3/ds_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic: prediction of survival of a disaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We develop a model that predicts which passengers survived the Titanic shipwreck. The baseline model given to beat throws an accuracy of  0.72727 on the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset and explore some of its statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import sklearn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_table('train.csv', sep=',')\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_table('test.csv', sep=',')\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([549.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.]),\n",
       "  array([549.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 342.])],\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Survived')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Survived'].dropna().values,data_train.query('Survived == 1').loc[:,'Survived'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is a bit imbalanced so we need to take this into account when we asses the performance of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 80.,   0.,   0.,   0.,   0.,  97.,   0.,   0.,   0., 372.]),\n",
       "  array([216.,   0.,   0.,   0.,   0., 184.,   0.,   0.,   0., 491.])],\n",
       " array([1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Pclass')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Pclass'].dropna().values,data_train.query('Survived == 1').loc[:,'Pclass'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is a tendency of Pclass=1 and Pclass=2 for 'Survived'. We will try to use this in the develpment of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([468.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  81.]),\n",
       "  array([577.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 314.])],\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Sex')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Sex'].dropna().values,data_train.query('Survived == 1').loc[:,'Sex'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is a tendency of Sex=female for 'Survived'. We will try to use this in the develpment of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 18.,  27., 114., 104.,  66.,  46.,  24.,  15.,   9.,   1.]),\n",
       "  array([ 54.,  46., 177., 169., 118.,  70.,  45.,  24.,   9.,   2.])],\n",
       " array([ 0.42 ,  8.378, 16.336, 24.294, 32.252, 40.21 , 48.168, 56.126,\n",
       "        64.084, 72.042, 80.   ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Age')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Age'].dropna().values,data_train.query('Survived == 1').loc[:,'Age'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is a tendency of Age<16 for 'Survived'. We will try to use this in the develpment of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([398.,  97.,  15.,  12.,   0.,  15.,   5.,   0.,   0.,   7.]),\n",
       "  array([608., 209.,  28.,  16.,   0.,  18.,   5.,   0.,   0.,   7.])],\n",
       " array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('SibSp')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'SibSp'].dropna().values,data_train.query('Survived == 1').loc[:,'SibSp'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([445.,  53.,   0.,  40.,   0.,   2.,   4.,   0.,   4.,   1.]),\n",
       "  array([678., 118.,   0.,  80.,   0.,   5.,   4.,   0.,   5.,   1.])],\n",
       " array([0. , 0.6, 1.2, 1.8, 2.4, 3. , 3.6, 4.2, 4.8, 5.4, 6. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Parch')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Parch'].dropna().values,data_train.query('Survived == 1').loc[:,'Parch'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([499.,  36.,   8.,   0.,   4.,   2.,   0.,   0.,   0.,   0.]),\n",
       "  array([732., 106.,  31.,   2.,  11.,   6.,   0.,   0.,   0.,   3.])],\n",
       " array([  0.     ,  51.23292, 102.46584, 153.69876, 204.93168, 256.1646 ,\n",
       "        307.39752, 358.63044, 409.86336, 461.09628, 512.3292 ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Fare')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Fare'].dropna().values,data_train.query('Survived == 1').loc[:,'Fare'].dropna().values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([427.,   0.,   0.,   0.,   0.,  47.,   0.,   0.,   0.,  75.]),\n",
       "  array([501.,   0.,   0.,   0.,   0.,  48.,   0.,   0.,   0., 123.])],\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Embarked')\n",
    "hist = ax.hist([data_train.query('Survived == 0').loc[:,'Embarked'].dropna().values,data_train.query('Survived == 1').dropna().loc[:,'Embarked'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()\n",
    "hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With further exploration we obtain two examples that show that the dataset is problematic in the sense that two almost identical passengers are classfied completely different. This will heavely impact on the performanse of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "      <td>400</td>\n",
       "      <td>401</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Niskanen, Mr. Juha</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O 2. 3101289</td>\n",
       "      <td>7.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                Name   Sex   Age  SibSp  \\\n",
       "400          401         1       3  Niskanen, Mr. Juha  male  39.0      0   \n",
       "\n",
       "     Parch             Ticket   Fare Cabin Embarked  \n",
       "400      0  STON/O 2. 3101289  7.925   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.query('PassengerId == 401')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "      <td>528</td>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Salonen, Mr. Johan Werner</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101296</td>\n",
       "      <td>7.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                       Name   Sex   Age  \\\n",
       "528          529         0       3  Salonen, Mr. Johan Werner  male  39.0   \n",
       "\n",
       "     SibSp  Parch   Ticket   Fare Cabin Embarked  \n",
       "528      0      0  3101296  7.925   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.query('PassengerId == 529')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function preprocess the dataset, creating and deleting features based on the past graphical analysis. This is done in preparation for the training and testing step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(data):    \n",
    "    data.loc[:,'Pclass_1'] = data.loc[:,'Pclass']\n",
    "    data.loc[:,['Pclass_1']] = data.loc[:,['Pclass_1']].applymap(lambda x:1. if (not pd.isna(x) and x==1) else 0.)\n",
    "    data.loc[:,'Pclass_2'] = data.loc[:,'Pclass']\n",
    "    data.loc[:,['Pclass_2']] = data.loc[:,['Pclass_2']].applymap(lambda x:1. if (not pd.isna(x) and x==2) else 0.)\n",
    "    data.loc[:,'Pclass_3'] = data.loc[:,'Pclass']\n",
    "    data.loc[:,['Pclass_3']] = data.loc[:,['Pclass_3']].applymap(lambda x:1. if (not pd.isna(x) and x==3) else 0.)\n",
    "    data = data.drop('Pclass', axis=1, inplace=False)\n",
    "    data = data.drop('Name', axis=1, inplace=False)\n",
    "    data.loc[:,'Sex_male'] = data.loc[:,'Sex']\n",
    "    data.loc[:,['Sex_male']] = data.loc[:,['Sex_male']].applymap(lambda x:1. if (not pd.isna(x) and x=='male') else 0.)\n",
    "    data.loc[:,'Sex_female'] = data.loc[:,'Sex']\n",
    "    data.loc[:,['Sex_female']] = data.loc[:,['Sex_female']].applymap(lambda x:1. if (not pd.isna(x) and x=='female') else 0.)\n",
    "    data = data.drop('Sex', axis=1, inplace=False)\n",
    "    data.loc[:,'Age_1'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_1']] = data.loc[:,['Age_1']].applymap(lambda x:1. if (not pd.isna(x) and x<=8) else 0.)\n",
    "    data.loc[:,'Age_2'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_2']] = data.loc[:,['Age_2']].applymap(lambda x:1. if (not pd.isna(x) and 8<x<=15) else 0.)\n",
    "    data.loc[:,'Age_3'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_3']] = data.loc[:,['Age_3']].applymap(lambda x:1. if (not pd.isna(x) and 15<x<=25) else 0.)\n",
    "    data.loc[:,'Age_4'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_4']] = data.loc[:,['Age_4']].applymap(lambda x:1. if (not pd.isna(x) and 25<x<=35) else 0.)\n",
    "    data.loc[:,'Age_5'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_5']] = data.loc[:,['Age_5']].applymap(lambda x:1. if (not pd.isna(x) and 35<x<=50) else 0.)\n",
    "    data.loc[:,'Age_6'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_6']] = data.loc[:,['Age_6']].applymap(lambda x:1. if (not pd.isna(x) and 50<x<=65) else 0.)\n",
    "    data.loc[:,'Age_7'] = data.loc[:,'Age']\n",
    "    data.loc[:,['Age_7']] = data.loc[:,['Age_7']].applymap(lambda x:1. if (not pd.isna(x) and 65<x<=100) else 0.)\n",
    "    data = data.drop('Age', axis=1, inplace=False)    \n",
    "    data.loc[:,'Family_size'] = data.loc[:,'SibSp']+data.loc[:,'Parch']+1\n",
    "    data = data.drop('SibSp', axis=1, inplace=False)\n",
    "    data = data.drop('Parch', axis=1, inplace=False)\n",
    "    data = data.drop('Ticket', axis=1, inplace=False)\n",
    "    data.loc[:,'Fare_1'] = data.loc[:,'Fare']\n",
    "    data.loc[:,['Fare_1']] = data.loc[:,['Fare_1']].applymap(lambda x:1. if (not pd.isna(x) and x<=15) else 0.)\n",
    "    data.loc[:,'Fare_2'] = data.loc[:,'Fare']\n",
    "    data.loc[:,['Fare_2']] = data.loc[:,['Fare_2']].applymap(lambda x:1. if (not pd.isna(x) and 15<x<=30) else 0.)\n",
    "    data.loc[:,'Fare_3'] = data.loc[:,'Fare']\n",
    "    data.loc[:,['Fare_3']] = data.loc[:,['Fare_3']].applymap(lambda x:1. if (not pd.isna(x) and 30<x<=45) else 0.)\n",
    "    data.loc[:,'Fare_4'] = data.loc[:,'Fare']\n",
    "    data.loc[:,['Fare_4']] = data.loc[:,['Fare_4']].applymap(lambda x:1. if (not pd.isna(x) and 45<x<=60) else 0.)\n",
    "    data.loc[:,'Fare_5'] = data.loc[:,'Fare']\n",
    "    data.loc[:,['Fare_5']] = data.loc[:,['Fare_5']].applymap(lambda x:1. if (not pd.isna(x) and 60<x<=600) else 0.)\n",
    "    data = data.drop('Fare', axis=1, inplace=False)\n",
    "    data.loc[:,'Cabin_num'] = data.loc[:,'Cabin']\n",
    "    data.loc[:,['Cabin_num']] = data.loc[:,['Cabin_num']].applymap(lambda x:len(re.split(' ',x)) if not pd.isna(x) else 0.)\n",
    "    data = data.drop('Cabin', axis=1, inplace=False)\n",
    "    data.loc[:,'Embarked_C'] = data.loc[:,'Embarked']\n",
    "    data.loc[:,['Embarked_C']] = data.loc[:,['Embarked_C']].applymap(lambda x:1. if (not pd.isna(x) and x=='C') else 0.)\n",
    "    data.loc[:,'Embarked_Q'] = data.loc[:,'Embarked']\n",
    "    data.loc[:,['Embarked_Q']] = data.loc[:,['Embarked_Q']].applymap(lambda x:1. if (not pd.isna(x) and x=='Q') else 0.)\n",
    "    data.loc[:,'Embarked_S'] = data.loc[:,'Embarked']\n",
    "    data.loc[:,['Embarked_S']] = data.loc[:,['Embarked_S']].applymap(lambda x:1. if (not pd.isna(x) and x=='S') else 0.)\n",
    "    data = data.drop('Embarked', axis=1, inplace=False)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Age_1</th>\n",
       "      <th>Age_2</th>\n",
       "      <th>Age_3</th>\n",
       "      <th>...</th>\n",
       "      <th>Family_size</th>\n",
       "      <th>Fare_1</th>\n",
       "      <th>Fare_2</th>\n",
       "      <th>Fare_3</th>\n",
       "      <th>Fare_4</th>\n",
       "      <th>Fare_5</th>\n",
       "      <th>Cabin_num</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass_1  Pclass_2  Pclass_3  Sex_male  Sex_female  \\\n",
       "0            1         0       0.0       0.0       1.0       1.0         0.0   \n",
       "1            2         1       1.0       0.0       0.0       0.0         1.0   \n",
       "2            3         1       0.0       0.0       1.0       0.0         1.0   \n",
       "3            4         1       1.0       0.0       0.0       0.0         1.0   \n",
       "4            5         0       0.0       0.0       1.0       1.0         0.0   \n",
       "\n",
       "   Age_1  Age_2  Age_3  ...  Family_size  Fare_1  Fare_2  Fare_3  Fare_4  \\\n",
       "0    0.0    0.0    1.0  ...            2     1.0     0.0     0.0     0.0   \n",
       "1    0.0    0.0    0.0  ...            2     0.0     0.0     0.0     0.0   \n",
       "2    0.0    0.0    0.0  ...            1     1.0     0.0     0.0     0.0   \n",
       "3    0.0    0.0    0.0  ...            2     0.0     0.0     0.0     1.0   \n",
       "4    0.0    0.0    0.0  ...            1     1.0     0.0     0.0     0.0   \n",
       "\n",
       "   Fare_5  Cabin_num  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0     0.0        0.0         0.0         0.0         1.0  \n",
       "1     1.0        1.0         1.0         0.0         0.0  \n",
       "2     0.0        0.0         0.0         0.0         1.0  \n",
       "3     0.0        1.0         0.0         0.0         1.0  \n",
       "4     0.0        0.0         0.0         0.0         1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = preprocess(data_train)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Age_1</th>\n",
       "      <th>Age_2</th>\n",
       "      <th>Age_3</th>\n",
       "      <th>...</th>\n",
       "      <th>Family_size</th>\n",
       "      <th>Fare_1</th>\n",
       "      <th>Fare_2</th>\n",
       "      <th>Fare_3</th>\n",
       "      <th>Fare_4</th>\n",
       "      <th>Fare_5</th>\n",
       "      <th>Cabin_num</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass_1  Pclass_2  Pclass_3  Sex_male  Sex_female  \\\n",
       "0          892         0       0.0       0.0       1.0       1.0         0.0   \n",
       "1          893         1       0.0       0.0       1.0       0.0         1.0   \n",
