titanic.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "id": "cca11249-9635-4fa8-8ec4-389218fbbc6c"
    },
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     "slide_type": "slide"
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   "outputs": [],
   "source": [
    "from azureml import Workspace\n",
    "ws = Workspace(\n",
    "    workspace_id='6b764336ef1047f5a4bc5292088afbac',\n",
    "    authorization_token='dCZxFPMQy6YCoYxxYy5URZwl5Tu9fc5x5HCn+k3WKB3v8lJoZmFqnrMM1AdauRWZ43xgdLoW/lmKYVsm3zs+cg==',\n",
    "    endpoint='https://studioapi.azureml.net'\n",
    ")\n",
    "ds = ws.datasets['train-titanic.csv']\n",
    "data = ds.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "id": "7d9563e2-488a-4f39-a953-bf13b1dbfc01"
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     "slide_type": "slide"
    }
   },
   "source": [
    "# Parte 1. Analisis de los datos"
   ]
  },
  {
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   "execution_count": 3,
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     "id": "b39b97a2-a0f4-4bc7-b441-0529855c1431"
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     "slide_type": "-"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
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   "execution_count": 4,
   "metadata": {
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     "id": "0a7f9748-15a7-473f-a483-a80762389539"
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    "scrolled": true,
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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",
       "    <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  "
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "data.head()"
   ]
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   "execution_count": 5,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</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>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "data.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "nbpresent": {
     "id": "de06b3f0-9765-423f-9ab1-0f8d5490315c"
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   },
   "outputs": [],
   "source": [
    "#Rellenamos los casos basicos de edad, usando la mediana ya que nos da mayor exactitud que la media\n",
    "data['Age'].fillna(data['Age'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "id": "692e51eb-d6e9-4206-b00c-8a2df94cf4da"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.361582</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.019697</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.361582    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.019697    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "a0b93356-db14-4503-befc-d090fd954942"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1709c1eef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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35ogjjshpp52WSqWSf//3f8+OHTuqPB0A/cEv32/Wr1+f//mf/+n+zN9HHnkk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\nACiEgAMAACjE/wOxifM5t3+etAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1709c1e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_sex = data[data['Survived']==1]['Sex'].value_counts()\n",
    "dead_sex = data[data['Survived']==0]['Sex'].value_counts()\n",
    "df = pd.DataFrame([survived_sex,dead_sex])\n",
    "df.index = ['Survived','Dead']\n",
    "df.plot(kind='bar',stacked=True, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "1769b945-52d8-4db7-a2b5-a18ccae4dd11"
    }
   },
   "outputs": [],
   "source": [
    "#Sacamos que el sexo parece ser un factor clave ya que sobrevivieron mas mujeres, veamos comparandolo con las edades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "c69bbb39-1a09-49a9-8a47-2db8b7ff5782"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1709b067b8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9993zwgsvlLsWAAAAyqyk4b0hQ4bkyiuvzOjRo1NfX5+qqqrO504++eSyFQcA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1709c1e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([data[data['Survived']==1]['Age'],data[data['Survived']==0]['Age']], \n",
    "         stacked=True, color = ['g','r'], bins = 25,label = ['Survived','Dead'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()\n",
    "#Vemos que el grupo con menos muertos y mas supervivientes a la vez lo forman los menores de 10\n",
    "#lo que corresponde a Subid a los botes a mujeres y niÃ±os primero!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbpresent": {
     "id": "fb4689ba-906b-44bc-ad94-b9bc73f616c8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1707886278>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QCAAAUCCvGQLb29vzxBNPpK2trSvqAQAAoIJeMwRWV1fn+uuvT23t6zp9EAAA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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1709ae4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([data[data['Survived']==1]['Fare'],data[data['Survived']==0]['Fare']],\n",
    "         stacked=True, color = ['g','r'], bins = 25, label = ['Survived','Dead'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()\n",
    "#Comparando los tickets vemos que con los tickets de inferior valor era mas probable morir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "6d62d539-3431-4df9-bbfd-360804d057df"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f17076e55c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NN9+ckcIBAAAAAMxlOAQeOXJEY8eO1bFjx7R///7E9q5hEAAAAACQ3QyHwLlz\n52ayHABgOk8wmDxBwKxZTBAA5Ing3qBq3q1RpD2iUnepZl00i4mfAMAgwyGwU1tbm5qampK2+U6a\njhcA7OYJBjWwulqFXW7adtfVqVEiCAI5Lrg3qOo3qhVqPXF+1zXWSVUiCAKAAYZD4N///nctW7ZM\n77//frd/+9WvfmVqoQDgTJXU1iYFQEkqDIVUUlNDCARyXO322qQAKEmh1pBq3q0hBAKAAYZnB62t\nrdV5552nmpoaFRcXq7a2VldeeaXuvvvuTJYPAHqloKEh9fZw2OKSADBbQ2vq8zvcyvkNAEYYDoF7\n9+7VHXfcobKyMsXjcZWWluqOO+7Q888/n8nyAUCvdJSXp97u9VpcEgBmKy9KfX57izi/AcAIwyGw\nT58+isVikqTS0tLE4oiRSCQzJQOAM9AcCCh20v3KMZ9PzYGATSUCYJZAZUC+ouTz21fkU6CS8xsA\njDB8T+CoUaO0adMmTZ48WZdccokWLVqkPn36aPTo0ZksHwD0StTvV6OUPDtoIMD9gEAe8A/3S1VS\nzbs1ajrWpLI+ZQpUBrgfEAAM6jEENjY2auDAgZo/f35i25e//GV9+tOf1tGjR3XFFVdktIAA0FtR\nv5/QB+Qp/3C//MP98vl8idFJAABjehwOeu+990o6vii8y+XS4sWLVVBQoClTpujaa69VUVFRxgsJ\nAAAAADBHjyEwHo8nPd6xY0fGCgMAAAAAyKweQ6DL5bKiHAAAAAAAC/R4T2AsFtP27dsTjzs6OpIe\nS9KFF15ofskAAAAAAKbrMQQOGDBAP/vZzxKPS0pKkh67XC4tW7YsM6UDAAAAAJiqxxC4fPlyK8oB\nAAAAALCA4cXiAQAAAAC5jxAIAAAAAA7S43BQAPknuDeomndrFI6G5fV4NeuiWfIPZ1F1q3iCQZXU\n1KggHFaH16vmWbNY1N4i2XzsZ3PZAAD5hRAIOExwb1DVb1Qr1BpKbKtrrJOqxAWnBTzBoAZWV6sw\ndKL+3XV1apQIghmWzcd+NpcNAJB/GA4KOEzt9tqkC01JCrWGVPNujU0lcpaS2tqkAChJhaGQSmqo\n/0zL5mM/m8sGAMg/hEDAYRpaG1JuD7eGLS6JMxU0pK7/gjD1n2nZfOxnc9kAAPmHEAg4THlRecrt\n3iKvxSVxpo7y1PXf4aX+My2bj/1sLhsAIP8QAgGHCVQG5CvyJW3zFfkUqAzYVCJniY4bp7jLlbQt\n7nIpOn6WNJVPAAAgAElEQVS8TSVyjmw+9rO5bACA/MPEMIDD+If7pSodn4WwNSxvkVeBygCTT1jE\n8/bbcsXjSdtc8bg8W7ao2aYyOUU2H/vZXDYAQP4hBAIO5B/u5+LSJtwTaK9sPvazuWwAgPzCcFAA\nsBD3BAIAALsRAgHAQs2BgGK+5Hu/Yj6fmgPc+wUAAKzBcFAAsFDU71ejpJKaGhWEw+rwetUcCLBQ\nPAAAsAwhEAAsFvX7CX0AAMA2hEAgCwT3Bo/PChgNy+vxatZFs5ggIgd5gsHkHr5Zs3od9jgmkArH\nBQDADIRAwGbBvUFVv1GtUGsosa2usU6qEhd3OcQTDGpgdbUKQyc+R3ddnRqltIMgxwRS4bgAAJiF\niWEAm9Vur026qJOkUGtINe/W2FQi9EZJbW1SAJSkwlBIJTXpf44cE0iF4wIAYBZCIGCzhtbU68aF\nW1k3LpeYuf4fxwRS4bgAAJiFEAjYrLwo9bpx3iLWjcslZq7/xzGBVDguAABmIQQCNgtUBuQrSl43\nzlfkU6CSdeNyiZnr/3FMIBWOCwCAWZgYBrCZf7hfqtLxGf9aw/IWeRWoDDDRQ44xc/0/jgmkwnEB\nADCLKx6Px+0uhFn27dtndxHk8/kUOmlyCFiH+rcPdW8v6t9e1L99qHt7Uf/2oe7tla31P2TIEEPP\nYzgoAAAAADiIJcNBQ6GQli9frsbGRrlcLk2bNk1f+MIX1NzcrCVLlujQoUMaNGiQ5s+fr5KSEsXj\nca1YsULbtm2Tx+PRnDlzNGLECCuKCgAAAAB5zZKewMLCQt1+++1asmSJfvzjH2vdunX629/+pjVr\n1qiyslJLly5VZWWl1qxZI0natm2b9u/fr6VLl2rWrFmqra21opgAAAAAkPcsCYFerzfRk9evXz8N\nHTpUDQ0N2rx5syZPnixJmjx5sjZv3ixJ2rJli6qqquRyuXT++eerpaVF4V6stQUAAAAASGb57KAH\nDx7UBx98oJEjR+rw4cPyfrKGltfrVVNTkySpoaFBvi5TrVdUVKihoSHx3E7r16/X+vXrJUmLFi1K\n+hu7uN3urCiHU1H/9qHu7UX924v6tw91by/q3z7Uvb1yvf4tDYGtra169NFHNXPmTBUXF5/yeakm\nLHW5XN22TZs2TdOmTUs8zoYZerJ1piCnoP7tQ93bi/q3F/VvH+reXtS/fah7e2Vr/Wfd7KDt7e16\n9NFH9fnPf16XXHKJJGnAgAGJYZ7hcFhlZWWSjvf8da3U+vr6br2AAAAAAID0WRIC4/G4nnrqKQ0d\nOlTXX399Yvv48eO1ceNGSdLGjRs1YcKExPbXX39d8Xhcu3fvVnFxMSEQAAAAAExgyXDQXbt26fXX\nX9ewYcP07W9/W5J066236qabbtKSJUu0YcMG+Xw+3XfffZKksWPHauvWrbrnnnvUt29fzZkzx4pi\nAgAAAEDesyQEjho1Ss8//3zKf/ve977XbZvL5dJdd92V6WIBAAAAgONYdk8gAAAAAMB+hEAAAAAA\ncBBCIAAAAAA4CCEQAAAAAByEEAgAAAAADkIIBAAAAAAHIQQCAAAAgIMQAgEAAADAQQiBAAAAAOAg\nhEAAAAAAcBBCIAAAAAA4CCEQAAAAAByEEAgAAAAADkIIBAAAAAAHIQQCAAAAgIMQAgEAAADAQQiB\nAAAAAOAghEAAAAAAcBC33QUAAGRecG9QNe/WKBwNy+vxatZFs+Qf7re7WLARxwQAOBchEADyXHBv\nUNVvVCvUGkpsq2usk6rERb9DcUwAgLMxHBQA8lzt9tqki31JCrWGVPNujU0lgt04JgDA2QiBAJDn\nGlobUm4Pt4YtLgmyBccEADgbIRAA8lx5UXnK7d4ir8UlQbbgmAAAZyMEAkCeC1QG5CvyJW3zFfkU\nqAzYVCLYjWMCAJyNiWEAIM/5h/ulKh2fCbI1LG+RV4HKABOAOBjHBAA4GyEQABzAP9zPBT6ScEwA\ngHMRAgEAjuEJBlVSU6OCcFgdXq+aZ81S1N/7INS51l6kPaJSdylr7SGrsTYkgE6EQACAI3iCQQ2s\nrlZh6MTSCO66OjVKvQqCrLWHXMLxCqArJoaxUHBvUNNfnq6pL0zV9Jena92H61I+zxMMqmL6dA2a\nOlUV06fLsy7184B8Y/QcMRPnm3OU1NYmBUBJKgyFVFLTu7XxWGsPuYTjFUBX9ARaxGgLnNkt1UCu\nsKOVmvPNWQoaUq+NVxDu3dp4rLWHXMLxCqAregItYrQFzuyWaiBX2NFKzfnmLB3lqdfG6/D2bm08\n1tpDLuF4BdAVIdAiRlvgzG6pBnKFHa3UnG/O0hwIKOZLXhsv5vOpOdC7tfFYaw+5hOMVQFcMB7WI\n0RY4s1uqgVxhRyu1XecbM/TZI+r3q1FKnh00EOj10N+ua+01HWtSWZ8y1tpD1vIP92vrBVu1aucq\nRWNReQo9unXUrRyvgEMRAi0SqAxoT3hP0nC3VC1wzYGA3Hv2JA1RO5OWaiBXGD1HzGTH+cYMffaK\n+v2m3u/Zudaez+dT6KShxUA2Ce4NavWu1Yoci0iS2jra9Nx7z2ns4LF89wAORAi0SNcW43BrWN4i\nb8oWY7NbqoFcYfQcMZMd59vp7n3kQgxApvDdA6ArQqCFOluMe2J2SzWQK4yeI2ay+nxjhj4AduC7\nB0BXTAwDABZihj4AduC7B0BXhEAAsBAz9AGwA989ALpiOCgAWMiOex8BgO8eAF0RAgHAYnbc+wgA\nfPcA6EQIBJA1PMFg8kyds2YxSVKeM7JmIusqAgBgLkIggKzgCQY1sLo6ac0+d12dGiWCYJ4ysmZi\ntq+r2BlQI+0RlbpLCbEAgJxACASQFUpqa5MCoCQVhkIqqanJuxBIKDjOyLpl2by2WT6E2FxmJIAD\nAFIjBALICgUNqdewKgjn1xpWhIITjKxbls1rm+V6iM1lnEcAcGZYIgJAVugoT72GVYc3v9awOl0o\ncBoj65Zl89pmuR5icxnnEQCcGUIggKzQHAgo5ktewyrm86k5kF9rWBEKTjCyblk2r22W6yE2l3Ee\nAcCZYTgogKwQ9fvVKCXPDhoI5N39gISCE4ysW5bNa5sFKgPaE96T1COVKsT29Bykj/MIAM4MIRBA\n1oj6/XkX+k5GKEhmZN2ybF3brGtAbTrWpLI+ZTkVYnMZ5xEAnBlCIABYiFCQXzoDqs/nU+ik2W1P\nfg7MYySAAwBOjRAIABYjFABnzkgABwCkxsQwAAAAAOAghEAAAAAAcBCGg8IxgnuDx+/Diobl9Xg1\n66JZGR+SZ8c+gXyz8K2FWrVzlaKxqDyFHs0cM1MLJiywu1gAAOQsQiAcIbg3qOo3qpNmkqtrrJOq\nlLFQZsc+gXyz8K2FWv6/yxVXXJLU1tGmZX9eJkkEQQAAeonhoHCE2u21SWFMkkKtIdW8W5NX+wTy\nzaq/rEoEwE5xxbVy50p7CgQAQB4gBMIRGlobUm4Pt4bzap9Avom2R9PaDgAAekYIhCOUF5Wn3O4t\n8ubVPoF843F70toOAAB6RgiEIwQqA/IV+ZK2+Yp8ClQG8mqfThHcG9T0l6dr6gtTNf3l6Vr34Tq7\ni5T1crXOZo6eKZdcSdtccmnm6Jn2FAjIYZ5gUBXTp2vQ1KmqmD5dnnW58T0AwHxMDANH8A/3S1U6\nPlNna1jeIq8ClYGMTtBixz6dgAl30pfLddY5+cvKnSsVbY/K4/Zo5mhmBwXS5QkGNbC6WoWhE98D\n7ro6NUqK+rP7ewCA+VzxeDze89Nyw759++wugnw+n0KhUM9PREZQ//YtS2FV3d+89ma9+Y83u22/\n9FOX6oXrX8j4/s2Qic/odPWfD3VmtXQ/I757zOUJBlVSU6OCcFgdXq+aZ806ZVCh7o2puPlmed7s\n/j0QvfRS1b/Q++8B6t8+1L29srX+hwwZYuh59AQCeSSXe3yMyvUJd+z4jHK9zqzmhPMom9FjlRkF\nDam/BwrCfA8ATsQ9gUAeMbosRS7fF5LrE+7YsXRIrteZ1VjexV4ltbVJAVCSCkMhldRQ/2eiozz1\n90CHl+8BwInoCQTyyEdNH/W4Pddb2QOVAe0J70m6SM+lCXfM7pXrHLYYaY+o1F2acthirteZ1eg5\ntZfRHqvOIaPuSEQVpaWnHTIKqTkQkHvPnqTv/pjPp+YA3wOAExECgTzSdKypx+2na2XPhQuoXJ9w\nx8xeOaPDFnO9zqxGz6m9jPRYndyY5VFuNWbZIer3q1FKvtcyEKC+AIciBAJ5pLRPqSJtkW7by/qU\nJf4/H+4L8Q/352yAMbNX7nTDFk+un1yuM6vRc2ovIz1Wud6YZZeo30/9AJBECATyyvCy4drX0n2W\n3GFlwxL/z30h9jKzV45hi5lBz6m9jPRY5UNjFgDYiRAI5BEjPRjcF2I/s3rlGLaYOfSc2qunHisa\nswDgzFgSAp988klt3bpVAwYM0KOPPipJam5u1pIlS3To0CENGjRI8+fPV0lJieLxuFasWKFt27bJ\n4/Fozpw5GjFihBXFBHKekR4M7gvJHwxbhFPRmAUAZ8aSEDhlyhRdc801Wr58eWLbmjVrVFlZqZtu\nuklr1qzRmjVrNGPGDG3btk379+/X0qVLtWfPHtXW1uqhhx6yophAXjDSg8F9Ifmha+hvOtaksj5l\nDFuEI3RtzOrT1KRjZWU0ZgFAGiwJgaNHj9bBgweTtm3evFk/+MEPJEmTJ0/WD37wA82YMUNbtmxR\nVVWVXC6Xzj//fLW0tCgcDsvLEA8A6KYz9Pt8PoVOmigDyGedjVk+n0/1HPsAkBbb7gk8fPhwIth5\nvV41NR2fwr6hoUE+ny/xvIqKCjU0NKQMgevXr9f69eslSYsWLUr6O7u43e6sKIdTUf/2oe7tRf3b\ni/q3D3VvL+rfPtS9vXK9/rNuYph4PN5tm8vlSvncadOmadq0aYnH2dAKTmu8vah/+1D39qL+7UX9\n24e6txf1bx/q3l7ZWv9Dhgwx9DzbQuCAAQMSwzzD4bDKyo6vY1ZRUZFUofX19QwFBYBTCO4Nqubd\nGkXaIyp1l2rWRbO4JxBIU+d5FI6G5fV4OY8A5D3bQuD48eO1ceNG3XTTTdq4caMmTJiQ2B4MBnX5\n5Zdrz549Ki4uJgQCQArBvUFVv1GdNDtoXWOdVCUuYAGDOI8AOJElIfDxxx/Xzp07FYlE9I1vfEO3\n3HKLbrrpJi1ZskQbNmyQz+fTfffdJ0kaO3astm7dqnvuuUd9+/bVnDlzrCgigBSc0Dqey++xdntt\n0oWrJIVaQ6p5tyZn3gNgN84jAE5kSQicN29eyu3f+973um1zuVy66667Ml0kAD1wQut4rr/HhtaG\nlNvDreGM7tfM4Gx1CM/l0J8OTzCYvBborFksn3AKdp1HAGCnrJsYBkB2cELruF3v0awgUl5UnnK7\ntyhzQ+jNDM5Wh/BcD/1GeYJBDayuTlpI3V1Xp0YpZ4KglWHdjvMIAOxGCASQkhNax+14j2YGkUBl\nQHvCe5Jey1fkU6AyYFp5T2ZmcLY6hDuhYUOSSmprkwKgJBWGQiqpqcmJEGh1WLfjPAIAuxECgRxh\n9TA2J7SO2/EezQwi/uF+bb1gq1btXKW2jjb1LeirW0fdmtHjwszgbHUId0LDhiQVNKR+nwXh3Hif\nVod1/3C/VKXj36+tYXmLvApUBvKqYQAATkYIBHKAHcPYnNA6bsd7NDOIBPcGtXrXakWORSRJ0VhU\nz733nMYOHmv5BWxc3dd47YnVIdwJDRuS1FGe+n125MhM23aEdf9wP6EPgKMQAoEcYMcwtnxoHe+p\n99SO92hmEMn14Y2ByoC2h7YnQqwklfYpzVgITyf05/IEMs2BgNx79iQNCY35fGoOJL/PbJ08xilh\n3ahcPhYBZC9CYJ7jxyM/2DWMLZdbx432nlr9HgOVAQ17fZu+8cdWVRyR6ouln32+SNdcnX7wyabh\njS650v6beDz93sMzYTT05/oEMlG/X41ScsALBJICXjZPHuOEUQhG5fqxCCB7EQLzGD8e+YOW8fRl\nay/Zpzdu1dI1rRrccmLb6FCr3qncJqVZLjuOC7N7Mrv2AkpS5Fgk4z3cPb12th476Yj6/acNc9k8\neUw+jEIwSz4ciwCyEyEwj/HjkT/Mbhl3Qg9xQ2uDbnhPuu9NJXrcHr1UqvPaOzlGxYpVSQFQkga3\nSOVPr5RmLEjrtezoMTFzn3bNztrTsZ9NPayZks7kMXYMG7W6hz5bh8Zm87HYeS5F2iMqdZfm1Hqh\nAAiBeS2bfzyQHjNbxu3qIbb6R/7G96T7XpbO7hK4RoWkx7y9G4JoVvmLDx9Nub1fU+rtp9P1uGg6\n1qSyPmUZ7zEx81g02qto1gW60WP/VJPc9Gbym2xldPKYbB42alRP564nGFTxt+bJEz7RK92xa4ek\nJ3p9nJn1XZeto0DsWC+UoAiYixCYx7L1xwO9Y1bLuB09xMG9QW14ap4Wvh5J9MrVVu2QZj+RsX3O\n35QcAKXjj+dvUtqX8mZepBwq7tCoFPsI9evQgDTLJZ04Lnw+n0InDe9Ll9GLLLOORSO9imaGEKPH\nfm/ub8w1RiePMXvYqNFjzKwLfiPnbtuyn6ginDwsuV84osaf/kRK8z2a/V2XrfdHWr1eKLe3AOYj\nBOaxbP3xgL3s6CF+79mHtfjFSHKv3IsRPVbyE/kfyMwPuO/IKbYfdelQmq9l5kXK05MH6LyD4aS6\n2N9f+vnkgfpxmuUykx0XWUZ6Fc0MIWd67OdTODQyeYxk7pqD6TSmmHUsGjl3jxz4OOXfthz4SMVp\n7c3877psvT/S6vVCub0FMB8hMI9l648H7GVHD/H0Vz9K2St386sfSQ9kZp9mrpVm5kXK7ktHKdD6\n5vF7FY9K9f2kxy6V6i+9IO1ymcmui6yeehXNDCFGj32njKLoafIYydzzyOgxZuaxaOTcDRVL56b6\n22KlHQIz8V2XjbM0mzlk2sj5xu0tgPkIgXkuG388YK9AZUAj/rhddyUNVyrVlb1YosCoU/XKlZ9i\nuxmaAwG5t29XYeTEMK9YaWm34W5GmHmREqgM6DvhPZo6KrmH/mGbe+izdSIdM0OI0dERjKI4weiw\nUSOMniNmXvAbOXf/86phGv6PXd1651+4apjmpbk/O77r7GBmr7iR880pDTOAlQiBgMPc+Je4vrRW\n6tfleuqStdKRK6To8Mzsc3B7kaTuE5+c1V6kSPenmyMe73aZ0tvLFjMvUrK1h97siXTMYmYI8Q/3\na+sFW7Vq5ypFY1F5Cj26ddSt3eo+Wz8jOxgdNmqEHT2xRs7dz95erW8136uvvR5J9M4/XVWqK2+v\nTnt/xWcNk/bt6ra9/1nD8mhaoVPrTTg0cr7RMAOYjxAIOExJbW3SLHjS8UkQCjK4Plj/SOspt2cq\nBJbU1qogkvzqBZFIr+4lM/siJRt76M2cSMdMUb9fGw5uVfnTq1TSHFVziUfhu27V6F5OSrJ61+rE\n2oRtHW167r3nNHbw2JRBMNs+I7sYGTZqhB09sUbOXf9wvzT7CX130pmH/r5zq3X0/nvVr8t37FFv\nqfrOrVY07VfLXmb3zPV0vtEwk6xzxmR3JKKK0tKsWdIEuYUQCDiMmfdYGeWKxdLabgaz36eZFymD\nPvtZuZuaEo/bBwzQoZ07e1Uus5g5kY6ZgnuDqtZqhW7rvKhuky/+nB7+sHtw6wmTS9jL6Dli9gW/\nkUBvVug/fiH+hAq69Jwe6WXPaTazo2eOhpnjTp4x2aPcW7YF2YEQCDiMmfdYGRX3eORqa0u5PVM+\ncEd0fortewub1D9D+zRykdIZALsOmnIfPqxBo0fbGgTtOC6MsHqSEGSW0Qv5XL7gN6vnNJvZsUYp\njjN72RY4FyEQcBgz77EyvM+ZM1W6bJlc8RMDC+Mul5pnzkx6npmLAf/b/23QE3vVbbKHB/5vg5b0\n6hXNcXIAlI7fq+g+fDhpm1kLpBtlx3FhhNWThAAwxsw1SmGcHaN5kJ8IgYDDmDnRg1HNCxZIkkpW\nrpQrGlXc41HzzJmJ7ZL569StOa9DR25Qt6UYfn9eh60h0AgzF0g3yo7jwgirJwlBZpnZ0AM4UbaO\n2kDuIQQCDmTHcKXmBQuSQt/J0hn2Z+RCsrCgUGtHSWtHJe+nX0Hhmb0RC9g13Ccbh7FZPUkIMsfs\nhh7AibJ11AZyDyEQQFYwOuzP6IWk1+PV0fbuy1J4Pfa2lrYPGCD34cNJQ0Ljn2zvxHCfE+yYJASZ\nwcQ8yCZWD7k3S9dRG32amnSsrCwrRm0g9xACAWQFo8P+jF5IlvYtlU5a8kCSyvqWnXlhz8ChnTs1\naPTopHsAT54dlOE+yQhu9jLrYtmuiXly9WIfmWPHkHszdY7a8Pl8qud+TPQSIRBAVjA67M/ohWQ2\nTwLS0yygDPdBtjDzYtmOc9KOi33ue8x+zLAJEAIBZAmjw/6MXkjm8iQg2TpJC5zHzItlO85Jqy/2\nue8xNzDkHiAEAjnDaOtyLrdCGxn2Z/RCMtcnATE6SUvn5x1pj6jUXZqXxwXSZ9bnbebFcjrnZDaW\n3wjue8wNDLkHCIFATjDauuyEVuh0LiSNhEo7wvU799yoiWvfVlG71OqW3rxhvC5+4jdpv47Zx8XC\ntxZq1c5Visai8hR6NHPMTC2YcOoZXfNZLodmM78HzL5YNnpOGil/571+7khEFaWlKe/1s/pi3677\nHpEehtwDhEAgJxhtXc71VmijF95mTRRiR7h+554bddV/vq3OhSqKYtLVL2zR7/XFtIOgmcfFwrcW\navn/LldccUlSW0eblv15mSQ5LgjmemOKmd8DdlwsGyn/yff6eZT6Xj+ry5/N9yLjBIbcA4RAICcY\nbV02uxXayln17LjwNjtcGwmxE9eeCICdCiVd+vIWHXkivfKbeVys+suqRADsFFdcK3eu7BYCzewl\nM/O1zDpec70xxczvATsulo2U3+i9flaXP5fvRXaabFwXFbASIRDIAUZbl81shbZ6Vj07LrzNDFFG\nQ2xRe+qyeNqlI0YL/ol0josb3pPue1OqOCLVF0uPXirVX3HiedH2aMrXOnm7mWE9uDeoea/NU+RY\nJLFtR/0OPXHFE2m/licYVPG35skTPvFaHbt2SHoi7ePVriF9O55ZqIqfr1JJJKrmUo8a7pqp0TPS\n74U1uzfKyMWy0bIbCf1Gyp/OvX5mXuz3VH7/cL/+j7aq/Bdd6+JWjc6BxgMAzkIIBHKA0dZlM1uh\nrZ5Vz44LbzPDtdEQ2+5SSrFTbD8do5/3g+FxGv//3tTALnnuc/ulLWPHJx573B61tbV124fH7Ul6\nbGZYf3jLw0kBUJIixyL6yeafpP1abct+oopw8mv1C0fU+NOfSDmwlMGOZxbq4h8t1+CWT3pj97fp\n4IPL9I6UdhC0ujfKaNmNNiAYKb8dE3sYKb8nGNQVi1erMPTJsbi/TbFHnlPjoLH0OgHIKoRAIAcY\nnQzFzBkxrZ5VLxMX3j212psZro2G2EPFUklT9+cdKj5+X1M6un7eTceaVNanLOXnXbXiVfU9qaNv\nYFSq+vnvFfrkIn3m6Jla9udlSUNCXXJp5uiZSX9nZlj/KPJRWttP58iBj1NubznwkYrTfC07hvRV\nrFh1IkR9YnBLXOVPr5TSDIFWz4xrtOxGGxCMlD9b71Vk/TlkEytv6UDuIQQCOcLoZChmTZpidUv7\nuLPGadM/NnULIePPGn+avzo1I632ZoZroyG24BTldfWiJ7CzbP7hfvl8PoVOuvjs5P4odajqur3z\nvr+VO1cq2h6Vx+3RzNHdZwc1NazH09x+GqFi6dwU2xuKlXYItGN5kZKm1MNx+0dSb++JWd8DRhgt\nezoNCD2Vv+u9fn2amnSsrCwr7lVk/TlkC6tv6UDuIQRajFaZ9JhZX3bUfS6X3+qW9rcPvJ1yYpIt\nB7b06vXS6XUwI1wb7T0acIpr+oGt6d8TaLYFExb0OBOomb1kw8qGaVd4V8rt6frPq4Zp+D926eyW\nE9v295deuGqY5qX9ataGKElqLvNI+7sPx20p9ai0y+Ns/A0xWvZM3avo8/lUf4oGEDMZKT/rzyFb\n0CuNnhACLUSrTHrMrC876j7Xy2/1rHpm3xNo9T2GRnuPCvsWSdHWbn9f4CnKSLkkqX3YMPXd1T1s\ntQ9LP2yZ2UtWPaFa9/7h3qT7Akv7lKp6QnXar/XZ26v1reZ79bXXI6o4KtX3k56uKtWVt6f/WnZo\n+NpMHXxwWdKwyoP9XWr42kyd/cnjbP0NMVJ2KfdnzjRSftafQ7agVxo9IQRaiFaZ9JhZX3bUfa6X\nX7J2Cm2zewnsmNzDSO9Rf/WR1D0ElsT7qDlD5YpUV8t7770qiHSZObO0VJHq3gUks3rJ/MP9euKK\nJ0wJlP7hfmn2E/ruJOuGcJpp9IwFekdS+dMr1T8SVUupRw1fS55hM1t/Q4yUXbJnmK2ZjJSf9eeQ\nLeiVRk8IgRaiVSY9ZtaXHXWf6+W3mtm9BNna69BRWpoUxhLby8oyts+o36/wE09k5YWpmcMurR7C\nabbRMxYkJlIplZJ60aTs/h7oqeydcv0zMlJ+1p9DNqBXGj0hBFrI7FYZMxdZzkZm1pcdLWK5Xn6r\n+Yf7tXXUVq3asUrRWFSeQo9uHXVrr49po70OVt9j1VFamnp7SUnG9ilxYZoPnPA9AMAc9EqjJ4RA\nC5nZKmPmgs3Zysz6sqNFLNfLb7Xg3qBWv7c6cX9YW0ebnnvvOY0dPPaMguDp/jZb77FCsmycDMUO\nTvgeAGAeGv9wOoRAC0X9fr3zhxc08tdB9T3WobY+BfrrFy7RYH/6PRNGZz60o7fQrAs2M1ux7GgR\ni/r9atm6VSWrVskVjSru8ajl1lt7XX4jx04uM3MRcqPsuMfqVCtBuHq7RkSey/ag3vkdG2mPqNRd\nmvI7Nhu/E81m9D3m+wgWJ+n8zN2RiCpKSx3bOAPkKkKghXY8s1AXv/g7lbYen0HNE+vQkP98Re+M\nWTNWkpMAACAASURBVJS4gd7oBY+RmQ/t6C00+4LNzFYsq1vEPMGg+q9enbj/y9XWpv7PPadjY8em\nXQ4jx06us3o2T8mee6xOuTRevBeL4zlAtk6GIhn7js3m70SzGH2PThjB4hQnf+YeZVfjDICenWrd\nYmRAxYpVSVNoS9LglrjKn16ZeHy6C56ujMx8eLqelUwxWn7p+AXB9Jena+oLUzX95ela9+G6jJXL\nDunURU+MHDuZ4AkGVTF9ugZNnaqK6dPlWZe5z8iO2TyddI+VlZ+lmbJ5MhQj37Fmfg9kK6Pv0Y7f\nJGSGE45rIN/RE2ihfoePpt7edGK70QseIzMfZnPPihNahM28eC1pSr3CeP/IKVYeN4HVw/DsmM3T\njnus7BgOmu1DKk8nm4O6ke/YbA6xZg1TNfoe7fhNygfZOIQ2m49rnMD91DgdQqCFQsUdqbf369CA\nT/7f6AWPkZkPs7lnxej9X7n8BWbmxWtzmUfa39Zte0upR6nnmjxzZg/D6+mztGMNMTvusTp05JCG\npNx+MGP7zOYhlT3J5slQjHzHZmuINbNhwOh7LC8q1w3vSfe9KVUckeqLpUcvleqvsD/QZ6tsbTA1\n+pnn8m94rsvlxj9YgxBooZ9PHqjzDjbo7JYT2/b3P779x588TueCp6eZDzPRs9JTi6TR8htpEc71\nLzAzL14LLvm8YrtfUWGXbTFJBROrzrygp9qniS29Rj9LO9YQs/oeq3C0IWUIDLc2KFOXwna02pvV\ne5HNk6EY+Y7N1hBrZsNAdNw49d20Sa4u97XGXS5Fx49Pet6D4XG66OVNSUPbR4dcemds8vNwgh0T\nZhlh5Lj2BIMaOG+eCrusi+resUONTzyRFedvvsvlxj9YgxBooV2XXqBA65vHW0GPSvX9pMculeov\nvSDxnHRmlOzpIss/3K//o60q/8UqlUSiai71qOGuWzU6xQ+HkQu24N6gNjw1TwtfjyRacWurdkiz\nn0g81+gFm5EWdKt7osxm5sXrZ+vCSQFQkgolfXZPg+rNKGwKZvZg8GN0Qv/W1NuLT7G9J0Zmp7S6\nN8rs3gszg7rRcFqycGHS93DzzJlqXpA8CZN/uF9bL9iqVTtXqa2jTX0L+nZb2zJbQ6ypjTxvv50U\nACXJFY/Ls2WLmrts+/xv3pYnxb3Nn1+zRfUz0t6tIzS0NqTsPa3z2jvssutx3aepScfKyrod16UP\nP5wUACWpMBJR6U9+Yvvx7wQM2UVPCIEWClQG9J3wHk0dldxq/HBlcsuZkRkljc5Kd8Xi1SoMffIl\nvL9NsUeeU+Og9F9Lkt579mEtfjGS1JM56sWIHiv5ifwPJF/09PQFb6QF3f3eeyn/1r1r12lfOxW7\nehV/c0FcNV+JKxyNy+uJa9YoqTd7s+PLvDkQUMd729UvfOJH/Ki3VEd605OZxT9GRkOB0YaSnp5z\nVotSOrtF6lobRhotjJ67RnujUpX/xr/E0248sav3oqc6M1pfJQsXqnT58kSwcbW1qXTZMklKCoLB\nvUGt3nVibctoLJpybctsnNHTzIYBo+d3PnwPmMnIOX7je9J9Lyv5dzckPebt3WzCZjaGdh7XPp9P\n9Sc18kmS+6OPUv7dqbbDXNk6FD0fGGl8zQWEQIt0HjB9C/uqtE+pyvqWaVjZsG73PBntMTFykWXm\na0nS9Fc/Svohko7/MN386kfSAye2GfmRMdJLWXD4cLd6lKSCxsaU20/Hjp4oM3tD7Pgy/80FcW24\nXvra6yd6rp+ukq7sRZA1Wv7/396Zx0dV3f3/M1tmsky2CTsmEEDS1K1AwCiLrHmw0FrhRyv6/IzV\ngIoK2qcSH3Gp2LI8AuUHsUhBodXqU9MqhUdDHxeCW2QJKAZksRCQLduETLbZf38MmbnLucmZcDM3\nYb7v18uXmcOZc7/3e8+ce77nfL/fE+mJGO8z4qnH21aMly2LsJx30YL3t8uzG8WSf8hnFfjFDsAs\nWAjgWTzRIgEIj8549ZWwdStzZythyxaREdhdXfV4UFoYcI4cCdusWWEZCby/b7XHMbXOqevOxyk9\nXgbme/fxMuXjZq70msTVQXd1Re/pdNc43c5ARmAEYHUYs8HMTHphUFghk5bzTLLUbAsA0pqZ1ZAq\nKOd9yXDtUur1gI+RTEcvP9mko8mAFivQak4QtRjMN32zCV8MceCNIcJSB/7VRfJ3xcDakVHJ+4x4\n6qn5vHkXLcJxFetoN4ol//27HYiVNGWoqYFr3XLMcm1U1KvaSal4FpZ4dMY71uma2YOdtLyutQ5L\nPwAW7AViPUCLEVg3GnhzdufGFTV3aTrq+6zQg9bRoxH/1lthGwm845Oa45ia59RpYczz/saV3rtp\nLTpUd9E11cKTno4YhueOJz1d9WsRcrqrK3pPpyc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YXBm+p0I48CaSajt/sbHRhcaEGNn5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\nX3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1707825b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Combinando los tickets, la edad y si sobrevivieron o no podemos ver\n",
    "plt.figure(figsize=(15,8))\n",
    "ax = plt.subplot()\n",
    "ax.scatter(data[data['Survived']==1]['Age'],data[data['Survived']==1]['Fare'],c='green',s=40)\n",
    "ax.scatter(data[data['Survived']==0]['Age'],data[data['Survived']==0]['Fare'],c='red',s=40)\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Fare')\n",
    "ax.legend(('survived','dead'),scatterpoints=1,loc='upper right',fontsize=15,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbpresent": {
     "id": "125ec916-e4c6-41b9-b8f7-630073ac83b2"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Survived  Pclass                                Name  \\\n",
      "258          259         1       1                    Ward, Miss. Anna   \n",
      "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
      "737          738         1       1              Lesurer, Mr. Gustave J   \n",
      "\n",
      "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \n",
      "258  female  35.0      0      0  PC 17755  512.3292          NaN        C  \n",
      "679    male  36.0      0      1  PC 17755  512.3292  B51 B53 B55        C  \n",
      "737    male  35.0      0      0  PC 17755  512.3292         B101        C  \n"
     ]
    }
   ],
   "source": [
    "print(data[data['Fare']>400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "nbpresent": {
     "id": "a66c5df1-31d9-41c2-99ff-e9561f8ddb00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1709c18c50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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13pihdvvtt+f2229Pkhw4cCB///d/n3vuuScPP/xw9u3bl+uuuy7PPvts1q9ff34TAwAA\nkOQ8/h21X//1X8+ePXuydevWDA4OZtOmTVM5FwAAwJw1oX/4bM2aNVmzZk2SZPny5fmTP/mTugwF\nAAAwl036jhoAAAD1IdQAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QA\nAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAK\nI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QA\nAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAK\nI9QAAAAKI9QAAAAKI9QAAAAKUxnrhDNnzuTBBx/M0NBQhoeHs3Hjxtx22205evRodu3alcHBwaxa\ntSpbt25NpTLmrwMAAGAMY5bVRRddlAcffDDz58/P0NBQduzYkXXr1mXPnj25+eabc9111+XP/uzP\nsnfv3vzyL//yhZgZAABgVhvz0ceGhobMnz8/STI8PJzh4eE0NDTkwIED2bhxY5Lk+uuvT09PT30n\nBQAAmCPG9aziyMhI7r///vzwhz/MjTfemOXLl6epqSmNjY1JkpaWlhw/fryugwIAAMwV4wq1efPm\n5aGHHsobb7yRT3/603n11VfHfYGurq50dXUlSdrb21OtVic36Sx2ZLoHYMawfuD8VCoV6wiYcvYW\n6mFC3/6xYMGCXHnllTl48GBOnjyZ4eHhNDY25vjx42lpaTnne2q1Wmq12tnjvr6+85sY5jDrB85P\ntVq1joApZ29hIlpbW8d13pifUTtx4kTeeOONJP/1DZD79+/PihUrsmbNmuzbty9J8uyzz2b9+vXn\nMS4AAAA/MuYdtf7+/jzyyCMZGRnJ6Oho3v/+9+fnfu7nsnLlyuzatStPP/10Vq1alU2bNl2IeQEA\nAGa9htHR0dELecHDhw9fyMvNCMN33TrdIzBDND7+zHSPADOax5OAerC3MBFT9ugjAAAAF5ZQAwAA\nKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQ\nAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAA\nKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQ\nAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAAKIxQAwAA\nKExlrBP6+vryyCOP5LXXXktDQ0NqtVpuuummDA4OpqOjI729vVm2bFm2bduWhQsXXoiZAQAAZrUx\nQ62xsTEf/vCHs3r16rz55pvZvn17rrrqqjz77LNZu3ZtNm/enM7OznR2dmbLli0XYmYAxmH4rlun\ne4TiHJnuAQrV+Pgz0z0CAP+PMR99bG5uzurVq5MkP/ETP5EVK1bk+PHj6enpSVtbW5Kkra0tPT09\n9Z0UAABgjpjQZ9SOHj2a73//+3n3u9+dgYGBNDc3J/mvmDtx4kRdBgQAAJhrxnz08UdOnTqVnTt3\n5o477khTU9O4L9DV1ZWurq4kSXt7e6rV6sSnnOU8isN4WT9MhL2F8bK3wPmpVCrWEVNuXKE2NDSU\nnTt35hd+4Rdy7bXXJkkWL16c/v7+NDc3p7+/P4sWLTrne2u1Wmq12tnjvr6+KRgb5ibrB6gHewuc\nn2q1ah0xbq2treM6b8xHH0dHR/PYY49lxYoVueWWW86+vn79+nR3dydJuru7s2HDhkmOCgAAwH83\n5h21l156Kf/0T/+Uyy+/PPfdd1+S5EMf+lA2b96cjo6O7N27N9VqNffee2/dhwUAAJgLxgy197zn\nPfniF794zp/t2LFjygcCAACY6yb0rY8AAADUn1ADAAAojFADAAAojFADAAAojFADAAAojFADAAAo\njFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFAD\nAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAojFADAAAo\njFADAAAoTGW6BwAAYOYYvuvW6R6hOEeme4BCNT7+zHSPMKO5owYAAFAYoQYAAFAYoQYAAFAYoQYA\nAFAYoQYAAFAYoQYAAFAYoQYAAFAYoQYAAFAYoQYAAFAYoQYAAFAYoQYAAFAYoQYAAFAYoQYAAFCY\nylgnfO5zn8tzzz2XxYsXZ+fOnUmSwcHBdHR0pLe3N8uWLcu2bduycOHCug8LAAAwF4x5R+3666/P\nAw888GOvdXZ2Zu3atdm9e3fWrl2bzs7Oug0IAAAw14wZaldeeeXb7pb19PSkra0tSdLW1paenp76\nTAcAADAHTeozagMDA2lubk6SNDc358SJE1M6FAAAwFw25mfUzldXV1e6urqSJO3t7alWq/W+5Ixz\nZLoHYMawfpgIewvjZW9hIuwtjJe95fxMKtQWL16c/v7+NDc3p7+/P4sWLfr/nlur1VKr1c4e9/X1\nTeaSQKwfoD7sLUA92FvOrbW1dVznTerRx/Xr16e7uztJ0t3dnQ0bNkzm1wAAAHAOY95R27VrV779\n7W/n9ddfz913353bbrstmzdvTkdHR/bu3ZtqtZp77733QswKAAAwJ4wZar/zO79zztd37Ngx5cMA\nAAAwyUcfAQAAqB+hBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAA\nUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBih\nBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAA\nUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBih\nBgAAUBihBgAAUBihBgAAUBihBgAAUJjK+bz5hRdeyJNPPpmRkZF88IMfzObNm6dqLgAAgDlr0nfU\nRkZG8sQTT+SBBx5IR0dHvva1r+UHP/jBVM4GAAAwJ0061A4dOpTLLrssy5cvT6VSyQc+8IH09PRM\n5WwAAABz0qQffTx+/HiWLl169njp0qU5ePDg287r6upKV1dXkqS9vT2tra2TveTs9b++Md0TALOR\nvQWoB3sLXBCTvqM2Ojr6ttcaGhre9lqtVkt7e3va29sneynmoO3bt0/3CMAsZG8B6sHeQj1MOtSW\nLl2aY8eOnT0+duxYmpubp2QoAACAuWzSofaud70r//mf/5mjR49maGgoX//617N+/fqpnA0AAGBO\nmvRn1BobG3PnnXfmk5/8ZEZGRnLDDTfkHe94x1TOxhxWq9WmewRgFrK3APVgb6EeGkbP9WEzAAAA\nps2kH30EAACgPoQaAABAYYQaAABAYYQaALPWq6++mv379+fUqVM/9voLL7wwTRMBs8GhQ4dy6NCh\nJMkPfvCD7NmzJ88999w0T8VsM+lvfYQL4atf/WpuuOGG6R4DmIG+/OUv5x//8R+zYsWKPPbYY7nj\njjuyYcOGJMlf//VfZ926ddM8ITAT/c3f/E1eeOGFDA8P56qrrsrBgwezZs2a/N3f/V1efvnl/Oqv\n/up0j8gsIdQo2he/+EWhBkzKV77ylXzqU5/K/Pnzc/To0Tz88MPp7e3NTTfdFF94DEzWvn378tBD\nD+Wtt97Kb/7mb+bRRx9NU1NTbr311jzwwANCjSkj1Jh2v/d7v3fO10dHRzMwMHCBpwFmi5GRkcyf\nPz9Jcumll+YP//APs3PnzvT29go1YNIaGxszb968XHLJJVm+fHmampqSJBdffHEaGhqmeTpmE6HG\ntBsYGMjHP/7xLFiw4MdeHx0dzR/8wR9M01TATLdkyZK8/PLLeec735kkmT9/frZv355HH300//Ef\n/zG9wwEzVqVSyenTp3PJJZekvb397OsnT57MvHm+/oGp4x+8Zto9+uijueGGG/Ke97znbT/7zGc+\nk9/+7d+ehqmAme7YsWNpbGzMkiVL3vaz7373u+fccwDG8tZbb+Wiiy562+snTpzIa6+9lssvv3wa\npmI2EmoAAACFcX8WAACgMEINAACgMEINgFnpwIEDufvuu6d7DACYFN/6CMCM8LGPfSyvvfZa5s2b\nl/nz5+dnf/Znc+edd579Cn4AmE3cUQNgxrj//vvzV3/1V/nUpz6V733ve/nbv/3b6R4JAOrCHTUA\nZpyWlpasW7cur7zySgYHB/OXf/mX+da3vpUzZ87kZ37mZ/L7v//7b3tPZ2dnvvKVr2RgYCBLly7N\nhz70oVxzzTVJkh/+8Id59NFH8/LLL6dSqeS9731vtm3bltHR0Xz+85/PP//zP+ett97KsmXLcs89\n9/j6bQDqTqgBMOP09fXl+eefzzXXXJPPfvazmT9/fnbu3Jn58+fnX//1X8/5nuXLl+eP/uiPsmTJ\nkuzbty+f/exns3v37jQ3N+fpp5/O1VdfnQcffDBDQ0P5t3/7tyTJt771rXznO9/JZz7zmTQ1NeXV\nV1/NggULLuSfCsAcJdQAmDEeeuihNDY2pqmpKe973/ty44035u67784TTzyRhQsXJkmuvPLKc773\n/e9//9n//sAHPpAvfelLOXToUDZs2JBKpZLe3t709/dn6dKlZ/8x7EqlklOnTuXVV1/Nu9/97qxc\nubL+fyQARKgBMIPcd999ueqqq84eHzp0KAsXLjwbaf+T7u7u7NmzJ729vUmSU6dO5fXXX0+SbNmy\nJU8//XQeeOCBLFiwILfccks2bdqU9773vbnxxhvzxBNPpK+vL9dcc00+/OEPp6mpqT5/IAD8H0IN\ngBlr6dKlGRwczBtvvPE/PpLY29ubP/3TP82OHTvyUz/1U5k3b17uu+++jI6OJkmWLFly9qv8v/vd\n7+YTn/hErrzyylx22WW56aabctNNN2VgYCAdHR155pln8mu/9msX5O8DYO7yrY8AzFjNzc1Zt25d\n/vzP/zyDg4MZGhrKt7/97bedd/r06TQ0NGTRokVJkq9+9at55ZVXzv78X/7lX3Ls2LEkORt88+bN\ny6FDh3Lw4MEMDQ3lkksuyUUXXZR58/yvE4D6c0cNgBlt69ateeqpp7Jt27YMDQ1lzZo1b/uc2sqV\nK3PLLbfk4x//eObNm5df/MVfzE//9E+f/fn3vve9PPXUUzl58mSWLFmS3/iN38ill16aI0eO5POf\n/3yOHDmSiy++OFdffXVuvfXWC/0nAjAHNYz+6LkPAAAAiuD5DQAAgMIINQAAgMIINQAAgMIINQAA\ngMIINQAAgMIINQAAgMIINQAAgMIINQAAgMIINQAAgML8bzDIGqh1QLueAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17098ed630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "data.groupby('Pclass').mean()['Fare'].plot(kind='bar',figsize=(15,8), ax = ax)\n",
    "#Mirando las clases de los tickets vemos que la mayoria de muertos se presentan en los billetes mas baratos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbpresent": {
