titanic-2 Completo.ipynb

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   },
   "source": [
    "# Machine Learning Clássico"
   ]
  },
  {
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   },
   "source": [
    "## Data Cleaning"
   ]
  },
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    "tags": []
   },
   "source": [
    "### Importando bibliotecas "
   ]
  },
  {
   "cell_type": "code",
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   "id": "be7e4941",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
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   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d973a793",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
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   },
   "source": [
    "### Importando dados"
   ]
  },
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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>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
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       "      <th>Embarked</th>\n",
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       "  </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</td>\n",
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       "      <td>891</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
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       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
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       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                      891   891   \n",
       "unique          NaN         NaN         NaN                      891     2   \n",
       "top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male   \n",
       "freq            NaN         NaN         NaN                        1   577   \n",
       "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                      NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                      NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare    Cabin  \\\n",
       "count   714.000000  891.000000  891.000000     891  891.000000      204   \n",
       "unique         NaN         NaN         NaN     681         NaN      147   \n",
       "top            NaN         NaN         NaN  347082         NaN  B96 B98   \n",
       "freq           NaN         NaN         NaN       7         NaN        4   \n",
       "mean     29.699118    0.523008    0.381594     NaN   32.204208      NaN   \n",
       "std      14.526497    1.102743    0.806057     NaN   49.693429      NaN   \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000      NaN   \n",
       "25%      20.125000    0.000000    0.000000     NaN    7.910400      NaN   \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200      NaN   \n",
       "75%      38.000000    1.000000    0.000000     NaN   31.000000      NaN   \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200      NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "\n",
    "\n",
    "titanic_train_c = titanic_train.copy()\n",
    "\n",
    "titanic_train.describe(include = 'all')"
   ]
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    "tags": []
   },
   "source": [
    "Informações"
   ]
  },
  {
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "Titanic Train data types\n",
      "<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",
      "None\n",
      "--------------------------------------------------\n",
      "Titanic Test data types\n",
      "<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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('Titanic Train data types')\n",
    "print(titanic_train.info())\n",
    "print('-'*50)\n",
    "print('Titanic Test data types')\n",
    "print(titanic_test.info())"
   ]
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   "source": [
    "Valores \"NAn\""
   ]
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     "text": [
      "Titanic Train data NaN Values\n",
      "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\n",
      "--------------------------------------------------\n",
      "Titanic Test data NaN Values\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "print('Titanic Train data NaN Values')\n",
    "print(titanic_train.isnull().sum())\n",
    "print('-'*50)\n",
    "print('Titanic Test data NaN Values')\n",
    "print(titanic_test.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a082c8",
   "metadata": {
    "papermill": {
     "duration": 0.067112,
     "end_time": "2021-12-20T22:28:34.286196",
     "exception": false,
     "start_time": "2021-12-20T22:28:34.219084",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Retirar valores não uteis (Cabin e Ticket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d20c527c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:28:34.425611Z",
     "iopub.status.busy": "2021-12-20T22:28:34.424595Z",
     "iopub.status.idle": "2021-12-20T22:28:34.431897Z",
     "shell.execute_reply": "2021-12-20T22:28:34.432385Z",
     "shell.execute_reply.started": "2021-12-20T21:38:42.834718Z"
    },
    "papermill": {
     "duration": 0.079323,
     "end_time": "2021-12-20T22:28:34.432566",
     "exception": false,
     "start_time": "2021-12-20T22:28:34.353243",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [titanic_train_c, titanic_test]:\n",
    "    data = data.drop([\"Cabin\",\"Ticket\"],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b702e6f6",
   "metadata": {
    "papermill": {
     "duration": 0.067244,
     "end_time": "2021-12-20T22:28:34.567438",
     "exception": false,
     "start_time": "2021-12-20T22:28:34.500194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Retiando \"PassengerID\"do data frame de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e06bb6b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:28:34.704558Z",
     "iopub.status.busy": "2021-12-20T22:28:34.703646Z",