       "2          894         0       0.0       1.0       0.0       1.0         0.0   \n",
       "3          895         0       0.0       0.0       1.0       1.0         0.0   \n",
       "4          896         1       0.0       0.0       1.0       0.0         1.0   \n",
       "\n",
       "   Age_1  Age_2  Age_3  ...  Family_size  Fare_1  Fare_2  Fare_3  Fare_4  \\\n",
       "0    0.0    0.0    0.0  ...            1     1.0     0.0     0.0     0.0   \n",
       "1    0.0    0.0    0.0  ...            2     1.0     0.0     0.0     0.0   \n",
       "2    0.0    0.0    0.0  ...            1     1.0     0.0     0.0     0.0   \n",
       "3    0.0    0.0    0.0  ...            1     1.0     0.0     0.0     0.0   \n",
       "4    0.0    0.0    1.0  ...            3     1.0     0.0     0.0     0.0   \n",
       "\n",
       "   Fare_5  Cabin_num  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0     0.0        0.0         0.0         1.0         0.0  \n",
       "1     0.0        0.0         0.0         0.0         1.0  \n",
       "2     0.0        0.0         0.0         1.0         0.0  \n",
       "3     0.0        0.0         0.0         0.0         1.0  \n",
       "4     0.0        0.0         0.0         0.0         1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = preprocess(data_test)\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we make different plots of the preprocessed dataset in an attempt to extract some additional information for the development of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb16744d710>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvJM33nij40tj6dKlzJ0794Ty8+bNY+nSpR33n376aa677jpefvll6uvrefPNN1m1ahUrV65k+fLlrFy5kiVLlvCXv/yF3/zmNx3XiKcCIjJORFYd97dfRL4iIoNF5BURqff+D/LKi4g8IiJvi8hbIlKS7PdgGGHRk9x0wQUXnPAcy03+cc5tcs4Ve38TnHP3e9t3O+eucM4Vef/3eNudc+5W59wFzrlJzrnq5L6DntHb6t+f//znM/2oDCNh1HXaXnvttcAuMzn577zzCkNxzsrKCmW/YaLRORnk5uZyww038Mgjj5ywvbKyks9+9rMAfP7zn+8YeHo8Jw+Qv+mmm1i3bh3XXXcdFRUVfOQjH+kYvHs60tPT+fu//3sg/uvmJz/5SV544QVaW1t58cUXufbaa08of8kll7Bz507ee+89amtrGTRoEOeddx4vv/wyL7/8MpdccgklJSWsX7+e+vp6XnvtNWbPnk3//v3Jzc3l6quv9v0ZhYVzboNzbrJzbjJwKXAQeJZj6yAVAX/07sOJ6yDNJ74OUqCElZ8sNx1Do3My6ElughPzk+Umwy89rX9wrA5qqH+zZs06wVkLGvNpmM7nnVcYynf4a6+9Fohfal0EngB33303la+dMqFSIJRPDaexdTsbTAqi0TlZfOUrX6GkpISbbrrptGU6S+TZ2dmnbBs5ciQ333wzN998MxMnTmTNmjVkZGR0zNQFJ06D3K9fvxPW/5g7dy4/+MEPGDx4MJdddhk5OTmnvMacOXN45pln2L59O/PmzQPiExLdeeedfPGLXzyh7He/+90T3Pv27Xva95hkrgDecc79VURCXQepK8LKT5abjqHROVmcaW6CU/OT5SbDLz2pf3BiHUz1+teZswY05tMwnbds+Wto3+H/5//8nx7vR92ZtjvvvDPZCr5pn/5UExqdk8XgwYO5/vrreeyxxzq2ffSjH2XJkiUAPPXUU0yZMgWAnJycjumB26d4becPf/hDx7TJ27dvZ/fu3eTn51NYWMiqVas4evQoW7Zs4c033zyty4wZM6ipqeEnP/nJKZd/tDNv3jyWLFnCM888w5w5cwC46qqrePzxxzuctm7dys6dO5k2bRrPPvsszc3NNDU18fzzz5/JRxQF84BferdDXQepK7TlJ43tXKNzsjjT3AQn5ifLTcaZ0JP6B8fqoIb698ILL5zgrAWN+VSjc1DHBurOtOXl5XVfKMVoX+NCExqdk8nChQv5/ve/33H/kUce4eabb+ahhx5i6NCh/OxnPwPiXwpf+MIXeOSRR3jiiScoLi7ueM7LL7/M7bffTr9+/YD44OcRI0YwfPhwRo8ezaRJk5g4cSIlJacfipWens4111zDE088weLFizstM2HCBJqamsjPz+fcc88F4Morr2TdunWUl5cD8V8Ln3zySUpKSpg7dy6TJ0/m/PPP73g8lRCRvsAsoLusGMg6SF2t25KXl8e+WA2trfF1W87JKeXw4R00t8T7igOyikhLy6TpQHx9nb59hjGg/1j27otfIpSWlsmg3HI+aKqmrS3+5T8wp4zZs2d3eLSvr9M+01pP1td5//33qaioiGx9nWnTprF27doera/jnIt0fR3wtw7SoUOHaGpq6livKgzaD25Pt17V8WUWLFjA97//fVpaWmhqauKhhx7in/7pn/jP//xPhgwZwo9+9CMAZs2axZe//GW++93v8utf/5qhQ4d27OMPf/gDCxYs6JgZ74EHHmDo0KH079+fgoICJk6cyKRJkyguLubgwYMdzzt+var+/fszc+ZMfvGLX3T4pKen45wjFovRv39/xo8fz759+xgxYkTH+7j88sv5u7/7O8rKykhLS6N///78+Mc/pqioiDlz5lBcXExBQQEf+chHOHLkSMf+4NiaYC0tLR3tpz1ORjScyXfjM888wwUXXNBRpzV8N06dOhWgw1kLGo/1NDoH1XeRVKhgpaWlrv2LvjsWLVrE5WULQ/EonyqhNLiKigpmzJgR+H7DJNWd161bx4c+9KGO+4WFhQnNapUo559/fsdBYFg0NTV1eolGKnM655PjASAiK51zpWE7eZdD3uqcu9K7vwGY4ZzbJiLnAhXOuXEi8qh3+5cnlzvdvv3kJggvP1luOkaqO58NuQn05aeufJOZn8Kks/x0ttQ/0FcH4VTnzupeKpHq+bQzwnQWkVAuj3y9ahELFyZ2bNBVblJ3eeQ3v/nNZCv4JhXPTnSHNueGhgba2toCWwg1ii+lAQMGhP4aQZOizp/h2KWREPE6SMejLT9pa+egz1ljboKUbeunRZtvVDQ0NOCcC6wORlX/QGdMtTlry6eg0zmoYwN1nbaZM2cmW8E3mzdvTraCbzQ6dzebVKqhzRdSz1lE+hNf6+g3x22OdB2k49GWnzS2c43OqdZuEkGbszbfqNH4+Zhz+GjMpxqdgzo2UNdpu+yyy5Kt4Jv2cSSa0OjcPm5GC9p8IfWcnXMHnXN5zrl9x21L2jpI2vKTxnau0TnV2k0iaHPW5hs1Gj8fcw4fjflUo3NQxwbqOm2GYRiGYRiGYRi9CXWdtieffDLZCr4ZP358shV8o9G5fWYpLWjzBZ3OUaItP2ls5xqdNbYbbc7afKNG4+djzuGjMZ9qdA7q2EBdp61Pnz7JVvBN+5THmtDonAozofpBmy/odI4SbflJYzvX6Kyx3Whz1uYbNRo/H3MOH435VKNzUMcG6jptp1sUMZXZsGFDshV8o9E5GQOA77//fiZMmMDFF1/M5MmTfS36eDrf559/ngcffLDTx/zSvtZRUGgbZB012vKTxnau0VlbboLOnS036SXqz6en9Q9OdU7l+teOtnqoMZ9qdA7q2EDd4tqG0RnnnVfIli3BrUUzatT5vPtuQ5dlKisr+d3vfkdNTQ2ZmZns2rWLw4cPJ7T/rgYrz5o1i1mzZvnRNQwjRdGYmzIyOj80sNykD6t/hnH2oK7T9vrrr4e2uHZY5OfnJ1vBN9qct2z5a6ALIpZPlW7LbNu2jSFDhpCZmQnAkCFDgPhiptXV1QwZMoTq6mr+5V/+hYqKCu69917ee+89GhoaGDJkCPX19TzxxBNMmDABgBkzZrBo0SJWr15NdXU1999/P8XFxWzatIm0tDQOHjzIuHHj2LRpE++++y633nor77//Pv379+cnP/kJF110EZs3b+azn/0sra2tfPKTnwzs82hH2+V/UaMtP2lr56DPWWNueuedd/jhD39ISUkJYLlJM1rr3+OPP84FF1wA6Kh/7Wirh9ryKeh0fv311xNeXLsr1F0e+dprryVbwTcFBQXJVvCNRueoufLKK9myZQtjx47lS1/6EsuWLev2OStXruS5557jv//7v/nMZz7D008/DcS/5N577z0uvfTSjrLnnHMOxcXFHft94YUXuOqqq+jTpw/z58/ne9/7HitXruTb3/42X/pSfMmx22+/nX/+53/mz3/+MyNGjAj8Pfft2zfwfZ5NaMtPGtu5Rueo6WlumjdvHr/97W8By02Gf4Kof08//TR9+/ZVU//a0VYPNeZTjc5BHRuo67TdcccdyVbwzZlcy51sNDpHTXZ2NitXruTHP/4xQ4cOZe7cuTzxxBNdPmfWrFlkZWUB8KlPfYpf/epXADz99NNcd911p5SfO3cuS5cuBWDJkiXMnTuXWCzGn/70J6677jomT57MF7/4RbZt2wbEf835zGc+A8DnP//5oN5qBwcOHAh8n2cT2vKTxnau0Tlqepqbrr/++o4flCw3GX4Jov796le/4sCBA2rqXzva6qHGfKrROahjA3WXRxpGKpGens6MGTOYMWMGkyZNYvHixWRkZHD06FEADh06dEL5AQMGdNweOXIkeXl5vPXWWyxdupRHH330lP3PmjWLO++8kz179rBy5Uo+/vGPc+DAAQYOHMiqVas6dRLp/vIVwzDObnqSm/Lz8xk8eLDlJuOM6Wn9y8vLY82aNVb/DOM41J1pa//VRBNhzVIUJhqdo2bDhg3U19d33F+1ahXnn38+hYWFrFy5EoBf//rXp31+Wloa8+bN41vf+hb79u1j0qRJp5TJzs7mwx/+MLfffjvXXHMN6enp5ObmMnr06I6zdM45amtrAbj88stZsmQJAE899VRg7/V4Z+P0aMtPGtu5Rueo6WluApgzZ47lJuOMCKL+zZs3j4cfflhN/WtHWz3UmE81Ogd1bKCrdgGLFi1KtoJvSktLk63gG43OUROLxbjxxhsZP348F198MXV1ddx7773cc8893H777UydOpX09PTTPn/AgAHMmTOHJUuWcP3115+23Ny5c3nyySdPmDL2qaee4rHHHqO4uJgJEybw3HPPAfDwww/zgx/8gMsuu4x9+/YF92aPczZOj7b8pLGda3SOmp7mJoDPfe5zlpuMMyKI+jdnzhyeeeYZNfWvHW31UGM+1egc1LGBpMJCgKWlpa66ujqhsvfccw8zP/HvoXiUT5VQFkasrKykvLw88P2GSao7r1u3jg996EMd95MxrXFPicVi6n4xOp3zyfEAEJGVzjl92fU4/OQmCC8/WW46Rqo7nw25CfTlp658e1N+OlvqH+irg3Cqc2d1L5VI9XzaGWE6i0igs6228/tX7uHf/z2xY4OucpO6MW25ubnJVvCNtsUWQZ/zu+820NTURE5OTrJVEiYVfjDxi0bnKNGWn7S1c9DnrDE3gb62rs03Kto7WFYHo0Gbs7Z8Cjqdgzo2UHd5pGEYhmEYhmEYRm9CXaft3/7t35Kt4JspU6YkW8E3Gp21XUahzRd0OkeJtvyksZ1rdNbYbrQ5a/ONGo2fjzmHj8Z8qtE5qGMDdZ22OXPmJFvBNxs3bky2gm80OJ98GcLJUwinOtp8oXNnbZeDhIm2/KShnZ+MBmftuQn0OZ/Otzfmp87es7Z4gn5nDXVPQz49GY3OQR0bqOu0TZ48OdkKvtm5c2eyFXyT6s79+vVj9+7dJyTF1tbWJBr5R5svnOrsnGP37t3069cvSUaphbb8lOrtvDNS3flsyE2gz7kz396Ynzqrf6AvnqDbWUvdS/V82hkanYM6NkhoIhIRGQj8FJgIOOBmYAOwFCgEGoDrnXN7Jb564cPA1cBB4B+dczWB2BqGR0FBAY2Njbz//vsd2w4dOpTyCfJ4tPlC5879+vWjoKAgSUaGkVqcDbkJ9Dmfzre35afO6h/oiyfod+5tdc8In0Rnj3wY+INzbo6I9AX6A3cBf3TOPSgidwB3AN8AZgJF3l8Z8EPvfyA8/vjjXF62MKjdRcLEiROTreCbVHfu06cPo0ePPmHbrl27GDJkSJKM/KPNF3Q6R4m2/JTq7bwzUt35bMhNoM9Zm29YdFb/QOfnY87hk+r5tDM0Oj/++OMsXNjzY4NuL48UkVxgGvAYgHPusHPuA+BaYLFXbDHwae/2tcDPXZw3gIEicm6PTT0GDRoU1K4iQ+P0pOYcPtp8QadzlGjLTxrjac7RoM1Zm2/UaPx8zDl8tPmCTuegjg0SGdM2Bngf+JmI/EVEfioiA4DhzrltAN7/YV75fGDLcc9v9LYFwuzZs4PaVWTU19cnW8E35hw+2nxBp3OUaMtPGuNpztGgzVmbb9Ro/HzMOXy0+YJO56CODRK5PDIDKAG+7JyrEpGHiV8KeTqkk22nTKEjIvOB+QAjR46koqICgDFjxpCTk0NtbS0AeXl5TJgwgeXLlwOQnx/v/+2L1dDauh+Ac3JKOXx4B80t8b7igKwi0tIyaTqwBoC+fYYxoP9Y9u5bAUBaWiaDcsv5oKmatrYYAANzypg9e3aHx7hx40hPT6eurg6AESNGMHr0aCorKwHIysqirKyMqqoqmpubASgvL2fz5s1s374dgPHjx9PW1kYsFqOiooL8/HwKCgqoqqoC4lPDlpaWUllZ2fHLwZQpU9i4cWPHQMuJEyfS0tLSUUlHjRrF8OHDqa6uBuIL9pWUlLBixYqOAbDTpk1j7dq17N69G4Di4mKamprYtGkTAIWFhQwePJiamvhQw0GDBlFcXMyyZctwzhEflgi1tbXs3bsXgJKSEvbs2UNDQ0NCccrIyGDKlCnU1NSwf388TqWlpezYsYMtW+JxKioqIjMzkzVr4nEaNt99kPIAACAASURBVGwYY8eOZcWKeJwyMzMpLy+nurqaWCwep7KyMhobG9m6despcYrFYqxfv/6M47Rhw4aOOhZFnJxzrF69ukdxmj59eqRxisVi1NXVJRQnwzAMwzAMIxikuylJRWQE8IZzrtC7P5V4p+1CYIZzbpt3+WOFc26ciDzq3f6lV35De7nTvUZpaalrP7jtjmuuuYZ/u+N3CZX1S/lUCWWK1nfeeYcLLrgg8P2GiTmHjzZf8OcsIiudc6UhK4WKn9wE4eUny03HMOdo0Obs17e35Sdt8QRzjgJtvhCus4hQ+Vrw37X3PXgNv/tdYscGXeWmbi+PdM5tB7aIyDhv0xVAHfA8cKO37UbgOe/288ANEucjwL6uOmx+aT/roInhw4cnW8E35hw+2nxBp3OUaMtPGuNpztGgzVmbb9Ro/HzMOXy0+YJO56CODRJdp+3LwFMi8hYwGXgAeBD4hIjUA5/w7gO8BGwC3gZ+AnwpEFOPIGZfiRo/v9SnCuYcPtp8QadzlGjLTxrjac7RoM1Zm2/UaPx8zDl8tPmCTuegjg0SmvLfObcK6OxU3RWdlHXArT30MgzDMAzDMAzDMEj8TFvK8O677yZbwTe5ubnJVvCNOYePNl/Q6Rwl2vKTxniaczRoc9bmGzUaPx9zDh9tvqDTOahjg24nIokCP4NpwxokCOEN9jeM3kgUA/1FZCDwU2Ai8VlqbwY2AEuBQqABuN45t1fi06I+DFwNHAT+0TnX5YXmficiCSs/WW4yjGDpbRORGIbRPanwHd6jiUhSjfvuuy/ZCr5pnxpdE+YcPtp8ISWdHwb+4Jy7CCgG1hGf3faPzrki4I8cW6JkJlDk/c0Hfhi0jLb8lILx7BZzjgZtztp8o0bj52PO4aPNF3Q6B3VsoK7T1q9fv2Qr+KZ9XS5NmHP4aPOF1HIWkVxgGvAYgHPusHPuA+BaYLFXbDHwae/2tcDPXZw3gIHeciWBoS0/pVI8E8Wco0GbszbfqNH4+Zhz+GjzBZ3OQR0bqOu0GYZheIwB3gd+JiJ/EZGfisgAYHj7MiPe/2Fe+Xxgy3HPb/S2GYZhGIZhpDTqxrSlp6fz+rK2UDzCGjdy9OhR0tJ09Y/NOXy0+YI/57DHjIhIKfAGcLlzrkpEHgb2A192zg08rtxe59wgEXkR+A/n3Apv+x+BrzvnVp603/nEL59k5MiRlz711FMAjBkzhpycHGprawHIy8tjwoQJLF++HICMjAymT5/OSy/+mdbW/QCck1PK4cM7aG6J9xUHZBWRlpZJ04E1APTtM4wB/ceyd1/8co+0tEwG5ZbzQVM1bW0xAAbmlLHo4c+xYMECAMaNG0d6ejp1dXUAjBgxgtGjR1NZWQlAVlYWZWVlVFVV0dzcDEB5eTmbN29m+/btAIwfP562tjY2bNgAQH5+PgUFBVRVVQGQnZ1NaWkplZWVtLS0ADBlyhQ2btzIzp07AZg4cSItLS3U19cDMGrUKIYPH94xHXNubi4lJSWsWLGi45fRadOmsXbtWnbv3g1AcXExTU1NbNq0CYDCwkIGDx7csabNoEGDKC4uZtmyZTjnEBGmTp3K6tWr2bt3LwAlJSXs2bOHhoaGhOM0ZcoUampq2L8/HqfS0lJ27NjBli3xOBUVFZGZmcmaNfE4DRs2jLFjx3ZclpOZmUl5eTnV1dXEYvE4lZWV0djYyNatW8+KOK1Zs4Y9e/accZymT59ObW1tysZp7NixvWpM29n+fZMqaHPW5gvhOoc1pu3y6em0tSXWd+nq2Eldp+2WW27hCzc9FopHWJ221atXM2nSpMD3GybmHD7afMGfcwSdthHAG865Qu/+VOLj1y4EZjjntnmXP1Y458aJyKPe7V965Te0lzvda/gd6B9WfrLcdAxzjgZtzn59e9tEJNriCeYcBdp8IVznsDptP/nZLTz2WGLHBmfVRCTjx49PtoJv2n9V1oQ5h482X0gtZ+fcdmCLiIzzNl0B1AHPAzd6224EnvNuPw/cIHE+AuzrqsN2JmjLT6kUz0Qx52jQ5qzNN2o0fj7mHD7afEGnc1DHBgktrm0YhpGifBl4SkT6ApuAm4j/GPW0iNwCvAtc55V9ifh0/28Tn/L/puh1DcMwDMMw/KPuTNujjz6abAXfFBcXJ1vBN+YcPtp8IfWcnXOrnHOlzrmLnXOfds7tdc7tds5d4Zwr8v7v8co659ytzrkLnHOTnHOBL3CkLT+lWjwTwZyjQZuzNt+o0fj5mHP4aPMFnc5BHRuo67QVFBQkW8E3TU1NyVbwjTmHjzZf0OkcJdryk8Z4mnM0aHNOVV8RSfdmt/2dd3+0iFSJSL2ILPWuEkBEMr37b3uPFwbpkaqfT1eYc/ho8wWdzkEdG6jrtH3qU59KtoJv2mfc0oQ5h482X9DpHCXa8pPGeJpzNGhzTmHf24F1x93/T+A7zrkiYC9wi7f9FmCvc+5C4DteucBI4c/ntJhz+GjzBZ3OQR0bqOu0GYZhGIZhpDoiUgB8Cvipd1+AjwPPeEUWA5/2bl/r3cd7/AqvvGEYBqCw0/byyy8nW8E3hYWFyVbwjTmHjzZf0OkcJdryk8Z4mnM0aHNOUd/vAl8Hjnr384APnHOt3v1GIN+7nQ9sAfAe3+eVD4QU/Xy6xJzDR5sv6HQO6thA3eyR7YuMamLw4MHJVvCNOYePNl/Q6Rwl2vKTxniaczRoc041XxG5BtjpnFspIjPaN3dS1CXw2PH7nQ/MBxg5ciQVFRVA94uVr169mgULFrBgwQLOO+88ABYtWkRJSQkf+9jHAHj22WfZu3cvN998MwCrVq3imWee4b777gNg//79fPOb32ThwoWce+65ACxe/At+/etfhbKo/KZNm2hoaIh0Ufm1a9d2TCl/JovKX3LJJSm9qPzJcWr/jHsSp/bvvajiNHnyZFavXt2jOE2fPr3TOH37299m9wcV9O83hoz0HPYfiMepb588svtPYM++eJxEMhh8zhT2xWpobY3H6ZycUg4f3kFzSzxOA7KKSEvLpOnAGq655hrq6uoSilNXqFtce9GiRVxetjAUj7AWsK2oqGDGjBmB7zdMzDl8tPmCP+fetngthJefLDcdw5yjQZuzX9+w85OI/AfweaAV6AfkAs8CVwEjnHOtIlIO3Oucu0pE/se7XSkiGcB2YKjrouGnwrFTWLkJ9NVB0OeszRfCdQ5rce3XqxaxcGFi7e+sWlzbMAzDMAwjlXHO3emcK3DOFQLzgP91zn0OeBWY4xW7EXjOu/28dx/v8f/tqsNmGEbvQ12nrf0UqiYGDRqUbAXfmHP4aPMFnc5Roi0/aYynOUeDNmdFvt8AviYibxMfs/aYt/0xIM/b/jXgjiBfVFtuAlUx7UCbszZf0OkcVPtTd3lkWKcuIdzT/IbR2+iNl0eGlZ8sNxlGsPS2/GS5yTC6JxXayVl1eeS3vvWtZCv4ZtmyZclW8I05h482X9DpHCXa8pPGeJpzNGhz1uYbNdpyE+iMqTZnbb6g0zmo9qeu05aWpk5Z5a9Q5hw+2nxBp3OUaMtPGuNpztGgzVmbb9Roy02gM6banLX5gk7noNqfulZ89OjR7gulGBrXxzTn8NHmCzqdo0RbftIYT3OOBm3O2nyjRltuAp0x1easzRd0OgfV/mxM23HYtdmGERy9bcwIpMb18IZhdE9vy0+Wmwyje1KhnZxVY9q++MUvJlvBN+2LKGrCnMNHmy/odI4SbflJYzzNORq0OWvzjRptuQl0xlSbszZf0OkcVPtT12krKipKtoJv2ldc14Q5h482X9DpHCXa8pPGeJpzNGhz1uYbNdpyE+iMqTZnbb6g0zmo9qeu02YYhmEYhmEYhtGbUNdp+973vpdsBd+UlJQkW8E35hw+2nxBp3OUaMtPGuNpztGgzVmbb9Roy02gM6banLX5gk7noNqfuk7buHHjkq3gmz179iRbwTfmHD7afEGnc5Roy08a42nO0aDNWZtv1GjLTaAzptqctfmCTueg2p+6TtuVV16ZbAXfNDQ0JFvBN+YcPtp8QadzlGjLTxrjac7RoM1Zm2/UaMtNoDOm2py1+YJO56DaX0YihUSkAWgC2oBW51ypiAwGlgKFQANwvXNur8QXUHgYuBo4CPyjc64mEFvDMAzDMAzDMIyAyerXn/Kpwa8D9/8W/b9A9uPnTNvHnHOTj1s74A7gj865IuCP3n2AmUCR9zcf+GEgph4vvvhikLuLhDFjxiRbwTfmHD7afEGnc5Roy08a42nO0aDNWZtv1GjLTaAzptqctflCuM7Nhw7idh0J/O+F370QiF9PLo+8Fljs3V4MfPq47T93cd4ABorIuT14nRNobGwMaleRkZOTk2wF35hz+GjzBZ3OUaItP2mMpzlHgzZnbb5Roy03gc6YanPW5gs6nYNqf4l22hzwsoisFJH53rbhzrltAN7/Yd72fGDL8a7etkCwBSKjwZzDR5sv6HSOEm35SWM8zTkatDlr840abbkJdMZUm7M2X9DpHFT7S2hMG3C5c+49ERkGvCIi67so29nFoO6UQvHO33yAkSNHUlFRAcRPe+bk5HQEJS8vjwkTJrB8+XIA8vPj/b99sRpaW/cDcE5OKYcP76C5Jd5XHJBVRFpaJk0H1gDQt88wBvQfy959KwBIS8tkUG45HzRV09YWA2BgThmzZ8/u8Bg3bhzp6enU1dUBMGLECEaPHk1lZSUAWVlZlJWVUVVVRXNzMwDl5eVs3ryZ7du3AzB+/Hja2tqIxWJUVFSQn59PQUEBVVVVAGRnZ1NaWkplZSUtLS0ATJkyhY0bN7Jz504AJk6cSEtLC/X19QCMGjWK4cOHU11dDUBubi4lJSWsWLGC1tZWAKZNm8batWvZvXs3AMXFxTQ1NbFp0yYACgsLGTx4MDU18aGGgwYNori4mGXLluGcIz4sMd4w2hcxLCkpYc+ePR0DQLuLU0ZGBlOmTKGmpob9++NxKi0tZceOHWzZEo9TUVERmZmZrFkTj9OwYcMYO3YsK1bE45SZmUl5eTnV1dXEYvE4lZWV0djYyNatW0+JUywWY/369Wccpw0bNnTUsSji5Jxj9erVPYrT9OnTI41TLBajrq4uoTgZhmEYhmEYwSDOndKf6voJIvcCMeALwAzn3Dbv8scK59w4EXnUu/1Lr/yG9nKn22dpaalrP7jtjltuuYUv3PSYL+dEKZ8q+P08EmH16tVMmjQp8P2GiTmHjzZf8OcsIiuPGwOrEj+5CcLLT5abjmHO0aDN2a9vb8tP2nIT6KuDoM9Zmy+E6ywiuF1HAt/vLV//Io89llj76yo3ddtpE5EBQJpzrsm7/QrwTeAKYLdz7kERuQMY7Jz7uoh8CriN+OyRZcAjzrkPd/UafhJPeno6ry9rS6isX8JKPkePHiUtTdfqCuYcPtp8wZ9zbzsogvDyk+WmY5hzNGhz9uvb2/KTttwE+uog6HPW5gvhOofVaUsflklbW2Ltr6vclMi7Hg6sEJFa4E3gRefcH4AHgU+ISD3wCe8+wEvAJuBt4CfAlxKyTJBvfetbQe4uEtovRdOEOYePNl/Q6Rwl2vKTxniaczRoc9bmGzXachPojKk2Z22+oNM5qPbX7Zg259wmoLiT7buJn207ebsDbg3EzjAMwzAMwzAMo5ej65wocOjQoWQr+CYjI9H5XlIHcw4fbb6Qes4i0iAiq0VklYhUe9sGi8grIlLv/R/kbRcReURE3haRt0SkJGgfbfkp1eKZCOYcDdqctflGjbbcBDpjqs1Zmy/odA6q/fmeiCQM/FyXLSJUvhaOc5jXZhtGbyOKMSMi0gCUOud2HbftW8Ce48bbDnLOfUNErga+zLHxtg8757qc5tLvmLaw8pPlJsMIlt42ps1yk2F0T1hj2mRIn4TbSU/HtKUUCxYsSLaCb9qnbNeEOYePNl9Q43wtsNi7vRj49HHbf+7ivAEM9Ga+DQxt+UlJPE/AnKNBm7M236jRlptAZ0y1OWvzBZ3OQbU/dZ228847L9kKvmlf/0oT5hw+2nwhJZ0d8LKIrPTWfgQY3r7EiPd/mLc9H9hy3HMbvW2BoS0/pWA8u8Wco0GbszbfqNGWm0BnTLU5a/MFnc5BtT