00qy4VUWvFjY3F/DzPYWJsoldtYmyD+7+cKS0rORH6+ABtr4yL7PPkrodh9/u/QLdRxt0Hzxs4aiLrqiItLAQDY2FgoldvYWCA+PqVUbcjlcnz77c9o3rwe6tVzUXpu27bjWLRoOzIzZahVqwY2bfoKEkn1WAg1LqHgb8fGxlyp3MbGDPHxxf9dldasrwZj1txt6OgbAH19MUQiMb6ZMxwtfeqWq13SLUlpOciXC7CxUL79s62lBJEv04vdJz5ZBhvLwvWliE8q+IyuXdMUTnZGWPLLfcwd1xhGUn1sOfQE0QnZiEsqfigkKTUHa/Y8xOBurho4Ki3hhEmV1D47lpaWsLKyKrJZWlrCyMgIbm5uCAwMhFxecldVUFAQLCwslLagZUfKdSAlOfj7HTTrukix5eVVfBdatiwXh4/fxcDeut/rcPDgBTRrNkax5eXll7vNuXM34eHD51i6dGKR5/r2fQv79n2LX36ZBXf3Gvjii+Uq51JUZQcPX0GzVl8otrzc8p+7kvy8/QzCbkdizcpx+HVnAGZ8OQBzF+zEH6H3Kuw16b/BQF+MFdNb4GlUBlp/eAzNhv6Gy3cS0LG5XbE98umZufj0myuo42yKCUPrVX7AmsI5Dyqp/ZNu8+bN+PrrrzF69Gi0atUKAHDlyhVs2bIFM2fORFxcHBYtWgSpVIqvvvqq2DYCAgLg7++vVCZNr5gFprq0rwvvhq9n9OfkFHyAJyRmwN72dZd5QmIGvOo6FNkfAKwsjaGnJ0JCYqZSeUJiBmyti3a7Hz11H9nZuej3jm4M5ajSpUsLeHt7KB7n5BRMkEpISIG9/evZ2wkJKfDycntje/PmbcKZMzfxyy+zix2OMDMzhpmZMdzda8Dbuy5atfoYx49fQ+/e7TRwNJWry9tN4N3EXfH49blLhb3d656bhIQ0eHmVvYcqOzsHS5cfwI/LP0XnjgXvOS9PZ9yLeI7gLSfQrm31udKHVLMyk0BPLEJCinKPQHxyDmwL9S78w9ZSWmQyZXyyDLZWr+s3qmOJ/Us7Ii0jF7l5clhbSDF42gU0qqPcA5melYex867AxEgfP87wgYE+f71XV2onD1u2bMHixYsxePBgRVmfPn3QuHFjrFu3DidPnoSrqysWLFhQYvIglUohlRZ6I+dWzJCFqYkUpiavX0sQBNjZmCD02lPUr1eQLKRnyHDrbhSGvd+82DYkBnpo6OmI0OtP4dupIJOWywWEXvsLIwYUnST66+Hb6NK+LqytjIs8p2tMTY1gamqkeCwIAuzsLBEaGo769d0BAOnpmbh16zGGDfMtsR1BEDB//mYcP34NP/88Ey4uJV+a+K+9IAgCcnJKvhKhKjM1MYSpyetVWAVBgJ2tOUIvR6C+V8FwTXp6Fm79GYlhQzqU+XXy8vKRm5dfZJ0VPbEYglwoc7ukeyQGYjSsY4HQ2/Hwbe0IoOCz6tKf8Rj+jnux+zT1tELo7XiM6lNbUfbHrXg0LebSzn8uu3walY47j5Px+QeeiufSM3PhN/cyJAZirP6qJaQSPQ0emRZU0x4DTVE7efjjjz+wdu3aIuXNmjVDaGgogIIrMv6550VVIxKJMHJwS6zZ8gfcXKzh7GSB5T+dg72tGXw7vu5iGzVxO7p1qocRA30AAGOGtsL0bw6jkZcjmjRwwpZdV5GVnYv+vZsotf/Xi0RcDXuGnxYPRnUkEokwcmRPrFmzD25ujnB2tsPy5Xtgb28JX18fRb1RoxagWzcfjBjRA0DBUMXhw39g9eopMDExQlxcMoCCngZDQwmeP49BSMglvPVWY1hbmyM6OhE//XQQhoYSdOrUVAtHqnkikQgjR3TBmnUhcHO1g3NNWyz/8RDs7Szg26Wpot6oscvQrUtTjPigM4CCNRyePXt9xcmLlwm4d/85LCxM4FTDGqamRmjlUxc/LNkLQ0MJnGpY4+q1h9h/6DJmTB1QyUepfQYmxrD2eD3WblXLGQ7eXshKTEHq81dajKxyjO5bGzNWhKFRHQs0qWuJLYcjkZWdj/5dCxLW6ctvwt7aEFM+LOiR+rB3LYycGYqNBx6jcwsHHLnwEuGPkzFv3Oue06MXo2BlIYGTrREe/JWGBcHh6NrKEe2bFkzM/CdxyJLl44cvmiE9MxfpmQVJv7W5FHp6OvhFzORBJbWTBxcXFwQHBxe5CVZwcDBcXArenAkJCbCyKnlBEm37eEQbZGXnYvZ3vyE1PRstmrhgw5LBkEpfn47nL5ORlJyleNzLtwESkzOxYv15xCVmoH5de2xYMhi2hSZe/nr4NhztzdG+VW1UVx9/3AdZWTLMnr0BqamZaNGiHjZsmAGp9PVk2efPY5CUlKZ4vGNHwWVzH344X6mtoKBP0b9/J0gkEly7dh9btvyG1NQM2NhYwMfHCzt2zCkyOVOXffxRd2Rl5WD23O1ITctEi2Z1sGHtRKXJws+fxyEp+fXktjvhzzDyo6WKx0E//B8A4P2+bbBwwSgAwJIf/LBk2QF8OWMjUlIy4VTDGpMn9v1PLhLl5NMIo8/8rHjcY2lBD2jY5r04MCZAW2FVml7tnZCYKsPKnQ8QlyRD/VrmWD+7lWLYIiouS6mXqrmXNRZNboZl2yOw9JcIuNcwwY8zfFDP7fXE3tgkGRZuuouEFBnsrAzxXmdnjBv0ejJu+JMU3HqQDADo/tlppXhOrOsCZ3sd7IUVc8hFFZEgCGr1ax48eBCDBg2Cl5cXWrZsCQC4du0a7t27h19//RW9e/fGmjVr8PDhQyxZsqT0DSdsVicM+jcb3Z9boTU5pbtChIo3VzpO2yHorNnhvbUdgk4TNVhcoe0LN8drrC1Rs1Uaa6uqULvnoW/fvoiIiMDatWvx4EHBokrvvPMO9u/fj/T0gl9L48bxA4WIiHQYhy1UKtMF9O7u7ophi9TUVOzYsQNDhgzBtWvXkJ9fcZejERERVQomDyqVeVDn3LlzGDVqFJycnLB48WK8/fbbuHTpkiZjIyIioipIrZ6H6OhobN68GcHBwUhNTcXgwYMhk8mwf/9+NGjAZXCJiKia4AqTKpX67PTp0weenp64ffs2li1bhqioKKxcubIiYyMiItIO3hhLpVL3PPz222/4/PPPMW7cONSty/XyiYiI/qtK3fNw4cIFpKWloUWLFmjdujV+/PFHxMfHV2RsRERE2sF7W6hU6uShTZs2WL9+PV69eoVPP/0UO3fuhJOTE+RyOY4fP460tLQ3N0JERKQLRGLNbdWQ2kdlYmKCjz76CBcuXMCff/6JKVOmYOHChbC3t0ffvn0rIkYiIiKqQsqVEnl6euL777/HixcvsGPHDk3FREREpF0ctlCpTItEFaanp4d+/fqhX79+mmiOiIhIu6rpl76maCR5ICIiqlZ4YyyVeHaIiIhILex5ICIiKoLDFqoweSAiIiqMcx5U4rAFERERqYU9D0RERIVV08WdNIXJAxERUREctlCFqRURERGphT0PREREhXHCpEpMHoiIiArjnAeVeHaIiIhILex5ICIiKoLDFqoweSAiIiqMcx5UYvJARERUBEf1VeHZISIiIrWw54GIiKgwDluoxOSBiIioMCYPKnHYgoiIiNTCngciIqIi2POgCpMHIiKiwrjCpEpVJnkQzuzWdgi6y/GMtiPQXSnJ2o5Ap80O763tEHTWvIaHtR2CTgsUFms7hP80plZERESFiUSa29S0atUquLu7w9DQEK1bt8aVK1dU1t+zZw+8vLxgaGiIxo0bIyQkpKxHXWpMHoiIiIoQaXArvV27dsHf3x+BgYG4ceMGvL290aNHD8TGxhZb/48//sCwYcPg5+eHmzdvol+/fujXrx/u3Lmj/iGrgckDERFRFbFkyRJ8/PHHGDNmDBo0aIC1a9fC2NgYGzduLLb+8uXL0bNnT0ydOhX169fH/Pnz0bx5c/z4448VGieTByIiosJEYo1tMpkMqampSptMJivykjk5Obh+/Tp8fX0VZWKxGL6+vggNDS02zNDQUKX6ANCjR48S62sKkwciIqJCRCKRxragoCBYWFgobUFBQUVeMz4+Hvn5+XBwcFAqd3BwQHR0dLFxRkdHq1VfU6rM1RZERERVh+bWeQgICIC/v79SmVQq1Vj72sDkgYiIqAJJpdJSJQu2trbQ09NDTEyMUnlMTAwcHR2L3cfR0VGt+prCYQsiIqLCNDjnobQkEglatGiBkydPKsrkcjlOnjyJtm3bFrtP27ZtleoDwPHjx0usrynseSAiIipCO8tT+/v7Y9SoUfDx8UGrVq2wbNkyZGRkYMyYMQCAkSNHombNmoo5E5MmTUKnTp2wePFivPvuu9i5cyeuXbuGn376qULjZPJARERURQwZMgRxcXGYPXs2oqOj0bRpUxw9elQxKfLZs2cQi1/3ZrRr1w7bt2/HzJkz8dVXX6Fu3brYv38/GjVqVKFxigRBECr0FUpJ+LWXtkPQXY722o5Ad3F56vJxr6PtCHQWl6cun0AhomJf4NUKzbVV43PNtVVFsOeBiIioMN4YSyWeHSIiIlILex6IiIiK0M6ESV3B5IGIiKiwMtwN87+EwxZERESkFvY8EBERFcYJkyoxeSAiIiqCwxaqMHkgIiIqjHMeVGK/DBEREamFPQ9ERERF8Le1KkweiIiICuOwhUpMrYiIiEgt7HkgIiIqjD0PKjF5ICIiKoId86rw7BAREZFaypw8PH78GDNnzsSwYcMQGxsLAPjtt98QHh6useCIiIi0QiTS3FYNlSl5OHv2LBo3bozLly9j7969SE9PBwDcunULgYGBGg2QiIio8ok0uFU/ZUoeZsyYgW+++QbHjx+HRCJRlHfp0gWXLl3SWHBERERU9ZRpwuSff/6J7du3Fym3t7dHfHx8uYMiIiLSKt4YS6UynR1LS0u8evWqSPnNmzdRs2bNcgdFRESkVZzzoFKZeh6GDh2K6dOnY8+ePRCJRJDL5bh48SK+/PJLjBw5UtMxaty20BQEn09GfHo+vBwlmNnHFk1cDIutu/tqKg7cSMPDmBwAQMOaUkzubl2k/uPYHCw6moCrkdnIlwuoYy/BiuEOcLI0qPDjqUzbTkYj+GgU4lNy4OVigpnD3dGktlmxdR++zMSK/c8R/jQDUQkyBAx1x6juNYrUi0mSYdGeZzj3ZzKyc/Lham+Ibz/yQONaphV9OJVu24VEBJ9KRHxaHrycpJjZ3xFN3IyKrbs7NAkHrqbgYbQMANDQ2RCT37VX1M/NF7A8JA5n76XjRUIOTA310K6eCfx728HBQvffd9tCniJ4/2PEJ8vg5W6OmWMbokk9qxLrH70YheU7IvAyNgtuNUzw5UgvdGrhoHg+PlmGRVvv4WJYHNIycuHT0AYzxzaEu1PB+yw5LQcrdz7AxbA4vIrPgrW5BF1bO2LSME+Ymej++SwN1w4+aDfVD04tGsHMyR47+32GiAMntR2WllTPL31NKVPPw7fffgsvLy+4uLggPT0dDRo0QMeOHdGuXTvMnDlT0zFqVMjtdCwMicf4rlbYO94ZnjUkGLvpFRLS84qtf+VJFt71NsWWsU7Y+b+acLTQh9+mV4hJeV3/WUIuPlj3ErXtJNj6sRMOfO6Cz7pYQapfvd58IVfisXDXU4zv64y9gU3g6WKMsUvuISE1t9j62TlyuNhJMWWgK+xK+DJLycjDsG/Doa8nwvrJXjjyTVNMH+IOC5PqtwRJyM1ULNwfi/E9bLF3Si14Ohli7LpnSEgr4b33KBPvNjfHlvFu2DnJHY5WBvBb+wwxyQXnOztHjrsvsvFZN1v8OqUWVo5xRmSsDJ9teFGZh1UhQi5EYeGmuxg/pB72Lu4AT3dzjJ13BQnJsmLr37ifiClLbmJgV1fsW9wBvq0dMWHhNTz4KxUAIAgCxgddxYuYTKwOaIm9SzrCyc4IH825jMzsgvMfm5iN2MRsTBvdAIeWdULQxKY4fyMOX6+6VWnHrW0SE2PE3IpAyPi52g6FqjiRIAiCOjsIgoDnz5/Dzs4O8fHx+PPPP5Geno5mzZqhbt26ZQ5E+LVXmfdVx+DVL9DIWYrZfe0AAHK5gM7f/4URbS3wSaeSf9X8I18uoNX8SMzqY4d+zQt+cfvviIG+HvD9YIc37F1BHO0r5WUGz/8TjWqZYPaI2gD+PndfXseIrjXwybuqh6u6TL2BUd1qFOl5WLznL9x4lIZtAY0qLG6VUpIr7aUGL41EI1cjzB7gCODv8zfvEUa0t8InvrZv3D9fLqDVVw8wa4AD+rW0LLbOn8+yMGjpU5ya7QEnq0r4texep0KaHTztAhp5WGD2J40B/H2uPj6BEb1q4ZMBHkXqT150HZnZ+Vg3s5WibMj0C/ByN8fccU0Q+TId70w4g0PLO6Guq5mizfZjjmPyCC8M6uZabBxHL0Zh6rIw3NzZE/p6mh0Dn9fwsEbb07RAIaJK9zwEChEV+wIpOzXXlsVQzbVVRaj91yAIAjw8PPDixQu4uLigV69eGDx4cLkSh8qSkycgPEqGdh7GijKxWIS2dYwQ9iy7VG1k5QrIywcsjAtOnVwu4ExEBtxtDeC3KQrtFkRi8OoXOHE3o0KOQVty8uQI/ysd7RpYKsrEYhHaNrBE2OO0Mrd7KiwJjdxNMWl1BNpNuor359zC7rMxGoi4asnJExD+Ihvt