     "iopub.status.idle": "2021-12-20T22:28:34.708078Z",
     "shell.execute_reply": "2021-12-20T22:28:34.708585Z",
     "shell.execute_reply.started": "2021-12-20T21:38:33.369119Z"
    },
    "papermill": {
     "duration": 0.074747,
     "end_time": "2021-12-20T22:28:34.708746",
     "exception": false,
     "start_time": "2021-12-20T22:28:34.633999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c = titanic_train_c.drop([\"PassengerId\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dafe6f87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:28:34.845954Z",
     "iopub.status.busy": "2021-12-20T22:28:34.844987Z",
     "iopub.status.idle": "2021-12-20T22:28:34.973467Z",
     "shell.execute_reply": "2021-12-20T22:28:34.972781Z",
     "shell.execute_reply.started": "2021-12-20T21:38:49.444365Z"
    },
    "papermill": {
     "duration": 0.198569,
     "end_time": "2021-12-20T22:28:34.973752",
     "exception": true,
     "start_time": "2021-12-20T22:28:34.775183",
     "status": "failed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['PassengerId'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_19/2332208523.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_train_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtitanic_train_c\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"PassengerId\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4911\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4912\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4913\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4914\u001b[0m         )\n\u001b[1;32m   4915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4150\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   4183\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4184\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4185\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4186\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6017\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6019\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['PassengerId'] not found in axis\""
     ]
    }
   ],
   "source": [
    "titanic_train_c = titanic_train_c.drop([\"PassengerId\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6548239d",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Preenchendo valores Nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "568e5493",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Preenchendo os valores nulos da coluna \"Age\", com a mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ac565c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:38:53.038253Z",
     "iopub.status.busy": "2021-12-20T21:38:53.037812Z",
     "iopub.status.idle": "2021-12-20T21:38:53.043890Z",
     "shell.execute_reply": "2021-12-20T21:38:53.043202Z",
     "shell.execute_reply.started": "2021-12-20T21:38:53.038219Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [titanic_train_c, titanic_test]:\n",
    "    data['Age'].fillna(data['Age'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470bbdfc",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Preenchendo os valores nulos da coluna \"Fare\", coma mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "722be77b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:38:55.731054Z",
     "iopub.status.busy": "2021-12-20T21:38:55.730749Z",
     "iopub.status.idle": "2021-12-20T21:38:55.737790Z",
     "shell.execute_reply": "2021-12-20T21:38:55.737064Z",
     "shell.execute_reply.started": "2021-12-20T21:38:55.731022Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [titanic_train_c, titanic_test]:\n",
    "    data['Fare'].fillna(data['Fare'].median(), inplace = True)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b91a86",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Preenchendo os valores nulos da coluna \"Embarked\", com a moda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e93034ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:38:58.175951Z",
     "iopub.status.busy": "2021-12-20T21:38:58.175609Z",
     "iopub.status.idle": "2021-12-20T21:38:58.184375Z",
     "shell.execute_reply": "2021-12-20T21:38:58.183438Z",
     "shell.execute_reply.started": "2021-12-20T21:38:58.175915Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [titanic_train_c, titanic_test]:\n",
    "     data['Embarked'].fillna(data['Embarked'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14ad5c9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:00.531790Z",
     "iopub.status.busy": "2021-12-20T21:39:00.530927Z",
     "iopub.status.idle": "2021-12-20T21:39:00.540568Z",
     "shell.execute_reply": "2021-12-20T21:39:00.539870Z",
     "shell.execute_reply.started": "2021-12-20T21:39:00.531740Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "print('Titanic Train data NaN Values')\n",
    "print(titanic_train_c.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b5b7ab1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:02.466019Z",
     "iopub.status.busy": "2021-12-20T21:39:02.465694Z",
     "iopub.status.idle": "2021-12-20T21:39:02.475696Z",
     "shell.execute_reply": "2021-12-20T21:39:02.474836Z",
     "shell.execute_reply.started": "2021-12-20T21:39:02.465985Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "print('Titanic Test data NaN Values')\n",
    "print(titanic_test.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e4f0c56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:07.871718Z",
     "iopub.status.busy": "2021-12-20T21:39:07.871033Z",
     "iopub.status.idle": "2021-12-20T21:39:07.897154Z",
     "shell.execute_reply": "2021-12-20T21:39:07.895757Z",
     "shell.execute_reply.started": "2021-12-20T21:39:07.871670Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.qcut(titanic_test[\"Age\"], 5).value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b47f9d24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:10.749955Z",