     "id": "1ba57caf-ad3d-4d31-8759-b15a135e9d1f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1707647be0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddFM+/OEPZ/Hixfnrv/7rfPzjH6/3WDCo+W0PCjVmzJisW7eu/0O9n3rqqYwf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\nCqrKgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17076799b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_embark = data[data['Survived']==1]['Embarked'].value_counts()\n",
    "dead_embark = data[data['Survived']==0]['Embarked'].value_counts()\n",
    "df = pd.DataFrame([survived_embark,dead_embark])\n",
    "df.index = ['Survived','Dead']\n",
    "df.plot(kind='bar',stacked=True, figsize=(15,8))\n",
    "#La ubicacion donde se subieron parece no tener importancia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "11812fbc-22b9-4faf-9cef-f98d9197fedf"
    }
   },
   "source": [
    "# Parte 2. Ingenieria de las caracteristicas, columnas o features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "748f3dfb-c317-4017-bc8f-b3a84b5f1d51"
    }
   },
   "outputs": [],
   "source": [
    "#Suele ser buena idea combinar el set de testeo con el de training,\n",
    "#especialmente cuando el de testeo tiene una feature que el de training no\n",
    "\n",
    "def get_combined_data():\n",
    "    data_training = ws.datasets['train-titanic.csv']\n",
    "    data_test = ws.datasets['test-titanic.csv']\n",
    "    # reading train data\n",
    "    train = data_training.to_dataframe()\n",
    "    \n",
    "    # reading test data\n",
    "    test = data_test.to_dataframe();\n",
    "\n",
    "    # extracting and then removing the targets from the training data \n",
    "    targets = train.Survived\n",
    "    train.drop('Survived',1,inplace=True)\n",
    "    \n",
    "\n",
    "    # merging train data and test data for future feature engineering\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop('index',inplace=True,axis=1)\n",
    "    \n",
    "    return combined\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "f3750a56-fe81-4484-948e-9c38489731ad"
    }
   },
   "outputs": [],
   "source": [
    "combined = get_combined_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbpresent": {
     "id": "82103f2b-275c-4c8e-bc82-109b4c0eff36"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 11)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Devuelve la forma de la variable, en este caso devuelve que tiene 1309 filas y 11 columnas combinando entrenamiento y pruebas\n",
    "combined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "176c6565-99e0-4e47-b06a-bdee5234438a"
    }
   },
   "outputs": [],
   "source": [
    "def get_titles():\n",
    "\n",
    "    global combined\n",
    "    \n",
    "    # we extract the title from each name\n",
    "    combined['Title'] = combined['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "    \n",
    "    # a map of more aggregated titles\n",
    "    Title_Dictionary = {\n",
    "                        \"Capt\":       \"Officer\",\n",
    "                        \"Col\":        \"Officer\",\n",
    "                        \"Major\":      \"Officer\",\n",
    "                        \"Jonkheer\":   \"Royalty\",\n",
    "                        \"Don\":        \"Royalty\",\n",
    "                        \"Sir\" :       \"Royalty\",\n",
    "                        \"Dr\":         \"Officer\",\n",
    "                        \"Rev\":        \"Officer\",\n",
    "                        \"the Countess\":\"Royalty\",\n",
    "                        \"Dona\":       \"Royalty\",\n",
    "                        \"Mme\":        \"Mrs\",\n",
    "                        \"Mlle\":       \"Miss\",\n",
    "                        \"Ms\":         \"Mrs\",\n",
    "                        \"Mr\" :        \"Mr\",\n",
    "                        \"Mrs\" :       \"Mrs\",\n",
    "                        \"Miss\" :      \"Miss\",\n",
    "                        \"Master\" :    \"Master\",\n",
    "                        \"Lady\" :      \"Royalty\"\n",
    "\n",
    "                        }\n",
    "    \n",
    "    # we map each title\n",
    "\n",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)\n",
    "    \n",
    "get_titles()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbpresent": {
     "id": "0e22aa13-f671-414c-b1da-e1a8e648d7be"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "nbpresent": {
     "id": "2ab52cd1-1429-4dc5-94a2-4683f3de2993"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Miss</th>\n",
       "      <td>529.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.9625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>853.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>797.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>760.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Miss</th>\n",
       "      <td>606.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>533.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Miss</th>\n",
       "      <td>603.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>668.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Master</th>\n",
       "      <td>803.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>634.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>678.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>600.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>Master</th>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>723.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>513.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Master</th>\n",
       "      <td>789.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>640.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PassengerId   Age  SibSp  Parch      Fare\n",
       "Sex    Pclass Title                                             \n",
       "female 1      Miss           529.5  30.0    0.0    0.0   99.9625\n",
       "              Mrs            853.5  45.0    1.0    0.0   78.1125\n",
       "              Officer        797.0  49.0    0.0    0.0   25.9292\n",
       "              Royalty        760.0  39.0    0.0    0.0   86.5000\n",
       "       2      Miss           606.5  20.0    0.0    0.0   20.2500\n",
       "              Mrs            533.0  30.0    1.0    0.0   26.0000\n",
       "       3      Miss           603.5  18.0    0.0    0.0    8.0500\n",
       "              Mrs            668.5  31.0    1.0    1.0   15.5000\n",
       "male   1      Master         803.0   6.0    1.0    2.0  134.5000\n",
       "              Mr             634.0  41.5    0.0    0.0   47.1000\n",
       "              Officer        678.0  52.0    0.0    0.0   37.5500\n",
       "              Royalty        600.0  40.0    0.0    0.0   27.7208\n",
       "       2      Master         550.0   2.0    1.0    1.0   26.0000\n",
       "              Mr             723.5  30.0    0.0    0.0   13.0000\n",
       "              Officer        513.0  41.5    0.0    0.0   13.0000\n",
       "       3      Master         789.0   6.0    3.0    1.0   22.3583\n",
       "              Mr             640.5  26.0    0.0    0.0    7.8958"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hemos visto antes que faltaba 177 valores de edad, esto es un gran numero, un 13% de toda la muestra.\n",
    "# Reemplazar los NaN por la media o la mediana no suele ser lo mejor ya que la edad puede diferir por grupos o categorias de pasajeros.\n",
    "\n",
    "grouped = combined.groupby(['Sex','Pclass','Title'])\n",
    "grouped.median() # Calcula la mediana del resto de atributos, como la edad, hermanos o esposas, padres o hijos o el ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "5736c47e-dd7b-4148-8a26-5fa55925c2a2"
    }
   },
   "outputs": [],
   "source": [
    "# Se mira el sexo, clase y titulo para rellenar los valores vacios de ese grupo con la mediana de ese grupo\n",
    "def process_age():\n",
    "    combined[\"Age\"] = combined.groupby(['Sex','Pclass','Title'])['Age'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "7f44f865-f160-4046-9ee6-13c71d0f621f"
    }
   },
   "outputs": [],
   "source": [
    "process_age()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "nbpresent": {
     "id": "b7b0ac8e-c75b-428a-8aec-55d0a3d1034a"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Title          1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Vemos que hemos rellenado la edad y son 1309 campos, pero tambien vemos que en tickets o cabinas faltan muchos datos\n",
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "67f458df-4d87-4914-82a4-ef4ef52617d2"
    }
   },
   "outputs": [],
   "source": [
    "# Quitamos la columna del nombre ya que con lacolumna del titulo ya no nos hace falta\n",
    "# Y encodeamos los titulos con un dummy encoding http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html\n",
    "# Es decir ahora las variables de titulos seran binarias \n",
    "def process_names():\n",
    "    \n",
    "    global combined\n",
    "    # we clean the Name variable\n",
    "    combined.drop('Name',axis=1,inplace=True)\n",
    "    \n",
    "    # encoding in dummy variable\n",
    "    titles_dummies = pd.get_dummies(combined['Title'],prefix='Title')\n",
    "    combined = pd.concat([combined,titles_dummies],axis=1)\n",
    "    \n",
    "    # removing the title variable\n",
    "    combined.drop('Title',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "c92a2163-d341-4d1b-9df9-a4352d38a503"
    }
   },
   "outputs": [],
   "source": [
    "process_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "nbpresent": {