6pkoysRiEdrWNUHYX1mlaiMrR448uQALY70S66RlySESAeZGujvZKydXjvDHKWjnbacoE4tFaNvEDmERScXuExaRhHbeygnYW03tEPagoH5OnhwAIDV4fV7EYhEkBmJcv5dYYixpmXkwNdbXeOJAuoCXaqqi9l+EWCxG3bp1kZCQUOYXlclkSE1NVdpkufllbq+0kjLzkS8HbEyVP3xtTfURn1a61198NAH25npoV6dg3DkhIx+ZOQLWn01Gh7rGCB7jBN+GJpi4LRpXnpTuS0EXJKXlFZw7c+Vfs7bmBohPKX7YojSex2Vjx+louDkYYYN/Awzt7IgF2yOx72JseUOuUpIy/j5/ZoXee2Z6iE8tftiisMWHY2Fvrq+UgPybLFeORYdj8W4zc5galpxgVHVJaTnIlwuwsZAqldtaShBfwrBFfLIMNpaF60sRn1RQv3ZNUzjZGWHJL/eRkp6DnFw51u99hOiEbMQlFd9mUmoO1ux5iMEl9EoQ/ZeVKZ1euHAhpk6dijt37pTpRYOCgmBhYaG0Be19Uqa2KtNPZ5MQcjsdPw53VPyCkf896NOlvglGt7dEfScpPulkhc6exth5JVWL0eoGQQAauJnAf4ArGriZYEhnBwzq6ICdZ6pf70N5/HQiHiE3U/HjR85Kv57/kZsv4IstLwFBwJxBjlqIsGoz0BdjxfQWeBqVgdYfHkOzob/h8p0EdGxuB3ExPwzTM3Px6TdXUMfZFBOG1qv8gEn7eLWFSmWalTZy5EhkZmbC29sbEokERkbKs8UTE0vuBgSAgIAA+Pv7K5VJQgaVJRS1WBnrQU8MJKQr9zLEp+fB1kz1L7Xg88lYfzYZGz+qAc8ar3/hWBnrQV8MeNhLlOrXsZfg+tPSDYXoAisz/YJzV2hyZHxqLmzLMbPfztIAHk7GSmV1nIxw7HrZe7aqIiuTv89foR6u+LR82Jqr/jMMPp2A9ScTsHGcKzydil4VlJsvYPKWF4hKysXmz1x1utcBAKzMJNATi5CQotwjEJ+cA9tCvQv/sLWUFplMGZ8sg63V6/qN6lhi/9KOSMvIRW6eHNYW0oK5FXUslPZLz8rD2HlXYGKkjx9n+MBAn0MW/038/65KmZKHZcuWletFpVIppFLlDwHBoOI/8CT6IjR0kiL0USZ8GxR0/crlAi49zsLwthYl7rfhXBLWnk7GhjE10NhZ+cNboi9CI2cpIuNzlMqfxufCybL6XDEg0RejoZspQu+lwLe5NYC/z929FAzvUvZfus08zBAZrTy88zQ6G042xX9J6CqJvggNnQ0R+iADvo1fT9i79DADw9uXPFF3w8kErD0Rjw2fuqKxa9FLOv9JHP6Ky8WW8a6wqgZXqUgMxGhYxwKht+Ph2/r15NJLf8Zj+Dvuxe7T1NMKobfjMapPbUXZH7fi0bSYSzv/uezyaVQ67jxOxucfeCqeS8/Mhd/cy5AYiLH6q5aQSnQ7ESOqKGX6pBk1apSm46g0o9tbYsb/xaKRsxRNnA2x5WIKsnIE9P/7yonpe2Jgb66PKT1sAADrzyZhxYlELBrigJpW+oj7+7I6Y4kYJtKCzNSvgyX8d8bAp5YRWtc2wvkHmTh9PwNbxzpp5yAryOgeNTBjwyM0cjdBk1qm2HL8FbJk+ejfvmBi2/T1D2FvJcGUgW4ACiapPY4qSAxy8+SISZbh3rMMGEvFcHMo+CIc3d0Jw769g7WHX+Cdlja4HZmO3WdjMG9U7eKD0GGjO9tgxvYoNHIxRBM3I2w5m4isHDn6t7YEAEzfFgV7C31M6V1w9cz6k/FY8Vs8Fn3ohJrWBoj7e26EsbTgvZebL2DS5he4+yIba8e6IF8ORR0LYz1IdPhS4dF9a2PGijA0qmOBJnUtseVwJLKy89G/qwsAYPrym7C3NsSUD+sDAD7sXQsjZ4Zi44HH6NzCAUcuvET442TMG9dY0ebRi1GwspDAydYID/5Kw4LgcHRt5Yj2TQvev/8kDlmyfPzwRTOkZ+YiPbOgp83aXAo9Pd09n6VlYGIMa4/XczysajnDwdsLWYkpSH1edGHAaq2aDjdoSrl/pmRnZyMnR/lXt7m5eXmbrTC9mpgiMSMfK08kIS4tD/VrSLF+TA3YmhWciqjkPIj+9abZcTkVufnApO3KY/Dju1hhom/BL/BuDU0x5z05fjqbjAWH4lHLzgArPnBEC/fiF//RVb1a2SIxLRcr9z9HXEou6ruYYP3k+rC1KBiyiUrMgehfA8ixyTl4f85txeONR19h49FXaOlpjp+nNwQANK5lipXjPbHk17+w+uALONsZImCYO/q0tUN106uZORLT87DyaBziUvNRv6YU6z91ff3eS8pV+rzacTH57wThpVI743vYYmJPO8Sk5OLUnYKb0vVbFKlUZ8t4V7T2KH5ipS7o1d4JiakyrNz5AHFJMtSvZY71s1sphi2i4rKU/k6be1lj0eRmWLY9Akt/iYB7DRP8OMMH9dxefxbFJsmwcNNdJKTIYGdliPc6O2PcoNdXiYU/ScGtB8kAgO6fnVaK58S6LnC2Vx5eq46cfBph9JmfFY97LP0KABC2eS8OjAnQVljaweWpVVJ7nQcAyMjIwPTp07F79+5ir7rIz1f/yonKWuehWqqkdR6qpUpc56FaqqB1Hv4Lqvo6D1Vdha/zkL5Pc22Zvq+5tqqIMqVW06ZNw6lTp7BmzRpIpVJs2LABc+fOhZOTE7Zu3arpGImIiCoZ13lQpUzDFocOHcLWrVvRuXNnjBkzBh06dICHhwfc3Nywbds2DB8+XNNxEhERVR7OeVCpTD0PiYmJqF27YEKbubm54tLM9u3b49y5c5qLjoiISCvEGtyqnzIdVe3atREZWTBBy8vLC7t37wZQ0CNhaWmpseCIiIio6lEreXjy5AnkcjnGjBmDW7cK7jQ3Y8YMrFq1CoaGhpg8eTKmTp1aIYESERFVGq4wqZJacx7q1q2LV69eYfLkyQCAIUOGYMWKFbh//z6uX78ODw8PNGnSpEICJSIiqjzVc7hBU9Q6O4Wv6gwJCUFGRgbc3NzQv39/Jg5ERET/Abq/li0REZGmVdPhBk1RK3kQiURKq7r9U0ZERFSt8LtNJbWSB0EQMHr0aMVNrbKzs/G///0PJibKy+Du3btXcxESERFRlaJW8lD4hlgjRozQaDBERERVAydMqqJW8rBp06aKioOIiKjq4LCFSkytiIiISC282oKIiKgI9jyowuSBiIioMBE75lVh8kBERFQEex5UYWpFREREamHPAxERUWEctlCJZ4eIiKgIkQa3ipGYmIjhw4fD3NwclpaW8PPzQ3p6usr6EydOhKenJ4yMjODq6orPP/8cKSkpar82kwciIiIdNHz4cISHh+P48eM4fPgwzp07h08++aTE+lFRUYiKisKiRYtw584dbN68GUePHoWfn5/ary0SCt8qU0uEX3tpOwTd5Wiv7Qh0V0qytiPQbe51tB2BzprX8LC2Q9BpgUJExb5A3nmNNSXLbwWZTKZUJpVKFbd6KIt79+6hQYMGuHr1Knx8fAAAR48eRa9evfDixQs4OTmVqp09e/ZgxIgRyMjIgL5+6WcysOeBiIioMJFYY1tQUBAsLCyUtqCgoHKFFxoaCktLS0XiAAC+vr4Qi8W4fPlyqdtJSUmBubm5WokDwAmTREREFSogIAD+/v5KZeXpdQCA6Oho2Nsr9zrr6+vD2toa0dHRpWojPj4e8+fPVznUURL2PBARERWhuQmTUqkU5ubmSltJycOMGTMgEolUbvfv3y/30aWmpuLdd99FgwYNMGfOHLX3Z88DERFRYVq6MdaUKVMwevRolXVq164NR0dHxMbGKpXn5eUhMTERjo6OKvdPS0tDz549YWZmhn379sHAwEDtOJk8EBERVRF2dnaws7N7Y722bdsiOTkZ169fR4sWLQAAp06dglwuR+vWrUvcLzU1FT169IBUKsXBgwdhaGhYpjg5bEFERFSEWIOb5tWvXx89e/bExx9/jCtXruDixYuYMGEChg4dqrjS4uXLl/Dy8sKVK1cAFCQO3bt3R0ZGBoKDg5Gamoro6GhER0cjPz9frddnzwMREVFhWhq2UMe2bdswYcIEdO3aFWKxGAMGDMCKFSsUz+fm5iIiIgKZmZkAgBs3biiuxPDw8FBqKzIyEu7u7qV+bSYPRERERVT9jnlra2ts3769xOfd3d3x76WcOnfuDE0t7VT1zw4RERFVKex5ICIiKkwHhi20qcosT11VyWQyBAUFISAgoNyLevwX8fyVHc9d2fHclQ/PH70Jk4c3SE1NhYWFhWIJT1IPz1/Z8dyVHc9d+fD80ZtwzgMRERGphckDERERqYXJAxEREamFycMbSKVSBAYGctJQGfH8lR3PXdnx3JUPzx+9CSdMEhERkVrY80BERERqYfJAREREamHyQERERGph8kBERERqYfKghjNnzkAkEiE5OblCX2f06NHo169fhb6GJohEIuzfvx8A8PTpU4hEIoSFhWk1pv86d3d3LFu2TNthEFE1p5PJQ1xcHMaNGwdXV1dIpVI4OjqiR48euHjxYoW+brt27fDq1StYWFhU6OtUFW86z69evcI777yjVpv79u1DmzZtYGFhATMzMzRs2BBffPFFBUSvPaNHj4ZIJIJIJIJEIoGHhwfmzZuHvLy8crWbmpqKr7/+Gl5eXjA0NISjoyN8fX2xd+9ejd1mVxeEhoZCT08P7777rrZD0Rn/fk/+e3v06JG2QyMdpZN31RwwYABycnKwZcsW1K5dGzExMTh58iQSEhLK1J4gCMjPz4e+vurTIZFI4OjoWKbX0EVvOs/qnouTJ09iyJAhWLBgAfr27QuRSIS7d+/i+PHjFRG+VvXs2RObNm2CTCZDSEgIxo8fDwMDAwQEBKjVTn5+PkQiEVJTU9G+fXukpKTgm2++QcuWLaGvr4+zZ89i2rRp6NKlCywtLSvmYKqY4OBgTJw4EcHBwYiKioKTk5O2Q9IJ/7wn/83Ozk6tNv55P4rFOvm7kzRJ0DFJSUkCAOHMmTPFPh8ZGSkAEG7evFlkn9OnTwuCIAinT58WAAghISFC8+bNBQMDA2HdunUCAOHevXtK7S1ZskSoXbu20n5JSUlCSkqKYGhoKISEhCjV37t3r2BqaipkZGQIgiAIz549EwYNGiRYWFgIVlZWQt++fYXIyEhF/by8PGHy5MmChYWFYG1tLUydOlUYOXKk8N5775XvRJXTm86zIAgCAGHfvn2CILw+7zt27BDatm0rSKVSoWHDhkr7T5o0SejcubPK1w0MDBS8vb2FtWvXCs7OzoKRkZEwaNAgITk5WSPHVRlGjRpV5P9ft27dhDZt2giLFy8WGjVqJBgbGwvOzs7CuHHjhLS0NEW9TZs2CRYWFsKBAweE+vXrC3p6ekJkZKQwbtw4wcTERHj58mWR10tLSxNyc3MFQRAENzc3YcGCBcKYMWMEU1NTwcXFRVi3bl2FHm9lSktLE0xNTYX79+8LQ4YMERYsWKD0/IEDBwQPDw9BKpUKnTt3FjZv3qz4m/3H+fPnhfbt2wuGhoaCs7OzMHHiRCE9Pb2Sj6RyFfeeFAShzO/H7OxsYcqUKYKTk5NgbGwstGrVSvH5Sv8NOpc+mpqawtTUFPv374dMJitXWzNmzMDChQtx7949DBw4ED4+Pti2bZtSnW3btuGDDz4osq+5uTl69+6N7du3F6nfr18/GBsbIzc3Fz169ICZmRnOnz+PixcvwtTUFD179kROTg4AYPHixdi8eTM2btyICxcuIDExEfv27SvXcWlCWc/z1KlTMWXKFNy8eRNt27ZFnz59lHoqwsPDcefOHZVtPHr0CLt378ahQ4dw9OhR3Lx5E5999lm5jkfbjIyMkJOTA7FYjBUrViA8PBxbtmzBqVOnMG3aNKW6mZmZ+O6777BhwwaEh4fD3t4eO3fuxPDhw4v9lW1qaqrUa7Z48WL4+Pgoztu4ceMQERFR4cdYGXbv3g0vLy94enpixIgR2Lhxo2LIJjIyEgMHDkS/fv1w69YtfPrpp/j666+V9n/8+DF69uyJAQMG4Pbt29i1axcuXLiACRMmaONwtK6s78cJEyYgNDQUO3fuxO3btzFo0CD07NkTDx8+1NKRUKXTdvZSFv/3f/8nWFlZCYaGhkK7du2EgIAA4datW4IgqNfzsH//fqV2ly5dKtSpU0fxOCIiQqk34t89D4IgCPv27VPqZfinN+K3334TBEEQfv75Z8HT01OQy+WKNmUymWBkZCT8/vvvgiAIQo0aNYTvv/9e8Xxubq7g7Oys9Z4HQVB9ngWh+J6HhQsXKp7/51i+++47QRAEIT09XejVq5cAQHBzcxOGDBkiBAcHC9nZ2Yp9AgMDBT09PeHFixeKst9++00Qi8XCq1evKviINePfv/Lkcrlw/PhxQSqVCl9++WWRunv27BFsbGwUjzdt2iQAEMLCwhRlMTExAgBhyZIlb3xtNzc3YcSIEYrHcrlcsLe3F9asWVOOI6o62rVrJyxbtkwQhIL3l62treLvevr06UKjRo2U6n/99ddKf7N+fn7CJ598olTn/PnzglgsFrKysio8fm0ZNWqUoKenJ5iYmCi2gQMHFqlXmvfjX3/9Jejp6RXpBevatasQEBBQcQdBVYrO9TwABWPxUVFROHjwIHr27IkzZ86gefPm2Lx5s1rt+Pj4KD0eOnQonj59ikuXLgEo6EVo3rw5vLy8it2/V69eMDAwwMGDBwEAv/76K8zNzeHr6wsAuHXrFh49egQzMzPFL3lra2tkZ2fj8ePHSElJwatXr9C6dWtFm/r6+kXi0paynOe2bdsq/v3Psdy7dw8AYGJigiNHjuDRo0eYOXMmTE1NMWXKFLRq1QqZmZmK/VxdXVGzZk2lNuVyuU79ej58+DBMTU1haGiId955B0OGDMGcOXNw4sQJdO3aFTVr1oSZmRk+/PBDJCQkKB2/RCJBkyZNFI8FNSdD/ntfkUgER0dHxMbGlv+gtCwiIgJXrlzBsGHDABS8v4YMGYLg4GDF8y1btlTap1WrVkqPb926hc2bNyv+Hk1NTdGjRw/I5XJERkZWzoFoydtvv42wsDDFtmLFijK9H//880/k5+ejXr16Sufx7NmzePz4sTYOjbRAJydMAoChoSG6deuGbt26YdasWRg7diwCAwNx/vx5AMofuLm5ucW2YWJiovTY0dERXbp0wfbt29GmTRts374d48aNKzEGiUSCgQMHYvv27Rg6dCi2b9+OIUOGKLqQ09PT0aJFiyJDIYD6E5W0paTzPHr06DK3WadOHdSpUwdjx47F119/jXr16mHXrl0YM2aM5gLXsrfffhtr1qyBRCKBk5MT9PX18fTpU/Tu3Rvjxo3DggULYG1tjQsXLsDPzw85OTkwNjYGUDDEIRKJFG3Z2dnB0tIS9+/fL9VrGxgYKD0WiUSQy+WaOzgtCQ4ORl5entLQjSAIkEql+PHHH0vVRnp6Oj799FN8/vnnRZ5zdXXVWKxVkYmJCTw8PBSPy/p+TE9Ph56eHq5fvw49PT2l1zA1Na2cgyGt08meh+I0aNAAGRkZii/lV69eKZ5TZ+2B4cOHY9euXQgNDcWTJ08wdOjQN9Y/evQowsPDcerUKQwfPlzxXPPmzfHw4UPY29vDw8NDabOwsICFhQVq1KiBy5cvK/bJy8vD9evXSx1vZfvnPJfkn14b4PWx1K9fv8T67u7uMDY2Vmrz2bNniIqKUmpTLBbD09OznNFXnn8+qF1dXRXJ5PXr1yGXy7F48WK0adMG9erVUzrOkojFYgwdOhTbtm0rtn56enq5LwOt6vLy8rB161YsXrxY6dfzrVu34OTkhB07dsDT0xPXrl1T2u/q1atKj5s3b467d+8W+Xv08PCARCKpzEPSurK+H5s1a4b8/HzExsYWOYf/pavR/ut0LnlISEhAly5d8Msvv+D27duIjIzEnj178P333+O9996DkZER2rRpo5gIefbsWcycObPU7ffv3x9paWkYN24c3n777TdeBtaxY0c4Ojpi+PDhqFWrltIQxPDhw2Fra4v33nsP58+fR2RkJM6cOYPPP/8cL168AABMmjQJCxcuxP79+3H//n189tlnFb4IVWm86TyXZNWqVdi3bx/u37+P8ePHIykpCR999BEAYM6cOZg2bRrOnDmDyMhI3Lx5Ex999BFyc3PRrVs3RRuGhoYYNWoUbt26hfPnz+Pzzz/H4MGDdf6DycPDA7m5uVi5ciWePHmCn3/+GWvXri3VvgsWLICLiwtat26NrVu34u7du3j48CE2btyIZs2aIT09vYKj167Dhw8jKSkJfn5+aNSokdI2YMAABAcH49NPP8X9+/cxffp0PHjwALt371YMsf3zy3n69On4448/MGHCBISFheHhw4c4cODAf3LCZFnfj/Xq1cPw4cMxcuRI7N27F5GRkbhy5QqCgoJw5MiRSoicqgQtz7lQW3Z2tjBjxgyhefPmgoWFhWBsbCx4enoKM2fOFDIzMwVBEIS7d+8Kbdu2FYyMjISmTZsKx44dK3bC5L8v3/q3wYMHCwCEjRs3KpWXtN+0adMEAMLs2bOLtPXq1Sth5MiRgq2trSCVSoXatWsLH3/8sZCSkiIIQsGkr0mTJgnm5uaCpaWl4O/vXyUu1SzNeUYxEya3b98utGrVSpBIJEKDBg2EU6dOKdo8deqUMGDAAMHFxUWQSCSCg4OD0LNnT+H8+fOKOv9cqrl69WrByclJMDQ0FAYOHCgkJiZW6vGXR0mXxQlCwaW/NWrUEIyMjIQePXoIW7duVXpP/XNpXHGSk5OFGTNmCHXr1lWcP19fX2Hfvn2KSblubm7C0qVLlfbz9vYWAgMDNXNwWtK7d2+hV69exT53+fJlAYBw69atIpdqrlmzRgCgNBnyypUrQrdu3QRTU1PBxMREaNKkSZFLPqubkt6TZX0/5uTkCLNnzxbc3d0FAwMDoUaNGsL7778v3L59u2IPhKoMkSD8h5amoypvzpw52L9/P5e5Jo1YsGAB1q5di+fPn2s7FKJqRWcnTBIRFbZ69Wq0bNkSNjY2uHjxIn744Yf/5JAEUUVj8kBE1cbDhw/xzTffIDExEa6urpgyZYraS4IT0Ztx2IKIiIjUonNXWxAREZF2MXkgIiIitTB5ICIiIrUweSAiIiK1MHkgIiIitTB5ICIiIrUweSAiIiK1MHkgIiIitfw/zx5xcLpufjYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(dftrain[[\"Survived\",\"SibSp\",\"ParCh\",\"Age\",\"Fare\"]].corr(),annot=True, cmap = \"YlOrRd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90762044",
   "metadata": {
    "papermill": {
     "duration": 0.022809,
     "end_time": "2023-11-04T18:20:47.702993",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.680184",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Engenharia de Recursos\n",
    "\n",
    "Será aplicado um dos métodos de engenharia de recursos que envolve a criação de novos recursos a partir do que temos à disposição. Vale ressaltar que este é um processo fundamental para análise de dados e para o desenvolvimento de modelos de machine learning. Assim, podemos realizar uma análise mais robusta e nossos modelos serão treinados com mais dados, o que é excelente."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84aff84",
   "metadata": {
    "papermill": {
     "duration": 0.022592,
     "end_time": "2023-11-04T18:20:47.747613",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.725021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **Dados de treinamentos:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d0dbc27a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:47.791217Z",
     "iopub.status.busy": "2023-11-04T18:20:47.790849Z",
     "iopub.status.idle": "2023-11-04T18:20:47.810544Z",
     "shell.execute_reply": "2023-11-04T18:20:47.809099Z"
    },
    "papermill": {
     "duration": 0.044559,
     "end_time": "2023-11-04T18:20:47.813243",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.768684",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftrain['Number_Family'] = dftrain['SibSp'] + dftrain['ParCh']\n",
    "\n",
    "dftrain['IsAlone'] = (dftrain['Number_Family'] == 0).astype(int)\n",
    "\n",
    "dftrain['AgeCategory'] = pd.cut(dftrain['Age'], bins=[0, 12, 18, 30, 60, 100], labels=['Child', 'Adolescent', 'Youth', 'Adult', 'Elderly'])\n",
    "\n",
    "dftrain['FareClassification'] = pd.qcut(dftrain['Fare'], q=4, labels=['Low', 'Medium-Low', 'Medium-High', 'High'])\n",
    "\n",
    "dftrain['AgeXClass'] = dftrain['Age'] * dftrain['Pclass']\n",
    "\n",
    "dftrain['Title'] = dftrain['Name'].apply(lambda x: re.search('([A-Za-z]+)\\.', x).group(1))\n",
    "\n",
    "dftrain['Title'] = dftrain['Title'].replace(['Mlle', 'Mme', 'Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Others')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d53e52f",
   "metadata": {
    "papermill": {
     "duration": 0.020451,
     "end_time": "2023-11-04T18:20:47.855573",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.835122",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **Dados de Teste:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b762969e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:47.899784Z",
     "iopub.status.busy": "2023-11-04T18:20:47.898622Z",
     "iopub.status.idle": "2023-11-04T18:20:47.913535Z",
     "shell.execute_reply": "2023-11-04T18:20:47.912271Z"
    },
    "papermill": {
     "duration": 0.039131,
     "end_time": "2023-11-04T18:20:47.915755",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.876624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftest['Number_Family'] = dftest['SibSp'] + dftest['ParCh']\n",
    "\n",
    "dftest['IsAlone'] = (dftest['Number_Family'] == 0).astype(int)\n",
    "\n",
    "dftest['AgeCategory'] = pd.cut(dftest['Age'], bins=[0, 12, 18, 30, 60, 100], labels=['Child', 'Adolescent', 'Youth', 'Adult', 'Elderly'])\n",
    "\n",
    "dftest['FareClassification'] = pd.qcut(dftest['Fare'], q=4, labels=['Low', 'Medium-Low', 'Medium-High', 'High'])\n",
    "\n",
    "dftest['AgeXClass'] = dftest['Age'] * dftest['Pclass']\n",
    "\n",
    "dftest['Title'] = dftest['Name'].apply(lambda x: re.search('([A-Za-z]+)\\.', x).group(1))\n",
    "\n",
    "dftest['Title'] = dftest['Title'].replace(['Mlle', 'Mme', 'Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Others')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a99a29",