     "iopub.status.busy": "2021-12-20T21:39:10.749176Z",
     "iopub.status.idle": "2021-12-20T21:39:10.763157Z",
     "shell.execute_reply": "2021-12-20T21:39:10.762279Z",
     "shell.execute_reply.started": "2021-12-20T21:39:10.749911Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [titanic_train_c,titanic_test]:\n",
    "    data['Age_group'] = pd.cut(data['Age'].astype(int), 5)\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6f19cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:13.247484Z",
     "iopub.status.busy": "2021-12-20T21:39:13.247163Z",
     "iopub.status.idle": "2021-12-20T21:39:13.260587Z",
     "shell.execute_reply": "2021-12-20T21:39:13.259719Z",
     "shell.execute_reply.started": "2021-12-20T21:39:13.247446Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [titanic_train_c,titanic_test]:\n",
    "    data['Fare_group'] = pd.qcut(data['Fare'], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f364e7",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Encoding de Variáveis Categóricas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce426c73",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "#### Variáveis aleatórias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecb438b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:16.236019Z",
     "iopub.status.busy": "2021-12-20T21:39:16.234880Z",
     "iopub.status.idle": "2021-12-20T21:39:16.252041Z",
     "shell.execute_reply": "2021-12-20T21:39:16.251034Z",
     "shell.execute_reply.started": "2021-12-20T21:39:16.235972Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e721f4",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### identificar o nome das colunas numéricas e categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8735ec20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:19.422677Z",
     "iopub.status.busy": "2021-12-20T21:39:19.422204Z",
     "iopub.status.idle": "2021-12-20T21:39:19.438796Z",
     "shell.execute_reply": "2021-12-20T21:39:19.437706Z",
     "shell.execute_reply.started": "2021-12-20T21:39:19.422628Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c.select_dtypes(include=['float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a04d20b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:23.913699Z",
     "iopub.status.busy": "2021-12-20T21:39:23.913373Z",
     "iopub.status.idle": "2021-12-20T21:39:23.927542Z",
     "shell.execute_reply": "2021-12-20T21:39:23.926942Z",
     "shell.execute_reply.started": "2021-12-20T21:39:23.913634Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c.select_dtypes(include=['int64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af99aa6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:34.598640Z",
     "iopub.status.busy": "2021-12-20T21:39:34.597610Z",
     "iopub.status.idle": "2021-12-20T21:39:34.614862Z",
     "shell.execute_reply": "2021-12-20T21:39:34.613920Z",
     "shell.execute_reply.started": "2021-12-20T21:39:34.598586Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3edce7",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "One-hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d3bb29a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:37.861591Z",
     "iopub.status.busy": "2021-12-20T21:39:37.861131Z",
     "iopub.status.idle": "2021-12-20T21:39:37.894360Z",
     "shell.execute_reply": "2021-12-20T21:39:37.892956Z",
     "shell.execute_reply.started": "2021-12-20T21:39:37.861555Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "onehot = OneHotEncoder(sparse = False)\n",
    "\n",
    "def OH_encoder(data,cols):\n",
    "    data_encoded = data.copy()\n",
    "    for col in cols:      \n",
    " \n",
    "        OH_cols = pd.DataFrame(onehot.fit_transform(data[[col]]),dtype = 'int')\n",
    "        \n",
    " \n",
    "        OH_cols.columns = onehot.get_feature_names([col])\n",
    "        \n",
    " \n",
    "        data_encoded = data_encoded.drop([col], axis = 1)\n",
    "        data_encoded = pd.concat([data_encoded,OH_cols], axis = 1)\n",
    "    return data_encoded\n",
    "\n",
    "\n",
    "train_encoded = OH_encoder(titanic_train_c,[\"Sex\",\"Embarked\"])\n",
    "test_encoded = OH_encoder(titanic_test,[\"Sex\",\"Embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e963dfaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:40.921702Z",
     "iopub.status.busy": "2021-12-20T21:39:40.921374Z",
     "iopub.status.idle": "2021-12-20T21:39:40.958814Z",
     "shell.execute_reply": "2021-12-20T21:39:40.957686Z",
     "shell.execute_reply.started": "2021-12-20T21:39:40.921644Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label = LabelEncoder()\n",
    "\n",
    "for data in [train_encoded,test_encoded]:\n",
    "    data[\"Age_group_code\"] = label.fit_transform(data[\"Age_group\"])\n",
    "    data[\"Fare_group_code\"] = label.fit_transform(data[\"Fare_group\"])\n",
    "    data = data.drop([\"Age_group\",\"Fare_group\",\"Name\"], axis = 1, inplace = True)\n",
    "    \n",
    "train_encoded.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab52b37f",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Precisão do Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b409b526",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:44.403837Z",
     "iopub.status.busy": "2021-12-20T21:39:44.402758Z",
     "iopub.status.idle": "2021-12-20T21:39:44.663049Z",
     "shell.execute_reply": "2021-12-20T21:39:44.662100Z",
     "shell.execute_reply.started": "2021-12-20T21:39:44.403777Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "randomforest_model1 = RandomForestClassifier(random_state = 0)\n",
    "\n",
    "X_model1 = train_encoded.drop('Survived', axis = 1)\n",
    "y_model1 = train_encoded['Survived']\n",
    "\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X_model1,y_model1,test_size = 0.2,random_state = 0)\n",
    "\n",
    "randomforest_model1.fit(X_train,y_train)\n",