     "id": "23bcf057-52d8-4277-a1df-0f8ea0fdc5d8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>3</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0            1       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1            2       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2            3       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3            4       1  female  35.0      1      0            113803  53.1000   \n",
       "4            5       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  \\\n",
       "0   NaN        S             0           0         1          0   \n",
       "1   C85        C             0           0         0          1   \n",
       "2   NaN        S             0           1         0          0   \n",
       "3  C123        S             0           0         0          1   \n",
       "4   NaN        S             0           0         1          0   \n",
       "\n",
       "   Title_Officer  Title_Royalty  \n",
       "0              0              0  \n",
       "1              0              0  \n",
       "2              0              0  \n",
       "3              0              0  \n",
       "4              0              0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "4f0e82fc-3b17-466b-a3a5-fad138e066a7"
    }
   },
   "outputs": [],
   "source": [
    "# Sustituimos por la mediana los valores que falten\n",
    "def process_fares():\n",
    "    \n",
    "    global combined    \n",
    "    combined.Fare.fillna(combined.Fare.median(),inplace=True)\n",
    "\n",
    "process_fares()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "24d80b3b-bbee-467e-8f4b-b1d3fab6ea12"
    }
   },
   "outputs": [],
   "source": [
    "# Sustituimos por el valor mas frecuente los valores que falten\n",
    "def process_embarked():\n",
    "    \n",
    "    global combined\n",
    "    # two missing embarked values - filling them with the most frequent one (S)\n",
    "    combined.Embarked.fillna('S',inplace=True)\n",
    "    \n",
    "    # dummy encoding \n",
    "    embarked_dummies = pd.get_dummies(combined['Embarked'],prefix='Embarked')\n",
    "    combined = pd.concat([combined,embarked_dummies],axis=1)\n",
    "    combined.drop('Embarked',axis=1,inplace=True)\n",
    "    \n",
    "process_embarked()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "051be64c-3d66-477d-afb1-d1752c13e737"
    }
   },
   "outputs": [],
   "source": [
    "# Sustituimos por 'U' de Unknown los valores que falten\n",
    "def process_cabin():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # replacing missing cabins with U (for Uknown)\n",
    "    combined.Cabin.fillna('U',inplace=True)\n",
    "    \n",
    "    # mapping each Cabin value with the cabin letter\n",
    "    combined['Cabin'] = combined['Cabin'].map(lambda c : c[0])\n",
    "    \n",
    "    # dummy encoding ...\n",
    "    cabin_dummies = pd.get_dummies(combined['Cabin'],prefix='Cabin')\n",
    "    \n",
    "    combined = pd.concat([combined,cabin_dummies],axis=1)\n",
    "    \n",
    "    combined.drop('Cabin',axis=1,inplace=True)\n",
    "    \n",
    "process_cabin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "nbpresent": {
     "id": "76821e33-cc9b-4e83-a524-0985cca56f4b"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 26 columns):\n",
      "PassengerId      1309 non-null int64\n",
      "Pclass           1309 non-null int64\n",
      "Sex              1309 non-null object\n",
      "Age              1309 non-null float64\n",
      "SibSp            1309 non-null int64\n",
      "Parch            1309 non-null int64\n",
      "Ticket           1309 non-null object\n",
      "Fare             1309 non-null float64\n",
      "Title_Master     1309 non-null uint8\n",
      "Title_Miss       1309 non-null uint8\n",
      "Title_Mr         1309 non-null uint8\n",
      "Title_Mrs        1309 non-null uint8\n",
      "Title_Officer    1309 non-null uint8\n",
      "Title_Royalty    1309 non-null uint8\n",
      "Embarked_C       1309 non-null uint8\n",
      "Embarked_Q       1309 non-null uint8\n",
      "Embarked_S       1309 non-null uint8\n",
      "Cabin_A          1309 non-null uint8\n",
      "Cabin_B          1309 non-null uint8\n",
      "Cabin_C          1309 non-null uint8\n",
      "Cabin_D          1309 non-null uint8\n",
      "Cabin_E          1309 non-null uint8\n",
      "Cabin_F          1309 non-null uint8\n",
      "Cabin_G          1309 non-null uint8\n",
      "Cabin_T          1309 non-null uint8\n",
      "Cabin_U          1309 non-null uint8\n",
      "dtypes: float64(2), int64(4), object(2), uint8(18)\n",
      "memory usage: 104.9+ KB\n"
     ]
    }
   ],
   "source": [
    "combined.info() # Comprobamos que todas las filas de la muestra esten completas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "0bd45cd6-4066-4334-a012-b9b068308954"
    }
   },
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    \n",
    "    global combined\n",
    "    # mapping string values to numerical one \n",
    "    combined['Sex'] = combined['Sex'].map({'male':1,'female':0})\n",
    "\n",
    "process_sex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "4cf2474a-118a-4bea-a0f5-422466fb788d"
    }
   },
   "outputs": [],
   "source": [
    "def process_pclass():\n",
    "    \n",
    "    global combined\n",
    "    # encoding into 3 categories:\n",
    "    pclass_dummies = pd.get_dummies(combined['Pclass'],prefix=\"Pclass\")\n",
    "    \n",
    "    # adding dummy variables\n",
    "    combined = pd.concat([combined,pclass_dummies],axis=1)\n",
    "    \n",
    "    # removing \"Pclass\"\n",
    "    \n",
    "    combined.drop('Pclass',axis=1,inplace=True)\n",
    "\n",
    "process_pclass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "9fd67817-9699-4b08-9429-ce1536a428b9"
    }
   },
   "outputs": [],
   "source": [
    "def process_ticket():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit)\n",
    "    def cleanTicket(ticket):\n",
    "        ticket = ticket.replace('.','')\n",
    "        ticket = ticket.replace('/','')\n",
    "        ticket = ticket.split()\n",
    "        ticket = map(lambda t : t.strip() , ticket)\n",
    "        ticket = list(filter(lambda t : not t.isdigit(), ticket))\n",
    "        if len(ticket) > 0:\n",
    "            return ticket[0]\n",
    "        else: \n",
    "            return 'XXX'\n",
    "    \n",
    "\n",
    "    # Extracting dummy variables from tickets:\n",
    "\n",
    "    combined['Ticket'] = combined['Ticket'].map(cleanTicket)\n",
    "    tickets_dummies = pd.get_dummies(combined['Ticket'],prefix='Ticket')\n",
    "    combined = pd.concat([combined, tickets_dummies],axis=1)\n",
    "    combined.drop('Ticket',inplace=True,axis=1)\n",
    "\n",
    "process_ticket()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "a895050c-740c-4d94-9a13-1869085f907b"
    }
   },
   "outputs": [],
   "source": [
    "#Creamos una variable nueva suponiendo que las familias grandes se agrupon y tienen mayor % de salvarse\n",
    "def process_family():\n",
    "    \n",
    "    global combined\n",
    "    # introducing a new feature : the size of families (including the passenger)\n",
    "    combined['FamilySize'] = combined['Parch'] + combined['SibSp'] + 1\n",
    "    \n",
    "    # introducing other features based on the family size\n",
    "    combined['Singleton'] = combined['FamilySize'].map(lambda s : 1 if s == 1 else 0)\n",
    "    combined['SmallFamily'] = combined['FamilySize'].map(lambda s : 1 if 2<=s<=4 else 0)\n",
    "    combined['LargeFamily'] = combined['FamilySize'].map(lambda s : 1 if 5<=s else 0)\n",
    "\n",
    "process_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "nbpresent": {
     "id": "b953d6d2-2c9c-45e4-92c6-04551445411a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 68)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape # Hemos pasado de 12 features a 68"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "nbpresent": {
     "id": "a339fa91-d372-4b83-becc-48af7d265ff6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Ticket_A</th>\n",
       "      <th>Ticket_A4</th>\n",
       "      <th>Ticket_A5</th>\n",
       "      <th>Ticket_AQ3</th>\n",
       "      <th>Ticket_AQ4</th>\n",
       "      <th>Ticket_AS</th>\n",
       "      <th>Ticket_C</th>\n",
       "      <th>Ticket_CA</th>\n",
       "      <th>Ticket_CASOTON</th>\n",
       "      <th>Ticket_FC</th>\n",
       "      <th>Ticket_FCC</th>\n",
       "      <th>Ticket_Fa</th>\n",
       "      <th>Ticket_LINE</th>\n",
       "      <th>Ticket_LP</th>\n",
       "      <th>Ticket_PC</th>\n",
       "      <th>Ticket_PP</th>\n",
       "      <th>Ticket_PPP</th>\n",
       "      <th>Ticket_SC</th>\n",
       "      <th>Ticket_SCA3</th>\n",
       "      <th>Ticket_SCA4</th>\n",
       "      <th>Ticket_SCAH</th>\n",
       "      <th>Ticket_SCOW</th>\n",
       "      <th>Ticket_SCPARIS</th>\n",
       "      <th>Ticket_SCParis</th>\n",
       "      <th>Ticket_SOC</th>\n",
       "      <th>Ticket_SOP</th>\n",
       "      <th>Ticket_SOPP</th>\n",
       "      <th>Ticket_SOTONO2</th>\n",
       "      <th>Ticket_SOTONOQ</th>\n",
       "      <th>Ticket_SP</th>\n",
       "      <th>Ticket_STONO</th>\n",
       "      <th>Ticket_STONO2</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_XXX</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>SmallFamily</th>\n",
