Parte2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "321aee4f",
   "metadata": {},
   "source": [
    "# Titanic - Machine Learning from Disaster\n",
    "\n",
    "<img src=\"https://storage.googleapis.com/kaggle-competitions/kaggle/3136/logos/header.png\" width=900>\n",
    "\n",
    "- Vamos utilizar os [dados disponíveis no Kaggle](https://www.kaggle.com/competitions/titanic)\n",
    "    - É um dataset de **competição**\n",
    "    - O resultado é avaliado através da **acurácia**:\n",
    "        - _\"Sua pontuação é a porcentagem de passageiros que você prevê corretamente. Isso é conhecido como acurácia.\"_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e860c1c7",
   "metadata": {},
   "source": [
    "### Importando novamente as bases e fazendo o tratamento dos dados\n",
    "- Vamos apenas replicar o que fizemos no **[primeiro arquivo dessa análise](https://github.com/lucaslealx/Titanic/blob/main/Parte1.ipynb)** (para visualizar a parte 1 completa, visite esse link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3c866a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c7a7e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </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>NaN</td>\n",
       "      <td>S</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",
       "    </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>NaN</td>\n",
       "      <td>S</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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a base de treino\n",
    "treino = pd.read_csv('train.csv')\n",
    "treino.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8bf6800",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
       "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
       "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "2          894       2         Myles, Mr. Thomas Francis    male  62.0      0   \n",
       "\n",
       "   Parch  Ticket    Fare Cabin Embarked  \n",
       "0      0  330911  7.8292   NaN        Q  \n",
       "1      0  363272  7.0000   NaN        S  \n",
       "2      0  240276  9.6875   NaN        Q  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a base de teste\n",
    "teste = pd.read_csv('test.csv')\n",
    "teste.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9d4907",
   "metadata": {},
   "source": [
    "- **Fazendo o mesmo tratamento inicial que fizemos na aula anterior**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf0433e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminando as colunas com elevada cardinalidade\n",
    "treino = treino.drop(['Name','Ticket','Cabin'],axis=1)\n",
    "teste = teste.drop(['Name','Ticket','Cabin'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb7cfa69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usando a média para substituir valores nulos na coluna de idade\n",
    "treino.loc[treino.Age.isnull(),'Age'] = treino.Age.mean()\n",
    "teste.loc[teste.Age.isnull(),'Age'] = teste.Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ae224c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tratando a coluna Embarked da base de treino usando a moda \n",
    "treino.loc[treino.Embarked.isnull(),'Embarked'] = treino.Embarked.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d38ce71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# E também a coluna Fare da base de teste usando a média\n",
    "teste.loc[teste.Fare.isnull(),'Fare'] = teste.Fare.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48849a0",
   "metadata": {},
   "source": [
    "### Podemos agora entender as colunas de texto\n",
    "- Vamos agora **adicionar um novo tratamento das colunas de texto**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a6b5bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sex', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando as colunas de texto na base de treino\n",
    "treino.columns[treino.dtypes == 'object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f92bccdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores na coluna Sex\n",
    "treino.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c35b70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# e na coluna Embarked\n",
    "treino.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95aec76f",
   "metadata": {},
   "source": [
    "- Para tratar a coluna Sex, podemos criar uma nova coluna chamada **\"Male_Check\"** que **vai receber 1 se o gênero for masculino e 0 se o gênero for feminino**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ef39208",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usando uma lambda function para fazer esse tratamento\n",
    "treino['MaleCheck'] = treino.Sex.apply(lambda x: 1 if x == 'male' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee525036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     MaleCheck\n",
       "male    1            577\n",
       "female  0            314\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores\n",
    "treino[['Sex','MaleCheck']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b131965",
   "metadata": {},
   "source": [
    "- O próximo passo é fazer o mesmo para a base de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6c305d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usando uma lambda function para fazer esse tratamento\n",
    "teste['MaleCheck'] = teste.Sex.apply(lambda x: 1 if x == 'male' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2aa3710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     MaleCheck\n",
       "male    1            266\n",
       "female  0            152\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores\n",
    "teste[['Sex','MaleCheck']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f6a080a",
   "metadata": {},
   "source": [
    "- Agora, para tratar a coluna **Embarked** usando o **[OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)** que irá **criar uma nova coluna para cada um dos rótulos da coluna original**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b9ce911",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o OneHotEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7948f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o encoder\n",
    "ohe = OneHotEncoder(handle_unknown='ignore',dtype='int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "39cfa842",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo o fit com os dados\n",