    "preds = randomforest_model1.predict(X_valid)\n",
    "\n",
    "accuracy = round(accuracy_score(preds,y_valid)*100,2)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd2d764",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "## Pipilines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e34195f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:47.704873Z",
     "iopub.status.busy": "2021-12-20T21:39:47.704207Z",
     "iopub.status.idle": "2021-12-20T21:39:47.721319Z",
     "shell.execute_reply": "2021-12-20T21:39:47.720339Z",
     "shell.execute_reply.started": "2021-12-20T21:39:47.704817Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c_na = titanic_train_c.copy()\n",
    "titanic_train_c_na = titanic_train_c_na.drop([\"Age\",\"Embarked\"], axis = 1)\n",
    "titanic_train_c_na = pd.concat([titanic_train_c_na,titanic_train[[\"Age\",\"Embarked\"]]],axis = 1)\n",
    "\n",
    "titanic_train_c_na.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79a6f93",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Função para avaliar estratégias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a912dffe",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Argumentos:\n",
    "\n",
    "● data: Banco de dados a partir do qual será produzido o modelo.\n",
    "\n",
    "● encoder: a estratégia de encoding de variáveis categóricas,\n",
    "como one-hot ou label encoder.\n",
    "\n",
    "● model: o algoritmo que irá produzir o modelo, inicialmente será\n",
    "o random forest classifier.\n",
    "\n",
    "● numerical_imputer: a estratégia usada para substituir valores\n",
    "nulos nas features numéricas, como \"mean\" ou \"median\".\n",
    "\n",
    "● categorical_imputer: estratégia para preencher valores nulos nas features categóricas, aqui será usada apenas \"most frequent\", que é substituir pelo valor que mais aparece no banco de dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eb039ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:51.284223Z",
     "iopub.status.busy": "2021-12-20T21:39:51.283328Z",
     "iopub.status.idle": "2021-12-20T21:39:51.295567Z",
     "shell.execute_reply": "2021-12-20T21:39:51.294794Z",
     "shell.execute_reply.started": "2021-12-20T21:39:51.284176Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "def pipeline_maker(data, encoder, model, numerical_imputer = SimpleImputer(),\n",
    "                   categorical_imputer = SimpleImputer(strategy = 'most_frequent')):\n",
    "    \n",
    "    X_pipe = data.drop('Survived', axis = 1)\n",
    "    y_pipe = data['Survived']\n",
    "\n",
    "    X_train_pipe,X_valid_pipe,y_train_pipe,y_valid_pipe = train_test_split(X_pipe,y_pipe,\n",
    "                                                                           test_size = 0.2,\n",
    "                                                                           random_state = 0)\n",
    "\n",
    "    \n",
    "    num_transf = numerical_imputer\n",
    "    cat_transf = Pipeline(steps = [('imputer', categorical_imputer),\n",
    "                                               ('encoder',encoder)])\n",
    "    preprocessor = ColumnTransformer(transformers = [\n",
    "                                                ('num',num_transf,['Age']),\n",
    "                                                ('cat',cat_transf,['Age_group','Fare_group',\n",
    "                                                                          'Sex','Embarked'])])\n",
    "    \n",
    "\n",
    "    pipe = Pipeline(steps = [('preprocessor', preprocessor),\n",
    "                                         ('model', model)])\n",
    "    pipe.fit(X_train_pipe,y_train_pipe)\n",
    "    preds_pipe = pipe.predict(X_valid_pipe)\n",
    "\n",
    "   \n",
    "    accuracy = round(accuracy_score(preds_pipe,y_valid_pipe)*100,2)\n",
    "    return accuracy, preds_pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647be395",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Definindo estratégias\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3a1196",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Imputers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a203bfd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:55.564005Z",
     "iopub.status.busy": "2021-12-20T21:39:55.563518Z",
     "iopub.status.idle": "2021-12-20T21:39:55.571726Z",
     "shell.execute_reply": "2021-12-20T21:39:55.570604Z",
     "shell.execute_reply.started": "2021-12-20T21:39:55.563963Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mean_imputer = SimpleImputer()\n",
    "mostfreq_imputer = SimpleImputer(strategy = 'most_frequent')\n",
    "median_imputer = SimpleImputer(strategy = 'median')\n",
    "zero_imputer = SimpleImputer(strategy = 'constant', fill_value = 0)\n",
    "imputers = [mean_imputer, mostfreq_imputer, median_imputer, zero_imputer]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b260dc",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f32fedd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:39:58.642836Z",
     "iopub.status.busy": "2021-12-20T21:39:58.642124Z",
     "iopub.status.idle": "2021-12-20T21:39:58.648273Z",
     "shell.execute_reply": "2021-12-20T21:39:58.647004Z",
     "shell.execute_reply.started": "2021-12-20T21:39:58.642792Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "onehot_encoder = OneHotEncoder(sparse = False)\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "encoders = [onehot_encoder, ordinal_encoder]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d128e824",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f67c1c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:02.164227Z",
     "iopub.status.busy": "2021-12-20T21:40:02.163253Z",
     "iopub.status.idle": "2021-12-20T21:40:02.169564Z",
     "shell.execute_reply": "2021-12-20T21:40:02.168887Z",