   "metadata": {
    "papermill": {
     "duration": 0.021347,
     "end_time": "2023-11-04T18:20:47.958882",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.937535",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Visualização dos Dados - Parte 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b356f28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:48.002621Z",
     "iopub.status.busy": "2023-11-04T18:20:48.001523Z",
     "iopub.status.idle": "2023-11-04T18:20:48.020247Z",
     "shell.execute_reply": "2023-11-04T18:20:48.019171Z"
    },
    "papermill": {
     "duration": 0.042581,
     "end_time": "2023-11-04T18:20:48.022626",
     "exception": false,
     "start_time": "2023-11-04T18:20:47.980045",
     "status": "completed"
    },
    "tags": []
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Number_Family</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>FareClassification</th>\n",
       "      <th>AgeXClass</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>missing</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Youth</td>\n",
       "      <td>Low</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>High</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>missing</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Youth</td>\n",
       "      <td>Medium-Low</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>High</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>missing</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Medium-Low</td>\n",
       "      <td>105.0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   ParCh            Ticket     Fare    Cabin Embarked  Number_Family  IsAlone  \\\n",
       "0      0         A/5 21171   7.2500  missing        S              1        0   \n",
       "1      0          PC 17599  71.2833      C85        C              1        0   \n",
       "2      0  STON/O2. 3101282   7.9250  missing        S              0        1   \n",
       "3      0            113803  53.1000     C123        S              1        0   \n",
       "4      0            373450   8.0500  missing        S              0        1   \n",
       "\n",
       "  AgeCategory FareClassification  AgeXClass Title  \n",
       "0       Youth                Low       66.0    Mr  \n",
       "1       Adult               High       38.0   Mrs  \n",
       "2       Youth         Medium-Low       78.0  Miss  \n",
       "3       Adult               High       35.0   Mrs  \n",
       "4       Adult         Medium-Low      105.0    Mr  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de460f13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:48.066808Z",
     "iopub.status.busy": "2023-11-04T18:20:48.065884Z",
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    },
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     "status": "completed"
    },
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   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>ParCh</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Number_Family</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>AgeXClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.904602</td>\n",
       "      <td>0.602694</td>\n",
       "      <td>64.966817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.002015</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>1.613459</td>\n",
       "      <td>0.489615</td>\n",
       "      <td>32.947541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</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>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>89.097353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>222.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            ParCh        Fare  Number_Family     IsAlone   AgeXClass  \n",
       "count  891.000000  891.000000     891.000000  891.000000  891.000000  \n",
       "mean     0.381594   32.204208       0.904602    0.602694   64.966817  \n",
       "std      0.806057   49.693429       1.613459    0.489615   32.947541  \n",
       "min      0.000000    0.000000       0.000000    0.000000    0.920000  \n",
       "25%      0.000000    7.910400       0.000000    0.000000   40.000000  \n",
       "50%      0.000000   14.454200       0.000000    1.000000   63.000000  \n",
       "75%      0.000000   31.000000       1.000000    1.000000   89.097353  \n",
       "max      6.000000  512.329200      10.000000    1.000000  222.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8201f8f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:48.145638Z",
     "iopub.status.busy": "2023-11-04T18:20:48.145240Z",
     "iopub.status.idle": "2023-11-04T18:20:48.163178Z",
     "shell.execute_reply": "2023-11-04T18:20:48.161464Z"
    },
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     "start_time": "2023-11-04T18:20:48.122661",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  ParCh            Ticket     Fare    Cabin Embarked  Number_Family  \\\n",
       "0        1      0         A/5 21171   7.2500  missing        S              1   \n",
       "1        1      0          PC 17599  71.2833      C85        C              1   \n",
       "2        0      0  STON/O2. 3101282   7.9250  missing        S              0   \n",
       "3        1      0            113803  53.1000     C123        S              1   \n",
       "4        0      0            373450   8.0500  missing        S              0   \n",
       "..     ...    ...               ...      ...      ...      ...            ...   \n",
       "886      0      0            211536  13.0000  missing        S              0   \n",
       "887      0      0            112053  30.0000      B42        S              0   \n",
       "888      1      2        W./C. 6607  23.4500  missing        S              3   \n",
       "889      0      0            111369  30.0000     C148        C              0   \n",
       "890      0      0            370376   7.7500  missing        Q              0   \n",
       "\n",
       "     IsAlone AgeCategory FareClassification   AgeXClass   Title  \n",
       "0          0       Youth                Low   66.000000      Mr  \n",
       "1          0       Adult               High   38.000000     Mrs  \n",
       "2          1       Youth         Medium-Low   78.000000    Miss  \n",
       "3          0       Adult               High   35.000000     Mrs  \n",
       "4          1       Adult         Medium-Low  105.000000      Mr  \n",
       "..       ...         ...                ...         ...     ...  \n",
       "886        1       Youth         Medium-Low   54.000000  Others  \n",
