titanic-final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "5767a33c-8f18-4034-e52d-bf7a8f7d8ab8",
    "_uuid": "847a9b3972a6be2d2f3346ff01fea976d92ecdb6"
   },
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "e7319668-86fe-8adc-438d-0eef3fd0a982",
    "_uuid": "13f38775c12ad6f914254a08f0d1ef948a2bd453"
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../input/train.csv')\n",
    "test_df = pd.read_csv('../input/test.csv')\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "ce473d29-8d19-76b8-24a4-48c217286e42",
    "_uuid": "ef106f38a00e162a80c523778af6dcc778ccc1c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "8d7ac195-ac1a-30a4-3f3f-80b8cf2c1c0f",
    "_uuid": "e068cd3a0465b65a0930a100cb348b9146d5fd2f"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </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    ...        Fare Cabin  Embarked\n",
       "0            1         0       3    ...      7.2500   NaN         S\n",
       "1            2         1       1    ...     71.2833   C85         C\n",
       "2            3         1       3    ...      7.9250   NaN         S\n",
       "3            4         1       1    ...     53.1000  C123         S\n",
       "4            5         0       3    ...      8.0500   NaN         S\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "9b805f69-665a-2b2e-f31d-50d87d52865d",
    "_uuid": "817e1cf0ca1cb96c7a28bb81192d92261a8bf427"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "859102e1-10df-d451-2649-2d4571e5f082",
    "_uuid": "2b7c205bf25979e3242762bfebb0e3eb2fd63010"
   },
   "source": [
    "**What is the distribution of numerical feature values across the samples?**\n",
    "\n",
    "This helps us determine, among other early insights, how representative is the training dataset of the actual problem domain.\n",
    "\n",
    "- Total samples are 891 or 40% of the actual number of passengers on board the Titanic (2,224).\n",
    "- Survived is a categorical feature with 0 or 1 values.\n",
    "- Around 38% samples survived representative of the actual survival rate at 32%.\n",
    "- Most passengers (> 75%) did not travel with parents or children.\n",
    "- Nearly 30% of the passengers had siblings and/or spouse aboard.\n",
    "- Fares varied significantly with few passengers (<1%) paying as high as $512.\n",
    "- Few elderly passengers (<1%) within age range 65-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "58e387fe-86e4-e068-8307-70e37fe3f37b",
    "_uuid": "380251a1c1e0b89147d321968dc739b6cc0eecf2"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>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",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived     ...           Parch        Fare\n",
       "count   891.000000  891.000000     ...      891.000000  891.000000\n",
       "mean    446.000000    0.383838     ...        0.381594   32.204208\n",
       "std     257.353842    0.486592     ...        0.806057   49.693429\n",
       "min       1.000000    0.000000     ...        0.000000    0.000000\n",
       "25%     223.500000    0.000000     ...        0.000000    7.910400\n",
       "50%     446.000000    0.000000     ...        0.000000   14.454200\n",
       "75%     668.500000    1.000000     ...        0.000000   31.000000\n",
       "max     891.000000    1.000000     ...        6.000000  512.329200\n",
       "\n",
       "[8 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking numerical features\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "8066b378-1964-92e8-1352-dcac934c6af3",
    "_uuid": "daa8663f577f9c1a478496cf14fe363570457191"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Dean, Master. Bertram Vere</td>\n",
       "      <td>male</td>\n",
       "      <td>1601</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                              Name   Sex Ticket    Cabin Embarked\n",
       "count                          891   891    891      204      889\n",
       "unique                         891     2    681      147        3\n",
       "top     Dean, Master. Bertram Vere  male   1601  B96 B98        S\n",
       "freq                             1   577      7        4      644"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking categorical features\n",
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "0964832a-a4be-2d6f-a89e-63526389cee9",
    "_uuid": "97a845528ce9f76e85055a4bb9e97c27091f6aa1"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "68908ba6-bfe9-5b31-cfde-6987fc0fbe9a",
    "_uuid": "00a2f2bca094c5984e6a232c730c8b232e7e20bb"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "01c06927-c5a6-342a-5aa8-2e486ec3fd7c",
    "_uuid": "a8f7a16c54417dcd86fc48aeef0c4b240d47d71b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "e686f98b-a8c9-68f8-36a4-d4598638bbd5",
    "_uuid": "5d953a6779b00b7f3794757dec8744a03162c8fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "50294eac-263a-af78-cb7e-3778eb9ad41f",
    "_uuid": "d3a1fa63e9dd4f8a810086530a6363c94b36d030"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdbbb7c7e48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "db57aabd-0e26-9ff9-9ebd-56d401cdf6e8",
    "_uuid": "c0e1f01b3f58e8f31b938b0e5eb1733132edc8ad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:720: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdbbb7c7518>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CNRTd4Eyn8VSpWES2AMuA54F5qtpqEQAvg2SLyDVAnqre0GjfXkA+kIfT3WUJcIKq7m3ufFap2LRGVSld/Aol7zxZvyx91BT6nH8jvsTkKEZ2WKxSsUdeW2PHq2pbi3XWD5INICJ1g2R7GUvibOAtVS1x930LmI4Nom0Og4iQPeUbJPTsR+Erf0YDNZSvWURgfxH9Lp5FQkZ2tEM0EdRapeKfuh9ni8ifG0+tHNvrINkXisgKEXlRROqGXvQ8wLYxbZUxagq537oDf7qT3Kp3rmfn3FnUFH4V5chMJLX2zG61+zMf51ay8XS45gNHqup44C3g8bbsbINkm/ZKGTiCAd+5m8Q+zv+vgdJCdjx+CxWbPo9yZCZSWivLPt/9uFJVH288tXLsVgfJVtViVa12Z/8GnOB1X3d/GyTbtFtiVl8GXjWb1GHHAqDVFex+djb7l74Z5chMJHhtjb1XRFaLyJ0iMtbjPq0Oki0iuWGz53PwSnIBcJY7WHZP4Cx3mTEdypeSTv9LbyHz+LOcBRqi6I2HKf7342go2OK+u56+g20P3sCup+/ohEjN4fJavPMMEemP093kYRHpATynqne1sE9AROoGyfYDj9YNkg3kq+o84L/dAbMDQAlwjbtviYjciZMwAe6oa6yIRbc9/DEFJRX07ZXGnddNjXY4po3E5ydn+kwSe+VS8u8nAKX0k3nU7t1N3wv+B19SSpP7BUoLqC2xehhdhdfWWFR1N/BnEXkX+CnwC6DZZOfu8zrweqNlvwj7fDNOnbym9n0UeNRrfNFUUFLBziIbkqMrExGyJ51PYnY/Cl65D62tpmLdp+x88hf0v2QWCZm9oh2iOUxeKxWPFpFfishK4C/AxzjP0YzpVtKPnsSAb9+JP6MnADW7N7LjsVlU79kS3cDMYfP6zO5RYC9wtqqerqoPqmpBBOMyJmqSc4cz8Dv3kNT3CACCZcXsfOIWKjZ0RAcEEy2tJjv3TYjNqnqfqlpxfxMXEnrkMOCq2aQOPx4Arali9/P3UJr/RpQjM+3V6jM7VQ2KyGARSVLVms4IyphY4EtOpf8lsyh+ay77818HDVG84G9U79mMLyWD2n3OzU2gbC9V29eQ4g7gbWKT1waKzcBCEZlH2ODYqvqHiERlTIwQn5+cs79LYq9cit96DDTEgUYVU7S2ip2P30qf835A5rFfi1KkpjVen9ltBF51t88Mm+Ja0b5K5r66isJ9lQDsL6+huLQyylGZSMg68Vz6XzwLpLl/