       "      <th>LargeFamily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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": [
       "   PassengerId  Sex   Age  SibSp  Parch     Fare  Title_Master  Title_Miss  \\\n",
       "0            1    1  22.0      1      0   7.2500             0           0   \n",
       "1            2    0  38.0      1      0  71.2833             0           0   \n",
       "2            3    0  26.0      0      0   7.9250             0           1   \n",
       "3            4    0  35.0      1      0  53.1000             0           0   \n",
       "4            5    1  35.0      0      0   8.0500             0           0   \n",
       "\n",
       "   Title_Mr  Title_Mrs  Title_Officer  Title_Royalty  Embarked_C  Embarked_Q  \\\n",
       "0         1          0              0              0           0           0   \n",
       "1         0          1              0              0           1           0   \n",
       "2         0          0              0              0           0           0   \n",
       "3         0          1              0              0           0           0   \n",
       "4         1          0              0              0           0           0   \n",
       "\n",
       "   Embarked_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  \\\n",
       "0           1        0        0        0        0        0        0        0   \n",
       "1           0        0        0        1        0        0        0        0   \n",
       "2           1        0        0        0        0        0        0        0   \n",
       "3           1        0        0        1        0        0        0        0   \n",
       "4           1        0        0        0        0        0        0        0   \n",
       "\n",
       "   Cabin_T  Cabin_U  Pclass_1  Pclass_2  Pclass_3  Ticket_A  Ticket_A4  \\\n",
       "0        0        1         0         0         1         0          0   \n",
       "1        0        0         1         0         0         0          0   \n",
       "2        0        1         0         0         1         0          0   \n",
       "3        0        0         1         0         0         0          0   \n",
       "4        0        1         0         0         1         0          0   \n",
       "\n",
       "   Ticket_A5  Ticket_AQ3  Ticket_AQ4  Ticket_AS  Ticket_C  Ticket_CA  \\\n",
       "0          1           0           0          0         0          0   \n",
       "1          0           0           0          0         0          0   \n",
       "2          0           0           0          0         0          0   \n",
       "3          0           0           0          0         0          0   \n",
       "4          0           0           0          0         0          0   \n",
       "\n",
       "   Ticket_CASOTON  Ticket_FC  Ticket_FCC  Ticket_Fa  Ticket_LINE  Ticket_LP  \\\n",
       "0               0          0           0          0            0          0   \n",
       "1               0          0           0          0            0          0   \n",
       "2               0          0           0          0            0          0   \n",
       "3               0          0           0          0            0          0   \n",
       "4               0          0           0          0            0          0   \n",
       "\n",
       "   Ticket_PC  Ticket_PP  Ticket_PPP  Ticket_SC  Ticket_SCA3  Ticket_SCA4  \\\n",
       "0          0          0           0          0            0            0   \n",
       "1          1          0           0          0            0            0   \n",
       "2          0          0           0          0            0            0   \n",
       "3          0          0           0          0            0            0   \n",
       "4          0          0           0          0            0            0   \n",
       "\n",
       "   Ticket_SCAH  Ticket_SCOW  Ticket_SCPARIS  Ticket_SCParis  Ticket_SOC  \\\n",
       "0            0            0               0               0           0   \n",
       "1            0            0               0               0           0   \n",
       "2            0            0               0               0           0   \n",
       "3            0            0               0               0           0   \n",
       "4            0            0               0               0           0   \n",
       "\n",
       "   Ticket_SOP  Ticket_SOPP  Ticket_SOTONO2  Ticket_SOTONOQ  Ticket_SP  \\\n",
       "0           0            0               0               0          0   \n",
       "1           0            0               0               0          0   \n",
       "2           0            0               0               0          0   \n",
       "3           0            0               0               0          0   \n",
       "4           0            0               0               0          0   \n",
       "\n",
       "   Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  Ticket_SWPP  Ticket_WC  \\\n",
       "0             0              0              0            0          0   \n",
       "1             0              0              0            0          0   \n",
       "2             0              1              0            0          0   \n",
       "3             0              0              0            0          0   \n",
       "4             0              0              0            0          0   \n",
       "\n",
       "   Ticket_WEP  Ticket_XXX  FamilySize  Singleton  SmallFamily  LargeFamily  \n",
       "0           0           0           2          0            1            0  \n",
       "1           0           0           2          0            1            0  \n",
       "2           0           0           1          1            0            0  \n",
       "3           0           1           2          0            1            0  \n",
       "4           0           1           1          1            0            0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "fe29f47c-99bc-4990-a322-354da051a729"
    }
   },
   "outputs": [],
   "source": [
    "# Como las columnas o features tienen distintos rangos, los normalizamos a entre 0 y 1, excepto PassengerID que kaggle lo pide asi\n",
    "def scale_all_features():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    features = list(combined.columns)\n",
    "    features.remove('PassengerId')\n",
    "    combined[features] = combined[features].apply(lambda x: x/x.max(), axis=0)\n",
    "    \n",
    "scale_all_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "nbpresent": {
     "id": "8665f022-149c-4fb5-8865-e2560d05e579"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Ticket_A</th>\n",
       "      <th>Ticket_A4</th>\n",
       "      <th>Ticket_A5</th>\n",
       "      <th>Ticket_AQ3</th>\n",
       "      <th>Ticket_AQ4</th>\n",
       "      <th>Ticket_AS</th>\n",
       "      <th>Ticket_C</th>\n",
       "      <th>Ticket_CA</th>\n",
       "      <th>Ticket_CASOTON</th>\n",
       "      <th>Ticket_FC</th>\n",
       "      <th>Ticket_FCC</th>\n",
       "      <th>Ticket_Fa</th>\n",
       "      <th>Ticket_LINE</th>\n",
       "      <th>Ticket_LP</th>\n",
       "      <th>Ticket_PC</th>\n",
       "      <th>Ticket_PP</th>\n",
       "      <th>Ticket_PPP</th>\n",
       "      <th>Ticket_SC</th>\n",
       "      <th>Ticket_SCA3</th>\n",
       "      <th>Ticket_SCA4</th>\n",
       "      <th>Ticket_SCAH</th>\n",
       "      <th>Ticket_SCOW</th>\n",
       "      <th>Ticket_SCPARIS</th>\n",
       "      <th>Ticket_SCParis</th>\n",
       "      <th>Ticket_SOC</th>\n",
       "      <th>Ticket_SOP</th>\n",
       "      <th>Ticket_SOPP</th>\n",
       "      <th>Ticket_SOTONO2</th>\n",
       "      <th>Ticket_SOTONOQ</th>\n",
       "      <th>Ticket_SP</th>\n",
       "      <th>Ticket_STONO</th>\n",
       "      <th>Ticket_STONO2</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_XXX</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>SmallFamily</th>\n",
       "      <th>LargeFamily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Sex     Age  SibSp  Parch      Fare  Title_Master  Title_Miss  \\\n",
       "0            1  1.0  0.2750  0.125    0.0  0.014151           0.0         0.0   \n",
       "1            2  0.0  0.4750  0.125    0.0  0.139136           0.0         0.0   \n",
       "2            3  0.0  0.3250  0.000    0.0  0.015469           0.0         1.0   \n",
       "3            4  0.0  0.4375  0.125    0.0  0.103644           0.0         0.0   \n",
       "4            5  1.0  0.4375  0.000    0.0  0.015713           0.0         0.0   \n",
       "\n",
       "   Title_Mr  Title_Mrs  Title_Officer  Title_Royalty  Embarked_C  Embarked_Q  \\\n",
       "0       1.0        0.0            0.0            0.0         0.0         0.0   \n",
       "1       0.0        1.0            0.0            0.0         1.0         0.0   \n",
       "2       0.0        0.0            0.0            0.0         0.0         0.0   \n",
       "3       0.0        1.0            0.0            0.0         0.0         0.0   \n",
       "4       1.0        0.0            0.0            0.0         0.0         0.0   \n",
       "\n",
       "   Embarked_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  \\\n",