     "shell.execute_reply.started": "2021-12-20T21:40:02.164159Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "randomforest = RandomForestClassifier(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbbccfe1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:04.506008Z",
     "iopub.status.busy": "2021-12-20T21:40:04.505389Z",
     "iopub.status.idle": "2021-12-20T21:40:06.955273Z",
     "shell.execute_reply": "2021-12-20T21:40:06.954038Z",
     "shell.execute_reply.started": "2021-12-20T21:40:04.505940Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for encoder in encoders:\n",
    "    for imputer in imputers:\n",
    "        accuracy,_ = pipeline_maker(titanic_train_c_na, encoder, randomforest, numerical_imputer = imputer)\n",
    "        print(\"pipe_\"+str(encoder)+'_'+str(imputer)+'='+str(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56326d3",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Cross Validation e Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1ad36c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:09.322673Z",
     "iopub.status.busy": "2021-12-20T21:40:09.322320Z",
     "iopub.status.idle": "2021-12-20T21:40:10.614844Z",
     "shell.execute_reply": "2021-12-20T21:40:10.613904Z",
     "shell.execute_reply.started": "2021-12-20T21:40:09.322626Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def cross_validation(model, X, y):\n",
    "    scores = cross_val_score(model, X, y, cv = 5, scoring = 'accuracy')\n",
    "    return round(scores.mean()*100,2)\n",
    "\n",
    "\n",
    "accuracy_crossval = cross_validation(randomforest_model1, X_model1, y_model1)\n",
    "print(accuracy_crossval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e6f925",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4855744a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:13.561286Z",
     "iopub.status.busy": "2021-12-20T21:40:13.560915Z",
     "iopub.status.idle": "2021-12-20T21:40:14.377047Z",
     "shell.execute_reply": "2021-12-20T21:40:14.376088Z",
     "shell.execute_reply.started": "2021-12-20T21:40:13.561235Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "\n",
    "def make_gbc(X,y):\n",
    "    gbclass = GradientBoostingClassifier(random_state = 0, n_iter_no_change = 100) \n",
    "    gbclass.fit(X,y)\n",
    "    score = cross_validation(gbclass, X, y)\n",
    "    return score, gbclass\n",
    "\n",
    "X_model2 = X_model1\n",
    "y_model2 = y_model1\n",
    "\n",
    "score,gbclass = make_gbc(X_model2,y_model2)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae794077",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d65cd0e",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Determinar quais são as features discretas (todas menos “Age” e “Fare”)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09ee7c56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:17.227420Z",
     "iopub.status.busy": "2021-12-20T21:40:17.226464Z",
     "iopub.status.idle": "2021-12-20T21:40:17.234469Z",
     "shell.execute_reply": "2021-12-20T21:40:17.233723Z",
     "shell.execute_reply.started": "2021-12-20T21:40:17.227357Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "discrete_features = train_encoded.drop(['Age','Fare'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9aebcc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:21.533841Z",
     "iopub.status.busy": "2021-12-20T21:40:21.533424Z",
     "iopub.status.idle": "2021-12-20T21:40:21.540363Z",
     "shell.execute_reply": "2021-12-20T21:40:21.539055Z",
     "shell.execute_reply.started": "2021-12-20T21:40:21.533804Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Função para produzir o gráfico de MI score;\n",
    "Features que serão utilizados na função;\n",
    "Produzindo gráfico a partir das funções e argumentos necessários.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "197c93f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:45.243552Z",
     "iopub.status.busy": "2021-12-20T21:40:45.243224Z",
     "iopub.status.idle": "2021-12-20T21:40:45.671086Z",
     "shell.execute_reply": "2021-12-20T21:40:45.670181Z",
     "shell.execute_reply.started": "2021-12-20T21:40:45.243514Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_MI (X, y, discrete_features):\n",
    "\n",
    "    mi_scores = mutual_info_classif(X, y, discrete_features = discrete_features )\n",
    "    mi_scores = pd.Series(mi_scores, index = X.columns)\n",
    "    mi_scores = mi_scores.sort_values(ascending = False)\n",
    "    \n",
    "    \n",
    "    sns.set_style('whitegrid')\n",
    "    scores = mi_scores.sort_values(ascending = True)\n",
    "    width = np.arange(len(scores))\n",
    "    ticks = list(scores.index)\n",
    "    plt.barh(width, scores)\n",
    "    plt.yticks(width, ticks)\n",
    "    plt.title(\"Mutual Information Scores\")\n",
    "    \n",
    "    return mi_scores\n",
    "\n",
    "X = train_encoded.drop(['Survived'],axis = 1)\n",
    "y = train_encoded['Survived']\n",
    "\n",
    "make_MI(X, y, discrete_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd8476a",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Construir dois gráficos do tipo sns.histplot para a análise de “Fare”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ba22701",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:52.598092Z",
     "iopub.status.busy": "2021-12-20T21:40:52.597758Z",
     "iopub.status.idle": "2021-12-20T21:40:54.075816Z",
     "shell.execute_reply": "2021-12-20T21:40:54.074862Z",
     "shell.execute_reply.started": "2021-12-20T21:40:52.598052Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Criando dois gráficos, plotados um ao lado do outro\n",
    "fig, axs = plt.subplots(1,2, figsize = (18,7))\n",
    "#Criando gráficos da distribuição de \"Fare\"\n",
    "axs[0].set_title('Distribuição Total das Taxas')\n",
    "sns.histplot(x = 'Fare', kde = True, data = titanic_train_c, ax = axs[0]);\n",
    "axs[1].set_title('Relação Entre Taxas e Sobreviventes ')\n",