    "ohe = ohe.fit(treino[['Embarked']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3f7d2a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       ...,\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 1, 0]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fazendo a transformação\n",
    "ohe.transform(treino[['Embarked']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "413d1add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked_C  Embarked_Q  Embarked_S\n",
       "0           0           0           1\n",
       "1           1           0           0\n",
       "2           0           0           1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformando esse resultado em um DataFrame\n",
    "ohe_df = pd.DataFrame(ohe.transform(treino[['Embarked']]).toarray(),columns=ohe.get_feature_names_out())\n",
    "ohe_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "766de833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Podemos agora adicionar essa coluna na nossa base de treino\n",
    "treino = pd.concat([treino,ohe_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c04b67b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked  Embarked_C  Embarked_Q  Embarked_S\n",
       "S         0           0           1             646\n",
       "C         1           0           0             168\n",
       "Q         0           1           0              77\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores\n",
    "treino[['Embarked','Embarked_C','Embarked_Q','Embarked_S']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f5764c",
   "metadata": {},
   "source": [
    "- Agora vamos **fazer o mesmo para a base de teste usando o encoder ohe que criamos acima**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fa70517",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformando esse resultado em um DataFrame\n",
    "ohe_df = pd.DataFrame(ohe.transform(teste[['Embarked']]).toarray(),columns=ohe.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "664c0ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adicionando o resultado na base de teste\n",
    "teste = pd.concat([teste,ohe_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c8f36e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked  Embarked_C  Embarked_Q  Embarked_S\n",
       "S         0           0           1             270\n",
       "C         1           0           0             102\n",
       "Q         0           1           0              46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando também os valores\n",
    "teste[['Embarked','Embarked_C','Embarked_Q','Embarked_S']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6795fb4c",
   "metadata": {},
   "source": [
    "### Usando essa nova base no modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51d5ea38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>MaleCheck</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</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>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  22.0      1      0   7.2500   \n",
       "1            2         1       1  female  38.0      1      0  71.2833   \n",
       "2            3         1       3  female  26.0      0      0   7.9250   \n",
       "\n",
       "  Embarked  MaleCheck  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0        S          1           0           0           1  \n",
       "1        C          0           1           0           0  \n",
       "2        S          0           0           0           1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a base\n",
    "treino.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1ff56674",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Podemos então apagar essas duas colunas que já tratamos\n",
    "treino = treino.drop(['Sex','Embarked'],axis=1)\n",
    "teste = teste.drop(['Sex','Embarked'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6cf90b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.699117647058763"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treino.Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a7c3c7f",
   "metadata": {},
   "source": [
    "- Podemos selecionar os mesmos modelos que vimos anteriormente (consulte arquivo da **[parte 1](https://github.com/lucaslealx/Titanic/blob/main/Parte1.ipynb)**)\n",
    "    - **Árvore de classificação**\n",
    "        - https://scikit-learn.org/stable/modules/tree.html#classification\n",
    "    - **Classificação dos vizinhos mais próximos**\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier\n",
    "    - **Regressão Logística**\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression\n",
    "- Antes de usar os algoritmos, precisamos separar a base de treino em **treino e validação**\n",
    "    - Vamos fazer isso utilizando o **train_test_split**\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "36f0b066",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o train_test_split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9110b1f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando a base de treino em X e y\n",
    "X = treino.drop(['PassengerId','Survived'],axis=1)\n",
    "y = treino.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7540d766",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando em treino e validação\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a6433c0",
   "metadata": {},
   "source": [
    "- Para a **árvore de classificação**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ca0ea325",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo a importação\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "23f96bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o classificador\n",
    "clf_ac = tree.DecisionTreeClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0be23ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo o fit com os dados\n",
    "clf_ac = clf_ac.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "620fd26d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo a previsão\n",
    "y_pred_ac = clf_ac.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860e0bc0",
   "metadata": {},
   "source": [