       "887        1       Youth        Medium-High   19.000000    Miss  \n",
       "888        0       Youth        Medium-High   89.097353    Miss  \n",
       "889        1       Youth        Medium-High   26.000000      Mr  \n",
       "890        1       Adult                Low   96.000000      Mr  \n",
       "\n",
       "[891 rows x 18 columns]>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1eec2d11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:48.210650Z",
     "iopub.status.busy": "2023-11-04T18:20:48.210284Z",
     "iopub.status.idle": "2023-11-04T18:20:48.381459Z",
     "shell.execute_reply": "2023-11-04T18:20:48.380124Z"
    },
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     "exception": false,
     "start_time": "2023-11-04T18:20:48.187366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='Number_Family'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=dftrain, x= \"Survived\", y= \"Number_Family\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c87733",
   "metadata": {
    "papermill": {
     "duration": 0.021589,
     "end_time": "2023-11-04T18:20:48.427252",
     "exception": false,
     "start_time": "2023-11-04T18:20:48.405663",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Tratamento de Dados Não Numéricos\n",
    "\n",
    "Embora os modelos possam ser treinados com dados não numéricos, é importante ressaltar que, para obter maior precisão nas inferências feitas pelos modelos, é crucial que os dados sejam numéricos, visto que os algoritmos dos modelos processam os dados através de diversos cálculos matemáticos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15511ee",
   "metadata": {
    "papermill": {
     "duration": 0.021711,
     "end_time": "2023-11-04T18:20:48.471877",
     "exception": false,
     "start_time": "2023-11-04T18:20:48.450166",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Convertendo para Dados Númericos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c5aaf1ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T18:20:48.517896Z",
     "iopub.status.busy": "2023-11-04T18:20:48.517505Z",
     "iopub.status.idle": "2023-11-04T18:20:48.525924Z",
     "shell.execute_reply": "2023-11-04T18:20:48.524006Z"
    },
    "papermill": {
     "duration": 0.03434,
     "end_time": "2023-11-04T18:20:48.528076",
     "exception": true,
     "start_time": "2023-11-04T18:20:48.493736",
     "status": "failed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid decimal literal (1089691802.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[35], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    344243dftrain['Title'] = dftrain['Title'].replace('Master', 1).replace('Miss', 2).replace('Mr', 3).replace('Mrs', 4).replace('Ms', 5).replace('Others', 6).astype(int)\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid decimal literal\n"
     ]
    }
   ],
   "source": [
    "344243dftrain['Title'] = dftrain['Title'].replace('Master', 1).replace('Miss', 2).replace('Mr', 3).replace('Mrs', 4).replace('Ms', 5).replace('Others', 6).astype(int)\n",
    "dftest['Title'] = dftest['Title'].replace('Master', 1).replace('Miss', 2).replace('Mr', 3).replace('Mrs', 4).replace('Ms', 5).replace('Others', 6).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5298141d",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.072030Z",
     "iopub.status.idle": "2023-11-04T18:02:19.072425Z",
     "shell.execute_reply": "2023-11-04T18:02:19.072260Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.072241Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftrain['Sex'] = dftrain['Sex'].replace(['male', 'female'], [0, 1]).astype(int)\n",
    "dftest['Sex'] = dftest['Sex'].replace(['male', 'female'], [0, 1]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c952e347",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.073788Z",
     "iopub.status.idle": "2023-11-04T18:02:19.074178Z",
     "shell.execute_reply": "2023-11-04T18:02:19.074006Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.073989Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftrain['Embarked'] = dftrain['Embarked'].replace(['S', 'C', 'Q'], [0, 1, 2]).astype(int)\n",
    "dftest['Embarked'] = dftest['Embarked'].replace(['S', 'C', 'Q'], [0, 1, 2]).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb04d51",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "#### Aplicando o One-Hot nos Dados de Teste e Treinamento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf8021c6",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.075158Z",
     "iopub.status.idle": "2023-11-04T18:02:19.075499Z",
     "shell.execute_reply": "2023-11-04T18:02:19.075352Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.075336Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftrain = pd.get_dummies(dftrain, columns=['AgeCategory', 'FareClassification'], dtype=int)\n",
    "dftest = pd.get_dummies(dftest, columns=['AgeCategory', 'FareClassification'], dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3eacc3",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "#### Deletandos as colunas que não serão relevantes para classificação:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6110e17b",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.076467Z",
     "iopub.status.idle": "2023-11-04T18:02:19.076847Z",
     "shell.execute_reply": "2023-11-04T18:02:19.076684Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.076664Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del dftrain['PassengerId']\n",
    "del dftrain['Name']\n",
    "del dftrain['Ticket']\n",
    "del dftrain['Cabin']\n",
    "\n",
    "del dftest['PassengerId']\n",
    "del dftest['Name']\n",
    "del dftest['Ticket']\n",
    "del dftest['Cabin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e9ae027",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.078260Z",
     "iopub.status.idle": "2023-11-04T18:02:19.079585Z",
     "shell.execute_reply": "2023-11-04T18:02:19.079327Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.079300Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dftrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b39be0c",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Divisão entre Classificadores e Previsores\n",
    "\n",
