04-Exemplo - Análise de sobreviventes do Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de sobreviventes do Titanic\n",
    "O dataset de sobrevivents do Titanic é bastante usado como exemplo didático para ilustrar conceitos de tratamento e exploração de dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar importando dados para um pandas DataFrame a partir de um arquivo CSV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>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",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \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  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('datasets/titanic.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A informação acima mostra que esse dataset contém informações sobre 891 passageiros: seus nome, gênero, idade, etc (para uma descrição completa do significado de cada coluna, confira [este link](https://www.kaggle.com/c/titanic/data))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valores em branco\n",
    "Antes de iniciar a análise em si, precisamos checar como está a \"saúde\" dos dados verificando quanta informação está presente de fato em cada coluna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived        0.000000\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age            19.865320\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "Cabin          77.104377\n",
       "Embarked        0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# POrcentagem de valores em branco em cada coluna\n",
    "(raw_data.isnull().sum() / len(raw_data)) * 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pode-se ver que 77% dos passageiros não apresentam informação sobre em qual cabine eles estavam alojados. Essa informação pode ser útil para análise posterior mas, por enquanto, vamos descartar essa coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.6+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.drop('Cabin', axis='columns', inplace=True)\n",
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna *Embarked*, que informa em qual porto o passageiro embarcou, possui apenas algumas poucas linhas em branoc. Como a quantidade de passageiros sem essa informação é pequena, é razoável assumir que eles podem ser descartados do dataset sem grandes prejuízos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived        0.000000\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age            19.910011\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "Embarked        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.dropna(subset=['Embarked'], inplace=True)\n",
    "(raw_data.isnull().sum() / len(raw_data)) * 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, cerca de 20% dos passageiros não possuem informação de idade. Não parece razoável exluir todos eles e tampouco descartar a coluna inteira, então uma solução possível é preenchermos os valores em branco dessa coluna com o valor mediano dela no dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.0\n",
       "Survived       0.0\n",
       "Pclass         0.0\n",
       "Name           0.0\n",
       "Sex            0.0\n",
       "Age            0.0\n",
       "SibSp          0.0\n",
       "Parch          0.0\n",
       "Ticket         0.0\n",
       "Fare           0.0\n",
       "Embarked       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.fillna({'Age': raw_data.Age.median()}, inplace=True)\n",
    "(raw_data.isnull().sum() / len(raw_data)) * 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por que usar a mediana ao invés da média?\n",
    "A mediana representa uma *estatística robusta*. Uma *estatística* é um número que resume um conjunto de valores, enquanto que uma estatística é considerada *robusta* se ela não for afetada significativamente por variações nos dados.\n",
    "\n",
    "Suponha, por exemplo, que temos um grupo de pessoas cujas idades sejam [15, 16, 14, 15, 15, 19, 14, 17]. A média de idades nesse grupo é de 15.625. Se uma pessoa de 80 anos for adicionada a esse grupo, a média de idades será agora 22.77, o que não parece representar bem o perfil de idades prevalente desse grupo.\n",
    "A mediana nesses dois exemplos, por sua vez, é de 15 anos - isto é, o valor da mediana não é afetado pela presença de um *outlier* nos dados, o que a torna uma estatística robusta para as idades desse grupo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora que todas as informações sobre os passageiros no conjunto de dados foram \"limpas\", podemos começar a analisar os dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise exploratória"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar explorando quantas pessoas nesse dataset sobreviveram ao Titanic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119d124e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119cddeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall_fig = raw_data.Survived.value_counts().plot(kind='bar')\n",
    "overall_fig.set_xlabel('Survived')\n",
    "overall_fig.set_ylabel('Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No geral, 38% dos passageiros sobreviveram.\n",
    "\n",
    "Vamos agora segmentar a proporção de pessoas que sobreviveram ao longo de diferentes recortes (o código usado para gerar os gráficos abaixo foram retirados [desse link](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recorte de gênero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ytapWt9a2VdWaJHcNzt+S5Jg5jz96cOxRpqenH749NTWVqamp/f4CAAAAxsHMzExmZmaG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\nF7EsdudoNHgUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d37828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_sex = raw_data[raw_data['Survived']==1]['Sex'].value_counts()\n",