MkrhG3MIlpd2alzGO6/97H4V6UC6mvXb9nLbw4sorzw4Cv2Bylpu/P273HHdVI4aZC+VdzdJ/Y6ElkpCBWs5sOpDsiae12kxGe+8dj15V0TeaTxFOrhYFQopv39qSYNEV6esopZ7/77ERrTqhgJlrfdrD+wv6oRITHt4fWb3k7DPKcCFxPG4sSs3FrXYiXh7wQFWbSpm7PCcTozKRJqXjsUJPezPPFZ5vY1t3MFooYg0HjQ7buwpqWh1mwWLt5Kbk07vrNROiMh0hoQevUkdfhyVG5c1uV4SEsk45pROjsp45SnZuZWD6/hwKgjH7Zh0vXo0/a5kuPeWbue9pds5ekhPpozLZcq4XAb0yeiE6Ewk5Uz/L3Y+cRvBsuKGK0TIOfd6G6oxhnkty74Zpzw6OLevW3Bezv8ocqG1TWeWZQ8EQ3z3rjcp2V/d+sZhjuifyZRxA5gyLpehA3ogYhW1u6JgeSmln73OvkX/hFAASUoh94rbSRk4Mhrh2F8ij1q8shORE4FtqjrUnb8a53ndFryVV++WEvw+brzkOGY/9imBYMPWucQEH/996QTKK2r5eOUuvthUTCjk/D+xdXcZW3ev5dm31tKvV1r9Fd+oI3rh89nf2a7Cn55Fr9Mvp3z1QmpLdpGQ0TNaic60QYtXdiKyFJjmllw6FXgWuBGYAIxW1Ys6J8zWRWPAnQ3b9/HSO+tZuGInqpCa7OfuH5zM8LBuJ/vLa/h01W4WrdzFsnUF1AYO7brQMzOZyWNzmTwul3HDc0hM8Dzom4mibQ/eQG3JLhJ75TL4+/dHKwz7X9Kj1p7Z+cPqyF0KzFHVl4CXRCTu61cfNSibn111Itfd/W92FpXTMzOlQaID6JGexLSJQ5g2cQiV1QGWrNnDopW7+OzLPVRWOw3ae8uqeWPRFt5YtIX01EROHNOPqeNyOe7ovqQkea7CZYxpQavJTkQSVDUAnAnMbMO+ppHU5AROPnYgJx87kNpAkOXri1i0cheLv9jF/nLntePyylreW7Kd95ZsJynRzwmj+jJlXC4njulPRmpilL+BMV1XawnrGeB9ESkCKoEPAUTkKKDV92JEZDpwH06l4r+p6j2N1v8Y+B5Oo0chcK2qbnXXBYGV7qZfqer5Xr9UV5CY4CdvdD/yRvfjBxcdy+rNxSxauYuPV+6iyH39rKY2yKKVu1i0chd+nzD+qBymjB/A5GP609NDi7Ax5qAWk52qzhaRt4Fc4E09+IDPh/Psrlluaaj/I2yQbBGZFz5INs7A23mqWiEi3wd+i3O7DFCpqhPa/I26IL9PGDs8h7HDc/jeBWPZuL2Uj1fuZNHKXWwvOABAMKQsW1fIsnWFPPjSckYd0Yup43OZPDaX/r3To/wNjIl9Xko8LW5i2ToPx251kGxVfTds+8XAtzwct1sTEY4anM1Rg7O56twxbNtT5lzdfbGLDdv2AaAKq7eUsHpLCY/MW8WwAVlMGZ/LlLG5DOmfaV1ajGlCJJ+7NTXQ9aQWtv8uEF4ZMUVE8nFuce9R1X92fIixb3C/TAb3y+SSaSMpKKlg8RdO4vtyUzFujxY27Sxl085S/v6vNQzISa/v0jJicE/r0hJBCVl9G/w0sS0mGhlE5Fs4b2WcFrb4CFXdISLDgHdEZKWqbmy030zcRpMhQ4Z0WrzR0rdXGuefOpzzTx1O6YFqPnG7tHy+rrC+v9/OonJeencDL727gd5ZKUwe6yS+scN64/d3fJeWeB5ZLfeKX7S+kYkZkUx2nga6FpFpwC3AaWEDZqOqO9yfm0TkPeA4nLp6hG0zB5gDTj+7Do7fs7690hr87AxZGcmcNekIzpp0BBVVteSv3sPHK3exZPUeqmqc8U6LS6t4beFmXlu4mcy0RCYe05+p4wYwYWQfkhL9HRKHjaxmuopIJrv6QbJxktxlwBXhG4jIccDDwPTwAXzcgbErVLVaRHKAk3AaL2JStK9o0lISOfW4QZx63CCqa4MsX1fIxyt38umq3ZRVOGWoyipqefuzbbz92TZSkvycMNrpy5c3uh9pKdalxXR/EUt2HgfJ/h2QAbzgPlSv62IyGmcw7hBOy+89jVpxTTOSE/1MPKY/E4/pTzAY4otNxfV9+YpLqwCoqgmycPlOFi7fSYLfx4SRfZgyLpdJx/QnKyM5yt/AmMjwVAigK4jG62JdSSikrN+2t74v364mbj19AmOG9WbKOKdLS9+erd+W1709MiAnnYdvnhaJ0E3LrAXKI0t2cUhV+Wp3GYu+2MWiFbvYtLPp/uFHDc5mitvAMbhfwyFHqmuDfLhsOw+/vJKqmiA90pN4eNaZZKQldcZXMAdZsvPIkp1hd3E5i7/YxccrdrFmawlN/ZUY3C+DyWNzmTpuAD17JHPbw4vYtqeswTbZGcnccd0Uhg6wmm6dyJKdR5bsTAN791exeNVuFq3YyYoNRQRDh/79SErwUdNE9RaA/r3TeGjWNPxx0L/v1ocWUlBSSb/eUe120/1/0R3Ekp1p1oGKGj5b7VRpWbKmgJraoKf9Tj9+EMMHZZGclEByop+UJD8pSQkkJ/lJTjo4n+LOJ/h9Xeqtj43b9/HMm2v5ZNVuABL8wg8vOpYzTxwSje/RdX5xUWbJznhSVRNg2doC5n+4mZUbO3YELZ9P3AToJzkxoUFCPJggDybHBvOJfpLdz3XbpzTapiM7U6/dWsLPH1xITe2hV7aXThvJt84Z3WHn8siSnUcx8QaFiX0pSQlMGTeArIxkfnZ/x1bjD4WUiqoAFVUBoG2l7r1I8EtYQmyUUJMT3ITZckJNSUogOdnPX577vMlEB/D82+uYNnGIFWaIUZbsTJscfUQv+vVKa3aEtQS/cNu1k/H7heqaINU1QapqAlS5P6trglTXBhvOh21T7S6rcqfGZe/bIxBUApW1TY7z25FU4YNlO7hkmpVoj0WW7Eyb+H3Cdf85jrse+7R+bI1w15x3DMeP6rgX44PBkJMEaw8mx6rqINW1gfqE2DBBHvxc3WA+UJ9kq2sO7tvUdzgcZRU1HXo803Es2Zk2O3FMf2ZfP5Vn31rL8vXO87vEBB8/uvx4TpkwsEPP5ff7SE/1kR6hKs21gVBY8msiUYYlyNIDNbz07vomu+bUadwf0cQOS3amXcYOz+Gu4TnMvPvf7Coqp092aocnus6QmOAjMSGJDI81HIpLK3l3yfYm12WmJXFqF/wdxAsbxsoclnhrCpz5n+MZMTj7kOWpyQncfM2JpCTb9UOssmRnTBtkpCbymxtO4aYrTyAlySmTlZmWyEOzzmTc8JwoR2daYsnOmDZKTPBx+vGD6OUOepSZllT/2cQuu+Y2hyUahUtjRTx/967I3qAwpmuLt8em7Wa3scaYuBDRZCci00VkrYhsEJFZTaxPFpHn3PWfiMiRYetudpevFZGzIxmnMab7i1iyCxsk+xxgDHC5iIxptNl3gb2qehTwR+A37r5jcMasOAaYDjzgHs8YY9olkld29YNkq2oNUDdIdrgLgMfdzy8CZ4pTI+cC4FlVrVbVzcAG93jGGNMukUx2TQ2S3bh7ef02qhoASoHeHvc1xhjPunQDhYjMFJF8EckvLCyMdjjGmBgWyWTnZZDs+m1EJAHIAoo97ouqzlHVPFXN69OnTweGbozpbiKZ7OoHyRaRJJwGh3mNtpkHXO1+vgh4R52Of/OAy9zW2qHACODTCMZqjOnmoj1I9iPAkyKyASjBSYi42z0PfAkEgB+qqrcBEIwxpgn2BoUxXZu9QeFRl26gMMYYryzZGWPigiU7Y0xcsGRnjIkL3aaBQkQKga1RDCEH6NjRo7sO++7RU6Sq06N4/i6j2yS7aBORfFXNi3Yc0WDfPT6/e1djt7HGmLhgyc4YExcs2XWcOdEOIIrsu5uYZ8/sjDFxwa7sjDFxwZKdMSYuWLIzxsQFS3bGmLhgyc4YExcs2Rlj4oIlO2NMXLBkZ4yJC5bsuigRCYrI52HTrDbse7qIvHqY539PRNr1AnxHnN89TqKI3CMi60VkqYgsEpFzDve4pnuK2IA7JuIqVXVCNE4sIv5onLcJdwK5wFhVrRaRfsBpUY7JxCi7sutmRGSLiNztXu3li8jxIrJARDaKyPVhm/YQkddEZK2IPCQiPnf/B939VonIrxod9zcishS4OGy5T0Tmishd7vxZ7hXWUhF5QUQy3OXTRWSNu/83O+B7pgH/BdyoqtUAqrpHVZ8/3GOb7smSXdeV2ug29tKwdV+5V30fAnNxxuSdDPwqbJuJwI3AGGA4BxPQLW59tvHAaSIyPmyfYlU9XlWfdecTgL8D61X1VhHJAW4Fpqnq8UA+8GMRSQH+CswATgD6N/WFROToRt8pfMputPlR7vfc7/H3ZeKc3cZ2XS3dxtYNRr4SyFDVMqBMRKrDksanqroJQESeAU4GXgQuEZGZOH83cnGS4Qp3n+canedh4HlVne3OT3a3XygiAEnAImAUsFlV17vnewqY2ThoVV0LROXW3HR/luy6p2r3Zyjsc9183Z9543I3KiJDgZ8AJ6rqXhGZC6SEbVPeaJ+PgTNE5F5VrcIZw/QtVb08fCMR8ZTARORoDk2odU5X1X1h8xuAISLSw67ujBd2Gxu/JorIUPdZ3aXAR0APnIRW6j7sb61l8xHgdeB5EUkAFgMnichRACKSLiIjgTXAkSIy3N3v8qYOpqprVXVCM9O+RttWuOe/T0SS3PP1EZGLmzq2MZbsuq7Gz+zuaeP+nwH3A6uBzcDLqrocWIaTnJ4GFrZ2EFX9g7vPk0AxcA3wjIiswL2Fda/6ZgKvuQ0UBW2MtTm3AoXAlyLyBfAqYFd5pklWvNMYExfsys4YExcs2Rlj4oIlO2NMXLDDSXVdAAAgAElEQVRkZ4yJC5bsjDFxodsku+nTpytOR1mbbIqnyXjUbZJdUVFRtEMwxsSwbpPsjDGmJfZurGm3UEjZVVxOIBhiYJ8MEvz2f6eJXRFLdiLyKHAeUKCqY5tYL8B9wLlABXCNqi51112N8yoQwF2q+nik4jTt88Gy7fz9X2vYWeTUBsjOTOY/TzuK/zx9OG7FE2NiSiT/K54LTG9h/TnACHeaCTwIICK9gNuBSTg1124XkZ4RjNO00b8//YrfPbWkPtEB7Cur5rFXV/H4a19GMTJjmhexKztV/UBEjmxhkwuAJ9R5OXexiGSLSC5wOk6ZoBIAEXkLJ2k+E6lY22tPSQWvfrSJz9cVAjBhZB9mnDyMvr3SohxZ5NQGQsx9bVWz619+fyMzThlG76zUTozKmNZF85ndQGBb2Px2d1lzy2PK2q0l/GLOIiqqAvXLtuzaz5ufbOXO66YyckjnXowGgyGqa4POVBOkxv1cUxuiuiZYv67mkPXBBusPWVcbpLo2RE3dNjUBQi10eAiFlMVf7OY/ThraeV/eGA+6dAOFW1F3JsCQIUM67bzBkHLv35c2SHR1KqoC/OHpJTzw0zNR1YMJx00U9Z+bSToHE0zdfIjq2tb3C7aUgTpZRWVNtEMw5hDRTHY7gMFh84PcZTtwbmXDl7/X1AFUdQ4wByAvL6/T/rV/saGIXcWNi/YetKOwnG/+bH5MJaC28PmE5EQ/yYl+kpL8JCf6nM+JfnwifLGpuMX953+0mf69Mzh5wgBrrDAxI5rJbh5wg4g8i9MYUaqqu0RkAfDrsEaJs4CboxVkU3aXVLS6TUcnupYSUP3PJP/Bbdz5Q9f7SE5MICnR1+w+rXUh+f1TS3h/2fZm1+8tq+a3T+Xzygc9ufb8YxgztHeH/i6MaY9Idj15BucKLUdEtuO0sCYCqOpDOOW8z8UZS6AC+I67rkRE7sSppAtwR11jRazonZXS6jbDB2aRk53qJJImkk6Sm3SSE33NJKW2JaDO9IOLxrO3rIoVGxq+tZKbk8aQfj34ZNVuANZ+tZef3f8RU8fncvV/jGFATkY0wjUG6EaVivPy8jQ/P79TzhUIhvje7LcoLq1qcn2fnqn89eZp+GMoQXU0VWXFhiLyV+8hEAwxdngOk47pT4Lfx7qv9vLo/FWsCrvdTfAL504dyqVfP5oe6UlRjLzbsecEHlmya6dlawu489FPqA2EGixPTPBx+/cmc+yIPp0WSyxSdVpl5766qkF/vPSUBC6ZdjTnnTyUpER/FCPsNizZeWTJ7jBs3lnKP97bwAdLdxBSJTU5gd/eeApH5vbo1DhiWSAY4l+LtvD0grWUVRxspe3bK42rzx3NKRMGWiPG4bFfnkeW7DrAdXf/m51F5QzISefhm6dFJYZYV15Zywtvr2Peh5saXA2PHJLNtTPGcswwa8RoJ0t2HnXfh0qdqG+vNAbkpHfrNycOV3pqItecdwwP/exMTj9+UP3ydV/tY9b/fcSv537KzsIDUYzQdHd2ZWeiYv22vTwyr2Ejht8nnHvSUC6dNpKsjOQoRtel2JWdR5bsTNSoKp+u2s1jr65iR2HjRoyRnHfyMGvEaJ0lO48s2ZmoCwRDLFi0haffXMv+cmvEaCP7xXhkyc7EjPLKWl58Zz2vfLDRGjG8s2TnkSU7E3MK9lbw5BureW9Jw1fSpoxz3sQY2MfexAhjyc4jS3YmZm3Yto9H5n/BFxsbNmKcM/VILvv60daI4bBk55ElOxPTVJXPvtzDo/NXsSOsa0paSgKXnDmSGafEfSOGJTuPLNmZLiEQDLFg8VaeXrCmYSNGz1SuOncMp0wYiM8Xl//u4/JLt4clO9OllFfW8tK763nl/Y3UhDVijBiczbUzjmHs8JxOi+W2hz+moKSCvr3SuPO6qZ123kYs2Xlkb1CYLiU9NZGrzh3Dg7PO5IwTDr6JsX7bPm5+YCGzH/ukwe1uJBWUVLCzqJwCD/UNTfRZsjNdUt+eafz4ihP4449OY1zY1dziL3bzw9++w8P/WEHpgeooRmhijSU706UdNSib2d+fym3fncSgvk6XlGBIeXXhZmbe/W9efGc9NbXBKEdpYkFEk52ITBeRtSKyQURmNbH+jyLyuTutE5F9YeuCYevmRTJO07WJCBPH9Of+n5zB9y8cT1aGUxy0oirA4699yfW/eZv3lmwj1EXHBDEdI5Jl2f3A/wFfxxkO8TMRmaeq9aMoq+qPwra/ETgu7BCVqjohUvGZ7sfv93Hu1KGcfvwg500MtxGjcG8l9z69lFc+3MS1M45pcNtr4kckr+wmAhtUdZOq1gDP4gyM3ZzLicGBsE3Xk5biNGI8NGsaX8sbTN1rtRu27ePnDyzkrkc/YXtBWXSDNJ0uksnO82DXInIEMBR4J2xxiojki8hiEflG5MI03VWfnqn86PLj+cP/nsb4ow5ezX2yajc//N27PGSNGHElVhooLgNeVNXwJ8lHqGoecAXwJxEZ3ngnEZnpJsT8wsLCzorVdDFHDcrmruun8ovvTmJwP6cRIxRSXnMbMV54ex3V1ojR7UUy2TU3CHZTLqPRLayq7nB/bsIZJPu4xjup6hxVzVPVvD594nuAG9MyEeHEMf35y01n8IOLjiXbfa+2oirAE6+v5vp73uZda8To1iKZ7D4DRojIUBFJwkloh7SqisgooCewKGxZTxFJdj/nACcBXzbe15i28vt9nDPlSB6++UwunTay/r3aon2V/OHppdx03/usbDQerukeIpbsVDUA3AAsAFYDz6vqKhG5Q0TOD9v0MuBZbfje2mggX0SWA+8C94S34hpzuNJSEvnWOaN5eNaZDRsxtpfy8wedRoxte6wRozuxd2ONATZu38ej81exIuyqzucTpk8+gsvPGkV25qHlpGJkVDl7N9ajWGmgMCaqhruNGLd/bzKD+2UCTiPG6x9vabIRQ1UJus/3usflQvfX4pWdiJTRwp+lqsbMaNB2ZWc6SjAY4q1Pv+Lv/1rDvrCuKTnZqXz7nNH0yEjiyddXs2lHKQA+gcvPHsXFZ47E3/llpuzKziNPt7EiciewC3gS55d7JZCrqr+IbHjeWbIzHa2iqpZ/vLeBl9/b6On92rMmHcGNl3T6Sz+W7DzymuyWq+qxrS2LJkt2JlKKSyt56o01vJ3/Fa39c7n/J2dwRG6n3vBYsvPI6zO7chG5UkT8IuITkSuB8lb3MqYb6J2Vyv9cdhyzrjqx1W0XrtjZCRGZ9vCa7K4ALgH2uNPF7jJj4kZOdmqr21RWBzohEtMenqqeqOoWWn6J35hub1DfDJIT/S2+WnbUoOxOjMi0hacrOxEZKSJvi8gX7vx4Ebk1sqEZE1vSUhL5+qQhza7PyU5l6vjcTozItIXX29i/AjcDtQCqugLnzQdj4sp3zjuGiWP6H7K8d1YKt39vMokJcT2sY0zzmuzSVPXTRsvs4YSJO0mJfm69diJ3/+Ak0lMTAcjOSOahWWdyZOe2wpo28prsitwSSwogIhfh9LszJu6ICGOH55CV7pR/T0tJICUpYkW/TQfx+if0Q2AOMEpEdgCbcToWG2NMl+A12W1V1Wkikg74VNXKQRhjuhSvt7GbRWQOMBnonBGIjTGmA3lNdqOAf+Pczm4WkftF5OTIhWWMMR3La6fiCuB54HkR6QncB7wPWDs7sOvpOwiUFpCQ1ZfcK2KmNoIxJoznenYicpqIPAAsAVJwXh8zQKC0gNqSXQRKC6IdijGmGZ6u7ERkC7AM5+ru/6mqpyIAIjId5yrQD/xNVe9ptP4a4HccHIjnflX9m7vuaqDuLY27VPVxL+c0xjRvxk2vjAK+gXPBsgh4a/69F4SiFY+InA78RFXPi/S5vLbGjlfV/W05sIj4gf8Dvo4zZuxnIjKvibEknlPVGxrt2wu4HcjD6du3xN13b1tiMMY4Ztz0SgJO97HvNFq1YsZNr8yYf+8FX0UhrE7V4m2siPzU/ThbRP7ceGrl2BOBDaq6SVVrgGfxXkzgbOAtVS1xE9xbwHSP+xpjDnUnhyY6gPHAazNueqXdz99F5EgRWSMic0VknYj8XUSmichCEVkvIhPdaZGILBORj0Xk6CaOky4ij4rIp+52HVp8pLUru9Xuz/ZUxRwIbAub3w5MamK7C0XkVGAd8CNV3dbMvgPbEYMxcW/GTa9k4PSkaM5YnIuJ1w7jNEfhlH67FmcY1SuAk4HzgZ8DVwGnqGpARKYBvwYubHSMW4B3VPVaEckGPhWRf3t9bNaaFpOdqs53P65U1aUdccJG5gPPqGq1iFwHPA58zevOIjITmAkwZEjz1SiMiXNjgcxWtjmZw0t2m1V1JYCIrALeVlUVkZXAkUAW8LiIjMB5NJXYxDHOAs4XkZ+48ynAEA5edB0Wr62x94rIahG5U0TGetxnBzA4bH4QBxsiAFDVYlWtG9Hkb8AJXvd195+jqnmqmtenTx+PYRkTd6pb38TTNl73D4XNh3Auqu4E3lXVscAMnETWmAAXquoEdxqiqh2S6MBjslPVM4AzgELgYRFZ6aGe3WfACBEZKiJJOCWh5oVvICLhxb/O52AGXwCcJSI93X59Z7nLjDFttwJorQFiXivrD1cWBy9YrmlmmwXAjSLOkOUiclxHBuC5n52q7lbVPwPXA58DLfaeVdUAcAPOF1gNPK+qq0TkDhE5393sv0VklYgsB/4b95egqiU4/xN85k53uMuMMW00/94LgjjPw5rzwvx7L4jEY6pwvwXuFpFlNP/47E6c29sV7q3wnR0ZgNd+dqOBS3EeKBYDzwE3tbafqr4OvN5o2S/CPt+MUxS0qX0fBR71El80hWqrCdXWAM7AycbEovn3XvDUjJte8QGzcR4LAVThPD76SbM7euAO2zA2bP6aZtaNDNvtVnf9e8B77udK4LrDiaUlXvvZPYrTdeRsVbXhk3AS276PX6Z08SuEqpzaCIF9e9j38ctkTfkG7pW4MTFj/r0XPDHjpleexnk2ngosn3/vBXHTd7XVZOd2Dt6sqvd1Qjxdxt73n2XfwhcbLlSl5N2nCAVq6HXqpdEJzJgWzL/3ggDwSbTjiIZWn9mpahAY7DYyGCBYsZ99i//Z7PrSRS8TrLSSf91d315pDMhJp2+vtGiHYjzwehu7GVgoIvMIGxxbVf8QkahiXOWm5RBsfggODdRSuXkFGWNO6sSoTGe787qp0Q7BtIHXZLfRnXy03jmx29NgbavbFL/9OLXFO0gfNYWkPoNb3b6rsvJWpqvwWs/uV5EOpCtJHjiy1W2C+4vZ+8Fz7P3gORJzBpF+9GTSR08hqe8R3arxoq68lTGxzmvXk3dxRxYLp6qeX+3qTpJyBpE24kQq1n/W5Hp/ejbBqgP1t7q1RdvZV/Qi+xa+SELP/qSPmkzGqCkk5Q7vVonPxL5Nsy88pMTTsFteOqwSTyLy38D3gaWq2uEDcYnIL4EDqvr7wzmO19vY8H44KTj97eJ63Ng+599IwUu/o3LLygbLU4ceS79v3gQiVKxfwoE1i6jcuAwNOH3xAnt3U7ron5Qu+icJWX1IHzWZ9FFTSB44AhHPfbyNaZNNsy9stsTTptkXzhh2y0uHU+LpB8A0Vd1+GMeIOK+3sUsaLVooIo0HzY4r/pR0+l9xO9U71rL7+bsJVR7A36M3/S+/rf5qLWPsKWSMPYVQTSUVG5dRvmYxFeuXoLVVAARKCyn9ZD6ln8zHn9nLvdWdTMqgUYjPKt6bDtViiadNsy+cMOyWl4JtPaiIPAQMA94QkWeB4TidiBOBX6rqK26R3m8A6cAI4PdAEvBtnHdoz1XVEhH5L5zCHknABuDb7pAQ4ecbjlMnsw9QAfyXqq7xEqvX29heYbM+nKKaWV727c5EhJRBo/CnZhKqPIAvIanJ21JfUioZo6eSMXoqodpqKjd9TvmaxZSvz0ernT/LYFkJ+/NfZ3/+6/jTs0k7eiIZo6aQcsQxlvhiVFdpnNk0+8KIlXhS1evdiuRnAD+miRJNYec4DufOcAPwM1U9TkT+iFP+6U/AP1T1rwAichfwXeAvjU45B7heVdeLyCTgATxWSvJ6G7uEg8/sAsAWNxDTRr7EZNKPnkT60ZPqu6gcWLOYinWf1r+JESzfR9nSNylb+ia+1EzSR04kffQUUo8ci/ibqoxjoqELNc50RoknaL5EEzgVT8qAMhEpxSnvBrAS5+oSYKyb5LKBDBoV/xCRDGAq8ELYRUWy1+BaTHYiciKwTVWHuvNX4zyv2wI0Lq9u2kgSEkkbcQJpI05Ag9dRufUL54pv7SeEKpwq+KHKMsqWv03Z8rfxpaSTNuJE0kdNJnXYsfgSrJ+38aQzSjzBwRJNaxssdK7AWisBBTAX+IaqLndvfU9vdHwfsE9VJ7QnuNau7B4GprkBnwrcDdwITMC5nLyoPSc1hxJ/AmnDJpA2bAI50/+Lqq++dBLfmsUEy/cBEKoq58DK9ziw8j0kKZW0ESeQMWoKqcOPw5fo+T84E3/qSjy1VOG2I0o81ZVoutEt3Hmcqi5rw/6ZwC4RSQSu5ND6l/tFZLOIXKyqL7iloMar6nIvB28t2fnDSitdCsxR1ZeAl0Tk8zZ8CdMG4vOTeuQ4Uo8cR++zv0v19nUcWLOI8tWLCJYVA6A1lZSv+ojyVR8hicmkDT+e9NFTSBt+PL7k1Ch/AxNLht3yUnDT7AtvAZ5sZpMXht3yUkeUeLoT59nbCnG6FmwG2jJq2G047+0Wuj+buvW+EnjQraeZiFOgpGOSnYgkuLXpzsQtge5xX9MBRHykDB5FyuBR9J52NdU7N7hXfIsI7HPGqdXaasrXLKJ8zSLEn0jq8Amkj5pC+og8fCnpUf4GJhYMu+WlpzbNvjBSJZ6ODJs9pESTqs7FuUU9ZPvwdar6IPBgE/v/MuzzZto5+FZrCesZ4H0RKQIqgQ8BROQooLQ9JzTtJ+IjZeBIUgaOpNfXvk3Nns2Ur15E+ZrF1JY4lbc0WEvFus+oWPcZhb4EUoeOJ2P0FNJGnIg/Le7f9Itrw2556YlNsy9sUOJp2C0vWYknAFWdLSJvA7nAm3qwOqUP59ldizwMkv1j4Hs4LbyFwLWqutVdF8RpqQH4SlXPx9QTEZL7DyO5/zB6nn4FtYXbnFvdNYuoLXQHZgsFqNy4lMqNS0F8pB45zunEfPQk/Olx33MoLg275aW4LfHU6q2oqi5uYtm61vbzOEj2MiBPVStE5Ps4pZvrCsFVtrfVJd6ICEl9h9Cr7xB6nXopNUXb6xs3avZsdjbSEJWbl1O5eTlF//orKUNGO7e6R08iIbNXyycwDQT2F1P6yTxq3ccIgbISKreuIvWIY6IcmWlJJJ+71Q+SDeD2rr6AsC4rqvpu2PaLgW9FMJ64kZQziKSTL6LnyRdRu3e3k/hWL6J61wZnAw1RtXUVVVtXUbzgEZIHHU3GaDfxZdkobS2pLdnJziduJVh+8CmO1laz66lfkHPu9fQ47utRjM60JJLJzusg2XW+C7wRNp8iIvk4t7j3qGrz1TJNsxJ79id7yjfInvINaksLKF/zCeVrFlG9va4rlFK9fQ3V29dQ/NZjJA8Y4b6vO5nEnv2jGnssKvrX3xokugbrFvyNtBEnkpCR3clRGS9iokVVRL6F8wraaWGLj1DVHSIyDHhHRFaq6sZG+8XEINkJWX0b/IxViVl9yZ40g+xJMwjsL6Z87SeUr1lM1bbVoE7hi+qd66neuZ6Sd54kqd9Q0kdPIX3UZJJ6D2xwLA0FqVifT8D9hx+qriBUW92l+vtpMECoppJQdSVaU1n/ucGyuvmaSoIH9lG5uYVeDsEAB1Z9QPYke7wciyRSI2KJyBScF4HPdudvBlDVuxttNw3n/bfTVLWgmWPNBV5V1RebWg+Ql5en+fn5HRR9fAkc2EfFuk8pX7OIyi1f1Ce+cIl9htSXpvJlZLPnuV9TvXN9g22cd0RvI7HXgIjEqaposBatbpiYNCwh1S9rJXFpdaWnIqxtlTVpBr2nXdPhx22B1QjzKJLJLgFYh9M/bwfO+K9XqOqqsG2OA14Epqvq+rDlPYEKVa0WkRyculsXNGrcaMCSXccIVpRRXpf4Nq+E0KGVvCQpBa2panL/xJxBDJr5x/pyVaqK1lYRqq4KS0IVhGrc+brP1ZWEaioIVVcdcpUVnqgItbkwR6fqfda1ZJ34H515Skt2HkUs2QGIyLk4Par9wKNuV5Y7gHxVnedWRBgH1L1N/ZWqni8iU3FeVQvhdHP5k6o+0tK5LNl1vGBVORXrP6N89WIqN33u+UrI3yMHNESopspJik1cKXYuQZJS8CWl4ktOxZeUirg/6+eTUvAlpznL3M8Stt6XnIokpVLwzz85XXmaOktCEkNunNPZ/Rkt2XkU0WTXmSzZRVaoupKKDUsozX+D6u2eyocdHvG1kJiamG+8Td18UiqSlNxhhVED+4vY+cStBEoLD4m37wX/Q8YxJ3fIedrAkp1HluxMm1R+tYpdT7Zcu82XmklCZi/36qjuiioNX1JKk1dLvgaJyV3eTG3AWBCsKGP/kn+xd+GLEAwgSakM+NYdJOcOi0Y4sflLikEx0Rpruo6UQaPwZ/YiWFbS9AY+P4Nm/qlbd7/wp2XS85SLOfDF+9SW7CIhIztaic60gQ16YNpEfH63tbHpC4qeJ1/UrROd6brsys60WcaYk5DEZPa+/+zB19F8fnLO/h6Z9gaBiVF2ZWfaJX1EHgO/+zsSsvsBkJDdlx7HnxWzz9mMsWRn2k1EEJ/zV8hSnIl1luyMMXHBkp0xJi5YsjPGxAVrjTWmnbpKtRvjsGRnTDvlXtHymyQmtthtrDEmLliyM8bEBUt2xpi4YMnOGBMXLNkZY+JCRJOdiEwXkbUiskFEZjWxPllEnnPXfyIiR4atu9ldvlZEzo5knMaY7i9iyS5skOxzgDHA5SIyptFm3wX2qupRwB+B37j7jgEuA44BpgMPuMczxph2ieSVXf0g2apaA9QNkh3uAuBx9/OLwJnilM24AHhWVatVdTOwwT2eMca0SySTXVODZA9sbhtVDQClQG+P+xpjjGdduoFCRGaKSL6I5BcWFra+gzEmbkUy2e0ABofND3KXNbmNO85sFlDscV9UdY6q5qlqXp8+fTowdONVQlZfEnvl2vuhJuZF8t3Yz4ARIjIUJ1FdBlzRaJt5wNU4g2BfBLyjqioi84CnReQPwABgBPBpBGM17WTvh5quImLJTlUDInIDsICDg2SvCh8kG3gEeFJENgAlOAkRd7vngS+BAPBDVY3toeCNMTHNxo01pmuzivgedekGCmOM8cqSnTEmLliyM8bEhW7zzE5ECoGtUQwhByiK4vmjyb579BSp6vQonr/L6DbJLtpEJF9V86IdRzTYd4/P797V2G2sMSYuWLIzxsQFS3YdZ060A4gi++4m5tkzO2NMXLArO2NMXLBkZ4yJC5bsjDFxwZKdMSYuWLIzxsQFS3bGmLhgyc4YExcs2Rlj4oIluy5KRIIi8nnYNKsN+54uIq8e5vnfE5F2vQDfEed3j5MkIn8SkQ3u9KqIDDnc45ruKZID7pjIqlTVCdE4sYj4o3HeJvwayASOVtWgiHwHeEVETlDVUJRjMzHGruy6GRHZIiJ3u1d7+SJyvIgsEJGNInJ92KY9ROQ1EVkrIg+JiM/d/0F3v1Ui8qtGx/2NiCwFLg5b7hORuSJylzt/logsEpGlIvKCiGS4y6eLyBp3/292wPdMA74D/KhuMCZVfQw4AEw73OOb7seSXdeV2ug29tKwdV+5V30fAnNxhqmcDPwqbJuJwI3AGGA4BxPQLW59tvHAaSIyPmyfYlU9XlWfdecTgL8D61X1VhHJAW4Fpqnq8UA+8GMRSQH+CswATgD6N/WFROToRt8pfMputPlR7vfc32h5vvudjGnAbmO7rpZuY+e5P1cCGapaBpSJSHVY0vhUVTcBiMgzwMnAi8AlIjIT5+9GLk7iWOHu81yj8zwMPK+qs935ye72C0UEIAlnTOBRwGZVXe+e7ylgZuOgVXUtEJVbc9P9WbLrnqrdn6Gwz3XzdX/mjcvdqDug+U+AE1V1r4jMBVLCtilvtM/HwBkicq+qVuEM6/eWql4evpGIeEpgInI0hybUOqer6r6w+Y3AEBHJdJN5nROAl7ycz8QXu42NXxNFZKj7rO5S4COgB05CKxWRfsA5rRzjEeB14HkRSQAWAyeJyFEAIpIuIiOBNcCRIjLc3e/ypg6mqmtVdUIz075G25YDjwN/qGswEZGrgCpgYVt/Gab7syu7ritVRD4Pm/+XqnrufgJ8BtyP8+zrXeBlVQ2JyDKc5LQND0lDVf8gIlnAk8CVwDXAMyKS7G5yq6quc2+NXxORCpxniZltiLU5NwO/A9aKSCpQCExRK9JommDFO023ICL9gTeAB1XVqgebQ1iyM8bEBXtmZ4yJC5bsjDFxwZKdMSYuWLIzxsSFbpPspk+frjgdZW2yKZ4m41G3SXZFRUXRDsEYE8O6TbIzxpiWWLIzxsQFe13sMKgqVVtWUrnFKQqSOvRYUo4Yi1vxo1tTVap3rKViw1I0FCR18BhSh09AfLFS19OYhiKW7ETkUeA8oEBVxzaxXoD7gHOBCuAaVV3qrrsapy4awF2q+nik4myvYEUZu1+4h+rta+qX7fv4ZVIGj6bfxbPwp2ZEMbrICtVUsecf91K5cWn9stJF/ySp31D6X3IzCT16RzE6Y5oWydvYucD0FtafA4xwp5nAgwAi0gu4HZiEU2DydhHpGcE426Vg3n0NEl2dqm2rKZz/lyhE1HmKFvy1QaKrU7NnM3te+h32CqKJRRG7slPVD0TkyBY2uQB4wq1QsVhEskUkFzgdpyZaCYCIvIWTNJ+JVKxtVVP4FZUblzW7vmJ9PoWvP4w/rSMKe8SWUE0VB1a83+z66p3rqdq2mrgr0kMAABZtSURBVNQhVizYxJZoPrMbiFNGqM52d1lzy2NG9c4NrW5TtuzNTogkNlXvWGfJzsScLt0aKyIz3cFh8gsLCzvvvAmJnXaurqhs+btUbFyGDfBlYkk0r+x2AIPD5ge5y3bg3MqGL3+vqQO4dcvmAOTl5XXag6LUYROQhEQ0UNv0BgmJ5F5+O77k1M4KqdNoMMCuZ+5AqxpXaD+otng7u5+9i8TeA8g68T/IGHc6vqSUZrc3pjNEM9nNA24QkWdxGiNKVXWXiCwAfh3WKHEWTkXamOFPzSR76oXs/eDZJtf3PPkSUoeM7uSoOk/vM75F0RsPN7nOl5ZFqKIUgNrinRT966+UvPc0mROm0SNvOolZfTszVGPqRbLryTM4V2g5IrIdp4U1EUBVH8IZu+BcYANO15PvuOtKROROnLLhAHfUNVbEkuyTL8KXnMq+j18mWO4Mj+BPzyb7pG/SI+/cKEcXWT2OPwvxJ1DywXME9zuv6flSM/5/e/ceHlV9JnD8+2Zym1xJIFFUQAUsKF6QgFYs3gsFF93aKpV2sbXV9tlW2z7dttZtV7F23XZ7ta2VVretW7Xb2lYsUKqAVwQTripYRVARkSQkmdxvk3f/OAecXMichJw5k8z7eZ55MnNu8x4gL+d3zu/3eymceTmFsz9M+77XiJT/laZXNoJ20dXaRGTDo0Q2Pkbu+86hcNYCsk6YkhL9EU3yGDEzFZeVlWlFRUXCv1ejHbRXOs9TMkvHIaHUuZ+nXVHaq/ZCtJOMknGkZWR1W98ZqSKy6W80bHmCrtbGbusyjz2ZwlkLyJs62+6BHh37H8MjS3bGd13trTS++BSR8hV0HNzXbV0odxQFM+ZScPZcQrmFAUU4rFmy88iSnUkY1S5adm8jUr6idz/FUDp5p51P4cwFZB17cjABDk+W7DyyZGcC0V79NvUVq2jYvg7taOu2Lnv8qRTOvJycU8psrG18luw8smRnAhVtaaRh2xrqK1bRGeneVzK9sJSCmR8i/8xLCGXnBhRh0rNk55ElO5MUtCtK86vlRF74K617d3ZbJxnZ5J95EQVl88kcfVxAESYtS3YeWbIzSadt/24i5Sto3PEsRDu7rQtPPJvCWZcTPukM67risD8EjyzZmaTV2VhL/ea/07B5NdGmSLd1GWNOcEdnXNCry0ui7H9wKZ2RStILSxl77bcCiQFLdp7Z5J0maaXnFVE85xqKzvswjTueJfLCCtoP7AGgo/ptqlfdS82TvyP/rEspLPsQ6QVjEhpfZ6SSjpr9Cf1OM3iW7EzSk/QM8s+4iLzTL6R17w4iL6yg+dVyZ3RGSyOR5/9CZMNycqecS+Gsy8k6/hRr4ppeLNmZYUNECI8/jfD40+ioq3S6rmx9gq62ZtAumnaup2nnerLGTqJw1uXkTj03pUa0mP5ZsjPDUsaoUkZfuoSiOVfTsP1J6stX0lHzDgBt+3dR+eiPCK0pomDGPAqmX2ajM4wlOzO8pWWGKSz7EAUz5tLy+hZndMbubQBEG2upfeoh6p79I3nTPkDBzAVkHXNisAGbwFiyMyOCSBo5k2aQM2kG7VV7iZSvpPHFJ9HOdjTaQcO2tTRsW0v2hGkUzlxAzuQZNjojxViyMyNOZsk4SubfSPFF19Kw5QkiFauINhwEoPXNl2h98yXSRx1D4cz55J95MWlZOQFHbBLBkp0ZsZxJVv+ZwnP+iaZ/bCRSvoK2t/8BQGfdAQ4+/j/UPPUw+WdeRGHZfDKKxwYcsfGTJTsz4kkonbxTZ5N36mxa39lFffkKGnc8B11RtL2F+vKV1JevImfyDApnLiD7xNOt68oI5GuyE5F5OIWwQ8CvVPWuHut/CFzkfswBSlV1lLsuCrzorntLVRf6GatJDdnHTSL7ipspvvgT1G9aTf2Wv9PVXA8oza9V0PxaBRkl453RGdM+ENjoDDP0/JyWPQT8DLgMpxxiuYgsV9Udh7ZR1S/FbP8FYHrMIVpU9Sy/4jOpLT2/mOILP8ao86+i8aVnqC9fQXvlmwB0VL1F9cp7qFn3vxRMv4yCGfNILxjd6xiHhlqOjAGXI5+fV3azgF2quhvALaxzBbDjCNt/DKdOhTEJk5aeScFZl5B/5sW0vvkSkfIVNL9aAShdLQ3Urf8TdRseJXfq+50m7vGn0LrvVWqfeojO2ncB6KyrJFLxNwpmzLXmbxLzM9n1Vez6nL42FJEJwEnA2pjF2SJSAXQCd6nqX/wK1BgRIXzi6YRPPJ2O2neJVKyiYesatL0FuqI0vfwsTS8/S8aYcXQcfAc0+t7OXVEOrv4lnZEDjL5kSXAnYfqVLEWyFwF/VI39F8QEVS0DrgV+JCITe+4UVJFsM7JlFB3LmMs+yYSbfsnoD15PetGxh9d1VO/tnuhiRDYsPzyKwyQfP5PdkYpg92UR8FDsAlXd5/7cjVMke3rPnVR1maqWqWpZSUnJUMRszGFpWWEKZ85n3Ofu5pirbyHr+FPi7tO4Y30CIjOD4WeyKwcmi8hJIpKJk9CW99xIRKYARcDzMcuKRCTLfT8GmM2R7/UZ4yuRNHInlzH6sk/F3bZnyUiTPHy7Z6eqnSLyeWA1TteT+1X1ZRFZClSo6qHEtwh4WLvPIjoVuFdEunAS8l2xT3GNCUJG8VgklIFGO464TWbphARGZAbC1352qroSWNlj2bd6fL6tj/3WA6f7GZsxAxUK55E3bQ4N29b0uT4tp5DcqeclOCrjVbI8oDBmWBh92SfJHje11/K07FyOvfrr1gk5ifV7ZSciDfTTZ1JVC4Y8ImOSWFpWmLEfv53m1yqoXH432t5CWriAcZ/9CaGc/KDDM/3oN9mpaj6AiNwB7AcewCnwsRiwUdMmJUlaiNz3nUN63gN01LQQCudaohsGvDZjF6rqz1W1QVXrVfUenNEQxhgzLHhNdk0islhEQiKSJiKLgSY/AzPGmKHkNdldC1wNHHBfH3WXGWPMsOCp64mqvoE1W40xw5inKzsROUVE1ojIS+7nM0Tk3/0NzRhjho7XZuwvgVuADgBV3Y4z8sEYY4YFr8kuR1Vf6LGsc6iDMcYYv3hNdtXuFEsKICIfwel3Z4wxw4LXsbH/CiwDpojIPmAPTsdiY4wZFrwmuzdV9VIRyQXSVLXBz6CMMWaoeW3G7hGRZcC5gE3YZYwZdrwmuynAEzjN2T0i8lMROd+/sIxJfumFpWQUjyW9sDToUIwH0n3OTA87iBTh1IJdrKohX6IahLKyMq2oqAg6DGMSzcqZeeR5PjsRuUBEfg5sArJxho/F22eeiPxDRHaJyNf7WH+diFSJyFb39emYdUtE5DX3ZSWbjDFHxdMDChF5A9gC/B/wb6oadxIAL0WyXb9X1c/32LcYp4ZsGU53l03uvrVe4jXGmJ68Po09Q1XrB3jsgRbJjjUXeFxVa9x9Hwfm0aMCmTHGeBVvpuKvqup3gTtFpNfNPVW9qZ/dvRbJvkpE5gCvAl9S1b1H2Pf4/mI1xpj+xLuy2+n+9OvO/2PAQ6raJiI3Ar8BLva6s4jcANwAMH78eH8iNMaMCPGmZX/Mffuiqm4e4LHjFslW1YMxH38FfDdm3wt77PtkH/EtwxnZQVlZ2cAeKxtjUorXp7HfF5GdInKHiEzzuE/cItkiElvHYiHvXUmuBj7oFssuAj7oLjPGmEHxOnnnRSJyLE53k3tFpADnKeq3+9nHS5Hsm0RkIc4MKjXAde6+NW6Rn3L3cEsPPawwxpjBGEyn4tOBrwLXqGqmL1ENgnUqNinKOhV75HWm4qkicpuIvAjcDazHuY9mjDHDgtd+dvcDDwNzVfUdH+MxxhhfxE127kiIPar64wTEY4wxvojbjFXVKDDOfaJqjDHDktdm7B7gORFZTkxxbFX9gS9RGWPMEPOa7F53X2lAvn/hGGOMP7z2s7vd70CMMcZPXqd4WodbWSyWqnoex2qMMUHy2oz9Ssz7bOAqrG6sMWYY8dqM3dRj0XMi0rNotjHGJC2vzdjimI9pODMIF/oSkTHG+MBrM3YT792z6wTeAK73IyBjjPFDvJmKZwJ7VfUk9/MSnPt1b+BtenVjjEkK8UZQ3Au0A7hTp/8nzmzCEdxJM40xZjiI14wNxcwjdw2wTFUfAR4Rka3+hmaMMUMn3pVdSEQOJcRLgLUx67ze7zPGmMDFS3YPAU+JyKNAC/AMgIhMwmnK9stDkewvi8gOEdkuImtEZELMumhM8ezlPfc1xpiBiFdw504RWQOMBf6u701rnAZ8ob99PRbJ3gKUqWqziHwOp+DONe66FlU9a8BnZIwxfYjbFFXVDX0se9XDseMWyVbVdTHbbwA+7uG4xhgzYF6riw3GQAtdXw+sivmcLSIVIrJBRK70I0BjTOpIiocMIvJxnFEZF8QsnqCq+0TkZGCtiLyoqq/32M+KZBtjPPHzyi5ukWwAEbkUuBVYqKpth5ar6j73526cAtnTe+6rqstUtUxVy0pKSoY2emPMiOJnsvNSJHs6TsflhapaGbO8SESy3PdjgNnYiA1jzFHwrRnrsUj294A84A8iAvCWqi4EpuIU4+7CSch39XiKa4wxAzLgItnJyopkmxRlRbI98rMZa4wxScOSnTEmJViyM8akBEt2xpiUkBSdio0Zjr5573oqa5opLc7hjhvPCzocE4clO2MGqbKmmXeqm4IOw3hkzVhjTEqwZGeMSQmW7IwZIFVl+64qIo3OUO6m1k5a26xmfLKzZGfMALR1RFl630ZuvWc9Ta1Ogos0tnHjXU+w5524k3ebAFmyM2YAfv3Yy1TsPNBreU19G0vv20hHZzSAqIwXluyM8SjS2MrqjW8ccX11XQvrt+9PXEBmQKzriTGu1vZOqmpbOFDTTFVtM5W1LVTWNFNZ67xq6tviHmPX23VccPYJCYjWDJQlO5Mymlo6qKxtPpzQDr+vdZJbpLH9qL8jnGW/UsnK/mbMiKCqNDQ7ycy5GmuJee98bmrpGNSxw1khSotyKCnK4ZU3a2hsPvJxZp953GBPwfjMkp0ZFlSVuoa2w4krNokdSmqt7YN7OJAXzqC0KIfS4rD7M4fSojAlRTkcU5xDXjgDd3JZNr9Sye33baCrq/c8kHPPncCEYwuO6jyNfyzZHYV3qhtZ/vRutr5aBcD0U0pYOGciY8fkBhyZ/6rrWlj+zG4qdr5LNKqcdvJorpgzkQljB/fLHu1Sautbu98vi7kyq6ptob2za1DHLszLdJJYTCJzfjrvc7IzPB/r7Cml3P6Zc/nNyp3s2lsHQJoIi+dN4aqLJw8qPpMYvs5ULCLzgB/jTMv+K1W9q8f6LOC3wAzgIHCNqr7hrrsFp7xiFLhJVVf3912Jnql4x56D/Mey53tdTYSz0ll6w/uZcmJxwmJJtDf213PrPc9R39T9HldGehrfuG4WZVOP6bVPZ7SL6rqW3g8Aat9LZtE+rpbiEYGi/OzuCexQQivKoaQoTHamP/+nf/rOxzlQ08zYMbksu+VSX77DA5up2CPfruxEJAT8DLgMp2ZsuYgs71FL4nqgVlUnicgi4L+Aa0TkVJwCPacBxwFPiMgpqpoUnZiiXcr3H9zcZ7Oppa2THzy4mV98/RLS0kbev0NV5ce/39Ir0QF0dHbx3QcqWLLgVGrqW7vdP6uJtDCIXEaawJhRTpMyNqEdU5RDSXGYklFhMtJDQ3BmAxdy/35H3t/yyORnM3YWsMsthYiIPAxcQfcqYVcAt7nv/wj8VJybI1cAD7ulFfeIyC73eM/7GK9n21+rorKm+Yjr9x9s4tPfeZzszGB+Cf3U3tHFgX7OvaWtk1/8abvn46WHhJJR790vc+6TuffLinIYXZhNKGTdQc3R8zPZHQ/sjfn8NnDOkbZxq5FFgNHu8g099j3ev1AHprK2Je42VR62SQWZ6Wm9mpeHEllpcZii/OwReQVsks+wfkAhIjcANwCMHz8+Yd9bMiocd5ui/CwyM0belV1nZxcH61v73eby80/mohknUFqUQ2Fe5uEnmcYEyc9ktw8YF/P5BHdZX9u8LSLpQCHOgwov+6Kqy4Bl4DygGLLI4zhz8hjGjApTXdf31VtpcQ7Lbrn08D2dkURV+eIPn2L3vr4HveeFM1iyYKpvDwWSSWlxTrefJrn5eTOkHJgsIieJSCbOA4flPbZZDixx338EWKvO4+HlwCIRyRKRk4DJwAs+xjogoVAaX/7Y2WRm9P7jy8oI8aVF00dkogMQEW66+ixyw727a4RCws2LpqdEogO448bzuPeWS21K9mHC764n84Ef4XQ9uV9V7xSRpUCFqi4XkWzgAWA6UAMsinmgcSvwKaAT+KKqrurvu4Iokr33QAN/fnIXW1871M+ulCsvmMi4Y/ITGkcQ3j3YxKNPv075jgNEo11MmzSGK+dMZOIJo4IOLdWMzP9VfeBrskukIJKdMUnAkp1H9kzfGJMSLNkZY1KCJTtjTEoYMffsRKQKeDPAEMYA1QF+f5Ds3INTrarzAvz+YWPEJLugiUiFqpYFHUcQ7NxT89yHG2vGGmNSgiU7Y0xKsGQ3dJYFHUCA7NxN0rN7dsaYlGBXdsaYlGDJ7iiJyP0iUikiLwUdS6KJyDgRWSciO0TkZRG5OeiYEkVEskXkBRHZ5p777UHHZPpnzdijJCJzgEbgt6o6Leh4EklExgJjVXWziOQDm4Are0y9PyK5M2rnqmqjiGQAzwI3q+qGOLuagNiV3VFS1adxZmxJOaq6X1U3u+8bgJ0k0YzSflJHo/sxw33ZlUMSs2RnhoSInIgzVdfGYCNJHBEJichWoBJ4XFVT5tyHI0t25qiJSB7wCM68g/VBx5MoqhpV1bNwZtKeJSIpdRtjuLFkZ46Ke7/qEeB3qvqnoOMJgqrWAesAG6OaxCzZmUFzb9LfB+xU1R8EHU8iiUiJiIxy34dx6iO/EmxUpj+W7I6SiDyEU8/2fSLytohcH3RMCTQb+ARwsYhsdV/zgw4qQcYC60RkO069lcdV9a8Bx2T6YV1PjDEpwa7sjDEpwZKdMSYlWLIzxqQES3bGmJRgyc4YkxIs2ZnDRCTqdh95SUT+ICI5/Wx7m4h8JZHxGXM0LNmZWC2qepY7e0s78NmgAzJmqFiyM0fyDDAJQET+RUS2u3O3PdBzQxH5jIiUu+sfOXRFKCIfda8St4nI0+6y09x54La6x5yc0LMyKcs6FZvDRKRRVfNEJB1nvOvfgKeBPwPnqWq1iBSrao2I3AY0qup/i8hoVT3oHuPbwAFVvVtEXgTmqeo+ERmlqnUicjewQVV/JyKZQEhVWwI5YZNS7MrOxAq7UxZVAG/hjHu9GPiDqlYDqGpfc/dNE5Fn3OS2GDjNXf4c8GsR+QwQcpc9D3xDRL4GTLBEZxIlPegATFJpcacsOswZ6x/Xr3FmKN4mItcBFwKo6mdF5BxgAbBJRGao6oMistFdtlJEblTVtUN4Dsb0ya7sTDxrgY+KyGgAESnuY5t8YL873dPiQwtFZKKqblTVbwFVwDgRORnYrao/AR4FzvD9DIzBruxMHKr6sojcCTwlIlFgC3Bdj82+iTNDcZX7M99d/j33AYQAa4BtwNeAT4hIB/Au8B3fT8IY7AGFMSZFWDPWGJMSLNkZY1KCJTtjTEqwZGeMSQmW7IwxKcGSnTEmJViyM8akBEt2xpiU8P/t8pzzRK0lpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "a21f66ac-c30d-f429-cc64-1da5460d16a9",
    "_uuid": "c8fd535ac1bc90127369027c2101dbc939db118e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdbb7c295c0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Ayur6paqegw4EzhkDHVIktRZ4wgA2wF39k2vauZJkqQRWTjuAtYkyRJgSTP5cJKbxllPi54L3DfuIobxlnEXsP6YN68ZTPt1u7CqFs9kO/bZ9ZP99mfmzes2sj5bVTNpN2NJ9gWOr6pfbabfC1BVHxlpIeuJJMuqamLcdWh4vmbd5us/P/m6Pd04dgFcBeySZKckGwGvA84fQx2SJHXWyHcBVNXjSd4GfA1YAHy2qlaMug5JkrpsLMcAVNVXga+OY9vroVPGXYCmzdes23z95ydftylGfgyAJEkaP08FLElSBxkAJEnqIAOAJEkdZACQJKmDDACSJHWQAUCSpA4yAEiS1EEGAEmSOsgAIElSBxkAJEnqIAOAJEkdZACYoSRPJFne93PcNNrun+SCWW7/siQzurb1XGy/Wc8zk3w0yc1JrknyrSSvHrDc8UmOHjB/0yRnJLkuyfeSXJ5k89nW1az7T5McNAfrmavnaqckVyRZmeSs5lLYGjH7rf12mut5W9NnK8lzZ7u+9c1Yrga4gfhxVe0xjg0nWTCO7Q7wZ8C2wIur6tEk2wCvnEb7dwL3VNUvAyTZFfjpsI2TLKyqxwfdV1V/Mo06RuFjwIlVdWaSvwGOAT4z5pq6yH5rv52ObwAXAJeNuY5WOAIwx5LcluQjzaeLZUn2TPK1JP+e5C19i26R5CtJbkryN0me0bT/TNNuRZITpqz3Y0muAX67b/4zkpye5IPN9MFNor8myRdWJ/Mki5Pc2LT/zTl4nJsCvwe8vaoeBaiqe6rq7GmsZlvgrtUTVXVT84a0Y5Lv9W3r3UmOb25fluQTSZYB70tye99zt1mSO5tPOKcnOax53F/oW9fPPhmM8LkKcABwTjNrKXDobNeruWO/td8OUlXXVtVtc7Gu9ZEBYOY2yc8PJR7Rd98dzaeMfwVOBw4D9gFO6Ftmb+DtwO7Ai3jqD/Z9VTUBvAR4ZZKX9LX5QVXtWVVnNtMLgTOAm6vq/ekNUb0fOKiq9gSWAccm2Rj4W+DXgb2AXxz0gJLsOuUx9f9sOWXxnZvH+eCQz9cgnwXe03TmDybZZch2G1XVRFWdACznqU8vrwG+VlX9n0b+D/DyJJs100cAZ474udoaeKDvU88qYLshH6vmlv3Wfjvsc7XBcxfAzK1tKPH85vd1wOZV9RDwUJJH+/7IrqyqWwCSfB74L/Q+IR6eZAm912Zbem80323anDVlOycDZ1fVh5rpfZrlv9H70MlGwLeA3YBbq+rmZnv/ACyZWnRV3QSMbHi0qpYneSFwMHAQcFWSfYEfr6PpWVNuHwFcCrwO+Osp23g8yYXAryc5B/g14A/pvfnMm+dKc8Z+O0v22w2HAaAdjza/n+y7vXp69XNeU9pUkp2AdwO/UlX3Jzkd2LhvmUemtPkm8Kokf1VVPwECXFxVR/YvlGSoP/j09uVNfbNabf+qeqBveiWwQ5ItZvNpoqoeBr4IfDHJk8B/a2roH53aeEqz/ufhfODDSZ5DL/n/84DNnAm8DfgPYFlVPZTeu8eonqsfAFvmqX2f29M3hKr1hv12SB3ptxs8dwGMz97pHRn+DHpJ+HJgC3qd5IfpHZjztCNzpzgN+CpwdpKFwLeB/ZLsDD/bt/ZLwI3Ajkle1LQ7ctDKmn15e6zh54Epy/6o2f5JaY5oT7IoyW8PWvcgSfZLslVzeyN6yf524B7geUm2TvIL9IYIB2reiK4CTgIuqKonBiz2dWBPevs+Vw/DjvK5KnqfdA5rZh0FnLeWp0brL/ttR/ptFxgAZm7qvsSPTrP9VcCngBuAW4EvVdV3gGvp/TH/I70jUNeqqj7etPl7ep80jwY+n+S7NENjzaeMJcBX0jtA5t5p1rom7wcmgevTO/jnAmA6nypeBHw9yXXNY1gGnNvsC/xT4ErgYnrPx9qcBbyBNST75s3lAnpvzBc08yYZ7XP1Hnr7KlfSOybgtDlar6bHfmu/HVqSdyRZRW/U7rtJTp2L9a4v0vtwIrUnvSOBb6uq08dciqQh2W83fI4ASJLUQR4EqFG4DOjc/jVpnrsM++0GzV0AkiR1kLsAJEnqIAOAJEkdNC+OAVi8eHFdeOGF4y5D6oLMxUrss9LIzLjPzosRgPvuu2/cJUiaBvustP6bFwFAkiTNLQOAJEkdZACQJKmD5sVBgJI2XCdftHzcJWzQ3nywV7/VYK2OACR5V5IVSb6X5PNJNm6upHVFkpVJzlp9RSpJkjQ6rQWAJNsB7wAmqurFwALgdcDHgBOramfgfuCYtmqQJEmDtX0MwEJ6l99cCGwK3A0cAJzT3L8UOLTlGiRJ0hStBYCqugv4S+AOev/4fwhcDTxQVY83i60CtmurBkmSNFibuwC2Ag4BdgKeD2wGLJ5G+yVJliVZNjk52VKVkuaKfVaaX9rcBXAQcGtVTVbVT4EvAvsBWza7BAC2B+4a1LiqTqmqiaqaWLRoUYtlSpoL9llpfmkzANwB7JNk0yQBDgSuBy4FDmuWOQo4r8UaJEnSAG0eA3AFvYP9rgGua7Z1CvAe4NgkK4GtgdPaqkGSJA3W6omAquoDwAemzL4F2LvN7UqSpLXzVMCSJHWQAUCSpA4yAEiS1EEGAEmSOsgAIElSBxkAJEnqoFa/BihJ2jCdfNHycZewwXrzwXuMZDuOAEiS1EEGAEmSOsgAIElSBxkAJEnqIAOAJEkdZACQJKmDDACSJHWQAUCSpA4yAEiS1EEGAEmSOqjVAJBkyyTnJLkxyQ1J9k3ynCQXJ7m5+b1VmzVIkqSna3sE4CTgwqraDXgpcANwHHBJVe0CXNJMS5KkEWotACR5NvAK4DSAqnqsqh4ADgGWNostBQ5tqwZJkjRYmyMAOwGTwOeSXJvk1CSbAdtU1d3NMt8HtmmxBkmSNECbAWAhsCfwmap6GfAIU4b7q6qAGtQ4yZIky5Ism5ycbLFMSXPBPivNL20GgFXAqqq6opk+h14guCfJtgDN73sHNa6qU6pqoqomFi1a1GKZkuaCfVaaX1oLAFX1feDOJLs2sw4ErgfOB45q5h0FnNdWDZIkabCFLa//7cAZSTYCbgHeRC90nJ3kGOB24PCWa5AkSVO0GgCqajkwMeCuA9vcriRJWjvPBChJUgcZACRJ6iADgCRJHWQAkCSpgwwAkiR1kAFAkqQOMgBIktRBBgBJkjpoqACQnjck+ZNmeocke7dbmiRJasuwIwB/DewLHNlMPwR8upWKJElS64Y9FfDLq2rPJNcCVNX9zfn9JUnSPDTsCMBPkywACiDJIuDJ1qqSJEmtGjYAfBL4EvC8JB8CLgc+3FpVkiSpVUPtAqiqM5JcTe8qfgEOraobWq1MkiS1Zp0BoBn6X1FVuwE3tl+SJElq2zp3AVTVE8BNSXYYQT2SJGkEhv0WwFbAiiRXAo+snllVr22lKkmS1KphA8Aft1qFJEkaqWEPAvz6TDfQHEOwDLirql6TZCfgTGBr4GrgjVX12EzXL0mSpm/YUwHvk+SqJA8neSzJE0keHHIb7wT6vzHwMeDEqtoZuB84ZnolS5Kk2Rr2PACfonca4JuBTYDfZYhTASfZHvg14NRmOsABwDnNIkuBQ6dXsiRJmq2hrwZYVSuBBVX1RFV9Dlg8RLNPAH/IU2cN3Bp4oKoeb6ZXAdtNo15JkjQHhg0AP2rO/b88yZ8nede62iZ5DXBvVV09k8KSLEmyLMmyycnJmaxC0gjZZ6X5ZdgA8MZm2bfR+xrgC4DfWkeb/YDXJrmN3kF/BwAnAVsmWX3w4fbAXYMaV9UpVTVRVROLFi0askxJ42KfleaXtX4LIMkOVXVHVd3ezPoJcMIwK66q9wLvbdazP/Duqnp9ki8Ah9ELBUcB582w9oFOvmj5XK5Ofd588B7jLkGSNEfWNQLw5dU3kpw7R9t8D3BskpX0jgk4bY7WK0mShrSu8wCk7/YLZ7qRqroMuKy5fQuw90zXJUmSZm9dIwC1htuSJGkeW9cIwEubE/4E2KTv5D8Bqqq2aLU6SZLUirUGgKpaMKpCJEnS6Ax9IiBJkrThMABIktRBBgBJkjrIACBJUgcZACRJ6iADgCRJHWQAkCSpgwwAkiR1kAFAkqQOMgBIktRBBgBJkjrIACBJUgcZACRJ6iADgCRJHdRaAEjygiSXJrk+yYok72zmPyfJxUlubn5v1VYNkiRpsDZHAB4H/qCqdgf2Ad6aZHfgOOCSqtoFuKSZliRJI9RaAKiqu6vqmub2Q8ANwHbAIcDSZrGlwKFt1SBJkgYbyTEASXYEXgZcAWxTVXc3d30f2GYUNUiSpKe0HgCSbA6cC/x+VT3Yf19VFVBraLckybIkyyYnJ9suU9Is2Wel+aXVAJDkmfT++Z9RVV9sZt+TZNvm/m2Bewe1rapTqmqiqiYWLVrUZpmS5oB9Vppf2vwWQIDTgBuq6uN9d50PHNXcPgo4r60aJEnSYAtbXPd+wBuB65Isb+b9EfBR4OwkxwC3A4e3WIMkSRqgtQBQVZcDWcPdB7a1XUmStG5tjgBIQzn5ouXrXkgz9uaD9xh3CZLWQ54KWJKkDjIASJLUQQYASZI6yAAgSVIHGQAkSeogA4AkSR1kAJAkqYMMAJIkdZABQJKkDjIASJLUQQYASZI6yAAgSVIHGQAkSeogA4AkSR1kAJAkqYMMAJIkddBYAkCSxUluSrIyyXHjqEGSpC4beQBIsgD4NPBqYHfgyCS7j7oOSZK6bBwjAHsDK6vqlqp6DDgTOGQMdUiS1FnjCADbAXf2Ta9q5kmSpBFZOO4C1iTJEmBJM/lwkpvGWU+LngvcN+4ihvGWcRew/pg3rxlM+3W7sKoWz2Q79tn1k/32Z+bN6zayPltVM2k3Y0n2BY6vql9tpt8LUFUfGWkh64kky6pqYtx1aHi+Zt3m6z8/+bo93Th2AVwF7JJkpyQbAa8Dzh9DHZIkddbIdwFU1eNJ3gZ8DVgAfLaqVoy6DkmSumwsxwBU1VeBr45j2+uhU8ZdgKbN16zbfP3nJ1+3KUZ+DIAkSRo/TwUsSVIHGQAkSeogA4AkSR1kAJAkqYMMAJIkdZABQJKkDjIASJLUQQYASZI6yAAgSVIHGQAkSeogA4AkSR1kAJAkqYMMADOU5Ikky/t+jptG2/2TXDDL7V+WZGKGbWe9/WY9GyX5RJKVzc8FSXZYw7KnJ9l/wPxtmnbfSXJ9kjm7SmSSU5PsPgfrOTrJp+ZgPXslua55rj6ZJLNdp4Znn7XPzmA9H0pyZ5KHZ7uu9dFYLge8gfhxVe0xjg0nWTCO7Q7wYeBZwK5V9USSNwHnJdmrqp4cch1/ClxcVScBJHnJdApIsqCqnhh0X1X97nTWNQKfAX4PuILe5bAXA/801oq6xT5rn52u/w18Crh53IW0wRGAOZbktiQfaT5hLEuyZ5KvJfn3JG/pW3SLJF9JclOSv0nyjKb9Z5p2K5KcMGW9H0tyDfDbffOf0ST1DzbTByf5VpJrknwhyebN/MVJbmza/+YcPM5NgTcB71rdmavqc8DDwEHTWNW2wKrVE1X13Wb9P/eJJ8mnkhzd3O5/Lv7fJFf2Lbdjkuua25clmUjyliR/0bfMzz4dJHlDkiub1+vk1W/USd6U5N+ade83rSdngCTbAltU1berdw3uvwMOne16NXv2WfvsmjT99e65WNf6yAAwc5vk54cTj+i7747mk8a/AqcDhwH7ACf0LbM38HZgd+BFPNXB31dVE8BLgFdOSdc/qKo9q+rMZnohcAZwc1W9P8lzgfcDB1XVnsAy4NgkGwN/C/w6sBfwi4MeUJJdpzym/p8tpyy+c/M4H5wyf1nzmIb1aeC0JJcmeV+S5w/ZbvVz8VFgoyQ7NfOPAM6asuy5wG/0TR8BnJnk/25u79e8Xk8Ar2/+WZ9A703kv6zp8SR51Rqeq28OWHw7+t40m9vbDflYNTfss/bZ6fTZDZ67AGZubcOJ5ze/rwM2r6qHgIeSPNrXKa+sqlsAknye3h/tOcDhSZbQe222pfeH/N2mzdROcjJwdlV9qJnep1n+G+ntXt4I+BawG3BrVd3cbO8fgCVTi66qm4CRDpFW1deSvJDecPirgWuTvHiIpv3Pxdn03hQ+2vzuf2OnqiaT3JLidvnMAAAPX0lEQVRkH3pDebsB3wDeSu/N9arm+doEuBd4OXBZVU0CJDkL+KUBtV/KiJ8vzYp9dg7YZzccBoB2PNr8frLv9urp1c95TWlTTSJ+N/ArVXV/ktOBjfuWeWRKm28Cr0ryV1X1EyD09s0d2b9QkqH+4JPsytPfsFbbv6oe6Jv+d2CHJM9q3ixX24teeh9aVf0H8I/APzZDiK8A7uHnR6g2ntKs/7k4C/hCki/2VleD9tedCRwO3Ah8qaoqvXeQpVX13v4Fkww1NJ/kVcCJA+76UVX95ynz7gK275vevpmn9YN9dho60mc3eO4CGJ+9k+yU3n7EI4DLgS3odZIfJtmGXrpem9PoHUx2dpKFwLeB/ZLsDJBksyS/RK8D7ZjkRU27IwetrKpuqqo91vDzwJRlHwGWAh/v2wf3O8BP6CX1oSQ5IL19kyR5Fr2h1TuA24Hdk/xC8wnswDWto6r+nd5Q4B+z5jfDLwGHNI999XDsJcBhSZ7XbP85Sf4vegfpvTLJ1kmeSd/+2ynbvXQNz9XT3kia/YgPJtmneRP7HeC8tT03Wu/YZ+lOn+0CRwBmbpMky/umL6yqob9WBFxF7+jSnYFL6SXcJ5NcS6/z38kQnbKqPp7k2cDfA68HjgY+n+QXmkXeX1X/1gxRfiXJj+jt53zWNGpdk/cCfwHclGQTYBLYtznIbVh7AZ9K8ji9QHpqVV0FkORs4HvArcC161jPWU0tOw26s/l0dgOwe1Vd2cy7Psn7gYuaN/WfAm+tqm8nOZ7eUOwDwPJB65yB/0lv//Im9I7+9xsAo2Wftc9OS5I/B/47sGmSVfQe6/Fzse71Qab3ukuDJflFev/QPlNVpwy4/3Tg9Kq6bMSlSRrAPitHADQnqur7wMvGXYek4dhn5TEAGpUvA7eNuwhJQ7PPbuDcBSBJUgc5AiBJUgfNi2MAFi9eXBdeeOG4y5C6YE4uUGSflUZmxn12XowA3HfffeMuQdI02Gel9d+8CACSJGluGQAkSeogA4AkSR1kAJAkqYNaDQBJ3pVkRZLvJfl8ko2bi2lckWRlkrOSbNRmDZIk6elaCwBJtgPeAUxU1YuBBcDrgI8BJ1bVzsD9wDFt1SBJkgZrexfAQnpX4FoIbArcDRwAnNPcvxQY6jrOkiRp7rQWAKrqLuAv6V0n+m7gh8DVwANV9Xiz2Cpgu7ZqkCRJg7W5C2Ar4BB613p+PrAZsHga7ZckWZZk2eTkZEtVSpor9llpfmlzF8BBwK1VNVlVPwW+COwHbNnsEgDYHrhrUOOqOqWqJqpqYtGiRS2WKWku2Gel+aXNAHAHsE+STZMEOBC4HrgUOKxZ5ijgvBZrkCRJA7R5DMAV9A72uwa4rtnWKcB7gGOTrAS2Bk5rqwZJkjRYq1cDrKoPAB+YMvsWYO82tytJktbOMwFKktRBBgBJkjrIACBJUgcZACRJ6iADgCRJHWQAkCSpgwwAkiR1kAFAkqQOMgBIktRBBgBJkjrIACBJUgcZACRJ6iADgCRJHWQAkCSpgwwAkiR1kAFAkqQOMgBIktRBBgBJkjqo1QCQZMsk5yS5MckNSfZN8pwkFye5ufm9VZs1SJKkp2t7BOAk4MKq2g14KXADcBxwSVXtAlzSTEuSpBFqLQAkeTbwCuA0gKp6rKoeAA4BljaLLQUObasGSZI0WJsjADsBk8Dnklyb5NQkmwHbVNXdzTLfB7ZpsQZJkjRAmwFgIbAn8JmqehnwCFOG+6uqgBrUOMmSJMuSLJucnGyxTElzwT4rzS9tBoBVwKqquqKZPodeILgnybYAze97BzWuqlOqaqKqJhYtWtRimZLmgn1Wml9aCwBV9X3gziS7NrMOBK4HzgeOauYdBZzXVg2SJGmwhS2v/+3AGUk2Am4B3kQvdJyd5BjgduDwlmuQJElTtBoAqmo5MDHgrgPb3K4kSVo7zwQoSVIHGQAkSeogA4AkSR1kAJAkqYMMAJIkdZABQJKkDjIASJLUQUMFgPS8IcmfNNM7JNm73dIkSVJbhh0B+GtgX+DIZvoh4NOtVCRJklo37JkAX15Veya5FqCq7m9O7ytJkuahYUcAfppkAc2le5MsAp5srSpJktSqYQPAJ4EvAc9L8iHgcuDDrVUlSZJaNdQugKo6I8nV9C7iE+DQqrqh1cokSVJr1hkAmqH/FVW1G3Bj+yVJkqS2rXMXQFU9AdyUZIcR1CNJkkZg2G8BbAWsSHIl8MjqmVX12laqkiRJrRo2APxxq1VIkqSRGvYgwK/PdAPNMQTLgLuq6jVJdgLOBLYGrgbeWFWPzXT9kiRp+oY9FfA+Sa5K8nCSx5I8keTBIbfxTqD/GwMfA06sqp2B+4FjpleyJEmarWHPA/ApeqcBvhnYBPhdhjgVcJLtgV8DTm2mAxwAnNMsshQ4dHolS5Kk2Rr6aoBVtRJYUFVPVNXngMVDNPsE8Ic8ddbArYEHqurxZnoVsN006pUkSXNg2ADwo+bc/8uT/HmSd62rbZLXAPdW1dUzKSzJkiTLkiybnJycySokjZB9Vppfhg0Ab2yWfRu9rwG+APitdbTZD3htktvoHfR3AHASsGWS1Qcfbg/cNahxVZ1SVRNVNbFo0aIhy5Q0LvZZaX5Z16f4HQCq6vaq+klVPVhVJ1TVsc0ugTWqqvdW1fZVtSPwOuCfq+r1wKXAYc1iRwHnzfpRSJKkaVnXCMCXV99Icu4cbfM9wLFJVtI7JuC0OVqvJEka0rrOA5C+2y+c6Uaq6jLgsub2LcDeM12XJEmavXWNANQabkuSpHlsXSMAL21O+BNgk76T/wSoqtqi1eokSVIr1hoAqmrBqAqRJEmjM/SJgCRJ0obDACBJUgcZACRJ6iADgCRJHWQAkCSpgwwAkiR1kAFAkqQOMgBIktRBBgBJkjrIACBJUgcZACRJ6iADgCRJHWQAkCSpgwwAkiR1UGsBIMkLklya5PokK5K8s5n/nCQXJ7m5+b1VWzVIkqTB2hwBeBz4g6raHdgHeGuS3YHjgEuqahfgkmZakiSNUGsBoKrurqprmtsPATcA2wGHAEubxZYCh7ZVgyRJGmwkxwAk2RF4GXAFsE1V3d3c9X1gm1HUIEmSntJ6AEiyOXAu8PtV9WD/fVVVQK2h3ZIky5Ism5ycbLtMSbNkn5Xml1YDQJJn0vvnf0ZVfbGZfU+SbZv7twXuHdS2qk6pqomqmli0aFGbZUqaA/ZZaX5p81sAAU4Dbqiqj/fddT5wVHP7KOC8tmqQJEmDLWxx3fsBbwSuS7K8mfdHwEeBs5McA9wOHN5iDZIkaYDWAkBVXQ5kDXcf2NZ2JUnSunkmQEmSOsgAIElSBxkAJEnqoDYPAhyLky9avu6FNCNvPniPcZcgSZojG1wAkDS/GNrbZXDXmhgAJEnTZnBrz6hCm8cASJLUQY4AaOz8JNEuh4AlDeIIgCRJHWQAkCSpgwwAkiR1kAFAkqQOMgBIktRBBgBJkjrIACBJUgcZACRJ6iADgCRJHWQAkCSpg8YSAJIsTnJTkpVJjhtHDZIkddnIA0CSBcCngVcDuwNHJtl91HVIktRl4xgB2BtYWVW3VNVjwJnAIWOoQ5KkzhpHANgOuLNvelUzT5Ikjch6ezngJEuAJc3kw0luGmc9LXoucN+4ixjGW8ZdwPpj3rxmMO3X7cKqWjyT7dhn10/225+ZN6/byPpsVc2k3Ywl2Rc4vqp+tZl+L0BVfWSkhawnkiyrqolx16Hh+Zp1m6///OTr9nTj2AVwFbBLkp2SbAS8Djh/DHVIktRZI98FUFWPJ3kb8DVgAfDZqlox6jokSeqysRwDUFVfBb46jm2vh04ZdwGaNl+zbvP1n5983aYY+TEAkiRp/DwVsCRJHWQAmKUk70hyQ5IzWlr/8Une3ca6NTeS7J/kgnHXoeHYZ2Wf7VlvzwMwj/xP4KCqWjXuQiQNxT4r4QjArCT5G+CFwD8leV+Szya5Msm1SQ5pljk6yZeTXJzktiRvS3Jss8y3kzynWe73klyV5DtJzk2y6YDtvSjJhUmuTvKvSXYb7SPecCXZMcmNSU5P8m9JzkhyUJJvJLk5yd7Nz7ea1+6bSXYdsJ7NBv0daP1gn91w2GfnQFX5M4sf4DZ6Z5j6MPCGZt6WwL8BmwFHAyuBZwGLgB8Cb2mWOxH4/eb21n3r/CDw9ub28cC7m9uXALs0t18O/PO4H/+G8gPsCDwO/DK9YHw18Fkg9K5V8WVgC2Bhs/xBwLnN7f2BC5rbA/8Oxv34/Pm519o+uwH82Gdn/+MugLlzMPDavn1/GwM7NLcvraqHgIeS/BD4383864CXNLdfnOSD9P4AN6d3noSfSbI58J+BLyRZPfsX2nggHXZrVV0HkGQFcElVVZLr6L3ZPBtYmmQXoIBnDljHmv4Obmi7eE2bfXb+s8/OggFg7gT4rar6ufOfJ3k58GjfrCf7pp/kqdfgdODQqvpOkqPpJdR+zwAeqKo95rZs9VnX6/Rn9P4x/EaSHYHLBqxj4N+B1kv22fnPPjsLHgMwd74GvD1N1E/ysmm2fxZwd5JnAq+femdVPQjcmuS3m/UnyUtnWbOm59nAXc3to9ewzGz/DjQ69tkNn312LQwAc+fP6A0vfbcZivqzabb/Y+AK4BvAjWtY5vXAMUm+A6ygt59Lo/PnwEeSXMuaR89m+3eg0bHPbvjss2vhmQAlSeogRwAkSeogA4AkSR1kAJAkqYMMAJIkdZABQJKkDjIAaMaac6mvSPLdJMubE6hIWk/ZZ9XPMwFqRpLsC7wG2LOqHk3yXGCjMZclaQ3ss5rKEQDN1LbAfVX1KEBV3VdV/1+SvZJ8vbn62deSbJtkYXPVtP0BknwkyYfGWbzUQfZZ/RxPBKQZaS50cjmwKfB/gLOAbwJfBw6pqskkRwC/WlX/I8l/As4B3g78BfDyqnpsPNVL3WOf1VTuAtCMVNXDSfYC/ivwKnpvJh8EXgxc3JxWewFwd7P8iiR/D1wA7OsbiTRa9llNZQDQjFXVE/SurnVZc/nNtwIrqmrfNTT5ZeAB4HmjqVBSP/us+nkMgGYkya7NNbZX24Pe9bMXNQcbkeSZzTAiSX4TeA7wCuB/Jdly1DVLXWaf1VQeA6AZaYYS/xewJfA4sBJYAmwPfJLeZTgXAp8AvkRvX+OBVXVnkncAe1XVUeOoXeoi+6ymMgBIktRB7gKQJKmDDACSJHWQAUCSpA4yAEiS1EEGAEmSOsgAIElSBxkAJEnqIAOAJEkd9P8DkRPYFV3mvMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "da057efe-88f0-bf49-917b-bb2fec418ed9",
    "_uuid": "e328d9882affedcfc4c167aa5bb1ac132547558c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "#Removing unnecessary features\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "print(train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "df7f0cd4-992c-4a79-fb19-bf6f0c024d4b",
    "_uuid": "c916644bd151f3dc8fca900f656d415b4c55e2bc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "553f56d7-002a-ee63-21a4-c0efad10cfe9",
    "_uuid": "b8cd938fba61fb4e226c77521b012f4bb8aa01d0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "67444ebc-4d11-bac1-74a6-059133b6e2e8",
    "_uuid": "e805ad52f0514497b67c3726104ba46d361eb92c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</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>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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  ...       Fare Embarked  Title\n",
       "0            1         0       3  ...     7.2500        S      1\n",
       "1            2         1       1  ...    71.2833        C      3\n",
       "2            3         1       3  ...     7.9250        S      2\n",
       "3            4         1       1  ...    53.1000        S      3\n",
       "4            5         0       3  ...     8.0500        S      1\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "2c8e84bb-196d-bd4a-4df9-f5213561b5d3",
    "_uuid": "a1ac66c79b279d94860e66996d3d8dba801a6d9a"
   },
   "source": [
    "### Converting a categorical feature\n",
    "\n",
    "Now we can convert features which contain strings to numerical values. This is required by most model algorithms. Doing so will also help us in achieving the feature completing goal.\n",
    "\n",
    "Let us start by converting Sex feature to a new feature called Gender where female=1 and male=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "c20c1df2-157c-e5a0-3e24-15a828095c96",
    "_uuid": "840498eaee7baaca228499b0a5652da9d4edaf37"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "c311c43d-6554-3b52-8ef8-533ca08b2f68",
    "_uuid": "345038c8dd1bac9a9bc5e2cfee13fcc1f833eee0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdbb7a9a400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "a4f166f9-f5f9-1819-66c3-d89dd5b0d8ff",
    "_uuid": "6b22ac53d95c7979d5f4580bd5fd29d27155c347"
   },
   "source": [
    "Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "9299523c-dcf1-fb00-e52f-e2fb860a3920",
    "_uuid": "24a0971daa4cbc3aa700bae42e68c17ce9f3a6e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "ec9fed37-16b1-5518-4fa8-0a7f579dbc82",
    "_uuid": "8acd90569767b544f055d573bbbb8f6012853385"
   },
   "source": [
    "Now we iterate over Sex (0 or 1) and Pclass (1, 2, 3) to calculate guessed values of Age for the six combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "a4015dfa-a0ab-65bc-0cbe-efecf1eb2569",
    "_uuid": "31198f0ad0dbbb74290ebe135abffa994b8f58f3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "725d1c84-6323-9d70-5812-baf9994d3aa1",
    "_uuid": "5c8b4cbb302f439ef0d6278dcfbdafd952675353"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "797b986d-2c45-a9ee-e5b5-088de817c8b2",
    "_uuid": "ee13831345f389db407c178f66c19cc8331445b0"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>0</th>\n",
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       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex      ...       Embarked  Title       AgeBand\n",
       "0         0       3    0      ...              S      1  (16.0, 32.0]\n",
       "1         1       1    1      ...              C      3  (32.0, 48.0]\n",
       "2         1       3    1      ...              S      2  (16.0, 32.0]\n",
       "3         1       1    1      ...              S      3  (32.0, 48.0]\n",
       "4         0       3    0      ...              S      1  (32.0, 48.0]\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "875e55d4-51b0-5061-b72c-8a23946133a3",
    "_uuid": "1ea01ccc4a24e8951556d97c990aa0136da19721"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Title</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "7e6c04ed-cfaa-3139-4378-574fd095d6ba",
    "_uuid": "33d1236ce4a8ab888b9fac2d5af1c78d174b32c7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "5c778c69-a9ae-1b6b-44fe-a0898d07be7a",
    "_uuid": "3b8db81cc3513b088c6bcd9cd1938156fe77992f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "74ee56a6-7357-f3bc-b605-6c41f8aa6566",
    "_uuid": "1e3479690ef7cd8ee10538d4f39d7117246887f0"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "305402aa-1ea1-c245-c367-056eef8fe453",
    "_uuid": "aac2c5340c06210a8b0199e15461e9049fbf2cff"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "bf351113-9b7f-ef56-7211-e8dd00665b18",
    "_uuid": "1e3f8af166f60a1b3125a6b046eff5fff02d63cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "51c21fcc-f066-cd80-18c8-3d140be6cbae",