    "Agora é o momento de dividir nossa base de dados entre classificadores e previsores. Os previsores são os dados que serão usados para treinar o modelo, e os classificadores são os rótulos que queremos prever com base nesses dados. Essa divisão é fundamental para o processo de treinamento e avaliação de modelos de machine learning. Com os previsores e classificadores claramente definidos, podemos prosseguir com a construção e ajuste dos modelos para atingir os objetivos do projeto."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5242a470",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "#### **Definindo as variáveis previsores:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fa6af13",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.081104Z",
     "iopub.status.idle": "2023-11-04T18:02:19.081687Z",
     "shell.execute_reply": "2023-11-04T18:02:19.081426Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.081400Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xPredictors = dftrain.drop([\"Survived\"], axis=1)\n",
    "x_train = xPredictors.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2b8434",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "#### **Definindo a variável de classificação:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dc8199f",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.083205Z",
     "iopub.status.idle": "2023-11-04T18:02:19.083626Z",
     "shell.execute_reply": "2023-11-04T18:02:19.083427Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.083408Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_train = dftrain.iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c44a95",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Escalonamento de Valores\n",
    "\n",
    "O escalonamento de valores é uma etapa crítica no pré-processamento de dados para muitos algoritmos de machine learning. Essa técnica visa normalizar ou padronizar os valores das variáveis para que estejam em uma faixa comparável. Isso é importante porque muitos algoritmos são sensíveis à escala dos dados, e valores em escalas diferentes podem afetar negativamente o desempenho do modelo.\n",
    "\n",
    "Para este nosso desafio, será aplicado a técnica da normalização que dimensiona os dados para um intervalo fixo, geralmente de 0 a 1, tornando os dados comparáveis e mantendo as relações de ordem entre os valores originais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a70bfde",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.085999Z",
     "iopub.status.idle": "2023-11-04T18:02:19.086885Z",
     "shell.execute_reply": "2023-11-04T18:02:19.086698Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.086670Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler_train = StandardScaler()\n",
    "scaler_test = StandardScaler()\n",
    "\n",
    "\n",
    "x_train_std = scaler_train.fit_transform(x_train)\n",
    "x_test_std = scaler_test.fit_transform(dftest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbe0b8b",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# **Salvandos o dados:**\n",
    "\n",
    "Os dados estão pronto e podem ser salvos se necessário."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "680a3783",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.088344Z",
     "iopub.status.idle": "2023-11-04T18:02:19.089175Z",
     "shell.execute_reply": "2023-11-04T18:02:19.088956Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.088931Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('x_train_std.pkl', mode = 'wb') as file:\n",
    "    pickle.dump([x_train_std, y_train], file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d63c483",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Aplicação de Modelos\n",
    "\n",
    "Os modelos que serão utilizados para prever quais serão os sobreviventes são modelos de classificação de machine learning amplamente conhecidos e utilizados em tarefas de classificação e previsão, nas quais as inferências resultantes devem ser precisas e confiáveis. O objetivo é treinar esses modelos com os dados que foram preparados até o momento, testá-los com os dados de teste que também foram preparados e, por fim, classificar os sobreviventes com base nas previsões de cada modelo, determinando o melhor modelo para esta tarefa.\n",
    "\n",
    "Segue a lista dos modelos que serão implementados:\n",
    "\n",
    "1. Gaussian Naive Bayes\n",
    "2. Decision Tree\n",
    "3. Random Forest\n",
    "4. k-Nearest Neighbors\n",
    "5. Logistic Regression\n",
    "6. Support Vector Classifier\n",
    "7. Multi-Layer Perceptron Classifier\n",
    "\n",
    "**Atenção**: \n",
    "\n",
    "Uma vez que não temos acesso às classificações reais dos sobreviventes no conjunto de dados de teste para comparar as previsões dos modelos, usaremos a acurácia como métrica de desempenho de cada modelo durante o treinamento, além da média do desempenho do modelo em 10 dobras de validação cruzada.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c6f8d1",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **Gaussian Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8913bfe2",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.090413Z",
     "iopub.status.idle": "2023-11-04T18:02:19.090840Z",
     "shell.execute_reply": "2023-11-04T18:02:19.090658Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.090638Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
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     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "gaussian_titanic = GaussianNB()\n",
    "\n",
    "gaussian_titanic.fit(x_train_std, y_train)\n",
    "\n",
    "naive_predictions = gaussian_titanic.predict(x_test_std)\n",
    "\n",
    "accuracy_gaussian = round(gaussian_titanic.score(x_train_std, y_train) * 100, 2)\n",
    "score_gaussian = cross_val_score(gaussian_titanic, x_train_std, y_train, cv=10).mean()\n",
    "\n",
    "print('------------------- Gaussian Naive Bayes -------------------')\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_gaussian)\n",
    "print('Cross validation mean:', score_gaussian)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c5fc159",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a2c5053",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.092027Z",
     "iopub.status.idle": "2023-11-04T18:02:19.092629Z",
     "shell.execute_reply": "2023-11-04T18:02:19.092386Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.092362Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dicisiontree_titanic = DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "dicisiontree_titanic.fit(x_train_std, y_train)\n",
    "\n",
    "predictions_dicisiontree = dicisiontree_titanic.predict(x_test_std)\n",
    "\n",
    "accuracy_decisiontree = round(dicisiontree_titanic.score(x_train_std, y_train) * 100, 2)\n",
    "score_decisiontree = cross_val_score(dicisiontree_titanic, x_train_std, y_train, cv=10).mean()\n",
    "\n",