    "dead_sex = raw_data[raw_data['Survived']==0]['Sex'].value_counts()\n",
    "df = pd.DataFrame([survived_sex,dead_sex])\n",
    "df.index = ['Survivors','Non-survivors']\n",
    "df.plot(kind='bar',stacked=True, figsize=(15,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recorte de idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HJDk9yeFJTkry51W1Xd2YAAAAbFlfw19r7WtJNp8d9NQMzxya3p+n9Z6/Nsnl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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119de8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([raw_data[raw_data['Survived']==1]['Age'], raw_data[raw_data['Survived']==0]['Age']], \n",
    "         stacked=True, color=['g','r'],\n",
    "         bins=30, label=['Survivors','Non-survivors'])\n",
    "plt.xlabel('Idade')\n",
    "plt.ylabel('No. passengers')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por preço de passagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uq7V2f2vtlt76I0nuyNgTi40ZnqS1tq63umfG7rFvMVbYgqqaneRX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\nMAgAANBBwiAAAEAHCYMAAAAd9P8DAEaeIrjDO2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3a89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([raw_data[raw_data['Survived']==1]['Fare'], raw_data[raw_data['Survived']==0]['Fare']], \n",
    "         stacked=True, color=['g','r'],\n",
    "         bins=50, label=['Survivors','Non-survivors'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('No. passengers')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os gráficos acima indicam que passageiros que sejam do gênero feminino, com menos de 20 anos de idade e que pagaram passsagens mais caras tiveram uma maior chance de ter sobrevivido.\n",
    "\n",
    "Como podemos usar essa informação para tentar prever se um passageiro qualquer teria sobrevivido ao acidente?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Previsão das chances de sobrevivência"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iniciemos então mantendo apenas a informação que queremos utilizar - os nomes de passageiros também serão mantidos para análise posterior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 5 columns):\n",
      "Name        889 non-null object\n",
      "Sex         889 non-null object\n",
      "Age         889 non-null float64\n",
      "Fare        889 non-null float64\n",
      "Survived    889 non-null int64\n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_for_prediction = raw_data[['Name', 'Sex', 'Age', 'Fare', 'Survived']]\n",
    "data_for_prediction.is_copy = False\n",
    "data_for_prediction.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Codificação numérica de Strings\n",
    "Algumas informaçõe estão codificadas como strings: a informação sobre o gênero do passageiro, por exemplo, é representada pelas strings **male** e **female**. Para usar essa informação no nosso futuro modelo preditivo, devemos converter esses valores para um formato numérico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 5 columns):\n",
      "Name        889 non-null object\n",
      "Sex         889 non-null int64\n",
      "Age         889 non-null float64\n",
      "Fare        889 non-null float64\n",
      "Survived    889 non-null int64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_for_prediction['Sex'] = data_for_prediction.Sex.map({'male': 0, 'female': 1})\n",
    "data_for_prediction.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Divisão entre treinamento e teste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder avaliar a capacidade preditiva do modelo, uma parte dos dados (nesse caso, 25%) deve ser separada para um *conjunto de teste*.\n",
    "\n",
    "Um conjunto de teste é um dataset para o qual os valores a serem previstos são conhecidos mas que não são usados para treinar o modelo, sendo portanto usados para avaliar quantos acertos o modelo consegue fazer em um conjunto de exemplos que ele nunca viu durante o treinamento. Isso permite que avaliemos, de maneira não-enviesada, o quanto o modelo deve acertar ao ser aplicado a dados reais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(666, 223)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data = train_test_split(data_for_prediction, test_size=0.25, random_state=254)\n",
    "len(train_data), len(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Previsão de chances de sobrevivência usando árvores de decisão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usaremos um simples modelo de Árvore de Decisão para prever se um passageiro teria sobrevivido ao Titanic usando seu gênero, idade e preço de passagem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80269058295964124"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier().fit(train_data[['Sex', 'Age', 'Fare']], train_data.Survived)\n",
    "tree.score(test_data[['Sex', 'Age', 'Fare']], test_data.Survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com uma simples árvore de decisão, o resultado cima indica que seria possível prever corretamente a sobrevivência de cerca de 80% dos passageiros.\n",
    "\n",
    "Um exercício interessante de se fazer após treinar um modelo é dar uma olhada nos casos em que ele erra:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Albimona, Mr. Nassef Cassem</td>\n",
       "      <td>0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>18.7875</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>Frauenthal, Dr. Henry William</td>\n",
       "      <td>0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>133.6500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Sheerlinck, Mr. Jan Baptist</td>\n",