    "sns.histplot(x = 'Fare',hue = 'Survived', kde = True, data = titanic_train_c, ax = axs[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3a27ed",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Pode-se notar que as tarifas, obteeram uma maior frequencia no valor de até 50 dólares. Contudo no gráfico \"Relação entre taxas e sobreviventes\", é que mesmo tendo poucas pessoas que pagaram taxas maiores que 50 dólares, nesses \"Setores\", houve um maior número de sobreviventes do que de mortes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77cfbf46",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "i. Distribuição da idade dos passageiros utilizando o sexo como camada.\n",
    "\n",
    "ii. A distribuição da idade dos passageiros, apenas entre os\n",
    "sobreviventes, utilizando o sexo como camada.\n",
    "\n",
    "iii. Distribuição da idade dos homens, utilizando “Survived” como camada.\n",
    "\n",
    "iv. Distribuição da idade das mulheres, utilizando “Survived”.\n",
    "\n",
    "v. Faça uma análise comparando a distribuição dos sobreviventes, comparando homens e mulheres e qual e como a idade influencia na chance de sobrevivência"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c330b82a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:40:57.599306Z",
     "iopub.status.busy": "2021-12-20T21:40:57.598963Z",
     "iopub.status.idle": "2021-12-20T21:40:59.437492Z",
     "shell.execute_reply": "2021-12-20T21:40:59.436779Z",
     "shell.execute_reply.started": "2021-12-20T21:40:57.599252Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Organizando as plotagens para 4 gráficos.\n",
    "fig, axs = plt.subplots(2,2, figsize = (18,7), sharex = True, sharey = True)\n",
    "\n",
    "#Criando gráficos da distribuição de \"Fare\"\n",
    "axs[0,0].set_title('Sexo x idade')\n",
    "sns.histplot(x = 'Age',hue = 'Sex', kde = True, data = titanic_train_c, ax = axs[0,0], palette = \"YlOrRd\");\n",
    "\n",
    "axs[0,1].set_title('sobreviventes x idade')\n",
    "sns.histplot(data = titanic_train_c.loc[titanic_train_c['Survived']==1], x = 'Age', hue = 'Sex', kde = True, ax = axs[0,1], palette = \"YlOrRd_r\");\n",
    "\n",
    "axs[1,0].set_title('Sobreviventes x Homens ')\n",
    "sns.histplot(data = titanic_train_c.loc[titanic_train_c['Sex']=='male'], x = 'Age', hue = 'Survived', kde = True, ax = axs[1,0]);\n",
    "\n",
    "axs[1,1].set_title('Sobreviventes x Mulheres')\n",
    "sns.histplot(data = titanic_train_c.loc[titanic_train_c['Sex']=='female'], x = 'Age', hue = 'Survived', kde = True, ax = axs[1,1]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6efa57d7",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Observa-se que há uma maior relação de mulheres sobreviventes em relação a homens, idependente da idade."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be8b391",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Feature Construction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a83cbd9",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "A partir de “Name” criar a feature “Title” para separar os valores, primeiramente, pela vírgula, e depois pelo ponto. Para isso deve-se utilizar o método str.split, lembre-se de criar uma nova coluna para armazenar estas informações, após isso, pode-se excluir a feature “Names”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1a84f5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:43:37.318913Z",
     "iopub.status.busy": "2021-12-20T21:43:37.318096Z",
     "iopub.status.idle": "2021-12-20T21:43:37.415652Z",
     "shell.execute_reply": "2021-12-20T21:43:37.414157Z",
     "shell.execute_reply.started": "2021-12-20T21:43:37.318848Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adicionando \"Name\" à train_encoded\n",
    "train_encoded = pd.concat([train_encoded,titanic_train_c['Name']], axis = 1)\n",
    "\n",
    "for data in [train_encoded,titanic_test]:\n",
    "    #Utilizando o método string split do pandas para retirar informações de texto \n",
    "    #(https://pandas.pydata.org/docs/reference/api/pandas.Series.str.split.html)\n",
    "    data['Title'] = data['Name'].str.split(',',expand = True)[1].str.split('.',expand = True)[0]\n",
    "    data.drop(\"Name\", axis = 1, inplace = True)\n",
    "\n",
    "#Demonstrando quais os valores da coluna\n",
    "print(train_encoded['Title'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dd0a7df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:43:42.638010Z",
     "iopub.status.busy": "2021-12-20T21:43:42.637613Z",
     "iopub.status.idle": "2021-12-20T21:43:42.810064Z",
     "shell.execute_reply": "2021-12-20T21:43:42.809407Z",
     "shell.execute_reply.started": "2021-12-20T21:43:42.637971Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in [train_encoded,titanic_test]:\n",
    "    #Criando Feature para contar número de integrantes da família\n",
    "    data['Familysize'] = data['SibSp'] + data['Parch']\n",
    "    \n",
    "    #Criando Feature para identificar se a pessoa está ou não sozinha no navio\n",
    "    data['IsAlone'] = 1\n",
    "    for i in range(len(data.index)):\n",
    "        if data.loc[i,'Familysize'] != 0:\n",
    "            data.loc[i,'IsAlone'] = 0\n",
    "\n",
    "train_encoded.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5975a80",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354feda7",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "O primeiro passo é analisar a distribuição dos passageiros levando em consideração “Fare” e “Age”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b7bc63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:51:35.909882Z",
     "iopub.status.busy": "2021-12-20T21:51:35.909188Z",
     "iopub.status.idle": "2021-12-20T21:51:36.200266Z",
     "shell.execute_reply": "2021-12-20T21:51:36.199188Z",