       "0         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1         0.0      0.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "2         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3         1.0      0.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "4         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   Cabin_T  Cabin_U  Pclass_1  Pclass_2  Pclass_3  Ticket_A  Ticket_A4  \\\n",
       "0      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "1      0.0      0.0       1.0       0.0       0.0       0.0        0.0   \n",
       "2      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "3      0.0      0.0       1.0       0.0       0.0       0.0        0.0   \n",
       "4      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "\n",
       "   Ticket_A5  Ticket_AQ3  Ticket_AQ4  Ticket_AS  Ticket_C  Ticket_CA  \\\n",
       "0        1.0         0.0         0.0        0.0       0.0        0.0   \n",
       "1        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "2        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "3        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "4        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "\n",
       "   Ticket_CASOTON  Ticket_FC  Ticket_FCC  Ticket_Fa  Ticket_LINE  Ticket_LP  \\\n",
       "0             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "1             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "2             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "3             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "4             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "\n",
       "   Ticket_PC  Ticket_PP  Ticket_PPP  Ticket_SC  Ticket_SCA3  Ticket_SCA4  \\\n",
       "0        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "1        1.0        0.0         0.0        0.0          0.0          0.0   \n",
       "2        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "3        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "4        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "\n",
       "   Ticket_SCAH  Ticket_SCOW  Ticket_SCPARIS  Ticket_SCParis  Ticket_SOC  \\\n",
       "0          0.0          0.0             0.0             0.0         0.0   \n",
       "1          0.0          0.0             0.0             0.0         0.0   \n",
       "2          0.0          0.0             0.0             0.0         0.0   \n",
       "3          0.0          0.0             0.0             0.0         0.0   \n",
       "4          0.0          0.0             0.0             0.0         0.0   \n",
       "\n",
       "   Ticket_SOP  Ticket_SOPP  Ticket_SOTONO2  Ticket_SOTONOQ  Ticket_SP  \\\n",
       "0         0.0          0.0             0.0             0.0        0.0   \n",
       "1         0.0          0.0             0.0             0.0        0.0   \n",
       "2         0.0          0.0             0.0             0.0        0.0   \n",
       "3         0.0          0.0             0.0             0.0        0.0   \n",
       "4         0.0          0.0             0.0             0.0        0.0   \n",
       "\n",
       "   Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  Ticket_SWPP  Ticket_WC  \\\n",
       "0           0.0            0.0            0.0          0.0        0.0   \n",
       "1           0.0            0.0            0.0          0.0        0.0   \n",
       "2           0.0            1.0            0.0          0.0        0.0   \n",
       "3           0.0            0.0            0.0          0.0        0.0   \n",
       "4           0.0            0.0            0.0          0.0        0.0   \n",
       "\n",
       "   Ticket_WEP  Ticket_XXX  FamilySize  Singleton  SmallFamily  LargeFamily  \n",
       "0         0.0         0.0    0.181818        0.0          1.0          0.0  \n",
       "1         0.0         0.0    0.181818        0.0          1.0          0.0  \n",
       "2         0.0         0.0    0.090909        1.0          0.0          0.0  \n",
       "3         0.0         1.0    0.181818        0.0          1.0          0.0  \n",
       "4         0.0         1.0    0.090909        1.0          0.0          0.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "45b23d11-b985-4409-b1e5-4c940e24525c"
    }
   },
   "source": [
    "# Parte 3. Modelado de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "1ccfafc0-d82a-481c-af37-da7c5634136f"
    }
   },
   "source": [
    "Con todas las nuevas features generadas vamos a crear un modelo estadistico. Se puede pensar en este modelo como una caja negra en la que por un lado se introduce un pasajero y por el otro nos devuelve una papel diciendo si ha sobrevivido o no.  \n",
    "Hay gran cantidad de modelos para usar, como regresion logica, arboles de decision, arboles aleatorios o SVM.  \n",
    "Usaremos random forests o arboles aleatorios ya que suelen dar buenos resultados en las competiciones de kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ba6afe41-6d64-4c34-9e27-d7b125dca223"
    }
   },
   "source": [
    "Volviendo a nuestro problema tenemos que descomponer nuestro dataset en entrenamiento y testeo, usar el de entrenamiento para construir un modelo predictivo, evaluar este modelo con el dataset de entrenamiento, y por ultimo testear el modelo usando el dataset de testeo.   \n",
    "La parte de construir un modelo y evaluarlo sera un proceso iterativo hasta que tengamos una puntuacion que nos guste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "nbpresent": {
     "id": "ee2687dd-5264-485c-bba3-1f30d2b1bc2e"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/set92/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/set92/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "nbpresent": {
     "id": "08973d72-5f07-46de-8225-7933aa333ae8"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18.2\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "a6ecccf6-df04-4290-ac19-d1c828bf1225"
    }
   },
   "outputs": [],
   "source": [
    "# Para evaluar a nuestro modelo usaremos un 5-fold cross validation with the Accuracy metric.\n",
    "def compute_score(clf, X, y,scoring='accuracy'):\n",
    "    xval = cross_val_score(clf, X, y, cv = 5,scoring=scoring)\n",
    "    return np.mean(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "f7a5964e-ae7a-4ea9-95b0-4dd5c745f809"
    }
   },
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined\n",
    "    \n",
    "    train0 = (ws.datasets['train-titanic.csv']).to_dataframe()\n",
    "    \n",
    "    targets = train0.Survived\n",
    "    train = combined.ix[0:890]\n",
    "    test = combined.ix[891:]\n",
    "    \n",
    "    return train,test,targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "nbpresent": {
     "id": "5b657530-22e1-4fe2-83e6-51f07c4f8a70"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/set92/anaconda3/envs/ml/lib/python3.6/site-packages/ipykernel/__main__.py:7: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"
     ]
    }
   ],
   "source": [
    "train,test,targets = recover_train_test_target()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "cacdcd45-2808-432c-b5e6-7914f1b5cb87"
    }
   },
   "source": [
    "## Seleccion de caracteristicas\n",
    "Tenemos 68 caracteristicas, un numero bastante grande, en esta fase intentaremos reducir las dimensiones del dataset, quedandonos solo con aquellas caracteristicas o features que nos aporten mas datos para el resultado deseado. \n",
    "\n",
    "De esta manera conseguiremos reducir la redundancia de datos, acelerar el proceso de entrenamiento y reducir el overfitting o sobreajuste que se le llama cuando se intenta ajustar demasiado el modelo, pudiendo arrojar falsos resultados cuando se vayan introduciendo mas datos.\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/1/19/Overfitting.svg\" width=\"200\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "543351ad-eef8-46fa-ad8a-28a0bd5bc72a"
    }
   },
   "outputs": [],
   "source": [
    "#Usaremos tree-based estimators para computar la importancia de las features\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "clf = ExtraTreesClassifier(n_estimators=200)\n",
    "clf = clf.fit(train, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "d833e2e5-29f2-4e83-866b-49f355dca995"
    }
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['feature'] = train.columns\n",
    "features['importance'] = clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "nbpresent": {
     "id": "c383fceb-89e0-4bac-a89b-a2903ffc27a0"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0.128693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.118302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.113884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Title_Mr</td>\n",
       "      <td>0.104700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>0.093512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Title_Mrs</td>\n",
       "      <td>0.048542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Title_Miss</td>\n",
       "      <td>0.047813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pclass_3</td>\n",
       "      <td>0.042710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cabin_U</td>\n",
       "      <td>0.027493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>FamilySize</td>\n",
       "      <td>0.020643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SmallFamily</td>\n",