99F4YahmEc43Ln3HsiMgx4RUTWd1FWOtl2yvUKXudvPsDIkSOpqKgAEluoHGBfrIbW1viXyjk5pRw+vIPmlnhfcUBWEWlpmTQdiC8o37fPMAb0H8veffGFytPSMhmUW84HTdW0tcUXKh+YU8bs2bM7PI5fUB5gxIgRZ7ygfCwWo6KiIrIF5adNm8batWt7tKA8EOmC8gDDhg1LaEH5xsZGtm7dekqcYrEY69evP+M4bdiwASDSODU3N3fUuTOJ0/Tp0yONUywWo66uLuE4GYZhaEPdmLaRI0fym6ffC8UjrGuzY7EY2dnZge83TMw5fLT5gj/nqMeMiMi9QAz4AjDDObfNu/yxwjk3TkQe9W7/0iu/ob3c6fbpd0xbWPnJctMxzDkatDn79e1tY9q05SbQVwdBn7M2XwjXOawxbSMnncd77yXW/s6qMW0lJYFP+BY6O3bsSLaCb8w5fLT5Qmo5i8gAEclpvw1cCawBngdu9IrdCDzn3X4euMGbRfIjwL6uOmxngrb8lErxTBRzjgZtztp8o0ZbbgKdMdXmrM0XdDoH1f7UXR55xcevoHxqZ1c59Zysfv1D2e+WLVu44IILQtl3WJhz+GjzhZRzHg48610ylwH8t3PuDyLyZ+BpEbkFeBe4ziv/EvGZI98GDgI3BS30sY99LOhdhkqKxTMhzDkatDlr840abbkJdMZUm7M2X9DpHFT7U9dpO+qOhnLqEuJTchqGoQPn3CaguJPtu4ErOtnugFsjUDMMwzAMwwgUdZdHPvvss8lW8E1RUVGyFXxjzuGjzRd0OkeJtvykMZ7mHA3anLX5Ro223AQ6Y6rNWZsv6HQOqv2p67S1z0SliczMzGQr+Macw0ebL+h0jhJt+UljPM05GrQ5a/ONGm25CXTGVJuzNl/Q6RxU+1PXabv55puTreCb9mmjNWHO4aPNF3Q6R4m2/KQxnuYcDdqctflGjbbcBDpjqs1Zmy/odA6q/anrtBmGYRiGYRiGYfQm1HXaVq1alWwF3wwbNizZCr4x5/DR5gs6naNEW37SGE9zjgZtztp8o0ZbbgKdMdXmrM0XdDoH1f7ULa6dlZVFc2NTKB4ypE8oi0S2traSkaFrok5zDh9tvuDPubctXgvx/PTqK82Be4S1gO3ZXgdTBXMOH7++vS0/actNoK8Ogj5nbb4QrnNYi2tnFeTQ3JxY+zurFte+7777kq3gmxUrViRbwTfmHD7afEGnc5Roy08a42nO0aDNWZtv1GjLTaAzptqctfmCTueg2p+6TpthGIZhGIZhGEZvQl2nbf/+/clW8I3G6UnNOXy0+YJO5yjRlp80xtOco0GbszbfqNGWm0BnTLU5a/MFnc5BtT91Y9rCut4UwhvTZhi9kd42ZgTi+anyteBzSJjjRgyjN9Lb8pPlJsPonrD6GH76F2fVmLaFCxcmW8E3fg76UgVzDh9tvqDTOUq05SeN8TTnaNDmrM03arTlJtAZU23O2nxBp3NQ7U9dp+3cc89NtoJvYrFYshV8Y87ho80XdDpHibb8pDGe5hwN2py1+UaNttwEOmOqzVmbL+h0Dqr96Zrn0zAMI4VJkzTKp0rg+83q1z/wfRqGYRiGoQd1Y9qGDh3K++u3heIR1pi25uZmsrKyAt9vmJhz+GjzBX/OvW3MCISXnyw3HcOco0Gbs1/f3pafhg4dygvPvh+4Q5hj2rTVQdDnrM0XwnUOa0zb0IvO5f33E2t/Z9WYtqlTpyZbwTeNjY3JVvCNOYePNl/Q6Rwl2vKTxniaczRoc9bmGzXachPojKk2Z22+oNM5qPanrtN2+eWXJ1vBN1u3bk22gm/MOXy0+YJO5yjRlp80xtOco0GbszbfqNGWm0BnTLU5a/MFnc5BtT91nTbDMAzDMIxURkT6icibIlIrImtF5N+97aNFpEpE6kVkqYj09bZnevff9h4vTKa/YRiph7pO29KlS5Ot4Jtx48YlW8E35hw+2nxBp3OUaMtPGuNpztGgzTkFfVuAjzvnioHJwCdF5CPAfwLfcc4VAXuBW7zytwB7nXMXAt/xygWGttwEKRnTbtHmrM0XdDoH1f7UddqOHAlnYe0wSU9PT7aCb8w5fLT5gk7nKNGWnzTG05yjQZtzqvm6OO1zk/fx/hzwceAZb/ti4NPe7Wu9+3iPXyEigU1Fqy03QerFNBG0OWvzBZ3OQbU/dZ22f/iHf0i2gm/q6uqSreAbcw4fbb6g0zlKtOUnjfE052jQ5pyKviKSLiKrgJ3AK8A7wAfOuVavSCOQ793OB7YAeI/vA/KCctGWmyA1Y9od2py1+YJO56Dan63TZhiGYRiGETDOuTZgsogMBJ4FPtRZMe9/Z2fVTplLX0TmA/MBRo4cSUVFBQBjxowhJyeH2tpaAPLy8pgwYQLLly8HID8/3jfcF6uhtXU/AOfklHL48A6aW7YAMCCriLS0TJoOrAGgb59hDOg/lr37VgCQlpbJoNxyPmiqpq0tfhJxyJAh1NfXd0wOMW7cONLT0zsOrEeMGMHo0aOprKwEICsri7KyMqqqqmhubgagvLyczZs3s337dgDGjx9PW1sbsViMiooK8vPzKSgooKqqCoDs7GxKS0uprKykpaUFgClTprBx40Z27twJwMSJE2lpaaG+vh6AUaNGMXz4cNqXSMjNzaWkpIQVK1bQ2hrvQ0+bNo21a9eye/duAIqLi2lqamLTpk0AFBYWMnjwYGpqagAYNGgQxcXFLFu2DOcc7SdGa2tr2bt3LwAlJSXs2bOHhoaGhOKUkZHBlClTqKmpYf/+eJxKS0vZsWMHW7bE41RUVERmZiZr1sTjNGzYMMaOHcuKFfE4ZWZmUl5eTnV1dcdC1GVlZTQ2Np4Sp/bPuCdx2rBhQ0cdiyJOzjlWr17dozhNnz690zh9+9vfpmLVCsacW0hO/2xq34l/xnm5g5kw+iKW1/4pHqe0dKZcXE7Nxlr2H2yKx2ncJezYu5MtO+OfcVH+GDL7ZrJm8zoKCgqoq6tLKE5dkfA6bSKSDlQDW51z14jIaGAJMBioAT7vnDssIpnAz4FLgd3AXOdcQ1f79rPWyLx581jygycTKuuXsNZCWr9+PRdddFHg+w0Tcw4fbb7gz7m3rYME4eUny03HMOdo0Obs1zfq/CQi9wAHgW8AI5xzrSJSDtzrnLtKRP7Hu10pIhnAdmCo66Lh+z12+sptS3r+Rk4izHXatNVB0OeszRfCdQ5rnbZ5t/4DS5Yk1v6CWqftdmDdcfeTMpj297//fZC7i4TRo0cnW8E35hw+2nxBp3OUaMtPGuNpztGgzTnVfEVkqHeGDRHJAv6G+DHUq8Acr9iNwHPe7ee9+3iP/29XHTa/aMtNkHoxTQRtztp8QadzUO0voU6biBQAnwJ+6t0XkjSY9u677w5qV5HRfrpZE+YcPtp8QadzlGjLTxrjac7RoM05BX3PBV4VkbeAPwOvOOd+R/xM29dE5G3iY9Ye88o/BuR5278G3BGkjLbcBCkZ027R5qzNF3Q6B9X+Eh3T9l3g60COdz+PBAfTikj7YNpdx++wp9dlJ3odKcCwgUMYO+pCVqx+A4DMjL6UT/ww1etriB06CEDZhy5l9uzZHR52Xbau67Lr6uqIxWKsX7/erssOMU6xWCyQ67INwzDOZpxzbwGXdLJ9E/DhTrYfAq6LQM0wDKV0O6ZNRK4BrnbOfUlEZgD/AtwEVHqXQCIio4CXnHOTRGQtcJVzrtF77B3gw8653ad7DT/XZd911108sPCbCZX1S1jjRqqqqtQdxJpz+GjzBX/OvXFMW1j5yXLTMcw5GrQ5+/XtbfnprrvuYtbVDwTuEOaYNm11EPQ5a/OFcJ3DGtN216K7eeCBxNpfV7kpkU7bfwCfB1qBfkAu8VmQriIJg2nD+kAhvAMjw+iN9LaDIggvP1luMoxg6W35SUSofC34HBJmp80woiYVvsN7NBGJc+5O51yBc64QmEd8cOznSNJg2jvvvDOoXUVG+2V2mjDn8NHmCzqdo0RbftIYT3OOBm3O2nyjRltuAp0x1easzRd0OgfV/nqyuHZSBtPm5QW21mRktI+l0oQ5h482X0hNZ28B27+IyO+8+6NFpEpE6kVkqYj09bZnevff9h4vDNpFW35KxXh2hzlHgzZnbb5Roy03gc6YanPW5gs6nYNqf746bc65CufcNd7tTc65DzvnLnTOXeeca/G2H/LuX+g9vikQU8MwjM5JieVIDMMwDMMwwiLhxbXDxM912eeccw77Np12TpMeEda4kZaWFjIzMwPfb5iYc/ho8wV/zlGMGfGWI1kM3E/8zP7fAu+ThPG2EF5+stx0DHOOBm3Ofn1725i2c845h/95cV/gDmGOadNWB0GfszZfCNc5rDFt54zJY9++xNpfV7kp0Sn/U4aZM2cmW8E3mzdvVrfivDmHjzZfSEnnlFmOJCMjg5kzZ6pajqS2tpZ+/fqpWo5k+PDhtLS0qFqO5NChQxQWFqpZjmTatGlUVVVx9OjRM45T1MuRHDp0iPPOOy/hOPU27NgpGrQ5a/MFnc5BtT91Z9oWLVrEwn+8PRSPsH7NrqioYMaMGYHvN0zMOXy0+YI/57B/yU615UggvPxkuekY5hwN2pz9+va2M22LFi3i8rKFgTuEeaZNWx0Efc7afCFc57DOtC164mEWLkys/Z1VZ9oMwzA8LgdmicjVHFuO5LvAQBHJ8M62FQDveeUbgVFAo3d55DnAnui1DcMwDMMw/NGT2SOTwpNPPplsBd+MHz8+2Qq+Mefw0eYLqeWcasuRgL78lErxTBRzjgZtztp8o0ZbbgKdMdXmAwXTFAAAIABJREFUrM0XdDoH1f7Uddr69OmTbAXftLW1JVvBN+YcPtp8QY1zUpYjAX35SUk8T8Cco0GbszbfqNGWm0BnTLU5a/MFnc5BtT91nba5c+cmW8E37YPINWHO4aPNF1LXOVWWI9GWn1I1nl1hztGgzVmbb9Roy02gM6banLX5gk7noNqfuk6bYRiGYRiGYRhGb0Jdp+31119PtoJv8vPzuy+UYphz+GjzBZ3OUaItP2mMpzlHgzZnbb5Roy03gc6YanPW5gs6nYNqf+o6ba+99lqyFXxTUFCQbAXfmHP4aPMFnc5Roi0/aYynOUeDNmdtvlGjLTeBzphqc9bmCzqdg2p/6jptd9wR+NwBodO+EKomzDl8tPmCTuco0ZafNMbTnKNBm7M236jRlptAZ0y1OWvzBZ3OQbU/dZ02wzAMwzAMwzCM3oS6Ttu2bduSreCb7OzsZCv4xpzDR5sv6HSOEm35SWM8zTkatDlr840abbkJdMZUm7M2X9DpHFT7k4DXlj0jSktLXXV1dUJlRQS360goHjKkD6nweRjG2YCIrHTOlSbboyf4yU0QXn6y3GQYwdLb8pOIUPla8DmkfKpYbjLOGlLhO7yr3KTuTNvdd9+dbAXfVFZWJlvBN+YcPtp8QadzlGjLTxrjac7RoM1Zm2/UaMtNoDOm2py1+YJO56Dan7pOW25ubrIVfNPS0pJsBd+Yc/ho8wWdzlGiLT9pjKc5R4M2Z22+UaMtN4HOmGpz1uYLOp2Dan/qOm2GYRiGYRiGYRi9CXVj2rKysmhubArFI6xxI62trWRkZAS+3zAx5/DR5gv+nHvbmBEILz9ZbjqGOUeDNme/vr0tP2VlZfHqK82BO4Q5pk1bHQR9ztp8IVznsMa0ZRXk0NycWPs7q8a0zZkzJ9kKvtm4cWOyFXxjzuGjzRd0OkeJtvykMZ7mHA3anLX5Ro223AQ6Y6rNWZsv6HQOqv2p67RNnjw52Qq+2blzZ7IVfGPO4aPNF3Q6R4m2/KQxnuYcDdqctflGjbbcBDpjqs1Zmy/odA6q/anrtBmGYRiGYRiGYfQm1HXaHn/88WQr+GbixInJVvCNOYePNl/Q6Rwl2vKTxniaczRoc9bmGzXachPojKk2Z22+oNM5qPanrtM2aNCgZCv4RuP0pOYcPtp8QadzlGjLTxrjac7RoM1Zm2/UaMtNoDOm2py1+YJO56Dan7pO2+zZs5Ot4Jv6+vpkK/jGnMNHmy/odI4SbflJYzzNORq0OWvzjRptuQl0xlSbszZf0OkcVPtT12kzDMMwDMMwDMPoTajrtL366qvJVvDNqFGjkq3gG3MOH22+oNM5SrTlJ43xNOdo0OaszTdqtOUm0BlTbc7afEGnc1DtT12nraamJtkKvhk+fHiyFXxjzuGjzRd0OkeJtvykMZ7mHA3anLX5Ro223AQ6Y6rNWZsv6HQOqv2p67QtXLgw2Qq+qa6uTraCb8w5fLT5gk7nKNGWnzTG05yjQZuzNt+o0ZabQGdMtTlr8wWdzkG1P3WdNsMwDMMwDMMwjN5Et502EeknIm+KSK2IrBWRf/e2jxaRKhGpF5GlItLX257p3X/be7wwSOF33303yN1FQm5ubrIVfGPO4aPNF3Q6R4m2/KQxnuYcDdqctflGjbbcBDpjqs1Zmy/odA6q/YlzrusCIgIMcM7FRKQPsAK4Hfga8Bvn3BIR+RFQ65z7oYh8CbjYOfdPIjIPmO2cm9vVa5SWlrpET3eKCG7XkYTK+kWG9KG7z8MwjMQQkZXOudJke/QEP7kJwstPlpsMI1h6W34SESpfCz6HlE8Vy03GWUMqfId3lZu6PdPm4sS8u328Pwd8HHjG274Y+LR3+1rvPt7jV3gdv0C47777gtpVZKxYsSLZCr4x5/DR5gs6naNEW37SGE9zjgZtztp8o0ZbbgKdMdXmrM0XdDoH1f4SGtMmIukisgrYCbwCvAN84Jxr9Yo0Avne7XxgC4D3+D4gLxBboF+/fkHtKjJaW1u7L5RimHP4aPMFnc5Roi0/aYynOUeDNmdtvlGjLTeBzphqc9bmCzqdg2p/GYkUcs61AZNFZCDwLPChzop5/zs7q3bKOUERmQ/MBxg5ciQVFRUAjBkzhpycHGprawHIy8tjwoQJLF++HID8/HjfsGZjLfsPNgFQOu4SduzdyZadWwEoyh9DZt9M1mxeB8CwgUMYO+pCVqx+A4DMjL6UT/ww1etriB06CEDZhy5l9uzZHR7jxo0jPT2duro6AEaMGMHo0aOprKwEICsri7KyMqqqqmhubgagvLyczZs3s337dgDGjx9PW1sbsViMiooK8vPzKSgooKqqCoDs7GxKS0uprKykpaUFgClTprBx40Z27twJwMSJE2lpaelYAX7UqFEMHz68Y/ac3NxcSkpKWLFiRUdFnjZtGmvXrmX37t0AFBcX09TUxKZNmwAoLCxk8ODBHVOQDho0iOLiYpYtW4ZzjvYTo7W1tezduxeAkpIS9uzZQ0NDQ0JxysjIYMqUKdTU1LB///54nEpL2bFjB1u2bInHqaiIzMxM1qxZE4/TsGGMHTu241eUzMxMysvLqa6uJhaLn+wtKyujsbGRrVu3nhKnWCzG+vXrzzhOGzZs6KhjUcTJOcfq1at7FKfp06dHGqdYLEZdXV1CcTIMwzAMwzCCodsxbac8QeQe4CDwDWCEc65VRMqBe51zV4nI/3i3K0UkA9gODHVdvJCf67LT09Np29niyzlRwho3cvToUdLSdE3Uac7ho80X/DmHPWZERPoBy4FM4j9APeOcu0dERgNLgMFADfB559xhEckEfg5cCuwG5jrnGrp6Db9j2sLKT5abjmHO0aDN2a9vbxvTlp6ezuvL2gJ3CHNMm7Y6CPqctflCuM5hjWlLH5ZJW1ti7a9HY9pEZKh3hg0RyQL+Bvj/2zv/4Kyu885/HhCW5QhsfkSAEY6gFjRAq5gQE20wJk6d+sfuut5JMslMWtN612mTutmGTkOnM+lO17v1eo0zzoynSzrxxOnuJk7azsTjJNOkjmUiF5Ng2ZgfNj8KrBHGKAUCKMaykc/+8V5AlgV6L9xz7v2i5zOj0fteXa4+Ouc8D/e89z7nvgg8CXws2+0O4LvZ68ey92Q///G5Jmx5WblyZVGHSsbWrVvLVsiNO8dHzRcq5zwA3BBC6ADeB9xkZh8E/gfw5RBCO3AEuDPb/07gSAjhauDL2X6FopafKtafdeHOaVBzVvNNjVpuAs0+VXNW8wVN56Lir56p6kzgSTN7AfgZ8KMQwuPUrrR9wcx2UatZ+1q2/9eAqdn2LwCrCzHNWLBgQZGHS8Kp29+UcOf4qPlCtZyrtkgS6OWnKvVnvbhzGtSc1XxTo5abQLNP1ZzVfEHTuaj4G7WmLYTwAnDNCNt3A9eOsP114OOF2DmO45wDMxsPPAtcDTxEjkWSzOzUIkn/mlTacRzHcRwnJ3UtRFIl1q5dy6qVny9bIxcdHR1lK+TGneOj5gvVc67SIkkNDQ2sXbuWD/+bFTKLJA0ODsotktTR0SG3SNLg4KDUIknLly+nubn59JhTWCRpcHCw7kWSent7GWusXbuWDy1dVbZGLqr2/009qDmr+YKm89q1a1m16sLjL/dCJDHIU0x7ww038OPv/DCKR6xi/5dffpmrrrqq8OPGxJ3jo+YL+ZxTF/qXvUgSxMtPnpvO4M5pUHPO65tgoaTZ1BY+mgG8BXw1hPCgmU0BHgXagL3AJ0IIR7JbtR8EbqGWx1aGEHrO9Tvynjv997/88Xn+NWcn5kIkamMQ9JzVfCGuc6yFSG74+Ef58Y/ri78LWoikatx6661lK+Tm1KeTSrhzfNR8oVrOVVskCfTyU5X6s17cOQ1qzhX0PQmsCiG8F/gg8DkzW0Ctzv+JbKGkJzhT938z0J593QX8dZEyarkJKtmno6LmrOYLms5FxZ/c7ZGO4zgZM4FHsrq2ccC3QwiPm9k24Ftmdg/wHG9fJOlvs0WSDgOfLEPacZyLnxDCAeBA9vq4mb1Ira72NmBFttsjQBe1uwNuA76RfZD0jJldYWYzs+M4juPoTdp++MMfytW0tbW1la2QG3eOj5ovVMu5ioskqeWnKvVnvbhzGtScq+xrZm3UctUGYPqpiVgI4YCZtWS7nV4oKePUIkpvm7Sdb81tU1MTAEf7ezh5slYjePnEJbzxxkFODNR+7bua2hk3rpHjv6zVcl4yoYV3XTaPI0drNYLjxjUyeVInvzi+kcHBWo3gtGnT2Llz54i1nHBhNbdvvPGGXM1tW1ubVM3tqTa+kH5KXXP7nve8h82bN19QP52t5vb++++n6/lu5s5sY+JlzWz6l1obT500hYVzfpV1m/651k/jxrPs1zvp2bGprhr2SZMm1V1zey7katra2trY++yuKB6x6kaOHTvGpEmTCj9uTNw5Pmq+kM95rD28FuLlJ89NZ3DnNKg55/VNlZ/MrBl4CvhvIYR/MLNfhBCuGPLzIyGEyWb2PeCvQgjd2fYngD8NITx7tmPnPXf61v/eeyF/yojErGlTG4Og56zmC3GdY9W0tb3/6tMT9zocLp6atrvvvrtshdycmu0r4c7xUfMFTeeUqOUnxf505zSoOVfR18wmAH8P/J8Qwj9kmw+a2czs5zOBvmx7LzB7yD9vBV4pykUtN0E1+3Q01JzVfEHTuaj4k5u0OY7jOI7jVJlsNcivAS+GEB4Y8qOhCyINXyjpd6zGB4GjXs/mOM5Q5CZtp+57VWLy5MllK+TGneOj5guazilRy0+K/enOaVBzrqDvh4DfBm4ws+ezr1uAe4EbzWwncGP2HuD7wG5gF/A3wGeLlFHLTVDJPh0VNWc1X9B0Lir+5GraYt1vCvHqRhxnLDIWa9pi5SfPTY5TLGMtP5kZ639SfA6JWdPmOKmpwv/hF1VN23333Ve2Qm6eeuqpshVy487xUfMFTeeUqOUnxf505zSoOav5pkYtN4Fmn6o5q/mCpnNR8Sc3aRs3Tk5Z8lMod46Pmi9oOqdELT8p9qc7p0HNWc03NWq5CTT7VM1ZzRc0nYuKP7kofuutt8pWyE2tHlkLd46Pmi9oOqdELT8p9qc7p0HNWc03NWq5CTT7VM1ZzRc0nYuKP69pG3psrxtxnMIYazUjUI374R3HGZ2xlp+8ps1xRqcK/4dfVDVtn/nMZ8pWyM2mTZvKVsiNO8dHzRc0nVOilp8U+9Od06DmrOabGrXcBJp9quas5guazkXFn9ykrb29vWyF3Bw5cqRshdy4c3zUfEHTOSVq+UmxP905DWrOar6pUctNoNmnas5qvqDpXFT8NRRyFMdxHMdxHKeSjLNxdF5XfC1Q06WXFX5Mx3FGRq6mra2tjb3P7oriEatu5NixY0yaNKnw48bEneOj5gv5nMdazQjEy0+em87gzmlQc87rO9byk1puAr0xCHrOar4Q1zlWTVvb+69m79699TpcPDVt8+fPL1shN4cPHy5bITfuHB81X9B0TolaflLsT3dOg5qzmm9q1HITaPapmrOaL2g6FxV/cpO2j370o2Ur5Kbe2XWVcOf4qPmCpnNK1PKTYn+6cxrUnNV8U6OWm0CzT9Wc1XxB07mo+JObtDmO4ziO4ziO44wl5CZt3/ve98pWyM3cuXPLVsiNO8dHzRc0nVOilp8U+9Od06DmrOabGrXcBJp9quas5guazkXFn9ykrbe3t2yF3EycOLFshdy4c3zUfEHTOSVq+UmxP905DWrOar6pUctNoNmnas5qvqDpXFT8yU3a/AGRaXDn+Kj5gqZzStTyk2J/unMa1JzVfFOjlptAs0/VnNV8QdN5zD5c23Ecx3Ecx3EcZywhN2nbtm1b2Qq5mTp1atkKuXHn+Kj5gqZzStTyk2J/unMa1JzVfFOjlptAs0/VnNV8QdO5qPiTe7j2+PHjGewbiOIR6yGRb731FuPGac2P3Tk+ar6Qz3msPbwW4uUnz01ncOc0qDnn9R1r+UktN4HeGAQ9ZzVfiOsc6+Ha41saGRwcrNfh4nm49n333Ve2Qm7WrVtXtkJu3Dk+MX2vuqoNMyv866GHHormfDGglp/UYgbcORVqzmq+qVHLTaDZp2rOar6g6VxU/DUUchTHcSrFvn3/j/U/Kf7Tz6c3rCn8mI7jOI7jOM65GfVKm5nNNrMnzexFM9tqZp/Ptk8xsx+Z2c7s++Rsu5nZV8xsl5m9YGaLixR+/fXXizxcEhoa9ObG7hwfNV/QjL+UqLWP4hh05zSoOav5pkYtN4Fmn6o5q/mCpnNR8TdqTZuZzQRmhhB6zGwi8CzwW8BK4HAI4V4zWw1MDiF80cxuAe4GbgGWAg+GEJae63fkuS871v2mEPfebMdJiZlFudLWeZ3VHSNjrWYE4uUnz02OUyxjLT95bnKc0alCnFxQTVsI4UAIoSd7fRx4EZgF3AY8ku32CLWJHNn2b4QazwBXZBO/QvijP/qjog6VjJ6enrIVcuPO8VHzBc34S4la+yiOQXdOg5qzmm9q1HITaPapmrOaL2g6FxV/uRYiMbM24BpgAzA9hHAAahM7oCXbbRawb8g/6822FcJVV11V1KGScezYsbIVcuPO8VHzBc34S4la+yiOQXdOg5qzmm9q1HITaPapmrOaL2g6FxV/dd8YambNwN8D/zmEcMzMzrrrCNvecU3QzO4C7gK48sor6erqAmDu3LlMnDjx9BPPp06dysKFC0+vFjNrVm3+17NjE8deOw7AkvnXcPBIH/v69gPQPmsujZc0smXPiwC0XDGNebOvpnvzMwA0NlxC56Jr2fhSD/2vvwbA0ve+n9tvv/20x/z58xk/fvzpZyvMmDGDOXPmsH79egCamppYunQpGzZs4MSJEwB0dnayZ88eXn31VQAWLFjA4OAg/f39dHV1MWvWLFpbW9mwYQMAzc3NLFmyhPXr1zMwUFuKd9myZezYsYO+vj4AFi1axMDAADt37gRg9uzZTJ8+nVO3REyaNInFixfT3d3NyZMnAVi+fDlbt27l0KFDAHR0dHD8+HF2794NQFtbG1OmTDn9acXkyZPp6OjgqaeeIoTAqb7dtGkTR44cAWDx4sUcPnyYvXv31tVPDQ0NLFu2jJ6entMBtmTJEg4ePMi+fbU5fXt7O42NjWzZsqXWTy0tzJs3j+7u7lo/NTbS2dnJxo0b6e/vr/XT0qX09vayf//+d/RTf38/L7300nn30/bt20+PsRT9FEJg8+bNF9RP119//Yj9dP/993PoF11cdulcGsZP5Ngva/10yYSpNF+2kMNHa/1k1sCUy5dxtL+Hkydr/XT5xCW88cZBTgzU+uldTe2MG9fI8V9uobW1lW3bttXVT7Exs9nAN4AZwFvAV0MID5rZFOBRoA3YC3wihHDEagP7QWq3br8GrDx1F4HjOI7jOE6Vqes5bWY2AXgc+McQwgPZtu3AihDCgez2x64QwnwzW5u9/ubw/c52/Dz3ZV955ZW8svnluvbNS6x7s/v7+2lubi78uDFx5/jE9I1V0/YfPnElr7zySr0OUWtGqlZvC/Hyk+emM7hzGtSc8/qOtZo2tdwEemMQ9JzVfCH+uVOMmrYrf+2qQs6d6lk90oCvAS+emrBlPAbckb2+A/jukO2/k60i+UHg6LkmbHlZvLjQxSiTcPDgwbIVcuPO8VHzhWrFX9XqbaFa7VMPimPQndOg5qzmmxq13ASafarmrOYLms5FxV89NW0fAn4buMHMns++bgHuBW40s53Ajdl7gO8Du4FdwN8Any3ENOPDH/5wkYdLwqnbAZVw5/io+UJ1468K9bZQ3fY5G4pj0J3ToOas5psatdwEmn2q5qzmC5rORcXfqDVtIYRuRq5TA/jICPsH4HMX6OU4jlMXVam3PfXsGK+39Xrb4f2kVm+7fPlyTpw4cXrMFVlvG6uf+vv766637e3txXEcR426atpik+e+7GXLltH93a4oHrHuzd6/f//pBVRUcOf4xPSNVdP2J6uXnT4hqsMhes1IleptIV5+8tx0BndOg5pzXt+xVtOmlptAbwyCnrOaL8Q/d4pR07bsthWFnDvlWvK/Cpz61E6JxsbGshVy487xUfOFasVf1eptoVrtUw+KY9Cd06DmrOabGrXcBJp9quas5guazkXFn9yk7fd+7/fKVsjNqVtslHDn+Kj5QuXir1L1tlC59hkVxTHozmlQc1bzTY1abgLNPlVzVvMFTeei4q/u57Q5juNUCa+3dRzHcRxnrCB3pe35558vWyE3LS0to+9UMdw5Pmq+oBl/KVFrH8Ux6M5piOV81VVtmFnhX9/5zt9F8b1YUMtN4HGTAjVf0HQuKv7kFiJpamriRO/xKB6xCmpPnjx5emU5Fdw5PjF9Yy1E8uEbm06vdFeHw5gq9Id4+clz0xncOQ2xnKuQmzKPMZWf1HITeNykQM0X4p87xViIpKl1YiHnTnJX2u65556yFXJT74oxVcKd46PmC5rxlxK19lEcg+6cBjVntdhLjWL7qI1B0HNW8wVN56LiT27S5jiO4ziO4ziOM5aQm7SdesimEorLk7pzfNR8QTP+UqLWPopj0J3ToOasFnupUWwftTEIes5qvqDpXFT8ydW0xbrfFOLem+04KYlVN9J5ndUdI2OtZgTi5SfPTc7FQhVyU+YxpvKT5ybHGZ0qxMlFVdO2atWqshVyk+ekryq4c3zUfEEz/lKi1j6KY9Cd06DmrBZ7qVFsH7UxCHrOar6g6VxU/MlN2mbOnFm2Qm76+/vLVsiNO8dHzRc04y8lau2jOAbdOQ1qzmqxlxrF9lEbg6DnrOYLms5FxZ/cpM1xHMdxHMdxHGcsIVfT9u53v5ufv3Qgikese7NPnDhBU1NT4ceNiTvHJ6ZvrLqRf3f7u/n5z39er8OYqhmBePnJc9MZ3DkNsZyrkJsyjzGVn9RyE3jcpEDNF+KfO8WoaXv3r84s5NxJ7krbddddV7ZCbnp7e8tWyI07x0fNFzTjLyVq7aM4Bt05DWrOarGXGsX2URuDoOes5guazkXFn9yk7UMf+lDZCrnZv39/