     "shell.execute_reply.started": "2021-12-20T21:51:35.909814Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.scatterplot(x='Fare',y='Age',data = titanic_train_c);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c228777",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "criar os grupos utilizando a biblioteca K-means do sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d17473",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T21:58:47.332898Z",
     "iopub.status.busy": "2021-12-20T21:58:47.332580Z",
     "iopub.status.idle": "2021-12-20T21:58:47.475925Z",
     "shell.execute_reply": "2021-12-20T21:58:47.475028Z",
     "shell.execute_reply.started": "2021-12-20T21:58:47.332865Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#Criando dataframe com \"Age\" e \"Fare\" sem outliers\n",
    "fare_age_group = titanic_train_c.loc[titanic_train_c['Fare'] < 500,['Fare','Age']]\n",
    "\n",
    "#Criando feature a partir da interação entre \"Age\" e \"Fare\"\n",
    "kmeans = KMeans(n_clusters = 6, random_state = 0)\n",
    "fare_age_group['Fare_age_group'] = kmeans.fit_predict(fare_age_group)\n",
    "fare_age_group['Fare_age_group'] = fare_age_group['Fare_age_group'].astype('int')\n",
    "\n",
    "fare_age_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206f380d",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Agora será útil entender quais as características de cada grupo criado pelo algoritmo, para isso serão utilizados dois gráficos, um para identificar os grupos na distribuição “Age” x “Fare”, outro para avaliá-los em relação à chance de sobrevivência."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b754bdf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:02:48.530213Z",
     "iopub.status.busy": "2021-12-20T22:02:48.529904Z",
     "iopub.status.idle": "2021-12-20T22:02:49.540112Z",
     "shell.execute_reply": "2021-12-20T22:02:49.539164Z",
     "shell.execute_reply.started": "2021-12-20T22:02:48.530181Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Adicionando feature criada ao banco de dados de treino\n",
    "titanic_train_c['Fare_age_group'] = fare_age_group['Fare_age_group']\n",
    "\n",
    "#Criando visualização para entendimento da nova feature\n",
    "fig, axs = plt.subplots(1,2, figsize = (15,7))\n",
    "sns.barplot(x = 'Fare_age_group', y = 'Survived', data = titanic_train_c, ax = axs[1], palette = \"rainbow_r\")\n",
    "sns.scatterplot(x='Fare',y='Age', hue = 'Fare_age_group', data = titanic_train_c, ax = axs[0],palette = \"rainbow_r\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "662f577c",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Como observado anteriormente os grupos que tiveram uma taxa menor que 50 apresentou uma menor chance de sobrevivência, e pode-se observar na figua acima que os grupos 0, 4 e 5 apresentaram uma taxa menor que 50, ou seja, a chance de sobrevivência desses grupos é menor em relação aos outros grupos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fcf804",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Ao analisarmos a nova feature, é fácil notar que existem valores nulos, estes valores correspondem aos outliers que foram retirados para a construção dos grupos. Para substituir os NaN, deve-se identificar quem são esses outliers e designar, manualmente, seu grupo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85bf2b83",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Descobrindo os outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba07cd05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:09:26.517895Z",
     "iopub.status.busy": "2021-12-20T22:09:26.517208Z",
     "iopub.status.idle": "2021-12-20T22:09:26.541512Z",
     "shell.execute_reply": "2021-12-20T22:09:26.540897Z",
     "shell.execute_reply.started": "2021-12-20T22:09:26.517849Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c.loc[titanic_train_c['Fare_age_group'].isnull() == True,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda90f24",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Grupo para os outiliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12cf20bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:11:33.885140Z",
     "iopub.status.busy": "2021-12-20T22:11:33.884829Z",
     "iopub.status.idle": "2021-12-20T22:11:33.910646Z",
     "shell.execute_reply": "2021-12-20T22:11:33.909703Z",
     "shell.execute_reply.started": "2021-12-20T22:11:33.885104Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "titanic_train_c.loc[titanic_train_c['Fare_age_group'].isnull() == True,['Fare_age_group']] = 1\n",
    "titanic_train_c.loc[[258,679,737]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b5a6ea",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Nova feature sendo adicionado aos bancos de dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d340fd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:13:30.349492Z",
     "iopub.status.busy": "2021-12-20T22:13:30.349000Z",
     "iopub.status.idle": "2021-12-20T22:13:30.377156Z",
     "shell.execute_reply": "2021-12-20T22:13:30.376174Z",
     "shell.execute_reply.started": "2021-12-20T22:13:30.349452Z"
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    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
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     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_encoded['Fare_age_group'] = titanic_train_c['Fare_age_group']\n",
    "train_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799f3757",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
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    },
    "tags": []
   },
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a64915b",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
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    "tags": []
   },
   "source": [
    "As features originais não representam a mesma grandeza, “Age” conta a idade em anos e “Fare” a taxa de embarque em dólares, portanto, para que seja possível utilizar o PCA, deve-se primeiramente realizar os scaling das variáveis. para esta etapa será utilizado o MinMax Scaler do scikit learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55659a1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:15:48.665782Z",