    "print('------------------- Decision Tree Classifier -------------------')\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_decisiontree)\n",
    "print('Cross validation mean:', score_decisiontree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bf3810",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4711e964",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.093947Z",
     "iopub.status.idle": "2023-11-04T18:02:19.094333Z",
     "shell.execute_reply": "2023-11-04T18:02:19.094165Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.094146Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "randomforest_titanic  = RandomForestClassifier(n_estimators=50, criterion='entropy', random_state=0)\n",
    "\n",
    "randomforest_titanic.fit(x_train_std, y_train)\n",
    "\n",
    "predictions_randomforest = randomforest_titanic.predict(x_test_std)\n",
    "\n",
    "accuracy_randomforest = round(randomforest_titanic.score(x_train_std, y_train) * 100, 2)\n",
    "score_randomforest = cross_val_score(randomforest_titanic, x_train_std, y_train, cv=10).mean()\n",
    "\n",
    "print('------------------- Random Forest Classifier -------------------')\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_randomforest)\n",
    "print('Cross validation mean:', score_randomforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12dc66c8",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **k-Nearest Neighbors Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9413343",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.096360Z",
     "iopub.status.idle": "2023-11-04T18:02:19.097149Z",
     "shell.execute_reply": "2023-11-04T18:02:19.096962Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.096942Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_titanic = KNeighborsClassifier(n_neighbors=1)\n",
    "knn_titanic.fit(x_train_std, y_train)\n",
    "\n",
    "prediction_knn = knn_titanic.predict(x_test_std)\n",
    "\n",
    "accuracy_knn = round(knn_titanic.score(x_train_std, y_train) * 100, 2)\n",
    "score_knn = cross_val_score(knn_titanic, x_train_std, y_train, cv=15).mean()\n",
    "\n",
    "print('------------------- k-Nearest Neighbors -------------------')\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_knn)\n",
    "print('Cross validation mean:', score_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7d4549",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe9c57c3",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.098380Z",
     "iopub.status.idle": "2023-11-04T18:02:19.099248Z",
     "shell.execute_reply": "2023-11-04T18:02:19.098995Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.098960Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lregression = LogisticRegression(random_state=0,  C=5)\n",
    "lregression.fit(x_train_std, y_train)\n",
    "\n",
    "predictions_lregression =  lregression.predict(x_test_std)\n",
    "\n",
    "accuracy_lregression = round(lregression.score(x_train_std, y_train) * 100, 2)\n",
    "score_lregression = cross_val_score(lregression, x_train_std, y_train, cv=5).mean()\n",
    "\n",
    "print('------------------- Logistic Regression -------------------')\n",
    "\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_lregression)\n",
    "print('Cross validation mean:', score_lregression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ea58db",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **Support Vector Classification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1480a50c",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.100842Z",
     "iopub.status.idle": "2023-11-04T18:02:19.101555Z",
     "shell.execute_reply": "2023-11-04T18:02:19.101334Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.101307Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "SVC_titanic = SVC(kernel='linear', random_state=1, C=1.0)\n",
    "SVC_titanic.fit(x_train_std, y_train)\n",
    "\n",
    "predictions_SVC = SVC_titanic.predict(x_test_std)\n",
    "\n",
    "accuracy_SVC = round(SVC_titanic.score(x_train_std, y_train) * 100, 2)\n",
    "score_SVC = cross_val_score(SVC_titanic, x_train_std, y_train, cv=5).mean()\n",
    "\n",
    "print('------------------- Support Vector Classification -------------------')\n",
    "\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_SVC)\n",
    "print('Cross validation mean:', score_SVC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6456ffcc",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### **Multi Layer Perceptron Classifier - Artificial Neural Networks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7e58e72",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-11-04T18:02:19.103198Z",
     "iopub.status.idle": "2023-11-04T18:02:19.104342Z",
     "shell.execute_reply": "2023-11-04T18:02:19.104157Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.104134Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "MLPC_titanic = MLPClassifier(max_iter=5000, verbose=0, tol=0.0000100,\n",
    "                                   solver = 'adam', activation = 'identity',\n",
    "                                   hidden_layer_sizes = (60,60))\n",
    "\n",
    "MLPC_titanic.fit(x_train, y_train);\n",
    "\n",
    "accuracy_MLPC = round(MLPC_titanic.score(x_train_std, y_train) * 100, 2)\n",
    "score_MLPC = cross_val_score(MLPC_titanic, x_train_std, y_train, cv=15).mean()\n",
    "\n",
    "print('------------------- Multi Layer Perceptron Classifier -------------------')\n",
    "print()\n",
    "print('Model Accuracy: ', accuracy_MLPC)\n",
    "print('Cross validation mean:', score_MLPC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc00e3c",
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    "tags": []
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   "source": [
    "# Curva de Aprendizado\n",
    "\n",
    "Também será utilizado o gráfico de curva de aprendizado para análise do desempenho do modelo à medida que mais dados de treinamento são usados. Nesse gráfico, será medida a relação entre o tamanho do conjunto de treinamento e o desempenho do modelo, que é medido pela acurácia. Além disso, poderemos identificar se o modelo está sofrendo de overfitting ou underfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ef9688",
   "metadata": {
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     "end_time": null,
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    "tags": []
   },
   "source": [
    "### Gaussian Naive Bayes e Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8e64e4a",
   "metadata": {
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     "shell.execute_reply.started": "2023-11-04T18:02:19.105895Z"