    "- Para o **KNeighborsClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4d54247f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a5b0532d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o classificador\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f7093f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo o fit com os dados\n",
    "clf_knn = clf_knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0b62e22f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo a previsão\n",
    "y_pred_knn = clf_knn.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a681fb",
   "metadata": {},
   "source": [
    "- E para a **Regressão Logística**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "acdb8597",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "57a895ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o classificador\n",
    "clf_rl = LogisticRegression(random_state=42,max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a86ac3c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo o fit com os dados\n",
    "clf_rl = clf_rl.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4fd3c698",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo a previsão\n",
    "y_pred_rl = clf_rl.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76a8036",
   "metadata": {},
   "source": [
    "- Vamos novamente **avaliar os modelos**\n",
    "    - Acurácia (método de avaliação usado na competição):\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html\n",
    "    - Matriz de confusão (ajuda a visualizar a distribuição dos erros):\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf694913",
   "metadata": {},
   "source": [
    "- Avaliando a **acurácia**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "02fbd240",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "708543cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7491525423728813"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para a árvore\n",
    "accuracy_score(y_val, y_pred_ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e38b7270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7152542372881356"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para o knn\n",
    "accuracy_score(y_val, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "446439ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8169491525423729"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para a regressão logística\n",
    "accuracy_score(y_val, y_pred_rl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e8e994",
   "metadata": {},
   "source": [
    "- Avaliando a **matriz de confusão**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d8265dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9f7278ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[138,  37],\n",
       "       [ 37,  83]], dtype=int64)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para a árvore\n",
    "confusion_matrix(y_val, y_pred_ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b4e9c36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[147,  28],\n",
       "       [ 56,  64]], dtype=int64)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para o knn\n",
    "confusion_matrix(y_val, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4edc78f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[153,  22],\n",
       "       [ 32,  88]], dtype=int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para a regressão logística\n",
    "confusion_matrix(y_val, y_pred_rl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4656b058",
   "metadata": {},
   "source": [
    "### Fazendo a previsão para os dados de teste\n",
    "- Vamos usar o modelo com melhor precisão para fazer o predict na base de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d316d0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>MaleCheck</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>3</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>2</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass        Age  SibSp  Parch     Fare  MaleCheck  Embarked_C  \\\n",
       "6         1  54.000000      0      0  51.8625          1           0   \n",
       "718       3  29.699118      0      0  15.5000          1           0   \n",
       "685       2  25.000000      1      2  41.5792          1           1   \n",
       "\n",
       "     Embarked_Q  Embarked_S  \n",
       "6             0           1  \n",
       "718           1           0  \n",
       "685           0           0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando o X_train\n",
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b97efd5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>MaleCheck</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch    Fare  MaleCheck  Embarked_C  \\\n",
       "0          892       3  34.5      0      0  7.8292          1           0   \n",
       "1          893       3  47.0      1      0  7.0000          0           0   \n",
       "2          894       2  62.0      0      0  9.6875          1           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           1           0  \n",
       "1           0           1  \n",
       "2           1           0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a base de teste\n",
    "teste.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e34f5ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para a base de teste ser igual a base de treino, precisamos eliminar a coluna de id\n",
    "X_teste = teste.drop('PassengerId',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f0497a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilizando a regressão logística na base de teste\n",
    "y_pred = clf_rl.predict(X_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "08fffa82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando uma nova coluna com a previsão na base de teste\n",
    "teste['Survived'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8b1fb599",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando apenas a coluna de Id e Survived para fazer o envio\n",
    "base_envio = teste[['PassengerId','Survived']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2965c544",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exportando para um csv\n",
    "base_envio.to_csv('resultados4.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "203a1311",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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