    "_uuid": "d85b5575fb45f25749298641f6a0a38803e1ff22"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "89a91d76-2cc0-9bbb-c5c5-3c9ecae33c66",
    "_uuid": "e480a1ef145de0b023821134896391d568a6f4f9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age    ...      Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1    ...             0      1        0          3\n",
       "1         1       1    1    2    ...             1      3        0          2\n",
       "2         1       3    1    1    ...             0      2        1          3\n",
       "3         1       1    1    2    ...             0      3        0          2\n",
       "4         0       3    0    2    ...             0      1        1          6\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "3600cb86-cf5f-d87b-1b33-638dc8db1564",
    "_uuid": "aacb62f3526072a84795a178bd59222378bab180"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
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       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age    ...      Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2    ...             2      1        1          6\n",
       "1          893       3    1    2    ...             0      3        0          6\n",
       "2          894       2    0    3    ...             2      1        1          6\n",
       "3          895       3    0    1    ...             0      1        1          3\n",
       "4          896       3    1    1    ...             0      3        0          3\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "0e9018b1-ced5-9999-8ce1-258a0952cbf2",
    "_uuid": "b9a78f6b4c72520d4ad99d2c89c84c591216098d"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
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       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
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       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
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       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
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      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "385f217a-4e00-76dc-1570-1de4eec0c29c",
    "_uuid": "640f305061ec4221a45ba250f8d54bb391035a57"
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age    ...      Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1    ...             0      1        0          3\n",
       "1         1       1    1    2    ...             1      3        0          2\n",
       "2         1       3    1    1    ...             0      2        1          3\n",
       "3         1       1    1    2    ...             0      3        0          2\n",
       "4         0       3    0    2    ...             0      1        1          6\n",
       "5         0       3    0    1    ...             2      1        1          3\n",
       "6         0       1    0    3    ...             0      1        1          3\n",
       "7         0       3    0    0    ...             0      4        0          0\n",
       "8         1       3    1    1    ...             0      3        0          3\n",
       "9         1       2    1    0    ...             1      3        0          0\n",
       "\n",
       "[10 rows x 9 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "d2334d33-4fe5-964d-beac-6aa620066e15",
    "_uuid": "8453cecad81fcc44de3f4e4e4c3ce6afa977740d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age    ...      Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2    ...             2      1        1          6\n",
       "1          893       3    1    2    ...             0      3        0          6\n",
       "2          894       2    0    3    ...             2      1        1          6\n",
       "3          895       3    0    1    ...             0      1        1          3\n",
       "4          896       3    1    1    ...             0      3        0          3\n",
       "5          897       3    0    0    ...             0      1        1          0\n",
       "6          898       3    1    1    ...             2      2        1          3\n",
       "7          899       2    0    1    ...             0      1        0          2\n",
       "8          900       3    1    1    ...             1      3        1          3\n",
       "9          901       3    0    1    ...             0      1        0          3\n",
       "\n",
       "[10 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "0acf54f9-6cf5-24b5-72d9-29b30052823a",
    "_uuid": "04d2235855f40cffd81f76b977a500fceaae87ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "0edd9322-db0b-9c37-172d-a3a4f8dec229",
    "_uuid": "a649b9c53f4c7b40694f60f5c8dc14ec5ef519ec"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "e545d5aa-4767-7a41-5799-a4c5e529ce72",
    "_uuid": "6e6f58053fae405fc93d312fc999f3904e708dbe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.201527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.398234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.287163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.261762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.129140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.085150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.311200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.749007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.201527\n",
       "5      Title     0.398234\n",
       "2        Age     0.287163\n",
       "4   Embarked     0.261762\n",
       "6    IsAlone     0.129140\n",
       "3       Fare    -0.085150\n",
       "7  Age*Class    -0.311200\n",
       "0     Pclass    -0.749007"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "dd85f2b7-ace2-0306-b4ec-79c68cd3fea0",
    "_uuid": "1f94308b23b934123c03067e84027b507b989e52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "f0694a8e-b618-8ed9-6f0d-8c6fba2c4567",
    "_uuid": "483c647d2759a2703d20785a44f51b6dee47d0db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6",
    "_uuid": "06a52babe50e0dd837b553c78fc73872168e1c7d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Score\n",
       "1        Random Forest  86.76\n",
       "2        Decision Tree  86.76\n",
       "0  Logistic Regression  80.36"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': [ 'Logistic Regression', 'Random Forest', 'Decision Tree'],\n",
    "    'Score': [acc_log, acc_random_forest, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  }
 ],
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