       "      <td>0.020298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pclass_1</td>\n",
       "      <td>0.018991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0.016671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>LargeFamily</td>\n",
       "      <td>0.016407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.014634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Title_Master</td>\n",
       "      <td>0.013970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>0.012622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Ticket_XXX</td>\n",
       "      <td>0.012227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pclass_2</td>\n",
       "      <td>0.012040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Singleton</td>\n",
       "      <td>0.011331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>0.010805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cabin_E</td>\n",
       "      <td>0.009161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cabin_B</td>\n",
       "      <td>0.007664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>0.007382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cabin_D</td>\n",
       "      <td>0.007206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Title_Officer</td>\n",
       "      <td>0.006821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Ticket_PC</td>\n",
       "      <td>0.006653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Ticket_SWPP</td>\n",
       "      <td>0.006472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cabin_C</td>\n",
       "      <td>0.006363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Ticket_STONO</td>\n",
       "      <td>0.005657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Ticket_SOPP</td>\n",
       "      <td>0.001788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cabin_G</td>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Title_Royalty</td>\n",
       "      <td>0.001057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Ticket_WEP</td>\n",
       "      <td>0.000889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Ticket_PP</td>\n",
       "      <td>0.000832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Ticket_SOC</td>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ticket_FC</td>\n",
       "      <td>0.000739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Ticket_FCC</td>\n",
       "      <td>0.000635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Ticket_LINE</td>\n",
       "      <td>0.000593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Ticket_SCPARIS</td>\n",
       "      <td>0.000541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ticket_A4</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Ticket_SCParis</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Cabin_T</td>\n",
       "      <td>0.000295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Ticket_SCAH</td>\n",
       "      <td>0.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Ticket_SOP</td>\n",
       "      <td>0.000145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Ticket_SC</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Ticket_SP</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ticket_SOTONO2</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Ticket_PPP</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ticket_CASOTON</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ticket_Fa</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Ticket_SCA4</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ticket_AS</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Ticket_SCOW</td>\n",
       "      <td>0.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Ticket_SCA3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ticket_STONOQ</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Ticket_LP</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Ticket_AQ4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ticket_AQ3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ticket_A</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature  importance\n",
       "0      PassengerId    0.128693\n",
       "2              Age    0.118302\n",
       "5             Fare    0.113884\n",
       "8         Title_Mr    0.104700\n",
       "1              Sex    0.093512\n",
       "9        Title_Mrs    0.048542\n",
       "7       Title_Miss    0.047813\n",
       "26        Pclass_3    0.042710\n",
       "23         Cabin_U    0.027493\n",
       "64      FamilySize    0.020643\n",
       "66     SmallFamily    0.020298\n",
       "24        Pclass_1    0.018991\n",
       "3            SibSp    0.016671\n",
       "67     LargeFamily    0.016407\n",
       "4            Parch    0.014634\n",
       "6     Title_Master    0.013970\n",
       "14      Embarked_S    0.012622\n",
       "63      Ticket_XXX    0.012227\n",
       "25        Pclass_2    0.012040\n",
       "65       Singleton    0.011331\n",
       "12      Embarked_C    0.010805\n",
       "19         Cabin_E    0.009161\n",
       "16         Cabin_B    0.007664\n",
       "13      Embarked_Q    0.007382\n",
       "18         Cabin_D    0.007206\n",
       "10   Title_Officer    0.006821\n",
       "41       Ticket_PC    0.006653\n",
       "60     Ticket_SWPP    0.006472\n",
       "17         Cabin_C    0.006363\n",
       "57    Ticket_STONO    0.005657\n",
       "..             ...         ...\n",
       "53     Ticket_SOPP    0.001788\n",
       "21         Cabin_G    0.001348\n",
       "11   Title_Royalty    0.001057\n",
       "62      Ticket_WEP    0.000889\n",
       "42       Ticket_PP    0.000832\n",
       "51      Ticket_SOC    0.000753\n",
       "36       Ticket_FC    0.000739\n",
       "37      Ticket_FCC    0.000635\n",
       "39     Ticket_LINE    0.000593\n",
       "49  Ticket_SCPARIS    0.000541\n",
       "28       Ticket_A4    0.000457\n",
       "50  Ticket_SCParis    0.000402\n",
       "22         Cabin_T    0.000295\n",
       "47     Ticket_SCAH    0.000216\n",
       "52      Ticket_SOP    0.000145\n",
       "44       Ticket_SC    0.000127\n",
       "56       Ticket_SP    0.000109\n",
       "54  Ticket_SOTONO2    0.000091\n",
       "43      Ticket_PPP    0.000068\n",
       "35  Ticket_CASOTON    0.000059\n",
       "38       Ticket_Fa    0.000057\n",
       "46     Ticket_SCA4    0.000055\n",
       "32       Ticket_AS    0.000043\n",
       "48     Ticket_SCOW    0.000028\n",
       "45     Ticket_SCA3    0.000000\n",
       "59   Ticket_STONOQ    0.000000\n",
       "40       Ticket_LP    0.000000\n",
       "31      Ticket_AQ4    0.000000\n",
       "30      Ticket_AQ3    0.000000\n",
       "27        Ticket_A    0.000000\n",
       "\n",
       "[68 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.sort_values(['importance'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "nbpresent": {
     "id": "45166575-0fac-44a0-b759-2d5a0cf1d567"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 14)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Como vemos hay gran importancia en la edad, el titulo de la persona, el precio del ticket y el sexo, asi como una \n",
    "# gran correlacion con el passengerID\n",
    "\n",
    "# Asi que con esto vamos a crear unos datasets mas compactos \n",
    "model = SelectFromModel(clf, prefit=True)\n",
    "train_new = model.transform(train)\n",
    "train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "nbpresent": {
     "id": "76da287b-026e-4546-8112-e97dda09585a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 14)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new = model.transform(test)\n",
    "test_new.shape\n",
    "\n",
    "# Y esta vez como podemos ver hemos vuelto a tener pocas features, 15, lo cual esta cerca del numero inicial que eran 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e5409800-028f-403a-98d1-0028176d64bd"
    }
   },
   "source": [
    "# Hyperparameters Tuning\n",
    "Una vez terminado el ejercicio se pueden modificar ciertos parametros que permiten modificar el ratio de aciertos.\n",
    "https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "nbpresent": {
     "id": "d6246323-699c-4007-b1b4-9bf765036154"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8327721661054994\n",
      "Best parameters: {'criterion': 'gini', 'max_depth': 4, 'n_estimators': 210, 'n_jobs': -1}\n"
     ]
    }
   ],
   "source": [
    "forest = RandomForestClassifier(max_features='sqrt')\n",
    "\n",
    "parameter_grid = {\n",
    "                 'max_depth' : [4,5,6,7],\n",
    "                 'n_estimators': [200,210,208,212],\n",
    "                 'criterion': ['gini','entropy'],\n",
    "                 'n_jobs': [-1]\n",
    "                 }\n",
    "\n",
    "cross_validation = StratifiedKFold(targets, n_folds=5)\n",
    "\n",
    "grid_search = GridSearchCV(forest,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(train_new, targets)\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "2772460d-4fa6-4130-9969-69e31e9bb5e5"
    }
   },
   "outputs": [],
   "source": [
    "output = grid_search.predict(test_new).astype(int)\n",
    "df_output = pd.DataFrame()\n",
    "df_output['PassengerId'] = test['PassengerId']\n",
    "df_output['Survived'] = output\n",
    "df_output[['PassengerId','Survived']].to_csv('./titanic/output.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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