       "      <td>0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>Barah, Mr. Hanna Assi</td>\n",
       "      <td>0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Mellinger, Miss. Madeleine Violet</td>\n",
       "      <td>1</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Hamalainen, Mrs. William (Anna)</td>\n",
       "      <td>1</td>\n",
       "      <td>24.00</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
       "      <td>1</td>\n",
       "      <td>3.00</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Futrelle, Mr. Jacques Heath</td>\n",
       "      <td>0</td>\n",
       "      <td>37.00</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>0</td>\n",
       "      <td>36.00</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>Lulic, Mr. Nikola</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Olsen, Mr. Henry Margido</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>22.5250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Funk, Miss. Annie Clemmer</td>\n",
       "      <td>1</td>\n",
       "      <td>38.00</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>Marvin, Mr. Daniel Warner</td>\n",
       "      <td>0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Hosono, Mr. Masabumi</td>\n",
       "      <td>0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>Taylor, Mr. Elmer Zebley</td>\n",
       "      <td>0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Pengelly, Mr. Frederick William</td>\n",
       "      <td>0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>Hedman, Mr. Oskar Arvid</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>6.9750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Goodwin, Miss. Lillian Amy</td>\n",
       "      <td>1</td>\n",
       "      <td>16.00</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>Pickard, Mr. Berk (Berk Trembisky)</td>\n",
       "      <td>0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>Lam, Mr. Ali</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Ford, Miss. Robina Maggie \"Ruby\"</td>\n",
       "      <td>1</td>\n",
       "      <td>9.00</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Minahan, Dr. William Edward</td>\n",
       "      <td>0</td>\n",
       "      <td>44.00</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>1</td>\n",
       "      <td>31.00</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Parrish, Mrs. (Lutie Davis)</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Blackwell, Mr. Stephen Weart</td>\n",
       "      <td>0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Hart, Mr. Benjamin</td>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>26.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Blank, Mr. Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Hart, Mrs. Benjamin (Esther Ada Bloomfield)</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
       "      <td>26.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>Wilhelms, Mr. Charles</td>\n",
       "      <td>0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>Goodwin, Mrs. Frederick (Augusta Tyler)</td>\n",
       "      <td>1</td>\n",
       "      <td>43.00</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Petranec, Miss. Matilda</td>\n",
       "      <td>1</td>\n",
       "      <td>28.00</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Niskanen, Mr. Juha</td>\n",
       "      <td>0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Peuchen, Major. Arthur Godfrey</td>\n",
       "      <td>0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
       "      <td>1</td>\n",
       "      <td>26.00</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Collyer, Mr. Harvey</td>\n",
       "      <td>0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>26.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>Leader, Dr. Alice (Farnham)</td>\n",
       "      <td>1</td>\n",
       "      <td>49.00</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Andersson, Mr. August Edvard (\"Wennerstrom\")</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>7.7958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>1</td>\n",
       "      <td>48.00</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Jerwan, Mrs. Amin S (Marie Marthe Thuillard)</td>\n",
       "      <td>1</td>\n",
       "      <td>23.00</td>\n",
       "      <td>13.7917</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name  Sex    Age      Fare  \\\n",
       "207                        Albimona, Mr. Nassef Cassem    0  26.00   18.7875   \n",
       "660                      Frauenthal, Dr. Henry William    0  50.00  133.6500   \n",
       "81                         Sheerlinck, Mr. Jan Baptist    0  29.00    9.5000   \n",
       "762                              Barah, Mr. Hanna Assi    0  20.00    7.2292   \n",
       "446                  Mellinger, Miss. Madeleine Violet    1  13.00   19.5000   \n",
       "247                    Hamalainen, Mrs. William (Anna)    1  24.00   14.5000   \n",
       "43            Laroche, Miss. Simonne Marie Anne Andree    1   3.00   41.5792   \n",
       "137                        Futrelle, Mr. Jacques Heath    0  37.00   53.1000   \n",
       "679                 Cardeza, Mr. Thomas Drake Martinez    0  36.00  512.3292   \n",
       "821                                  Lulic, Mr. Nikola    0  27.00    8.6625   \n",
       "508                           Olsen, Mr. Henry Margido    0  28.00   22.5250   \n",
       "357                          Funk, Miss. Annie Clemmer    1  38.00   13.0000   \n",
       "748                          Marvin, Mr. Daniel Warner    0  19.00   53.1000   \n",