2Qq5cef4qPmCZvylRK19FMegO6dBzblqsWdmD5tZn5ltGbJtipn9yMx2Zt8nZ9vNzL5iZrvM7AUzW1y0T9Xapx7UxiDoOav5gqZzUfEnN2lzHMdxHMepOF8Hbhq2bTXwRAihHXgiew9wM9Cefd0F/HUiR8dxhJCbtD366KNlK+Rm/vz5ZSvkxp3jo+YLmvGXErX2URyD7pwGNeeqxV4IYR1weNjm24BHstePAL81ZPs3Qo1ngCvMrNDlHqvWPvWgNgZBz1nNFzSdi4o/uUnbm2/GebB2TMaPH1+2Qm7cOT5qvqAZfylRax/FMejOaVBzFom96SGEAwDZ95Zs+yxg35D9erNthSHSPm9DbQyCnrOaL2g6FxV/DYUcJSGf/vSny1bIzbZt22hpaRl9xwrhzvFR8wXN+EuJWvsojkF3ToOas1rsDcNG2Dbikoxmdhe1Wyi58sor6erqAmDu3LlMnDiRTZs2ATB16lQWLlzIunXrAPjiF78IQM+OTRx77TgAS+Zfw8Ejfezrq9UItc+aS+MljWzZ8yIALVdMY97sq+ne/AwAjQ2X0LnoWja+1EP/668BMG3aNHbu3Hm6zmj+/PmMHz+ebdu2ATBjxgzmzJnD+vXrAWhqamLp0qVs2LCBEydOANDZ2cmePXt49dVXAViwYAGDg4M8++yzNDc3M2vWLFpbW9mwYQMAzc3NLFmyhPXr1zMwMADAsmXL2LFjB319fQAsWrSIgYEBdu7cCcDs2bOZPn366QczT5o0icWLF9Pd3c3JkycBWL58OVu3buXQoUMAdHR0cPz4cXbv3g1AW1sbU6ZMoaenB4DJkyfT0dHBU089RQihtvJgCBw4cIAjR44AsHjxYg4fPszevXvr6qeGhgaWLVtGT08Px44dq/XTkiUcPHiQfftq8/r29nYaGxvZsqVWLtnS0sK8efPo7u6u9VNjI52dnWzcuPH0M82WLl1Kb2/vO/rppz/9Kc3NzRfUT9u3bwdI1k8hBA4ePHhB/XT99dezadOmd/TT/fffT9fz3cyd2cbEy5rZ9C+1Np46aQoL5/wq6zb9c62fxo1n2a931h1Pq1evZtu2bXX107mQW/J/zZo1rFr5+SgesZau7erqYsWKFYUfNybuHJ+YvrGW1X56wxpWrVpVr8OYWlIb4uUnz01ncOc0xHKuQm7KPKLnJzNrAx4PISzK3m8HVoQQDmS3P3aFEOab2drs9TeH73eu41fh3Cnmkv8eN/FR84X4504xlvxf8/UHCzl3krs98mc/+1nZCrmZMWNG2Qq5cef4qPmCZvylRK19FMegO6dBzVkk9h4D7she3wF8d8j238lWkfwgcHS0CVteRNrnbaiNQdBzVvMFTeei4k/u9sgf/OAHZSvkZs6cOWUr5Mad46PmC5rxlxK19lEcg+6cBjXnqsWemX0TWAFMM7Ne4C+Ae4Fvm9mdwMvAx7Pdvw/cAuwCXgN+t2ifqrVPPaiNQdBzVvMFTeei4k/uStuXvvSlshVyc+oeYSXcOT5qvqAZfylRax/FMejOaVBzrlrshRA+FUKYGUKYEEJoDSF8LYRwKITwkRBCe/b9cLZvCCF8LoTwKyGEXwsh1H9Pdp1UrX3qQW0Mgp6zmi9oOhcVf3KTNsdxHMdxHMdxnLGE3KTt1GoxSjQ1NZWtkBt3jo+aL2jGX0rU2kdxDLpzGtSc1WIvNYrtozYGQc9ZzRc0nYuKP7nVI2Ot7AJxV0FynJTEWqGt8zqrO0bG4uqRsfKT5ybnYqEKuSnzGFP5yXOT44xOFeLkolo98s/+7M/KVsjNqWdWKOHO8VHzBc34S4la+yiOQXdOg5qzWuylRrF91MYg6Dmr+YKmc1HxJzdpmzp1atkKuTn1YEIl3Dk+ar6gGX+R2jnjAAASv0lEQVQpUWsfxTHozmlQc1aLvdQoto/aGAQ9ZzVf0HQuKv7kJm2O4ziO4ziO4zhjiVFr2szsYeDfAn0hhEXZtinAo0AbsBf4RAjhiJkZ8CC15428BqwMIfSMJpHnvuzLL7+co7vjFNTGujd7YGCAxsbGwo8bE3eOT0zfWHUjv3nr5Rw9erRehzFVMwLx8pPnpjO4cxpiOVchN2UeYyo/qeUm8LhJgZovxD93ilHTdvncqYWcO9Vzpe3rwE3Dtq0GngghtANPZO8Bbgbas6+7gL+uyzAHN998c9GHjM6ePXvKVsiNO8dHzReqF39m9rCZ9ZnZliHbppjZj8xsZ/Z9crbdzOwrZrbLzF4ws8VF+1StfUZDcQy6cxrUnNViLzWK7aM2BkHPWc0XNJ2Lir9RJ20hhHXA4WGbbwMeyV4/AvzWkO3fyB4U+QxwhZnNLMQ04wMf+ECRh0vCq6++WrZCbtw5Pmq+UMn4+zoV+lCpgu1zThTHoDunQc1ZLfZSo9g+amMQ9JzVfEHTuaj4azjPfzc9hHAAIIRwwMxasu2zgH1D9uvNth0YfgAzu4vaiRNXXnklXV1dAMydO5eJEyeyadMmoFa8t3DhQtatW1f7BbNmAdCzYxPHXjsOwJL513DwSB/7+vYD0D5rLo2XNLJlz4sAtFwxjXmzr6Z78zMANDZcQueia9n4Ug/9r78GwNL3vp/bb7/9tMf8+fMZP34827ZtA2DGjBnMmTPn9JPYm5qaWLp0KRs2bDhdFNnZ2cmePXtOD6gFCxYwODhIf38/XV1dzJo1i9bW1tMr3zQ3N7NkyRLWr1/PwMAAAMuWLWPHjh309fUBsGjRIgYGBti5cycAs2fPZvr06Zy6JWLSpEksXryY7u5uTp48CcDy5cvZunXr6edCdHR0cPz4cXbv3g1AW1sbU6ZMoaendufq5MmT6ejo4KmnniKEQO0uV9i0aRNHjhwBYPHixRw+fJi9e/fW1U8NDQ0sW7aMnp4ejh07VuunJUs4ePAg+/bVhkh7ezuNjY1s2VK7SNLS0sK8efPo7u6u9VNjI52dnWzcuJH+/v5aPy1dSm9vL/v3739HP/X39/PSSy+ddz9t37799BhL0U8hBDZv3nxB/XT99deP2E/3338/h37RxWWXzqVh/ESO/bLWT5dMmErzZQs5fLTWT2YNTLl8GUf7ezh5stZPl09cwhtvHOTEQK2f3tXUzrhxjRz/5RZaW1vZtm1bXf2UghDCOjNrG7b5NmBF9voRoAv4IkM+VAKeMbMrzGzmqVzmOI7jOI5TVep6Tlt2UvT4kJq2X4QQrhjy8yMhhMlm9j3gr0II3dn2J4A/DSE8e67j57kv+5prruG5f/pZXfvmJda92X19fbS0tIy+Y4Vw5/jE9I1VN/IHd1/Dc889V69DkpqRHPnpceDeYfnpiyGEsyafvDVtsfKT56YzuHMaYjlXITdlHmOqpk0tN4HHTQrUfCH+uVOMmrZrfuMDhZw7ne+VtoOnPqHObn/sy7b3ArOH7NcKvHKev2NEJkyYUOThkjA4OFi2Qm7cOT5qvqAZf0OwEba942zjfO8CaGhoYMKECVJ3AWzZsoUJEyZI3QUwb948ubsA3nzzTWbPni1zF8Dy5cvZvXv36TGmcBfAZz7zmbrvAujt7WWsoZi7Ff+PVHNW8wVN56Li73yvtP1P4FAI4V4zWw1MCSH8qZndCvwhtdUjlwJfCSFcO9rx83xatGbNGlat/Hxd++Yl1idGXV1drFixovDjxsSd4xPTN9an2U9vWMOqVavqdSjrStt2YMWQD5W6QgjzzWxt9vqbw/c727HzXmmLlZ88N53BndMQy/mypndxIvtAokgeWPMAf/yFP657/7F2pU0tN4HHTQrUfCH+uVOMK21rvv5gIedOo15pM7NvUqsPmWZmvcBfAPcC3zazO4GXgY9nu3+f2oRtF7Ul/3+3LkPHcZzieAy4g1qeugP47pDtf2hm36L2odJRr2dznLSceP21aCdFjuM4FzP1rB75qRDCzBDChBBCawjhayGEQyGEj4QQ2rPvh7N9QwjhcyGEXwkh/Nq5akXOl6effrroQ0bn1OIpSrhzfNR8oXrxl32otB6Yb2a92QdJ9wI3mtlO4MbsPdQ+VNpN7UOlvwE+W7RP1dpnNBTHoDunQc1ZLfZSo9g+amMQ9JzVfEHTuaj4O9+attL4yU9+UrZCblpbW8tWyI07x0fNF6oXfyGET53lRx8ZYd8AfC6mT9XaZzQUx6A7p0HNWS32UqPYPmpjEPSc1XxB07mo+Kvn4dqVYvXq1aPvVDFOFY0r4c7xUfMFzfhLiVr7KI5Bd06DmrNa7KVGsX3UxiDoOav5gqZzUfEnd6XNcZzRabr0MjqvG2mxxAvjgTUPFH5Mx3Ecx3Ec59zITdoOHNBbN6C5ublshdy4c3xi+sYq9v+Tv9L7tDYlavlJLWbAnVOh5qwWe6lRbB+1MQh6zmq+oOlcVPzVteR/bPIsWxtrOU6Iu3St46QkVpzkiZGxtqQ2VKPdHafKVCVGxlp+qkq7O06VqUKcnCs3ydW0felLXypbITenHqaqhDvHR80XNOMvJWrtozgG3TkNas5qsZcaxfZRG4Og56zmC5rORcWf3KRt0qRJZSvkZmBgoGyF3LhzfNR8QTP+UqLWPopj0J3ToOasFnupUWwftTEIes5qvqDpXFT8yU3aHMdxHMdxHMdxxhJyNW1NTU2c6D0exSPWvdknT56koUFrzRd3jk9M31j3ZTe1TuTEiRP1OoypmhGIl588N53BndMQy7kKuSnzGFP5SS03gcdNCtR8YWyfO8ldafvYxz5WtkJuduzYUbZCbtw5Pmq+oBl/KVFrH8Ux6M5pUHNWi73UKLaP2hgEPWc1X9B0Lir+5CZt73vf+8pWyE1fX1/ZCrlx5/io+YJm/KVErX0Ux6A7p0HNWS32UqPYPmpjEPSc1XxB07mo+JObtDmO4ziO4ziO44wl5CZtDz/8cNkKuVm0aFHZCrlx5/io+YJm/KVErX0Ux6A7p0HNWS32UqPYPmpjEPSc1XxB07mo+JObtE2ePLlshdwoLk/qzvFR8wXN+EuJWvsojkF3ToOas1rspUaxfdTGIOg5q/mCpnNR8Sc3abv99tvLVsjNzp07y1bIjTvHR80XNOMvJWrtozgG3TkNas5qsZcaxfZRG4Og56zmC5rORcWf3KTNcRzHcRzHcRxnLCE3aXvyySfLVsjN7Nmzy1bIjTvHR80XNOMvJWrtozgG3TkNas5qsZcaxfZRG4Og56zmC5rORcWf3KStp6enbIXcTJ8+vWyF3LhzfNR8QTP+UqLWPopj0J3ToOasFnupUWwftTEIes5qvqDpXFT8yU3aVq1aVbZCbjZu3Fi2Qm7cOT5qvqAZfylRax/FMejOaVBzVou91Ci2j9oYBD1nNV/QdC4q/uQmbY7jOI7jOI7jOGMJuUnbyy+/XLZCbiZNmlS2Qm7cOT5qvqAZfylRax/FMejOaVBzVou91Ci2j9oYBD1nNV/QdC4q/iyEUMiBLoQlS5aEei93mhnhX9+M4mHTJlCF9nCcCyVWnOSJETN7NoSwpHCJhOTJTVCNdnecKlOVGBlr+akq7e44VaYKcXKu3CR3pe2ee+4pWyE33d3dZSvkxp3jo+YLmvGXErX2URyD7pwGNWe12EuNYvuojUHQc1bzBU3nouJPbtJ26aWXlq2Qm5MnT5atkBt3jo+aL2jGX0rU2kdxDLpzGtSc1WIvNYrtozYGQc9ZzRc0nYuKP7lJm+M4juM4juM4zlhCrqZt/PjxDPYNRPGIdW/2W2+9xbhxWvNjd45PTN9Y92WPb2lkcHCwXocxVTMC8fKT56YzuHMaYjlXITdlHmMqP6nlJvC4SYGaL4ztcyetngJWrlxZtkJutm7dWrZCbtw5Pmq+oBl/KVFrH8Ux6M5pUHNWi73UKLaP2hgEPWc1X9B0Lir+5CZtCxYsKFshN4cOHSpbITfuHB81X9CMv5SotY/iGHTnNKg5q8VeahTbR20Mgp6zmi9oOhcVf3KTNsdxHMdxHMdxnLFElEmbmd1kZtvNbJeZrS7y2GvXri3ycEno6OgoWyE37hwfNV/QjL/heH46g+IYdOc0qDmrxd5IeG56O2pjEPSc1XxB07mo+Ct80mZm44GHgJuBBcCnzKyw6/Ktra1FHSoZx48fL1shN+4cHzVf0Iy/oXh+ejuKY9Cd06DmrBZ7w/Hc9E7UxiDoOav5gqZzUfEX40rbtcCuEMLuEMIbwLeA24o6+K233lrUoZKxe/fushVy487xUfMFzfgbhuenISiOQXdOg5qzWuyNgOemYaiNQdBzVvMFTeei4i/GpG0WsG/I+95sm+M4Ttl4fnIcp4p4bnIc55wU/pw2M/s48JshhP+Yvf9t4NoQwt3D9rsLuCt7Ox/YXuevmAb8a0G6qXDnNKg5q/lCPuf3hBDeHVMmL/XkpwvITaDXp2q+4M6pUHPO61up/OTnTiPizvFR84WL3/msuamhOJ/T9AKzh7xvBV4ZvlMI4avAV/Me3Mw2qj0Q053ToOas5guazsMYNT+db24CvfZR8wV3ToWas5rvCPi50zDcOT5qvjC2nWPcHvkzoN3M5pjZJcAngcci/B7HcZy8eH5yHKeKeG5yHOecFH6lLYRw0sz+EPhHYDzwcAhB7/HljuNcdHh+chyninhuchxnNGLcHkkI4fvA92Mcm/O8balk3DkNas5qvqDp/DY8P70NNV9w51SoOav5vgPPTe/AneOj5gtj2LnwhUgcx3Ecx3Ecx3Gc4ohR0+Y4juM4juM4juMURGUnbWZ2k5ltN7NdZrZ6hJ83mtmj2c83mFlbest3OI3m/AUz22ZmL5jZE2b2njI8hzmd03nIfh8zs2Bmpa7YU4+vmX0ia+etZvZ/UzuO4DPauLjKzJ40s+eysXFLGZ5DfB42sz4z23KWn5uZfSX7e14ws8WpHcvEc1Ma1HJT5iKVn9RyU+bk+ekceH6Kj+emNKjlpyS5KYRQuS9qRbj/AswFLgE2AQuG7fNZ4H9lrz8JPCrg/GHgsuz1Hyg4Z/tNBNYBzwBLquwLtAPPAZOz9y1Vb2Nq9zr/QfZ6AbC3ZOflwGJgy1l+fgvwA8CADwIbyvStYH96bkrgnO1XidyUo50rk58Uc1Pm4fnpwvrU81Nk32w/z03xnSuVn1LkpqpeabsW2BVC2B1CeAP4FnDbsH1uAx7JXv8d8BEzs4SOwxnVOYTwZAjhteztM9Sew1Im9bQzwH8F7gNeTyk3AvX4/ifgoRDCEYAQQl9ix+HU4xyASdnryxnh2TwpCSGsAw6fY5fbgG+EGs8AV5jZzDR2peO5KQ1quQn08pNcbgLPT6Pg+Sk+npvSIJefUuSmqk7aZgH7hrzvzbaNuE8I4SRwFJiaxG5k6nEeyp3UZtxlMqqzmV0DzA4hPJ5S7CzU08bzgHlm9rSZPWNmNyWzG5l6nP8L8Gkz66W2ctjdadTOm7xj/WLCc1Ma1HIT6OWnizE3gecnz09x8dyUhosxP11wboqy5H8BjPSpz/BlLuvZJyV1+5jZp4ElwPVRjUbnnM5mNg74MrAyldAo1NPGDdQu86+g9mncT8xsUQjhF5HdzkY9zp8Cvh5CWGNmncDfZs5vxdc7L6oWeynx3JQGtdwEevnpYsxNUL34S4nnp/h4bkrDxZifLjj2qnqlrReYPeR9K++87Hl6HzNroHZp9FyXJWNTjzNm9hvAnwP/PoQwkMjtbIzmPBFYBHSZ2V5q9+A+VmJRbb3j4rshhDdDCHuA7dQSUVnU43wn8G2AEMJ64FJgWhK786OusX6R4rkpDWq5CfTy08WYm8Dzk+enuHhuSsPFmJ8uPDfFKMa70C9qM/7dwBzOFCAuHLbP53h7Me23BZyvoVZY2V52G9frPGz/LspdiKSeNr4JeCR7PY3apeipFXf+AbAye/3eLIit5LHRxtmLaW/l7cW0Py3TtYL96bkpgfOw/UvNTTnauTL5STU3ZS6en86/Tz0/RfYdtr/npnjOlctPsXNTaX9YHX/4LcCOLFD/PNv2l9Q+ZYHajPo7wC7gp8BcAed/Ag4Cz2dfj1Xdedi+VUg+o7WxAQ8A24DNwCer3sbUVj16OktKzwMfLdn3m8AB4E1qnwzdCfw+8PtD2vih7O/ZXPaYqGB/em5K4Dxs39JzU53tXKn8pJabMifPTxfWp56fIvsO29dzUzznSuWnFLnJsgM5juM4juM4juM4FaSqNW2O4ziO4ziO4zgOPmlzHMdxHMdxHMepND5pcxzHcRzHcRzHqTA+aXMcx3Ecx3Ecx6kwPmlzHMdxHMdxHMepMD5pcxzHcRzHcRzHqTA+aXMcx3Ecx3Ecx6kwPmlzHMdxHMdxHMepMP8fBb2iim26XTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Pclass_1')\n",
    "ax1.hist([data_train.query('Survived == 0').loc[:,'Pclass_1'].values,data_train.query('Survived == 1').loc[:,'Pclass_1'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax1.legend()\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Pclass_2')\n",
    "ax2.hist([data_train.query('Survived == 0').loc[:,'Pclass_2'].values,data_train.query('Survived == 1').loc[:,'Pclass_2'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax2.legend()\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "ax3.grid(b=True, linestyle='--')\n",
    "ax3.set_title('Pclass_3')\n",
    "ax3.hist([data_train.query('Survived == 0').loc[:,'Pclass_3'].values,data_train.query('Survived == 1').loc[:,'Pclass_3'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax3.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb16756c160>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax2 = fig.add_subplot(1, 2, 1)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Sex_male')\n",
    "ax2.hist([data_train.query('Survived == 0').loc[:,'Sex_male'].values,data_train.query('Survived == 1').loc[:,'Sex_male'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax2.legend()\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Sex_female')\n",
    "ax2.hist([data_train.query('Survived == 0').loc[:,'Sex_female'].values,data_train.query('Survived == 1').loc[:,'Sex_female'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb14fc6ac18>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 15))\n",
    "ax1 = fig.add_subplot(3, 3, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Age_1')\n",
    "ax1.hist([data_train.query('Survived == 0').loc[:,'Age_1'].values,data_train.query('Survived == 1').loc[:,'Age_1'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax1.legend()\n",
    "ax2 = fig.add_subplot(3, 3, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Age_2')\n",
    "ax2.hist([data_train.query('Survived == 0').loc[:,'Age_2'].values,data_train.query('Survived == 1').loc[:,'Age_2'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax2.legend()\n",
    "ax3 = fig.add_subplot(3, 3, 3)\n",
    "ax3.grid(b=True, linestyle='--')\n",
    "ax3.set_title('Age_3')\n",
    "ax3.hist([data_train.query('Survived == 0').loc[:,'Age_3'].values,data_train.query('Survived == 1').loc[:,'Age_3'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax3.legend()\n",
    "ax4 = fig.add_subplot(3, 3, 4)\n",
    "ax4.grid(b=True, linestyle='--')\n",
    "ax4.set_title('Age_4')\n",
    "ax4.hist([data_train.query('Survived == 0').loc[:,'Age_4'].values,data_train.query('Survived == 1').loc[:,'Age_4'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax4.legend()\n",
    "ax5 = fig.add_subplot(3, 3, 5)\n",
    "ax5.grid(b=True, linestyle='--')\n",
    "ax5.set_title('Age_5')\n",
    "ax5.hist([data_train.query('Survived == 0').loc[:,'Age_5'].values,data_train.query('Survived == 1').loc[:,'Age_5'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax5.legend()\n",
    "ax6 = fig.add_subplot(3, 3, 6)\n",
    "ax6.grid(b=True, linestyle='--')\n",
    "ax6.set_title('Age_6')\n",
    "ax6.hist([data_train.query('Survived == 0').loc[:,'Age_6'].values,data_train.query('Survived == 1').loc[:,'Age_6'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax6.legend()\n",
    "ax7 = fig.add_subplot(3, 3, 7)\n",
    "ax7.grid(b=True, linestyle='--')\n",
    "ax7.set_title('Age_7')\n",
    "ax7.hist([data_train.query('Survived == 0').loc[:,'Age_7'].values,data_train.query('Survived == 1').loc[:,'Age_7'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax7.