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     "duration": null,
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    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
    "\n",
    "common_params = {\n",
    "    \"X\": x_train_std,\n",
    "    \"y\": y_train,\n",
    "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
    "    \"cv\": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0),\n",
    "    \"score_type\": \"both\",\n",
    "    \"n_jobs\": 4,\n",
    "    \"line_kw\": {\"marker\": \"o\"},\n",
    "    \"std_display_style\": \"fill_between\",\n",
    "    \"score_name\": \"Accuracy\",\n",
    "}\n",
    "\n",
    "for ax_idx, estimator in enumerate([gaussian_titanic, dicisiontree_titanic]):\n",
    "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
    "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
    "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
    "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d6a92d",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
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    },
    "tags": []
   },
   "source": [
    "### Random Forest e k-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233af9c9",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2023-11-04T18:02:19.107493Z",
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     "shell.execute_reply.started": "2023-11-04T18:02:19.107315Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 6), sharey=True)\n",
    "\n",
    "common_params = {\n",
    "    \"X\": x_train_std,\n",
    "    \"y\": y_train,\n",
    "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
    "    \"cv\": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0),\n",
    "    \"score_type\": \"both\",\n",
    "    \"n_jobs\": 4,\n",
    "    \"line_kw\": {\"marker\": \"o\"},\n",
    "    \"std_display_style\": \"fill_between\",\n",
    "    \"score_name\": \"Accuracy\",\n",
    "}\n",
    "\n",
    "estimators = [randomforest_titanic, knn_titanic]\n",
    "\n",
    "for ax_idx, estimator in enumerate(estimators):\n",
    "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
    "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
    "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
    "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a14f585a",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
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    },
    "tags": []
   },
   "source": [
    "### Logistic Regression e Suport Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ac334d4",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2023-11-04T18:02:19.108769Z",
     "shell.execute_reply": "2023-11-04T18:02:19.108578Z",
     "shell.execute_reply.started": "2023-11-04T18:02:19.108560Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
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     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
    "\n",
    "common_params = {\n",
    "    \"X\": x_train_std,\n",
    "    \"y\": y_train,\n",
    "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
    "    \"cv\": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0),\n",
    "    \"score_type\": \"both\",\n",
    "    \"n_jobs\": 4,\n",
    "    \"line_kw\": {\"marker\": \"o\"},\n",
    "    \"std_display_style\": \"fill_between\",\n",
    "    \"score_name\": \"Accuracy\",\n",
    "}\n",
    "\n",
    "for ax_idx, estimator in enumerate([lregression, SVC_titanic]):\n",
    "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
    "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
    "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
    "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd277ad",
   "metadata": {
    "papermill": {
     "duration": null,
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    },
    "tags": []
   },
   "source": [
    "### Gaussian Naive Bayes e Multi Layer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51acac36",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2023-11-04T18:02:19.110648Z"
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
    "\n",
    "common_params = {\n",
    "    \"X\": x_train_std,\n",
    "    \"y\": y_train,\n",
    "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
    "    \"cv\": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0),\n",
    "    \"score_type\": \"both\",\n",
    "    \"n_jobs\": 4,\n",
    "    \"line_kw\": {\"marker\": \"o\"},\n",
    "    \"std_display_style\": \"fill_between\",\n",
    "    \"score_name\": \"Accuracy\",\n",
    "}\n",
    "\n",
    "for ax_idx, estimator in enumerate([gaussian_titanic, MLPC_titanic]):\n",
    "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
    "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
    "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
    "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611e1ca0",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# Resultado dos modelos\n",
    "\n",
    "**Gaussian Naive Bayes:** Accuracy 77.33\n",
    "\n",
    "**Decision Tree:** Accuracy 98.43\n",
    "\n",
    "**Random Forest Classifier:** Accuracy 98.43\n",
    "\n",
    "**k-Nearest Neighbors Classifier:** Accuracy 97.87\n",
    "\n",
    "**Logistic Regression:** Accuracy 81.82\n",
    "\n",
    "**Support Vector Classification:** Accuracy 81.59\n",
    "\n",
    "**Multi Layer Perceptron Classifier:** Accuracy 79.12\n",
    "\n",
    "\n",
    "# Conclusão:\n",
    "\n",
    "Com base nos resultados apresentados, podemos tirar as seguintes conclusões:\n",
    "\n",
    "Os modelos \"Decision Tree\" e \"Random Forest Classifier\" demonstraram um desempenho excepcional, alcançando as maiores acurácias. Isso sugere que esses modelos são altamente eficazes na tarefa de classificação e podem ser opções viáveis para prever os sobreviventes.\n",
    "\n",
    "O modelo \"k-Nearest Neighbors Classifier\" também obteve uma acurácia bastante alta, indicando sua capacidade de classificação precisa.\n",
    "\n",
    "Embora o \"Gaussian Naive Bayes\" e o \"Multi Layer Perceptron Classifier\" tenham acurácias um pouco mais baixas, eles ainda mostram um desempenho aceitável e podem ser considerados como opções alternativas.\n",
    "\n",
    "Os modelos \"Logistic Regression\" e \"Support Vector Classification\" apresentaram as acurácias mais baixas entre os modelos testados. Isso pode sugerir que esses modelos não se ajustaram tão bem aos dados ou que a escolha das características pode precisar ser revisitada."
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