       "288                               Hosono, Mr. Masabumi    0  42.00   13.0000   \n",
       "712                           Taylor, Mr. Elmer Zebley    0  48.00   52.0000   \n",
       "238                    Pengelly, Mr. Frederick William    0  19.00   10.5000   \n",
       "804                            Hedman, Mr. Oskar Arvid    0  27.00    6.9750   \n",
       "71                          Goodwin, Miss. Lillian Amy    1  16.00   46.9000   \n",
       "429                 Pickard, Mr. Berk (Berk Trembisky)    0  32.00    8.0500   \n",
       "498    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)    1  25.00  151.5500   \n",
       "692                                       Lam, Mr. Ali    0  28.00   56.4958   \n",
       "147                   Ford, Miss. Robina Maggie \"Ruby\"    1   9.00   34.3750   \n",
       "245                        Minahan, Dr. William Edward    0  44.00   90.0000   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...    1  31.00   18.0000   \n",
       "259                        Parrish, Mrs. (Lutie Davis)    1  50.00   26.0000   \n",
       "271                       Tornquist, Mr. William Henry    0  25.00    0.0000   \n",
       "339                       Blackwell, Mr. Stephen Weart    0  45.00   35.5000   \n",
       "314                                 Hart, Mr. Benjamin    0  43.00   26.2500   \n",
       "209                                   Blank, Mr. Henry    0  40.00   31.0000   \n",
       "440        Hart, Mrs. Benjamin (Esther Ada Bloomfield)    1  45.00   26.2500   \n",
       "673                              Wilhelms, Mr. Charles    0  31.00   13.0000   \n",
       "678            Goodwin, Mrs. Frederick (Augusta Tyler)    1  43.00   46.9000   \n",
       "100                            Petranec, Miss. Matilda    1  28.00    7.8958   \n",
       "400                                 Niskanen, Mr. Juha    0  39.00    7.9250   \n",
       "17                        Williams, Mr. Charles Eugene    0  28.00   13.0000   \n",
       "449                     Peuchen, Major. Arthur Godfrey    0  52.00   30.5000   \n",
       "312              Lahtinen, Mrs. William (Anna Sylfven)    1  26.00   26.0000   \n",
       "637                                Collyer, Mr. Harvey    0  31.00   26.2500   \n",
       "796                        Leader, Dr. Alice (Farnham)    1  49.00   25.9292   \n",
       "737                             Lesurer, Mr. Gustave J    0  35.00  512.3292   \n",
       "146       Andersson, Mr. August Edvard (\"Wennerstrom\")    0  27.00    7.7958   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...    1  48.00   25.9292   \n",
       "78                       Caldwell, Master. Alden Gates    0   0.83   29.0000   \n",
       "473       Jerwan, Mrs. Amin S (Marie Marthe Thuillard)    1  23.00   13.7917   \n",
       "\n",
       "     Survived  Predicted  \n",
       "207         1          0  \n",
       "660         1          0  \n",
       "81          1          0  \n",
       "762         1          0  \n",
       "446         1          0  \n",
       "247         1          0  \n",
       "43          1          0  \n",
       "137         0          1  \n",
       "679         1          0  \n",
       "821         1          0  \n",
       "508         0          1  \n",
       "357         0          1  \n",
       "748         0          1  \n",
       "288         1          0  \n",
       "712         1          0  \n",
       "238         0          1  \n",
       "804         1          0  \n",
       "71          0          1  \n",
       "429         1          0  \n",
       "498         0          1  \n",
       "692         1          0  \n",
       "147         0          1  \n",
       "245         0          1  \n",
       "18          0          1  \n",
       "259         1          0  \n",
       "271         1          0  \n",
       "339         0          1  \n",
       "314         0          1  \n",
       "209         1          0  \n",
       "440         1          0  \n",
       "673         1          0  \n",
       "678         0          1  \n",
       "100         0          1  \n",
       "400         1          0  \n",
       "17          1          0  \n",
       "449         1          0  \n",
       "312         0          1  \n",
       "637         0          1  \n",
       "796         1          0  \n",
       "737         1          0  \n",
       "146         1          0  \n",
       "862         1          0  \n",
       "78          1          0  \n",
       "473         1          0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.is_copy = False\n",
    "test_data['Predicted'] = tree.predict(test_data[['Sex', 'Age', 'Fare']])\n",
    "test_data[test_data.Predicted != test_data.Survived]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um exemplo de previsão incorreta acima é o caso da passageira **Mrs. Hudson J C Allison**, que não sobreviveu ao acidente do Titanic apesar de ser uma mulher de 25 anos que pagou uma passagem cara. Uma busca na [Encyclopedia Titanica](https://www.encyclopedia-titanica.org/titanic-victim/bessie-waldo-allison.html) mostra que ela foi informada, após já ter sido alocada em um bote salva-vidas, que seu filho seria embarcado em um outro bote que estava no lado oposto do navio - ela, então, fugiu do seu próprio bote em uma tentativa de se juntar a seu filho mas não obteve êxito.\n",
    "\n",
    "Uma coleção de histórias particularmente interesantes relacionadas a passageiros do Titanic pode ser encontrada [nesse post](https://blog.socialcops.com/engineering/machine-learning-python/)."
   ]
  }
 ],
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