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb1502162e8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WgIsvvvi4NlqJTG+3/C42xnwoItnAKyLynse8PZX9T40CR4roHAg3T+y6H8CYMWNIS0ujsbERCLeVGj9+PMuXLw8HTk6mqqqKuXPnsmd/+DMj0so5enQXh9vCNXd4ShGDBoVo/XgNAEMGZzN82Fj2HQh3sh00KERGeiX7W+vp6Aj3DhuZVsGRo80caQtvvg8fVsyrr74azTV69GgKCwuj949ISUmhoqKCurq6aIulyspKtmzZEr38aty4cXR0dERPzcjNzSUvLy+625aamkp5eTm1tbXRUwWqqqrYuHEjLS0tAJSUlNDW1hb9Jc/PzycnJ4euLjjp6emUlZVRU1MTvcRoypQprF27lj179gBQWlpKdnZ21KWgoIBRo0bR0NAAQEZGRnRsxxiDiDB16lQaGxvZt28fAGVlZezduzd6sX1v11NDQwMHDx4EoLy8nF27dtHUFF5PRUVFhEIh1qwJr6fs7GzGjh0b7TgcCoWorKykvr4+2uOtoqKCESNGRF2Ki4tJSkqK3pAnnuvp2LFj0S2eWJ3k29HRwSeffAKEt/KGDRsWXeauXbu455572LdvH0lJSYwZM4Zf/vKXrFmzhttvv53s7GwqKiowxtDa2kpbWxtDhgzBGBP9eV5xxRXcddddfPvb345+b2dnJ+3t7dHXf/u3f8tXvvIVXn75ZVpbW0lOTubpp59mzpw5fP/73+fYsWPceOONnHfeedx///3cdttt/OQnP4keMW5tbSUUCiEi0S3R5ORkQqFQtN29iJCamsqhQ4ei5wwOHz6ctra26O/10KFDMcZE18HgwYMZMmRI9Du6pndfT170uaWViNwLHAL+DphmjNkhImcC1caYYhF5NPL8d5H5N3TNd7LvTNSWVn965Xv867/+a0yXES9qa2sTthtKX0kUlxNbKMX7VJdE4tChQ9a3yH1vaSUiw0Ukres5cBmwBlgC3BKZ7RbghcjzJcDNkaO+FwEHgjrel56ebjuCbwSx68bJSFSXbdv61tLq4MGDnu8HpfBBMK/w6M1ubw7wfGQQMxn4rTHmzyKyAnhWRG4DtgFdh4peBr4MvA98Apz8pgKKoiiW0E7OHnzhSynOtEz3OqM/aCSKS0+7WX2ha3zVBRLBRTs5+0jXjVVcYOPGjbYj+EYiuZzOxsOJp6EEGdsu/VkPWvw8mDBhgu0IvtF19NgFEsVl6NCh7Nmzp98FMIgNQE+GTRdjDHv27OnzEXf7+w6KElDy8vJobm7mo48+6tfnjxw5Esg+eD1h22Xo0KF9vuxRi58HTzzxhDONDUpKSmxH8I1EcRk8eDCFhYX9/vzu3bujzQiCThBddLfXg4yMDNsRfCNRTw/pD664uOIBwXTR4ufBtddeazuCb3S/DCrouOLiigcE00WLn6IoAxItfh689tprtiP4Rn5+vu0IvuGKiyseEEwXLX4edF347wI5OTm2I/iGKy6ueEAwXbT4eeDKkV6Avl5Bk8i44uKKBwTTRYufoigDEi1+Hmzbts12BN9wqUONKy6ueEAwXbSxgQeVkyWQrXoURQmjjQ36yX333Wc7gm90dUd2AVdcXPGAYLpo8fPAlesuQS+iT0Rc8YBgumjxUxRlQKJjfh5cPDWJjo6OmC4jXnR2djJokBt/61xxccUDEtdFx/z6ya233mo7gm903VrQBVxxccUDgumixc+DcePG2Y7gG123sXQBV1xc8YBgumjxUxRlQKLFz4NHH33UdgTfKC0ttR3BN1xxccUDgumixc+DvrbFTmRaW1ttR/ANV1xc8YBgumjx8+DKK6+0HcE3Nm/ebDuCb7ji4ooHBNNFi5+iKAMSLX4eLF261HYE3ygoKLAdwTdccXHFA4LposXPgw0bNtiO4BujRo2yHcE3XHFxxQOC6aLFz4M777zTdgTfcKkrtSsurnhAMF20+CmKMiDR4udBEG/HdzJcugexKy6ueEAwXbSxgQfazFRRgo02NugnDz74oO0IvrFs2TLbEXzDFRdXPCCYLlr8PEjEFj39xaUtWFdcXPGAYLq48787BnR2dtqO4BsiYjuCb7ji4ooHBNNFx/w80DE/RQk2OubXT26//XbbEXyjsbHRdgTfcMXFFQ8IposWPw+KiopsR/CNffv22Y7gG664uOIBwXTR4qcoyoBEi58HP/3pT21H8I2ysjLbEXzDFRdXPCCYLlr8PCguLrYdwTf27t1rO4JvuOLiigcE06XXxU9EkkTkHRH5Y+R1oYjUicgmEVkkIkMi00OR1+9H3i+ITfTYc9lll9mO4Btbt261HcE3XHFxxQOC6dKXLb+7gPXdXv8/4EfGmCJgH3BbZPptwD5jzGeBH0XmUxRFSSh6VfxEJA+4EvhV5LUAlwCLI7M8BVwTeX515DWR9y+VIJ4BCbz00ku2I/jGmDFjbEfwDVdcXPGAYLr0dsvvx8A/A12XPGQC+40x7ZHXzUBu5Hku0AQQef9AZP7A0dzcbDuCb6SlpdmO4BuuuLjiAcF0ST7VDCJyFdBijFkpItO6Jvcwq+nFe92/dw4wB+Css86iuroaCP8FSUtLi540mZmZyfjx41m+fHk4cHIyVVVVzJ07lz37w58ZkVbO0aO7ONzWBMDwlCIGDQrR+vEaAIYMzmb4sLHsO1ADwKBBITLSK9nfWk9HxyEARqZVcORoM0fatoe/Y1gx3/rWt6K5Ro8eTWFhIbW1tQCkpKRQUVFBXV0dhw8fBqCyspItW7awc+dOIHzT846OjmhH6NzcXPLy8qirqwMgNTWV8vJyamtraWtrA6CqqoqNGzfS0tICQElJCW1tbdH2Wvn5+eTk5NB1RUx6ejplZWXU1NTQ3h7+WzRlyhTWrl0bvZF0aWkpK1asYMiQIUC45fioUaOiDSgzMjIoLS1l2bJlGGMQEaZOnUpjY2P0/K2ysjL27t0bHdvp7XpqaGjg4MGDAJSXl7Nr1y6amsLrqaioiFAoxJo14fWUnZ3N2LFjqakJr6dQKERlZSX19fUcOhReTxUVFbz99tuEQiEgfFAqKSmJdevWBW49GWPIyso6bj21trZGbwYUpPXU1tZGamrqceupubmZ7du3W11PXpzy8jYR+SHwNaAdGAqkA88DlwOjjTHtIlIJ3GuMuVxE/hJ5XisiycBO4AzjsaBEvbztjboFzJs3L6bLiBfV1dVMmzbNdgxfcMXFFQ9IXJfTurzNGDPfGJNnjCkAZgH/Y4z5KvAacF1ktluAFyLPl0ReE3n/f7wKXyLT9VfKBTIzAzny0COuuLjiAcF06VNjg8hu7z8aY64SkTHAQmAU8A5wkzGmTUSGAv8JTAT2ArOMMZ439UzULb+LpybR0dER02XEi87OTmdadLni4ooHJK6Lb40NjDHVxpirIs83G2MuNMZ81hhzvTGmLTL9SOT1ZyPvB+9uxhFcambaNcbjAq64uOIBwXRJvFKtKIoSB7T4eXDkyBHbEXwjOfmUB/YDgysurnhAMF20makH2sxUUYKNNjPtJ3PnzrUdwTeCeFPpk+GKiyseEEwXLX4enH322bYj+EbXCawu4IqLKx4QTBctfoqiDEi0+HmwYMEC2xF8o7y8x2GPQOKKiyseEEwXLX4eBLE77cnYtWuX7Qi+4YqLKx4QTJfgHZ+OkDJ0GJWTY9sp698W/FtMvz+eNDU18ZnPfMZ2DF9wxcUVDwimS2CL3+Ejn2B2H4vpMhY8+ZOYfr+iKPbQ3V4Pnn/+edsRfMOl23C64uKKBwTTRYufB0G8F+nJ6Op/5wKuuLjiAcF00eLnwezZs21H8I2uRpQu4IqLKx4QTBctfoqiDEi0+HmwatUq2xF8Izs723YE33DFxRUPCKZLoBsbxPpob0peWvR+AkGnvb09kJ03esIVF1c8IHFdtLFBP7nvvvtsR/CNrpvNuIArLq54QDBdtPgpijIg0eLnQRA7VZyMIJ6KcDJccXHFA4LpomN+XsvIGqzNTBUlwOiYXz9x5Z69AH3945LIuOLiigcE00WLnwdnnnmm7Qi+cejQIdsRfMMVF1c8IJguWvwURRmQ6JifB2d87kw++uijmC4jXhw+fJiUlBTbMXzBFRdXPCBxXXTMr59MnjzZdgTfaG5uth3BN1xxccUDgumixc+Diy++2HYE39i+fbvtCL7hiosrHhBMFy1+iqIMSLT4ebBo0SLbEXyjuLjYdgTfcMXFFQ8IposWPw+OHYvtAZV4kpSUZDuCb7ji4ooHBNNFi58HN910k+0IvrFu3TrbEXzDFRdXPCCYLlr8FEUZkGjx82DFihW2I/jG6NGjbUfwDVdcXPGAYLpo8fPgT3/6k+0IvlFYWGg7gm+44uKKBwTTRYufB9/97ndtR/CN2tpa2xF8wxUXVzwgmC5a/BRFGZBo8fNgz549tiP4RiJed9lfXHFxxQOC6aKNDbyWoc1MFSXQaGODfjJ//nzbEXyjrq7OdgTfcMXFFQ8IposWPw8yMzNtR/ANV27BCe64uOIBwXQ5ZfETkaEi8raINIrIWhH518j0QhGpE5FNIrJIRIZEpocir9+PvF8QWwVFUZS+c8oxPxERYLgx5pCIDAZqgLuAfwCeM8YsFJFfAI3GmJ+LyDeA840x/1dEZgHXGmNmei0jUcf8RozJ5MCBAzFdRrxoa2sL5B22esIVF1c8IHFdTmvMz4TpatA/OPIwwCXA4sj0p4BrIs+vjrwm8v6lkQIaOKZPn247gm9s2bLFdgTfcMXFFQ8IpkuvxvxEJElEVgEtwCvAX4H9xpj2yCzNQG7keS7QBBB5/wAQyMGzSZMm2Y7gGzt37rQdwTdccXHFA4LpktybmYwxHcAEERkJPA+c29NskX972sr71L61iMwB5gCcddZZVFdXAzBmzBjS0tJobGwEwgcdxo8fz/Lly8OBk5Opqqpi7ty5VK+qAaC8eCK79rXQ1BLuJluUO4bQkBBrtqwHIHtkFmPzP0vN6rcACCUPobLkQurfa+DQkU8AqDj3Apo/+pDtu3cAUJxfREpKSjTX6NGjKSwsjJ7JnpKSQkVFBXV1ddHB3srKSrZs2RL9RRg3bhwdHR1s2LABgNzcXPLy8qJHxlJTUykvL6e2tpa2tjYAqqqq2LhxIy0tLQCUlJTQ1tbGpk2bAMjPzycnJyd6q8D09HTKysqoqamhvT38t2jKlCmsXbs2ep5iaWkpR48ejboUFBQwatQoGhoaAMjIyKC0tJRly5ZhjEFEmDp1Ko2Njezbtw+AsrIy9u7dy9atW/u0nhoaGqI3fy8vL2fXrl00NTWF11NREaFQiDVr1oTXU3Y2Y8eOpaYmvF5DoRCVlZXU19dH7w5WUVFBW1tb1KW4uJikpKRoV5EgrSdjDKtXrz5uPbW2trJ58+bArSfgU+upubk52uHZ1nryos/n+YnI94BPgG8Do40x7SJSCdxrjLlcRP4SeV4rIsnATuAM47GgRB3zm/jFSbzzzjsxXUa8aGlpITs723YMX3DFxRUPSFyX0xrzE5EzIlt8iEgK8EVgPfAacF1ktluAFyLPl0ReE3n/f7wKXyIzePBg2xF8o6Ojw3YE33DFxRUPCKZLb8b8zgReE5F3gRXAK8aYPxLe8vsHEXmf8Jje45H5HwcyI9P/AbjH/9jxYeZMz4PUgaJrV8EFXHFxxQOC6XLKMT9jzLvAxB6mbwYu7GH6EeB6X9IpiqLECL3Cw4M33njDdgTfyM3NPfVMAcEVF1c8IJguWvw8eP31121H8I28vDzbEXzDFRdXPCCYLlr8PLjnnsAOV36KIF54fjJccXHFA4LposVPUZQBiRY/D3bs2GE7gm+kpqbajuAbrri44gHBdNFmpl7L0GamihJotJlpP9EbGCUmrri44gHBdNHi50F6errtCL7Rm2sdg4IrLq54QDBdtPgpijIg0TE/D1Ly0gLZnrsn2tvbSU7uVROfhMcVF1c8IHFddMyvn1x33XWnnikgbNy40XYE33DFxRUPCKaLFj8PJkyYYDuCb3T1nXMBV1xc8YBgumjxUxRlQKLFz4MnnnjCdgTfKCkpsR3BN1xxccUDgumixc+DjIwM2xF8I4inIpwMV1xc8YBgumjx8+Daa6+1HcE3uu4t4QKuuLjiAcF00eKnKMqARIufB6+99prtCL6Rn42dZqwAABQqSURBVJ9vO4JvuOLiigcE00WLnwddtwx0gZycHNsRfMMVF1c8IJguWvw8mDdvnu0IvtHXK2gSGVdcXPGAYLpo8VMUZUCixc+Dbdu22Y7gGy51qHHFxRUPCKaLNjbwWoY2M1WUQKONDfrJfffdZzuCb9TU1NiO4BuuuLjiAcF00eLnwdChQ21H8I329nbbEXzDFRdXPCCYLlr8FEUZkOiYnwdJ2SE6Ojpiuox40dnZyaBBbvytc8XFFQ9IXBcd8+snt956q+0IvrF27VrbEXzDFRdXPCCYLlr8PBg3bpztCL6xZ88e2xF8wxUXVzwgmC5a/BRFGZBo8fPg0UcftR3BN0pLS21H8A1XXFzxgGC6aPHzIC8vz3YE32htbbUdwTdccXHFA4LposXPgyuvvNJ2BN/YvHmz7Qi+4YqLKx4QTBctfoqiDEi0+HmwdOlS2xF8o6CgwHYE33DFxRUPCKaLFj8PNmzYYDuCb4waNcp2BN9wxcUVDwimixY/D+68807bEXzDpa7Urri44gHBdNHipyjKgESLnwdBvB3fyXDpHsSuuLjiAcF00cYGXsvQZqaKEmhOq7GBiOSLyGsisl5E1orIXZHpo0TkFRHZFPk3IzJdROQREXlfRN4VkTJ/deLHgw8+aDuCbyxbtsx2BN9wxcUVDwimS292e9uBecaYc4GLgDtEZBxwD/CqMaYIeDXyGmA6UBR5zAF+7nvqOJGILXr6i0tbsK64uOIBwXQ55f9uY8wOY0xD5HkrsB7IBa4GnorM9hRwTeT51cBvTJi3gJEicqbvyeNAZ2en7Qi+ISK2I/iGKy6ueEAwXZL7MrOIFAATgTogxxizA8IFUkSyI7PlAk3dPtYcmbbjhO+aQ3jLkLPOOovq6moAxowZQ1paGo2NjQBkZmYyfvx4li9fHg6cnExVVRVz586lelX4vgHlxRPZta+FppbtABTljiE0JMSaLesByB6Zxdj8z1Kz+i0AQslDqCy5kPr3Gjh05BMAKs69gOaPPmT77nDM4vwifvvb3zJp0iQARo8eTWFhIbW1tQCkpKRQUVFBXV0dhw8fBqCyspItW7awc+dOINwSq6OjI3q+YG5uLnl5edTV1QGQmppKeXk5tbW1tLW1AVBVVcXGjRtpaWkBoKSkhLa2tujBl/z8fHJycqL3SU1PT6esrIyamppoK/EpU6awdu3aaJuh0tJSCgsLoz/jgoICRo0aFT09ISMjg9LSUpYtW4YxBhFh6tSpNDY2sm/fPgDKysrYu3cvW7du7dN6amho4ODBg+H1VF7Orl27aGoK/3oUFRURCoVYs2ZNeD1lZzN27Njo/SBCoRCVlZXU19dz6NCh8HqqqDju96W4uJikpCTWrVsXyPW0evXq49ZTa2tr9FKxIK2nqVOnfmo9NTc3s337dqvryYteH/AQkVRgGXC/MeY5EdlvjBnZ7f19xpgMEXkJ+KExpiYy/VXgn40xK0/23Yl6wOP//n/f5Be/+EVMlxEvGhsbA9l5oydccXHFAxLX5bQ7OYvIYOD3wDPGmOcik3d17c5G/m2JTG8G8rt9PA/4sD/BbVNUVGQ7gm90bRm4gCsurnhAMF16c7RXgMeB9caYf+v21hLglsjzW4AXuk2/OXLU9yLgQNfusaIoSqJwyt1eEakCXgdWA11HAL5DeNzvWeBsYBtwvTFmb6RY/gy4AvgE+LoxxnOfNlF3ewsu+Gx07CToHDx4kPT0dNsxfMEVF1c8IHFdvHZ7T3nAIzJ2d7JDOZf2ML8B7uhTwgSluLjYdgTf2Lt3b0L+cvYHV1xc8YBgurhzIlsMuOyyy2xH8A1XtmDBHRdXPCCYLlr8FEUZkGjx8+Cll16yHcE3xowZYzuCb7ji4ooHBNNFi58Hzc3NtiP4Rlpamu0IvuGKiyseEEwXLX4e3H777bYj+EbXGf4u4IqLKx4QTBctfoqiDEi0+HnQdR2iC2RmZtqO4BuuuLjiAcF00WamHiRlh+jo6IjpMuJFZ2enMy26XHFxxQMS1+W0r+0dqLjUzLSri4cLuOLiigcE00WLn6IoAxItfh4cOXLEdgTfSE7uU+vGhMYVF1c8IJguOubntQy9gZGiBBod8+snc+fOtR3BN4J4U+mT4YqLKx4QTBctfh6cc845iEhMH2efXRAXl64W5S7giosrHhBMl+DtqMcRYwy1r8d2t7dycvBu/KIoLqBbfh4sWLDAdgTfKC/vcdgjkLji4ooHBNNFi58HZWWBvd/6p9i1a5ftCL7hiosrHhBMFy1+HnzhC1+wHcE3um5D6AKuuLjiAcF00eKnKMqARIufB88//7ztCL7h0m04XXFxxQOC6aLFz4Mg3ov0ZIRCIdsRfMMVF1c8IJguWvw8mD17tu0IvrFmzRrbEXzDFRdXPCCYLlr8FEUZkGjx82DVqlW2I/hGdna27Qi+4YqLKx4QTBctfh4sXrzYdgTfGDt2rO0IvuGKiyseEEwXLX4e3HfffbYj+EZNTY3tCL7hiosrHhBMFy1+iqIMSLT4eRDEThUnI4inIpwMV1xc8YBgumgzU69lZA2OS1eXRFgHiuIi2sy0n8ybN892BN/o6x+XRMYVF1c8IJguWvw8OPPMM21H8I1Dhw7ZjuAbrri44gHBdNHipyjKgESLnwcPPPCA7Qi+UVFRYTuCb7ji4ooHBNNFi58HkydPth3BN5qbm21H8A1XXFzxgGC6aPHz4OKLL7YdwTe2b99uO4JvuOLiigcE00WLn6IoAxItfh4sWrTIdgTfKC4uth3BN1xxccUDgumixc+DY8diexJ1PElKSrIdwTdccXHFA4LposXPg5tuusl2BN9Yt26d7Qi+4YqLKx4QTJdTFj8ReUJEWkRkTbdpo0TkFRHZFPk3IzJdROQREXlfRN4VEXfu/agoilP0ZsvvSeCKE6bdA7xqjCkCXo28BpgOFEUec4Cf+xPTDitWrLAdwTdGjx5tO4JvuOLiigcE0+WUxc8YsxzYe8Lkq4GnIs+fAq7pNv03JsxbwEgRCew1Yn/6059sR/CNwsJC2xF8wxUXVzwgmC79HfPLMcbsAIj829XDOhfofvfi5si0QPLd73435ssYMjiEiMT88dhjj8XcJV7U1tbajuALrnhAMF2Sff4+6WFaj/2aRGQO4V1jzjrrLKqrqwEYM2YMaWlpNDY2ApCZmcn48eNZvnx5OHByMlVVVcydO5fqVeHuseXFE9m1r4WmlvCJlkW5YwgNCbFmy3oAskdmMTb/s9SsfguAUPIQKksupP69Bg4d+QSAinMvoPmjD9m+ewcAxflFpKSksGd/OFdoyGiGDS1k38HwSk4alMLI9Ar2H6yjo/MwABnplXxyZAttR3cCkDpsHIYOPv5kAwBDQ7kMHZLH/ta68HckpXL0WBsvv/QmnZ1t4e8YUcXHn2zk6LEWANKGl9DZ2cbHhzcBkBLKZ8iQHA601kd+HumMSC1j74EajGkHYNSIKRz6ZC1Hj+0BIH14Kes3/Vf0Z1xQUMCoUaNoaGgILzMjg9LSUpYtW4YxBhFh6tSpNDY2Rm/fWVZWxt69e9m6dWuf1lNDQ0O0L2J5eTm7du2iqSn897GoqIhQKBS981d2djZjx46NdgUOhUJUVlZSX18fvXC+oqKCtra2qEtxcTFJSUnRAffRo0dTWFgY/c+YkpJCRUUFdXV1HD4cXk+VlZVs2bKFnTvD62ncuHF0dHSwYUN4PeXm5pKXl0ddXXg9paamUl5eTm1tLW1t4fVUVVXFxo0baWkJr6eSkhLa2trYtCm8nvLz88nJyYl2O0lPT6esrIyamhra28PryRjD6tWr2bMnvJ5KS0tpbW1l8+bNgVtPwKfWU3Nzc/TkZ1vryYte9fMTkQLgj8aYksjrDcA0Y8yOyG5ttTGmWEQejTz/3YnzeX1/ovbz+86C7zLjyz+I6TIqJ0vMewYCLHn5O/zgB7F1iRd1dXWBvJb0RFzxgMR1iUU/vyXALZHntwAvdJt+c+So70XAgVMVvkTmhz/8oe0IvuGSSyL+J+sPrnhAMF16c6rL74BaoFhEmkXkNuAB4Esisgn4UuQ1wMvAZuB94JfAN2KSOk7Mnz/fdgTfcMmlazcn6LjiAcF0OeWYnzHmKyd569Ie5jXAHacbKlHIzMy0HcE3XHLpGhMKOq54QDBd9AoPRVEGJHoDIw9GjMnkLy8diOky4nXA4/IrR3DgQGxd4kVbW1sg7xZ2Iq54QOK66A2M+sn06dNtR/ANl1y2bNliO4IvuOIBwXTR4ufBpEmTbEfwDZdcus77CjqueEAwXbT4KYoyINHi58HTTz9tO4JvuOQybtw42xF8wRUPCKaLFj8PBg8ebDuCb7jk0tHRYTuCL7jiAcF00eLnwcyZM21H8A2XXLqu7Qw6rnhAMF38bmzgFCJC5eSeejUoihJ0tPh5UFNTE/NzCSUrPrujb7zxBvPmzYvLsmJNbm5gu6QdhyseEEwX3e314PXXX7cdwTdccsnLy7MdwRdc8YBgumjx8+Cee+459UwBwSWXIF5E3xOueEAwXbT4KYoyINHi58GOHYFtRfgpXHJJTU21HcEXXPGAYLpoYwOvZWQNjssy4tHYoHKykAjrWlHiiTY26CfxuIFRvHDJJYg3y+kJVzwgmC5a/DxIT0+3HcE3XHLpzc1pgoArHhBMFy1+iqIMSHTMz4OUvDQON7fGdBnxGvP7wpdSAtlqvCfa29tJTg7++fmueEDiuuiYXz+57rrrbEfwDZdcNm7caDuCL7jiAcF00eLnwYQJE2xH8A2XXLpuFB50XPGAYLpo8VMUZUCiY34ejJ9SytrljTFdRuisNI4ePRLTZQBMnFBGwzsrY76ceLB7926ysrJsxzhtXPGAxHXxGvNLvBHKBCIjIyPmyzh69EjMizhA1dXTYr6MeBHE0yp6whUPCKaL7vZ6cO2119qO4BsuuWzatMl2BF9wxQOC6aLFT1GUAYkWPw9ee+012xF8wyWX/Px82xF8wRUPCKaLFj8PGhoabEfwDZdccnJybEfwBVc8IJguWvw8cKXtO7jl0tczAxIVVzwgmC5a/AYIIhLzx9lnF9jWVJReo6e6eLBt2zbbEXzjgw8+iPk1xPG6050rHWpc8YBguuhJzl7LiFMz03ic5xePBgraMFVJNLSxQT+57777bEfwDZdcHn54gRO78DU1NTFfRrwIoovu9nowdOhQ2xF8wyUXEZzYhW9vb4/5MuJFEF20+CmBY5AMinlxShk6LKbfr9hHx/w8SMoO0dES22sW4zXml5Qd4o1lHTFdRrzG/JKSkuKzXmLs0tnZyaBBbow8JaqLjvn1k1tvvdV2BN+Ih8uQwaG4nFJz2+zbYu4SD9auXWs7gm8E0UWLnwfjxo2zHcE3XHL53Lmfi/ky4lHIq6urY+4RL/bs2WM7Qp/RMT/FN44ea4vLLvyCJ38S82UcPdYW84Mqb9QtiOn3K97EZMxPRK4AfgIkAb8yxjzgNX+ijvmNrRzHxtp1MV1GvMb8xk2ewPr1sd01GTI4RNuOQzFdBsRnvcSjyWzJ+PNYvebdmC4jXpSVlfHOO+/EdBkjRoxg//79ffpMXJuZikgS8O/Al4BmYIWILDHGxPa3NQbk5eXZjuAbo0dns+71VTFdhmQNjun3dxGP9RKPJrNfnHkFIrE/paY/RaOvjBw5Mi4XBPhJLHZ7LwTeN8ZsBhCRhcDVQOCK35VXXmk7gm+oS+Ixffp0/nvRn2O+nHj8UQriOonFAY9coKnb6+bINEVRlITB9zE/EbkeuNwY838ir78GXGiMufOE+eYAcyIvi4ENvgbxhyxgt+0QPqEuiYcrHpC4LucYY87o6Y1Y7PY2A93buuYBH544kzHmMeCxGCzfN0Sk/mSDpUFDXRIPVzwgmC6x2O1dARSJSKGIDAFmAUtisBxFUZR+4/uWnzGmXUS+CfyF8KkuTxhjgnf6t6IoThOTk5yNMS8DL8fiu+NMQu+W9xF1STxc8YAAuiREYwNFUZR4o9f2KooyINHi1wMiki8ir4nIehFZKyJ32c50OohIkoi8IyJ/tJ3ldBCRkSKyWETei6ybStuZ+ouI3B353VojIr8TkcB0mxWRJ0SkRUTWdJs2SkReEZFNkX8zbGbsDVr8eqYdmGeMORe4CLhDRILcFuUuYL3tED7wE+DPxpjPAaUE1ElEcoG5QLkxpoTwgcFZdlP1iSeBK06Ydg/wqjGmCHg18jqh0eLXA8aYHcaYhsjzVsL/yQJ5lYqI5AFXAr+yneV0EJF0YArwOIAx5qgxJrYXrMaWZCBFRJKBYfRwLmyiYoxZDuw9YfLVwFOR508B18Q1VD/Q4ncKRKQAmAjU2U3Sb34M/DPQaTvIaTIG+Aj4dWQX/lciMtx2qP5gjNkOPAxsA3YAB4wxS+2mOm1yjDE7ILzxAGRbznNKtPh5ICKpwO+BbxljDtrO01dE5CqgxRiz0nYWH0gGyoCfG2MmAh8TgF2rnoiMh10NFAJnAcNF5Ca7qQYeWvxOgogMJlz4njHGPGc7Tz+5GJghIluBhcAlIvK03Uj9phloNsZ0bYEvJlwMg8gXgS3GmI+MMceA54DPW850uuwSkTMBIv+2WM5zSrT49YCEm6w9Dqw3xvyb7Tz9xRgz3xiTZ4wpIDyg/j/GmEBuYRhjdgJNIlIcmXQpAWyTFmEbcJGIDIv8rl1KQA/edGMJcEvk+S3ACxaz9AptY98zFwNfA1aLSFcH0O9ErlxR7HEn8EzkmvHNwNct5+kXxpg6EVkMNBA+s+AdAnSFhIj8DpgGZIlIM/A94AHgWRG5jXBxv95ewt6hV3goijIg0d1eRVEGJFr8FEUZkGjxUxRlQKLFT1GUAYkWP0VRBiRa/BRFGZBo8VMUZUCixU9RlAHJ/w8MwTwYiAWs8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Family_size')\n",
    "ax.hist([data_train.query('Survived == 0').loc[:,'Family_size'].values,data_train.query('Survived == 1').loc[:,'Family_size'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb150869dd8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 10))\n",
    "ax1 = fig.add_subplot(2, 3, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Fare_1')\n",
    "ax1.hist([data_train.query('Survived == 0').loc[:,'Fare_1'].values,data_train.query('Survived == 1').loc[:,'Fare_1'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax1.legend()\n",
    "ax2 = fig.add_subplot(2, 3, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Fare_2')\n",
    "ax2.hist([data_train.query('Survived == 0').loc[:,'Fare_2'].values,data_train.query('Survived == 1').loc[:,'Fare_2'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax2.legend()\n",
    "ax3 = fig.add_subplot(2, 3, 3)\n",
    "ax3.grid(b=True, linestyle='--')\n",
    "ax3.set_title('Fare_3')\n",
    "ax3.hist([data_train.query('Survived == 0').loc[:,'Fare_3'].values,data_train.query('Survived == 1').loc[:,'Fare_3'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax3.legend()\n",
    "ax4 = fig.add_subplot(2, 3, 4)\n",
    "ax4.grid(b=True, linestyle='--')\n",
    "ax4.set_title('Fare_4')\n",
    "ax4.hist([data_train.query('Survived == 0').loc[:,'Fare_4'].values,data_train.query('Survived == 1').loc[:,'Fare_4'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax4.legend()\n",
    "ax5 = fig.add_subplot(2, 3, 5)\n",
    "ax5.grid(b=True, linestyle='--')\n",
    "ax5.set_title('Fare_5')\n",
    "ax5.hist([data_train.query('Survived == 0').loc[:,'Fare_5'].values,data_train.query('Survived == 1').loc[:,'Fare_5'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax5.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb1509c0908>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('Cabin_num')\n",
    "ax.hist([data_train.query('Survived == 0').loc[:,'Cabin_num'].values,data_train.query('Survived == 1').loc[:,'Cabin_num'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb150e16da0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Embarked_C')\n",
    "ax1.hist([data_train.query('Survived == 0').loc[:,'Embarked_C'].values,data_train.query('Survived == 1').loc[:,'Embarked_C'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax1.legend()\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Embarked_Q')\n",
    "ax2.hist([data_train.query('Survived == 0').loc[:,'Embarked_Q'].values,data_train.query('Survived == 1').loc[:,'Embarked_Q'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax2.legend()\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "ax3.grid(b=True, linestyle='--')\n",
    "ax3.set_title('Embarked_S')\n",
    "ax3.hist([data_train.query('Survived == 0').loc[:,'Embarked_S'].values,data_train.query('Survived == 1').loc[:,'Embarked_S'].values], color=['#FFECFB', '#C7C3FF'], edgecolor='#000000', label=['Not Survived', 'Survived'], stacked=True)\n",
    "ax3.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function plots the confusion matrix of the model predictions and it will be used to analize its performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(model, X, Y):    \n",
    "    y_pred = model.predict(X).flatten()\n",
    "    y_true = Y.flatten()\n",
    "    cm = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[1.,0.])\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.set_xlabel('PREDICTED')\n",
    "    ax.set_ylabel('ACTUAL')\n",
    "    ax.set_xticklabels(2*['']+['Positive']+3*['']+['Negative']+2*[''])\n",
    "    ax.set_yticklabels(2*['']+['Positive']+3*['']+['Negative']+2*[''])\n",
    "    ax.text(0., 0., str(cm[0,0]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.text(1., 0., str(cm[0,1]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.text(0., 1., str(cm[1,0]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.text(1., 1., str(cm[1,1]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    im = ax.imshow(cm)\n",
    "    fig.colorbar(mappable=im, ax=ax, shrink=0.8)\n",
    "    print(classification_report(y_true, y_pred, digits=4, output_dict=False)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the datasets for training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = (data_train.iloc[:,2:].values).astype(float)\n",
    "Y_train = (data_train.iloc[:,[1]].values).astype(float)\n",
    "\n",
    "X_test = (data_test.iloc[:,2:].values).astype(float)\n",
    "Y_test = (data_test.iloc[:,[1]].values).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.max(X_train, axis=0)+0.000001\n",
    "a = np.min(X_train, axis=0)-0.000001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our first model and evaluate its performance. It is a KNN classifier with n=1 just to have a baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('lambda',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<function <lambda> at 0x7fb1505bd510>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('knn',\n",
       "                 KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                      metric='minkowski', metric_params=None,\n",
       "                                      n_jobs=None, n_neighbors=1, p=2,\n",
       "                                      weights='uniform'))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = Pipeline([('lambda', FunctionTransformer(lambda x:(x-a)/(b-a))),\n",
    "                ('knn', KNeighborsClassifier(n_neighbors=1))])\n",
    "\n",
    "knn.fit(X_train, Y_train.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_1():\n",
    "    \n",
    "    def predict(self, X):\n",
    "        A1 = knn.predict(X)\n",
    "        A2 = np.expand_dims(A1, axis=-1)   \n",
    "        return A2 \n",
    "    \n",
    "    def predict_logit(self, X):\n",
    "        A1 = self.predict(X)\n",
    "        A2 = A1*0.9999999999999999 + (1-A1)*0.0000000000000001\n",
    "        A3 = np.log(A2/(1-A2))\n",
    "        return A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = Model_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9035    0.8525    0.8772       549\n",
      "         1.0     0.7828    0.8538    0.8168       342\n",
      "\n",
      "    accuracy                         0.8530       891\n",
      "   macro avg     0.8432    0.8531    0.8470       891\n",
      "weighted avg     0.8572    0.8530    0.8540       891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_1, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7129186602870813"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(1-np.square(np.sign(Y_test-model_1.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is overfitting and performing worse than the baseline model on the test set, so we discard it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our second model and evaluate its performance. It is a \"women survive\" based classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_2():\n",
    "            \n",
    "    def predict(self, X):\n",
    "        A1 = X[:,4] \n",
    "        A2 = np.expand_dims(A1, axis=-1)   \n",
    "        return A2  \n",
    "    \n",
    "    def predict_logit(self, X):\n",
    "        A1 = self.predict(X)\n",
    "        A2 = A1*0.9999999999999999 + (1-A1)*0.0000000000000001\n",
    "        A3 = np.log(A2/(1-A2))\n",
    "        return A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2 = Model_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8111    0.8525    0.8313       549\n",
      "         1.0     0.7420    0.6813    0.7104       342\n",
      "\n",
      "    accuracy                         0.7868       891\n",
      "   macro avg     0.7766    0.7669    0.7708       891\n",
      "weighted avg     0.7846    0.7868    0.7849       891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_2, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7655502392344498"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(1-np.square(np.sign(Y_test-model_2.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing better than the baseline model on the test set, so we take it as the new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our third model and evaluate its performance. It is a \"women and children survive\" based classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_3():\n",
    "        \n",
    "    def predict(self, X):\n",
    "        A1 = X[:,4]+X[:,3]*X[:,5]     \n",
    "        A2 = np.expand_dims(A1, axis=-1)   \n",
    "        return A2  \n",
    "    \n",
    "    def predict_logit(self, X):\n",
    "        A1 = self.predict(X)\n",
    "        A2 = A1*0.9999999999999999 + (1-A1)*0.0000000000000001\n",
    "        A3 = np.log(A2/(1-A2))\n",
    "        return A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3 = Model_3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8324    0.8324    0.8324       549\n",
      "         1.0     0.7310    0.7310    0.7310       342\n",
      "\n",
      "    accuracy                         0.7935       891\n",
      "   macro avg     0.7817    0.7817    0.7817       891\n",
      "weighted avg     0.7935    0.7935    0.7935       891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_3, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7655502392344498"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(1-np.square(np.sign(Y_test-model_3.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing a bit better than the last one, so we take it as the new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our fourth model and evaluate its performance. It is a \"women and children of class 1 or 2 survive\" based classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_4():\n",
    "    \n",
    "    def predict(self, X):\n",
    "        A1 = (X[:,0]+X[:,1])*(X[:,4] + X[:,3]*X[:,5]+X[:,3]*X[:,6])  \n",
    "        A2 = np.expand_dims(A1, axis=-1)\n",
    "        return A2\n",
    "    \n",
    "    def predict_logit(self, X):\n",
    "        A1 = self.predict(X)\n",
    "        A2 = A1*0.9999999999999999 + (1-A1)*0.0000000000000001\n",
    "        A3 = np.log(A2/(1-A2))\n",
    "        return A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_4 = Model_4()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7616    0.9836    0.8585       549\n",
      "         1.0     0.9505    0.5058    0.6603       342\n",
      "\n",
      "    accuracy                         0.8002       891\n",
      "   macro avg     0.8561    0.7447    0.7594       891\n",
      "weighted avg     0.8341    0.8002    0.7824       891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_4, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7822966507177034"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(1-np.square(np.sign(Y_test-model_4.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing a better than the last one, so we take it as the new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our fifth model and evaluate its performance. This new model will boost the last model and to do so, we use a special trick and transform the original classification problem to a regression problem. Then we use the error of the last model as the output to predict. The new model will be an ensemble of the last one and the one we trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_ = Y_train*0.9999999999999999 + (1-Y_train)*0.0000000000000001\n",
    "Y_train_ = np.log(Y_train_/(1-Y_train_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "R1_train = Y_train_ - model_4.predict_logit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('lambda',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<function <lambda> at 0x7fb151c49ae8>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('adaboost',\n",
       "                 AdaBoostRegressor(base_estimator=None, learning_rate=1.0,\n",
       "                                   loss='linear', n_estimators=50,\n",
       "                                   random_state=None))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaboost = Pipeline([('lambda', FunctionTransformer(lambda x:(x-a)/(b-a))),\n",
    "                     ('adaboost', AdaBoostRegressor())]) \n",
    "\n",
    "adaboost.fit(X_train, R1_train.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_5():\n",
    "    \n",
    "    def predict(self, X):\n",
    "        A1 = self.predict_logit(X)       \n",
    "        A2 = 1/(1+np.exp(-A1))\n",
    "        A3 = np.sign(1+np.sign(A2-0.5))   \n",
    "        return A3\n",
    "    \n",
    "    def predict_logit(self, X):\n",
    "        A1 = model_4.predict_logit(X) + np.expand_dims(adaboost.predict(X), axis=-1)\n",
    "        return A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_5 = Model_5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7738    0.9781    0.8640       549\n",
      "         1.0     0.9391    0.5409    0.6865       342\n",
      "\n",
      "    accuracy                         0.8103       891\n",
      "   macro avg     0.8564    0.7595    0.7752       891\n",
      "weighted avg     0.8372    0.8103    0.7959       891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_5, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7918660287081339\n"
     ]
    }
   ],
   "source": [
    "np.mean(1-np.square(np.sign(Y_test-model_5.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we see that this model is an improvement of the last one, so we take it as the new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our sixth model and evaluate its performance. This new model will apply the same idea as before and boost the last model using the same special trick. The new model will be an ensemble of the last one and the one we trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "R2_train = Y_train_ - model_5.predict_logit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NN():\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Lambda(function=lambda x: (x-a)/(b-a),\n",
    "                                     input_shape=[X_train.shape[1]]))\n",
    "    model.add(tf.keras.layers.Dense(units=64, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(units=64, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(R2_train.shape[1], \n",
    "                                    activation='linear', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    model.compile(loss='mse', metrics=['mae'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    model.fit(X_train, R2_train, epochs=10000, batch_size=16, callbacks=[callback1, callback2], verbose=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda (Lambda)              (None, 22)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                1472      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 5,697\n",
      "Trainable params: 5,697\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 891 samples\n",
      "Epoch 1/10000\n",
      "891/891 [==============================] - 0s 548us/sample - loss: 822.7352 - mae: 23.0077\n",
      "Epoch 2/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 771.7184 - mae: 21.8115\n",
      "Epoch 3/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 734.3658 - mae: 21.1114\n",
      "Epoch 4/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 727.5127 - mae: 20.5791\n",
      "Epoch 5/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 726.5154 - mae: 20.3032\n",
      "Epoch 6/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 694.3156 - mae: 19.8120\n",
      "Epoch 7/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 710.1009 - mae: 20.2307\n",
      "Epoch 8/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 703.5855 - mae: 19.9298\n",
      "Epoch 9/10000\n",
      "891/891 [==============================] - 0s 84us/sample - loss: 705.0114 - mae: 20.2361\n",
      "Epoch 10/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 683.9167 - mae: 19.8013\n",
      "Epoch 11/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 709.9855 - mae: 20.6021\n",
      "Epoch 12/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 689.2319 - mae: 19.6381\n",
      "Epoch 13/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 686.7253 - mae: 20.0496\n",
      "Epoch 14/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 678.8962 - mae: 19.2399\n",
      "Epoch 15/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 650.8760 - mae: 18.9919\n",
      "Epoch 16/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 686.5134 - mae: 19.9631\n",