     "iopub.status.busy": "2021-12-20T22:15:48.665112Z",
     "iopub.status.idle": "2021-12-20T22:15:49.535771Z",
     "shell.execute_reply": "2021-12-20T22:15:49.534624Z",
     "shell.execute_reply.started": "2021-12-20T22:15:48.665726Z"
    },
    "papermill": {
     "duration": null,
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     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaller = MinMaxScaler(copy = False)\n",
    "\n",
    "stand_data = pd.DataFrame()\n",
    "for column in ['Fare','Age']:\n",
    "    stand_data[[column]] = titanic_train_c[[column]]\n",
    "    stand_data[[column]] = scaller.fit_transform(stand_data[[column]])\n",
    "    \n",
    "fig, axs = plt.subplots(1,2, figsize = (18,7))\n",
    "\n",
    "sns.histplot(x = 'Age', kde = True, data = stand_data, ax = axs[0]);\n",
    "sns.histplot(x = 'Fare', kde = True, data = stand_data, ax = axs[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60050f5",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Realizar o PCA no dataframe com features escaladas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ea1f65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:16:43.382674Z",
     "iopub.status.busy": "2021-12-20T22:16:43.382208Z",
     "iopub.status.idle": "2021-12-20T22:16:43.400623Z",
     "shell.execute_reply": "2021-12-20T22:16:43.399628Z",
     "shell.execute_reply.started": "2021-12-20T22:16:43.382624Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "pca_data = pca.fit_transform(stand_data)\n",
    "\n",
    "columns = ['PC1','PC2']\n",
    "pca_data = pd.DataFrame(pca_data, columns = columns)\n",
    "\n",
    "loadings = pd.DataFrame(pca.components_.T, columns = columns, index = [\"Fare\",\"Age\"])\n",
    "loadings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad48c975",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "MI scores do PC1 e PC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5447f540",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:18:04.060092Z",
     "iopub.status.busy": "2021-12-20T22:18:04.059780Z",
     "iopub.status.idle": "2021-12-20T22:18:04.329113Z",
     "shell.execute_reply": "2021-12-20T22:18:04.328202Z",
     "shell.execute_reply.started": "2021-12-20T22:18:04.060058Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "make_MI(pca_data, titanic_train_c[\"Survived\"], discrete_features = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5588e8e",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "É viável, com essas técnicas, a substituição das features “Fare” e “Age” por apenas uma?\n",
    "Como observado acima, não é viável utilizar nem a PC1 e nem a PC2 ao banco de dados, mesmo o PC2 sendo um pouco melhor que o PC1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43307734",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "## Target Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c874547",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Realizar o encoding da feature “Title”, após isso será interessante analisar a distribuição dos valores resultantes e compará-los com o target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b5cba5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:23:53.460346Z",
     "iopub.status.busy": "2021-12-20T22:23:53.459560Z",
     "iopub.status.idle": "2021-12-20T22:23:53.715694Z",
     "shell.execute_reply": "2021-12-20T22:23:53.714829Z",
     "shell.execute_reply.started": "2021-12-20T22:23:53.460304Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from category_encoders import MEstimateEncoder\n",
    "\n",
    "encoder = MEstimateEncoder(cols = ['Title'], m = 5)\n",
    "\n",
    "title = encoder.fit_transform(train_encoded['Title'], train_encoded['Survived'])\n",
    "title\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42592e52",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "#### Plotando a distribuição dos valores codificados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1833291",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "Criar um histograma com os valores da feature “Survived”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcae6bf7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:24:47.556027Z",
     "iopub.status.busy": "2021-12-20T22:24:47.555689Z",
     "iopub.status.idle": "2021-12-20T22:24:47.945967Z",
     "shell.execute_reply": "2021-12-20T22:24:47.945224Z",
     "shell.execute_reply.started": "2021-12-20T22:24:47.555990Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize = (9,7))\n",
    "\n",
    "sns.distplot(train_encoded['Survived'], kde=False, norm_hist=True)\n",
    "sns.kdeplot(title['Title'], color='r', ax=ax)\n",
    "ax.set_xlabel(\"Survived\")\n",
    "ax.legend(labels=['Title', 'Survived']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31f5033",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "\n",
    "Criar um gráfico do tipo kdeplot, sobreposto ao primeiro, que indique a distribuição dos valores da feature “Title” codificada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0da4d991",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T22:27:00.060934Z",
     "iopub.status.busy": "2021-12-20T22:27:00.060177Z",
     "iopub.status.idle": "2021-12-20T22:27:00.377648Z",
     "shell.execute_reply": "2021-12-20T22:27:00.377015Z",
     "shell.execute_reply.started": "2021-12-20T22:27:00.060884Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_encoded['Title-encoded'] = title\n",
    "train_encoded.info()\n",
    "\n",
    "X_enc = train_encoded.loc[:,['Fare_age_group','Title-encoded']]\n",
    "y_enc = train_encoded['Survived']\n",
    "make_MI(X_enc, y_enc, discrete_features = False )"
   ]
  }
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