      "Epoch 17/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 666.1918 - mae: 19.2963\n",
      "Epoch 18/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 674.7876 - mae: 19.4313\n",
      "Epoch 19/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 676.6120 - mae: 19.6168\n",
      "Epoch 20/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 668.3859 - mae: 19.2842\n",
      "Epoch 21/10000\n",
      "891/891 [==============================] - 0s 78us/sample - loss: 634.9267 - mae: 18.7910\n",
      "Epoch 22/10000\n",
      "891/891 [==============================] - 0s 84us/sample - loss: 668.1170 - mae: 19.2156\n",
      "Epoch 23/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 646.7648 - mae: 18.9033\n",
      "Epoch 24/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 662.4867 - mae: 19.0884\n",
      "Epoch 25/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 636.5039 - mae: 18.8615\n",
      "Epoch 26/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 656.8560 - mae: 19.1410\n",
      "Epoch 27/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 661.0584 - mae: 18.7500\n",
      "Epoch 28/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 655.9990 - mae: 18.7971\n",
      "Epoch 29/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 640.1882 - mae: 18.3418\n",
      "Epoch 30/10000\n",
      "891/891 [==============================] - 0s 67us/sample - loss: 651.5696 - mae: 18.7278\n",
      "Epoch 31/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 658.5119 - mae: 19.0827\n",
      "Epoch 32/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 639.3584 - mae: 18.7080\n",
      "Epoch 33/10000\n",
      "891/891 [==============================] - 0s 85us/sample - loss: 637.0411 - mae: 18.6237\n",
      "Epoch 34/10000\n",
      "891/891 [==============================] - 0s 80us/sample - loss: 632.2606 - mae: 18.2877\n",
      "Epoch 35/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 666.4033 - mae: 18.9492\n",
      "Epoch 36/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 622.4452 - mae: 18.0496\n",
      "Epoch 37/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 623.2478 - mae: 18.0725\n",
      "Epoch 38/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 623.6604 - mae: 18.3215\n",
      "Epoch 39/10000\n",
      "891/891 [==============================] - 0s 85us/sample - loss: 603.3203 - mae: 17.8620\n",
      "Epoch 40/10000\n",
      "891/891 [==============================] - 0s 82us/sample - loss: 636.3128 - mae: 18.2510\n",
      "Epoch 41/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 620.4528 - mae: 17.9291\n",
      "Epoch 42/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 632.4245 - mae: 17.8952\n",
      "Epoch 43/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 617.2047 - mae: 18.2600\n",
      "Epoch 44/10000\n",
      "891/891 [==============================] - 0s 82us/sample - loss: 597.2963 - mae: 17.6133\n",
      "Epoch 45/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 617.9845 - mae: 17.7017\n",
      "Epoch 46/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 621.3864 - mae: 17.8238\n",
      "Epoch 47/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 645.3274 - mae: 18.1101\n",
      "Epoch 48/10000\n",
      "891/891 [==============================] - 0s 67us/sample - loss: 624.6436 - mae: 18.0108\n",
      "Epoch 49/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 604.8460 - mae: 17.8628\n",
      "Epoch 50/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 625.7144 - mae: 17.9325\n",
      "Epoch 51/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 582.8303 - mae: 17.3468\n",
      "Epoch 52/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 631.1714 - mae: 17.8955\n",
      "Epoch 53/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 626.8870 - mae: 17.8376\n",
      "Epoch 54/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 622.2575 - mae: 18.0611\n",
      "Epoch 55/10000\n",
      "891/891 [==============================] - 0s 106us/sample - loss: 613.5309 - mae: 18.0459\n",
      "Epoch 56/10000\n",
      "891/891 [==============================] - 0s 80us/sample - loss: 619.0871 - mae: 17.7813\n",
      "Epoch 57/10000\n",
      "891/891 [==============================] - 0s 82us/sample - loss: 622.7829 - mae: 17.9141\n",
      "Epoch 58/10000\n",
      "891/891 [==============================] - 0s 90us/sample - loss: 586.4101 - mae: 17.5337\n",
      "Epoch 59/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 579.7078 - mae: 17.1367\n",
      "Epoch 60/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 628.6382 - mae: 17.8364\n",
      "Epoch 61/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 606.6510 - mae: 17.7158\n",
      "Epoch 62/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 591.5209 - mae: 17.2275\n",
      "Epoch 63/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 601.9344 - mae: 17.4704\n",
      "Epoch 64/10000\n",
      "891/891 [==============================] - ETA: 0s - loss: 592.3939 - mae: 17.46 - 0s 76us/sample - loss: 590.4264 - mae: 17.6244\n",
      "Epoch 65/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 606.4306 - mae: 17.5725\n",
      "Epoch 66/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 598.9914 - mae: 17.4746\n",
      "Epoch 67/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 72us/sample - loss: 597.9575 - mae: 17.1221\n",
      "Epoch 68/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 612.6522 - mae: 17.6653\n",
      "Epoch 69/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 601.6389 - mae: 17.6471\n",
      "Epoch 70/10000\n",
      "891/891 [==============================] - 0s 83us/sample - loss: 593.1426 - mae: 17.4092\n",
      "Epoch 71/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 610.7454 - mae: 17.5073\n",
      "Epoch 72/10000\n",
      "891/891 [==============================] - 0s 78us/sample - loss: 620.5903 - mae: 17.7539\n",
      "Epoch 73/10000\n",
      "891/891 [==============================] - 0s 80us/sample - loss: 600.0990 - mae: 17.5994\n",
      "Epoch 74/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 597.5094 - mae: 17.2917\n",
      "Epoch 75/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 590.4069 - mae: 17.0784\n",
      "Epoch 76/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 586.7951 - mae: 17.3714\n",
      "Epoch 77/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 581.1403 - mae: 16.8899\n",
      "Epoch 78/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 605.0839 - mae: 17.3983\n",
      "Epoch 79/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 589.4034 - mae: 17.1882\n",
      "Epoch 80/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 571.4241 - mae: 16.6807\n",
      "Epoch 81/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 611.8892 - mae: 17.6206\n",
      "Epoch 82/10000\n",
      "891/891 [==============================] - 0s 63us/sample - loss: 599.3543 - mae: 17.3376\n",
      "Epoch 83/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 578.0449 - mae: 16.6787\n",
      "Epoch 84/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 602.3706 - mae: 17.3529\n",
      "Epoch 85/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 579.3765 - mae: 17.1042\n",
      "Epoch 86/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 602.7765 - mae: 17.0354\n",
      "Epoch 87/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 587.6170 - mae: 17.2018\n",
      "Epoch 88/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 567.7935 - mae: 17.0262\n",
      "Epoch 89/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 591.0801 - mae: 16.8433\n",
      "Epoch 90/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 586.1679 - mae: 17.3426\n",
      "Epoch 91/10000\n",
      "891/891 [==============================] - 0s 67us/sample - loss: 598.6319 - mae: 17.5532\n",
      "Epoch 92/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 591.0813 - mae: 17.0422\n",
      "Epoch 93/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 592.4459 - mae: 17.2112\n",
      "Epoch 94/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 576.0378 - mae: 16.9235\n",
      "Epoch 95/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 600.1673 - mae: 17.4006\n",
      "Epoch 96/10000\n",
      "891/891 [==============================] - 0s 65us/sample - loss: 593.2239 - mae: 17.2846\n",
      "Epoch 97/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 575.9927 - mae: 17.0695\n",
      "Epoch 98/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 579.9058 - mae: 17.1329\n",
      "Epoch 99/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 562.1147 - mae: 16.2931\n",
      "Epoch 100/10000\n",
      "891/891 [==============================] - 0s 65us/sample - loss: 603.1823 - mae: 17.3075\n",
      "Epoch 101/10000\n",
      "891/891 [==============================] - 0s 93us/sample - loss: 586.6094 - mae: 17.0624\n",
      "Epoch 102/10000\n",
      "891/891 [==============================] - 0s 77us/sample - loss: 574.3351 - mae: 16.9325\n",
      "Epoch 103/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 598.3253 - mae: 16.7047\n",
      "Epoch 104/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 606.9137 - mae: 17.4918\n",
      "Epoch 105/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 576.4422 - mae: 16.6544\n",
      "Epoch 106/10000\n",
      "891/891 [==============================] - 0s 65us/sample - loss: 571.8275 - mae: 17.1553\n",
      "Epoch 107/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 571.2551 - mae: 16.5914\n",
      "Epoch 108/10000\n",
      "891/891 [==============================] - 0s 65us/sample - loss: 567.5835 - mae: 16.5176\n",
      "Epoch 109/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 576.1952 - mae: 16.7643\n",
      "Epoch 110/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 594.8493 - mae: 17.3752\n",
      "Epoch 111/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 582.7821 - mae: 17.0068\n",
      "Epoch 112/10000\n",
      "891/891 [==============================] - 0s 67us/sample - loss: 583.8546 - mae: 17.1974\n",
      "Epoch 113/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 586.1167 - mae: 16.6588\n",
      "Epoch 114/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 568.4042 - mae: 16.5833\n",
      "Epoch 115/10000\n",
      "891/891 [==============================] - 0s 67us/sample - loss: 559.4643 - mae: 16.3678\n",
      "Epoch 116/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 581.9282 - mae: 17.1229\n",
      "Epoch 117/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 595.6631 - mae: 17.3776\n",
      "Epoch 118/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 573.8416 - mae: 16.6247\n",
      "Epoch 119/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 580.4757 - mae: 16.8836\n",
      "Epoch 120/10000\n",
      "891/891 [==============================] - 0s 66us/sample - loss: 567.1973 - mae: 16.5745\n",
      "Epoch 121/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 592.5696 - mae: 17.0487\n",
      "Epoch 122/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 572.5375 - mae: 16.9779\n",
      "Epoch 123/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 573.6918 - mae: 16.8512\n",
      "Epoch 124/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 591.1015 - mae: 16.9665\n",
      "Epoch 125/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 561.9591 - mae: 16.3116\n",
      "Epoch 126/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 572.4865 - mae: 16.6266\n",
      "Epoch 127/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 569.6126 - mae: 16.6360\n",
      "Epoch 128/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 579.2602 - mae: 17.0224\n",
      "Epoch 129/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 578.2877 - mae: 17.2778\n",
      "Epoch 130/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 580.2843 - mae: 16.7451\n",
      "Epoch 131/10000\n",
      "891/891 [==============================] - 0s 67us/sample - loss: 589.9642 - mae: 16.8527\n",
      "Epoch 132/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 563.4532 - mae: 16.4649\n",
      "Epoch 133/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 577.4133 - mae: 16.6351\n",
      "Epoch 134/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 552.5221 - mae: 16.2639\n",
      "Epoch 135/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 579.2489 - mae: 16.8897\n",
      "Epoch 136/10000\n",
      "891/891 [==============================] - 0s 65us/sample - loss: 584.3451 - mae: 16.9692\n",
      "Epoch 137/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 558.3178 - mae: 16.6474\n",
      "Epoch 138/10000\n",
      "891/891 [==============================] - 0s 66us/sample - loss: 549.4033 - mae: 16.0624\n",
      "Epoch 139/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 565.4558 - mae: 16.7701\n",
      "Epoch 140/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 568.2353 - mae: 16.1759\n",
      "Epoch 141/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 564.8738 - mae: 16.8740\n",
      "Epoch 142/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 586.3142 - mae: 16.5725\n",
      "Epoch 143/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 577.5001 - mae: 16.8644\n",
      "Epoch 144/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 73us/sample - loss: 554.6283 - mae: 16.5497\n",
      "Epoch 145/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 553.1409 - mae: 16.0319\n",
      "Epoch 146/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 560.3681 - mae: 16.3632\n",
      "Epoch 147/10000\n",
      "891/891 [==============================] - 0s 66us/sample - loss: 557.0928 - mae: 16.1310\n",
      "Epoch 148/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 588.0021 - mae: 16.5787\n",
      "Epoch 149/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 550.7755 - mae: 16.1843\n",
      "Epoch 150/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 563.6091 - mae: 16.6106\n",
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    },
    {
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      "891/891 [==============================] - 0s 66us/sample - loss: 508.1945 - mae: 14.8193\n",
      "Epoch 833/10000\n",
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      "Epoch 834/10000\n",
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      "Epoch 835/10000\n",
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      "Epoch 836/10000\n",
      "891/891 [==============================] - 0s 59us/sample - loss: 497.5731 - mae: 14.7395\n",
      "Epoch 837/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 60us/sample - loss: 473.8243 - mae: 14.2562\n",
      "Epoch 838/10000\n",
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      "Epoch 839/10000\n",
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      "Epoch 840/10000\n",
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      "Epoch 841/10000\n",
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      "Epoch 842/10000\n",
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      "Epoch 843/10000\n",
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      "Epoch 844/10000\n",
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      "Epoch 845/10000\n",
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      "Epoch 846/10000\n",
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      "Epoch 847/10000\n",
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      "Epoch 848/10000\n",
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      "Epoch 850/10000\n",
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      "Epoch 851/10000\n",
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      "Epoch 852/10000\n",
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      "Epoch 853/10000\n",
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      "Epoch 858/10000\n",
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      "Epoch 859/10000\n",
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      "Epoch 860/10000\n",
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      "Epoch 861/10000\n",
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      "Epoch 862/10000\n",
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      "Epoch 863/10000\n",
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      "Epoch 864/10000\n",
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      "Epoch 865/10000\n",
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      "Epoch 867/10000\n",
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      "Epoch 868/10000\n",
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      "Epoch 869/10000\n",
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      "Epoch 870/10000\n",
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      "Epoch 880/10000\n",
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      "Epoch 888/10000\n",
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      "Epoch 892/10000\n",
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      "Epoch 893/10000\n",
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      "Epoch 894/10000\n",
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      "Epoch 895/10000\n",
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      "Epoch 896/10000\n",
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      "Epoch 897/10000\n",
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      "Epoch 898/10000\n",
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      "Epoch 899/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 483.2953 - mae: 14.4226\n",
      "Epoch 900/10000\n",
      "891/891 [==============================] - 0s 64us/sample - loss: 510.6435 - mae: 14.9056\n",
      "Epoch 901/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 487.4507 - mae: 14.7019\n",
      "Epoch 902/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 504.9208 - mae: 14.9868\n",
      "Epoch 903/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 494.0952 - mae: 14.5389\n",
      "Epoch 904/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 466.1509 - mae: 14.3169\n",
      "Epoch 905/10000\n",
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      "Epoch 906/10000\n",
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      "Epoch 907/10000\n",
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      "Epoch 908/10000\n",
      "891/891 [==============================] - 0s 66us/sample - loss: 495.4397 - mae: 14.6672\n",
      "Epoch 909/10000\n",
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      "Epoch 910/10000\n",
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      "Epoch 911/10000\n",
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      "Epoch 912/10000\n",
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      "Epoch 913/10000\n",
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      "Epoch 914/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 59us/sample - loss: 509.0799 - mae: 15.0202\n",
      "Epoch 915/10000\n",
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      "Epoch 916/10000\n",
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      "Epoch 917/10000\n",
      "891/891 [==============================] - 0s 58us/sample - loss: 481.2701 - mae: 14.6563\n",
      "Epoch 918/10000\n",
      "891/891 [==============================] - 0s 58us/sample - loss: 488.9745 - mae: 14.5680\n",
      "Epoch 919/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 512.6864 - mae: 14.8463\n",
      "Epoch 920/10000\n",
      "891/891 [==============================] - 0s 61us/sample - loss: 490.9229 - mae: 14.5832\n",
      "Epoch 921/10000\n",
      "891/891 [==============================] - 0s 59us/sample - loss: 496.0788 - mae: 14.7376\n",
      "Epoch 922/10000\n",
      "891/891 [==============================] - 0s 58us/sample - loss: 483.9353 - mae: 14.3202\n",
      "Epoch 923/10000\n",
      "891/891 [==============================] - 0s 59us/sample - loss: 483.1542 - mae: 14.4175\n",
      "Epoch 924/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 490.6148 - mae: 14.4483\n",
      "Epoch 925/10000\n",
      "891/891 [==============================] - 0s 61us/sample - loss: 486.9012 - mae: 14.6953\n",
      "Epoch 926/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 476.3871 - mae: 14.2722\n",
      "Epoch 927/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 509.0791 - mae: 14.8689\n",
      "Epoch 928/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 512.1785 - mae: 14.8547\n",
      "Epoch 929/10000\n",
      "891/891 [==============================] - 0s 62us/sample - loss: 477.5002 - mae: 14.5420\n",
      "Epoch 930/10000\n",
      "891/891 [==============================] - 0s 63us/sample - loss: 485.6592 - mae: 14.5351\n",
      "Epoch 931/10000\n",
      "891/891 [==============================] - 0s 61us/sample - loss: 509.0885 - mae: 14.7553\n",
      "Epoch 932/10000\n",
      "891/891 [==============================] - 0s 68us/sample - loss: 503.8688 - mae: 14.9390\n",
      "Epoch 933/10000\n",
      "891/891 [==============================] - 0s 66us/sample - loss: 495.3650 - mae: 14.7790\n",
      "Epoch 934/10000\n",
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      "Epoch 935/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 520.0428 - mae: 15.0329\n",
      "Epoch 936/10000\n",
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      "Epoch 937/10000\n",
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      "Epoch 938/10000\n",
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      "Epoch 939/10000\n",
      "891/891 [==============================] - 0s 99us/sample - loss: 516.8384 - mae: 15.0930\n",
      "Epoch 940/10000\n",
      "891/891 [==============================] - 0s 90us/sample - loss: 484.3783 - mae: 14.4861\n",
      "Epoch 941/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 488.8772 - mae: 14.6997\n",
      "Epoch 942/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 484.1535 - mae: 14.4279\n",
      "Epoch 943/10000\n",
      "891/891 [==============================] - 0s 85us/sample - loss: 481.2882 - mae: 14.6065\n",
      "Epoch 944/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 503.5899 - mae: 14.9955\n",
      "Epoch 945/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 495.9443 - mae: 14.7988\n",
      "Epoch 946/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 516.2464 - mae: 14.8620\n",
      "Epoch 947/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 500.1685 - mae: 14.7075\n",
      "Epoch 948/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 515.5934 - mae: 14.9709\n",
      "Epoch 949/10000\n",
      "891/891 [==============================] - 0s 86us/sample - loss: 472.1403 - mae: 14.3205\n",
      "Epoch 950/10000\n",
      "891/891 [==============================] - 0s 91us/sample - loss: 490.1333 - mae: 14.6242\n",
      "Epoch 951/10000\n",
      "891/891 [==============================] - 0s 101us/sample - loss: 506.1700 - mae: 14.8298\n",
      "Epoch 952/10000\n",
      "891/891 [==============================] - 0s 82us/sample - loss: 482.2591 - mae: 14.3501\n",
      "Epoch 953/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 506.9382 - mae: 14.8885\n",
      "Epoch 954/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 500.6721 - mae: 14.7680\n",
      "Epoch 955/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 498.1679 - mae: 14.7344\n",
      "Epoch 956/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 490.1963 - mae: 14.5084\n",
      "Epoch 957/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 520.5692 - mae: 15.3205\n",
      "Epoch 958/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 504.9586 - mae: 14.9781\n",
      "Epoch 959/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 507.1747 - mae: 14.8397\n",
      "Epoch 960/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 490.0339 - mae: 14.4319\n",
      "Epoch 961/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 494.6183 - mae: 14.9790\n",
      "Epoch 962/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 513.6293 - mae: 15.0216\n",
      "Epoch 963/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 484.1563 - mae: 14.4026\n",
      "Epoch 964/10000\n",
      "891/891 [==============================] - 0s 73us/sample - loss: 506.1330 - mae: 14.9110\n",
      "Epoch 965/10000\n",
      "891/891 [==============================] - 0s 72us/sample - loss: 491.7784 - mae: 14.5864\n",
      "Epoch 966/10000\n",
      "891/891 [==============================] - 0s 76us/sample - loss: 505.0252 - mae: 14.8354\n",
      "Epoch 967/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 497.3301 - mae: 14.6017\n",
      "Epoch 968/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 484.4425 - mae: 14.4823\n",
      "Epoch 969/10000\n",
      "891/891 [==============================] - 0s 74us/sample - loss: 499.6286 - mae: 14.7774\n",
      "Epoch 970/10000\n",
      "891/891 [==============================] - 0s 70us/sample - loss: 492.5920 - mae: 14.4398\n",
      "Epoch 971/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 477.8601 - mae: 14.5361\n",
      "Epoch 972/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 476.8737 - mae: 14.3174\n",
      "Epoch 973/10000\n",
      "891/891 [==============================] - 0s 71us/sample - loss: 496.4820 - mae: 14.6167\n",
      "Epoch 974/10000\n",
      "891/891 [==============================] - 0s 69us/sample - loss: 490.0459 - mae: 14.6214\n",
      "Epoch 975/10000\n",
      "891/891 [==============================] - 0s 75us/sample - loss: 486.8873 - mae: 14.5322\n"
     ]
    }
   ],
   "source": [
    "nn = NN() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_6():\n",
    "    \n",
    "    def predict(self, X):\n",
    "        A1 = self.predict_logit(X)       \n",
    "        A2 = 1/(1+np.exp(-A1))\n",
    "        A3 = np.sign(1+np.sign(A2-0.5))   \n",
    "        return A3\n",
    "    \n",
    "    def predict_logit(self, X):\n",
    "        A1 = model_5.predict_logit(X) + nn.predict(X)\n",
    "        return A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_6 = Model_6()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8904    0.9326    0.9110       549\n",
      "         1.0     0.8829    0.8158    0.8480       342\n",
      "\n",
      "    accuracy                         0.8878       891\n",
      "   macro avg     0.8867    0.8742    0.8795       891\n",
      "weighted avg     0.8875    0.8878    0.8868       891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_6, X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.80622009569378\n"
     ]
    }
   ],
   "source": [
    "np.mean(1-np.square(np.sign(Y_test-model_6.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we see that this model is perfoming better than the last one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This last model could be considered a candidate solution for the original problem presented. Additional exploration of machine learning and deep learning models would be necessary to make a final conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'PassengerId': data_test.loc[:,'PassengerId'], 'Survived': (model_6.predict(X_test).flatten()).astype(int)})\n",
    "output.to_csv('my_submission.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}